International Journal of Automation and Smart Technology: Announcements
The International Journal of Automation and Smart Technology (AUSMT) is a peer-reviewed, open-access journal devoted to publishing research papers in the fields of automation and smart technology. It is currently indexed in SCOPUS. AUSMT invites Special Issue (SI) Proposals for its 2025 edition. Distinguished researchers and Guest Editors are encouraged to submit proposals for high-impact Special Issues in automation and smart technology. Special Issue Tracks (Quotas: 3 SIs/Track) 25-01: Intelligent Robotics – Autonomous systems, human-robot collaboration, swarm robotics, SLAM, etc. 25-02: Smart Manufacturing – Digital twins, cyber-physical systems, predictive maintenance, automated inspection, etc. 25-03: Artificial Internet of Things (AIoT) – Edge AI, federated learning, low-power sensor networks, big data, etc. 25-04: AI & Computer Vision – Machine learning, multimodal perception, vision-language models, etc. 25-05: Smart Medical Mechatronics – Surgical robotics, rehabilitation robots, AI-driven diagnostics, wearable tech, etc. Proposals outside these tracks will also be considered. Key Dates Proposal Submission: February 20 – November 30, 2025 Decision Notification: Monthly and Within 30 days of submission Please find the detailed guidelines in the AUSMT 2025 SI Proposal Template Thank you very much and looking forward to your contribution! Editor-in-Chief Prof. Chung-Hsien Kuo
Call for papers: Special Issue (26-03-01): Computational Intelligence for Smart Cities, Energy Efficiency, and Sustainable Systems
Background The increasing interconnection of urban systems has accelerated the integration of computational intelligence into modern city networks. Through the integration of deep analytics, autonomous governance and adaptive learning, cities are becoming responsive systems with the capacity to integrate technological progress with sustainable ecosystems. By integrating decision-making in the major sectors such as transportation management, energy efficiency, waste disposal and environmental monitoring for increased efficiency and resilience. With growing urbanization, cities are still facing the enduring issues of scalability, interoperability and real-time coordination of complex networks. These issues highlight the requirement for intelligent frameworks capable of decoding heterogeneous data, forecasting city trends and dynamically optimizing distributed functions. Technologies such as networked sensors, intelligent networks and adaptive algorithms are the foundation for predictive decision support and decentralized management to enable rapid and more sustainable urban responses. By combining automation, communication and cognition, computational intelligence helps municipal governments enhance public services, eliminates operating inefficiencies and encourage resource-aware development. Ultimately, it represents a transformative route toward cleaner, smarter and more resilient urban environments that align with the broader vision of sustainable digital governance and human-centered innovation. Despite its expanding potential, computational intelligence within smart city systems continues to experience significant challenges. Protection of data, interoperability and the high computational expense of smart models tend to discourage efficient deployment and scalability. Most systems also suffer from a lack of transparency, such that policymakers and citizens cannot trust or understand automated decisions. Uneven technological preparedness and digital infrastructure further exacerbate the disparity in sustainable urban growth. To overcome these challenges, researchers ought to prioritize the development of explainable, lightweight and privacy-preserving models that promote accountability and equity. Interoperable frameworks for secure data sharing, adaptive algorithms that work well across different environments and energy-efficient computing systems that reduce resource tension should also be created with emphasis. By addressing these limitations, computational intelligence will evolve into a sustainable basis for future cities to spur innovation while securing equitable access, ethical governance and long-term environmental sustainability. Aim This special issue invites advance research, models and frameworks that examine the contribution of computational intelligence toward developing smart, sustainable urban ecosystems. It also invites contributions tackling data management challenges, interoperability, energy efficiency and system scalability in smart city environments. Researchers, technologists and policymakers are invited to submit advance solutions that exploit intelligent automation, adaptive learning and ethical AI to construct resilient, resource-conserving and human-centered cities that capture the principles of digital sustainability and urban innovation. Scope • IoT-Driven Predictive Analytics for Sustainable Infrastructure Management • Federated Learning Models for Privacy-Preserving Smart City Data Integration • Fuzzy Logic-Based Decision Systems for Intelligent Waste Management • Hybrid Neural Networks for Environmental Monitoring and Pollution Forecasting • Blockchain-Enabled Energy Trading Platforms for Decentralized Smart Grids • Swarm Intelligence Algorithms for Autonomous Public Transport Coordination • Knowledge Graphs for Context-Aware Urban Governance and Policy Support • Multi-Agent Systems for Adaptive Water Distribution and Management • Bio-Inspired Optimization for Green Urban Infrastructure Design • Explainable AI Techniques for Transparent Smart City Decision-Making • Big Data Analytics for Urban Mobility Pattern Prediction and Planning • Self-Organizing Networks for Efficient Urban Communication Systems • Cognitive Computing Approaches for Sustainable Urban Policy Development Important dates Paper Submission Deadline: 15.06.2026 Notification to Author: 20.08.2026 Revised Paper Submission: 10.10.2026 Final Decision Announcement: 10.12.2026 Lead Associate Editor of this Special Issue: Prof. Shang-Chih Lin Feng Chia University (FCU), Taiwan Details of the Our Guest Editor Team: Lead Guest Editor Dr. Marzieh Faridi Masouleh Ahrar Institute of Technology and Higher Education, Rasht, Iran. Official Email: m.faridi@ahrar.ac.ir Expert Domains: Artificial Intelligence, Computational intelligence, Data Mining, Optimization, Neural Network. Co-Guest Editors: Dr. Dena Bazazian University of Plymouth, PL4 8AA Plymouth, U.K. Official Email: dena.bazazian@plymouth.ac.uk Expert Domains: Computer vision, deep learning, point clouds, image processing, pattern recognition. Dr. Mehregan Mahdavi Kingsford Institute of Higher Education (KIHE), Sydney NSW, Australia. Official Email: mehrm@cse.unsw.edu.au Expert Domains: Recommender Systems, Big Data, Distributed Systems, Service Oriented Architecture Aref Akbari Kashali University of Yazd, Yazd Province, Iran. Official Email: arefakbari@stu.yazd.ac.ir Expert Domains: Large Language Models, software architectures, anomaly detection, financial forecasting
International Journal of Automation and Smart Technology
Notice: AUSMT 2026 Special Issue Proposal Google Form
The International Journal of Automation and Smart Technology (AUSMT) is a peer-reviewed, open-access journal devoted to publishing research papers in the fields of automation and smart technology. It is currently indexed in SCOPUS. AUSMT invites Special Issue (SI) Proposals for its 2026 edition. Distinguished researchers and Guest Editors are encouraged to submit proposals for high-impact Special Issues in automation and smart technology. Special Issue Tracks 26-01: Intelligent Robotics – Autonomous systems, human-robot collaboration, swarm robotics, SLAM, etc. 26-02: Smart Manufacturing – Digital twins, cyber-physical systems, predictive maintenance, automated inspection, etc. 26-03: Artificial Internet of Things (AIoT) – Edge AI, federated learning, low-power sensor networks, big data, etc. 26-04: AI & Computer Vision – Machine learning, multimodal perception, vision-language models, etc. 26-05: Smart Medical Mechatronics – Surgical robotics, rehabilitation robots, AI-driven diagnostics, wearable tech, etc. Proposals outside these tracks will also be considered. Key Dates Proposal Submission: December 3rd, 2025 – October 31st, 2026. Decision Notification: Within 30 days of submission. =================================================================================== Please make your Submission only through this Google Form: Submission Form for AUSMT 2026 Special Issue Proposals =================================================================================== NOTICE: AUSMT now only accepts Special Issue Proposal Submission through the Google Form. Please do not send special issue proposal as emails to the CIE, such emails will be redirected or ignored. If you already submitted your Proposal through AUSMT Website, you can ignore this message. Thank you very much and looking forward to your contribution! =================================================================================== If you have any questions regarding this announcement, feel free to contact us via: Principal contact: Editorial Office, AUSMT. (ausmt.ciae@gmail.com) Support contact: Thai-Nguyen, Chau. (nguyenbku97@gmail.com )
Call for papers: Special Issue (25-04-01): Human-Computer Interaction in the Virtual Reality: Challenges and Solutions
Background Human-Computer Interaction (HCI) plays a crucial role in defining how users engage with computer systems, especially in immersive environments like Virtual Reality (VR). The advancement of VR has introduced novel interaction techniques, replacing traditional input methods like keyboards and mice with motion tracking, gesture recognition, and eye-tracking systems. These technologies create an immersive virtual environment that enhances user engagement, leading to new challenges and opportunities in VR system design. By integrating cognitive and interaction design principles, VR systems can provide more intuitive and natural experiences. This study explores the impact of HCI methodologies on VR design strategies and highlights the usability challenges and solutions in enhancing user interaction within virtual environments. Aim The primary objective of this SI is to analyze how HCI methodologies influence VR design and to propose strategies for improving interaction efficiency, usability, and immersive experience. Additionally, this study aims to identify challenges in VR technology implementation and suggest future research directions to optimize human-computer interaction in virtual environments. Scope Human-Computer Interaction (HCI) Virtual Reality (VR) Gesture-Based Interaction Motion Tracking Technology Cognitive Ergonomics in VR Usability Evaluation in VR Immersive User Experience Important dates Paper Submission Deadline: 08.2025 Notification to Author: 10.2025 Revised Paper Submission: 12.2025 Final Decision Announcement: 15.02.2025 Details of the Our Guest Editor Team: Lead Guest Editor Dr. Mustafa Bin Man Professor, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, Malaysia Official Email: mustafaman@umt.edu.my, mustafabinman@outlook.com Google Scholar: https://scholar.google.com.my/citations?user=L4VqciUAAAAJ&hl=en Co-Guest Editors: Prof. Bishwajeet Kumar Pandey Professor, Department of Intelligent System and Cyber Security, Astana IT University, Astana, Kazakhstan Official Email: bk.pandey@astanait.edu.kz Google Scholar: https://scholar.google.co.in/citations?user=UZ_8yAMAAAAJ&hl=en Dr. Henderi Professor, Informatics Engineering, University of Raharja, Tangerang, Indonesia Official Email: henderi@raharja.info Google Scholar: https://scholar.google.co.id/citations?user=mOe2HxcAAAAJ&hl=en
Important: AUSMT 2025 Special Issue Proposal Google Form
The International Journal of Automation and Smart Technology (AUSMT) is a peer-reviewed, open-access journal devoted to publishing research papers in the fields of automation and smart technology. It is currently indexed in SCOPUS. AUSMT invites Special Issue (SI) Proposals for its 2025 edition. Distinguished researchers and Guest Editors are encouraged to submit proposals for high-impact Special Issues in automation and smart technology. Special Issue Tracks 25-01: Intelligent Robotics – Autonomous systems, human-robot collaboration, swarm robotics, SLAM, etc. 25-02: Smart Manufacturing – Digital twins, cyber-physical systems, predictive maintenance, automated inspection, etc. 25-03: Artificial Internet of Things (AIoT) – Edge AI, federated learning, low-power sensor networks, big data, etc. 25-04: AI & Computer Vision – Machine learning, multimodal perception, vision-language models, etc. 25-05: Smart Medical Mechatronics – Surgical robotics, rehabilitation robots, AI-driven diagnostics, wearable tech, etc. Proposals outside these tracks will also be considered. Key Dates Proposal Submission: February 20 – November 30, 2025 Decision Notification: Within 30 days of submission =================================================================================== Please make your Submission only through this Google Form. =================================================================================== <span data-themer-decoration-light-background-color="rgba(0, 0, 0, 0)" data-themer-decoration-light-border-color="rgb(117, 117, 117)" data-themer-decoration-light-color="rgb(117, 117, 117)" data-themer-decoration-dark-background-color="rgba(0, 0, 0, 0)" data-themer-decoration-dark-border-color="rgba(151, 153, 155, 1)" data-themer-decoration-dark-color="rgba(151, 153, 155, 1)" data-originalfontsize="11pt" data-originalcomputedfontsize="14.666667">NOTICE: AUSMT now only accepts Special Issue Proposal Submission through the Google Form. <span data-themer-decoration-light-background-color="rgba(0, 0, 0, 0)" data-themer-decoration-light-border-color="rgb(117, 117, 117)" data-themer-decoration-light-color="rgb(117, 117, 117)" data-themer-decoration-dark-background-color="rgba(0, 0, 0, 0)" data-themer-decoration-dark-border-color="rgba(151, 153, 155, 1)" data-themer-decoration-dark-color="rgba(151, 153, 155, 1)" data-originalfontsize="11pt" data-originalcomputedfontsize="14.666667">Please do not send special issue proposal as emails to the CIE, such emails will be redirected or ignored. If you already submitted your Proposal through AUSMT Website, you can ignore this message. Thank you very much and looking forward to your contribution!
Call for papers: Special Issue (24-30)Integration of sensor data with AI for personalised health interventions
Integration of sensor data with AI for personalised health interventions Wearable sensor data can be processed by machine learning (ML) and artificial intelligence (AI) algorithms to yield insights into a person's health status. This allows for the early identification of health problems and the delivery of individualised healthcare. Healthcare providers can create individualised treatment regimens that take into account each patient's unique traits with the help of AI algorithms. Based on particular facts, treatment plans may include recommendations for therapy, personalised drug dosages, and lifestyle changes. In order to anticipate and avoid machine issues, artificial intelligence (AI) processes and analyses data from sensors, including temperature, thermal, ultrasonic, photocell, inductive, radar, LiDAR, vision, and motion sensors. Artificial intelligence (AI) algorithms enhance system precision by gaining the ability to comprehend training data. This, in turn, facilitates humans in gaining unparalleled insights into treatment variability, care processes, diagnostics, and patient outcomes. AI can assist doctors in developing individualised treatment programs for each patient that take into account their particular medical background, genetic composition, lifestyle choices, and other aspects. Both patient outcomes and healthcare costs may be improved by this focused strategy. Artificial intelligence (AI) is driving the creation of adaptive learning platforms, which use student performance data analysis to generate individualised learning plans. These systems adjust the content delivery and difficulty level in real-time based on their understanding of each student's learning style through the use of machine learning algorithms. AI, for instance, uses sensory elements like cameras and temperature sensors to examine the surroundings. One use of AI perception is autonomous driving. By detecting and measuring a wide range of characteristics, including temperature, pressure, humidity, flow rate, motion, and position, sensors perform a critical function. They make production smarter and automated by converting physical signals into electrical signals and giving the control system information in real time. In robotic systems, sensors supply essential sensory data. Position, size, direction, velocity, distance, temperature, weight, force, and many other variables that let robots perceive their surroundings and perform tasks are included in these data. AI is being used in radiology to find anomalies in pictures from CT, MRI, and X-ray scans. In pathology, artificial intelligence (AI) is used to identify and categorise various tissue types by analysing biopsy slides and microscope images. In order to detect indicators of lung nodules, breast cancer, and many other illnesses earlier and more efficiently than before, AI is assisting clinicians in the analysis of images. By evaluating large patient datasets to suggest individualised treatment regimens, optimise medicine dosages, and anticipate possible bad effects, generative AI may also assist physicians in improving patient care. It enables medical providers to make data-driven choices, enhancing diagnostic precision and customising care for each patient. The potential advantages of AI in customised medicine are enormous, even though there are still obstacles to be addressed, such as prejudice and data protection. In the end, generative AI can be a useful tool to enhance human-machine interaction in medicine, but it cannot completely replace it. Contributions are invited from a range of disciplines and perspectives, including, but not restricted to: Integration of sensor data with AI for personalised health interventions. Potential topics include but are not limited to the following: Adaptive and customised health and wellness systems using machine learning approaches. Medical sensors with artificial intelligence to support clinical judgments. An overview of data science and AI tools for managing cancer. Artificial intelligence of the future for customised healthcare. Personalised medicine uses AI techniques for data processing and utilisation. Wearable and mobile sensors to enable customised feeding. Machine learning for customised preventive adolescent health care. Medical embedded technology for customised treatment. Limitations in individualised nutrition and health care. Integrating wearable sensor data and artificial intelligence to identify and forecast cardiovascular disease. Wearable technologies, blockchain, and artificial intelligence combined for the management of chronic illnesses. Wearable sensors for monitoring and analysis of data in the medical field. Details of the Our Guest Editor Team: Prof. Usman Ahmad Usmani Assistant Professor, Universiti Teknologi Petronas, Seri Iskandar, Malaysia. Official Email: usman_19001067@utp.edu.my , usmanahmadusmani@outlook.com Google Scholar: https://scholar.google.co.in/citations?user=fPSHmRIAAAAJ&hl=en Prof. Junzo Watada Professor, Faculty of Data Science, Shimonoseki City University, Shimonoseki, Yamaguchi 751-8510, Japan Official Email: watada@waseda.jp Official Page: https://w-rdb.waseda.jp/html/100002838_en.html#item_kenkyu_keyword_2 Google Scholar: https://scholar.google.com/citations?user=pU-e-HoAAAAJ&hl=en Dr. Abdulla Yousuf Usmani, Assistant Professor, Aligarh Muslim University, Uttar Pradesh, India. Official Email: usmani.fts@gmail.com Official Page: https://www.amu.ac.in/faculty/mechanical-engineering/abdullah-yousuf-usmani Google Scholar: https://scholar.google.co.in/citations?user=_vND8fwAAAAJ&hl=en Tentative timeline for organising this special issue: Submission Deadline - 30.11.2024 Authors Notification - 25.02.2025 Revised Papers Due - 15.04.2025 Final notification - 25.06.2025
Call for papers: Special Issue (24-29):Energy-Efficient Security Protocols for Wireless Sensor Networks in Harsh Environments
Energy-Efficient Security Protocols for Wireless Sensor Networks in Harsh Environments The goal of energy-efficient routing protocols is to extend the lifespan of each SN while lowering energy usage. Conversely, energy-balanced routing strategies increase network longevity by distributing power usage among network nodes in an equitable manner. While IlTESLA offers authenticated broadcast for networks with extreme resource constraints, SNEP concentrates on data confidentiality, two-party authentication of data, and data freshness. Among the clustering routing methods used in wireless sensor networks is LEACH (Low Energy Adaptive Clustering Hierarchy). The benefit of LEACH is that every node has an equal chance of becoming a cluster head, which results in a reasonably balanced energy dissipation across all nodes. A thorough introduction to the theoretical and practical elements of these issues is given in Energy Efficiency in Wireless Networks via Fractional Programming Theory, which also discusses the solutions made possible by this method. All engineers and researchers working on contemporary communication systems will find it useful. The deployment of a battery source to power the sensor node is the foundation of the battery-driven classification; the battery itself may be fixed, removable, or rechargeable. A hardware and software network combined, the Wireless Sensor Network (WSN) employs a set of sensors to identify physical occurrences. WSN is widely used in both consumer and industrial applications. A crucial component of WSN is privacy; all data that is used, transferred, or kept must be secured. For WSN routing methods to transmit precise and secure data to users across the network, security services are required. In WSNs, a routing protocol called LEACH (Low Energy Adaptive Clustering Hierarchy) groups sensor nodes into clusters. Multi-hop routing has a number of security and privacy-related problems. Snooping, sinkholes, tampering with Sybil, clones, wormholes, spoofing, and other concerns impact the WSNs' availability, integrity, and data secrecy. Power amplifiers (PAs) are widely acknowledged as one of the most important parts of wireless communication systems, and they account for a large portion of the system's energy consumption. An energy-efficient building or gadget requires very less energy to operate. Giving WSN nodes the ability to harvest energy from their surroundings results in Energy Harvesting-based WSNs (EHWSNs). Numerous energy sources, including wind, solar power, mechanical vibrations, temperature changes, magnetic fields, and others, can be harnessed by energy harvesting. The primary challenge in developing a data collection method for wireless sensor networks (WSNS) is figuring out how to minimise sensor node energy consumption while still fulfilling application and user requirements. As was already mentioned, because WEP transmits communications over radio waves, it has the least secure protocol. Hackers may already easily grab the data as it travels thanks to this. A series of actions designed to guarantee data security. When paired with a communications protocol, it enables two parties to send data securely. In general, the phrase describes a group of related parts that function together. The Secure Sockets Layer (SSL) protocol guarantees message integrity, authenticates the origins of data, and encrypts data. Through a handshake, SSL generates session keys and negotiates security session parameters to authenticate the server. Articles are invited that explore Energy-Efficient Security Protocols for Wireless Sensor Networks in Harsh Environments. Case studies and practitioner perspectives are also welcome. Potential topics include but are not limited to the following: An examination of wireless sensor network security mechanisms. Method and algorithm development for wireless sensor networks. Studying wireless sensor network security techniques. A description of wireless sensor network security concerns. A study of security measures and assaults for wireless sensor networks. An overview of vulnerabilities, defences, and issues with wireless sensor networks. Techniques for detecting intrusions in wireless sensor networks that are connected with the Internet of Things. A low-power protocol for wireless sensor network key management. A thin security protocol designed for sensor networks that are wireless. An overview of military specifics and security concerns related to wireless sensor networks. Regarding the safety of communication protocols based on clusters for wireless sensor networks. Techniques for secure localization in wireless sensor networks. Editor Details: Dr. Yusliza Yusoff Senior Lecturer in Computer Science Department, Universiti Teknologi Malaysia, Malayisa. Email: yusliza@utm.my, yuslizayusoff.my@gmail.com Google Scholar: https://scholar.google.co.in/citations?user=6lS61CwAAAAJ&hl=en Short Bio: Dr. Yusliza Yusoff holds a Ph.D. in Computer Science from Universiti Teknologi Malaysia, with research focused on Multi Objective Evolutionary Algorithms for Machining Parameters Optimization. She has international experience from Hochschule Darmstadt University of Applied Science, where she completed an academic attachment. Dr. Yusoff's expertise spans computational intelligence, AI, numerical analysis, optimization, and applied mathematics. Her research includes algorithms, simulation, modeling, and machining. She also holds a Master’s degree in Computer Science and a Bachelor's degree in Computer Science and System Engineering from institutions in Malaysia and Japan, respectively. Dr. Kai-Qing Zhou Associate Professor of Computer Science, Jishou University, China. Email: kqzhou@jsu.edu.cn Google Scholar: https://scholar.google.com/citations?user=XFvTC7YAAAAJ&hl=zh-CN Short Bio: Dr. Kai-Qing Zhou is an Associate Professor of Computer Science, as well as the deputy dean of the School of Communication and Electronic Engineering at Jishou University. He obtained his Ph.D. degree in Computer Science at Universiti Teknologi Malaysia in 2015, master's degrees in Computer Applied Technique at Changsha University of Science and Technology in 2011, and bachelor's degrees in Computer Science and Technology at Jishou University in 2006. Zhou's current research interest lies in knowledge representation and application, including fuzzy Petri net, soft computing technique, knowledge graph, and its applications. His broad research interests focus on clinical decision support system. He is also an author of over 50 published research papers in impact journals and conferences. Dr. Murad A. Rassam Assistant Professor of Cybersecurity, Qassim University, Saudi Arabia. Email: m.qasem@qu.edu.sa Google Scholar: https://scholar.google.com/citations?user=gT83IVcAAAAJ&hl=en Short Bio: Dr. Murad A. Rassam received a B.Sc. degree in Information Engineering from the Faculty of Information Engineering, Tishreen University, Syria, in 2005, an M.Sc. degree in Information Security from Universiti Teknologi Malaysia, UTM, Malaysia, in 2010, and a Ph.D. degree in Computer Science (information security) from the Faculty of Computing, UTM, in 2013. He is currently an Assistant Professor of Cybersecurity at the College of Computer, Qassim University, Saudi Arabia. He is the recipient of several academic awards and recognitions, including the UTM Alumni Award, UTM Best Postgraduate Student Award, and UTM Excellence Award. His research interests include, but are not limited to, information assurance and security, network security, wireless sensor networks, vehicular ad hoc networks, cloud computing security, and applied data mining and machine learning in the information security domain. Timeline: Manuscript Submission Deadline: 25th Jan, 2025 Authors Notification: 25th Mar, 2025 Revised Papers Due: 05th Jun, 2025 Final notification: 15th Aug, 2025
Call for papers: Special Issue(24-28): Evolution of Robotics: Autonomous Machines in Manufacturing, Healthcare, and Personal Assistance
Evolution of Robotics: Autonomous Machines in Manufacturing, Healthcare, and Personal Assistance Robots are revolutionising automated processes and are becoming more and more common in several sectors. They are used in conveyance, industry, medicine, and cultivation, among other industries. Robots are developing from basic automated devices to smart and autonomous systems that are able to see, learn, and make decisions thanks to advances in artificial intelligence (AI). The ability of traditional robotic automation systems to adapt to dynamic and complicated situations is limited. They frequently need programming expertise and are incapable of independent learning or decision-making. The goal of integrating AI approaches with robotics is to get over these restrictions and allow robots to function on their own. Usually, healthcare robots should be regulated, despite the fact that healthcare is an extraordinarily delicate field of application and that systems that have direct influence over the environment have the potential to inflict harm in ways that humans are not always able to oversee or repair. The majority of the time, current regulations are ill-equipped to offer direction for a subject that is changing so quickly and to take into account gadgets that use AI and machine learning. Furthermore, although the subject of healthcare robotics is rich and vast, there are still many loose ends and ambiguities when it comes to terminology, technical and medical classifications, product attributes, intended uses, and purposes. There have been changes in the operation of robots that follow preset rules and logic in the fields of robotics and health sciences. In the healthcare industry, robotics is changing how individuals care for one another in a variety of ways, including by encouraging reliance on and submission to technology. Circumstances like these carry the risk of legal ramifications due to the influences and effects of growing reliance on robotic technology. Because of the demand for robots that work together, the manufacturing sector has historically seen the greatest advancements in robotic technology. In contrast, this is not prevalent in the service industries, particularly in the medical field. The healthcare industry receives insufficient attention, which has created new potential for the development of assistance robots to help those with illnesses and cognitive impairments. An overview of several robotic technology types and their applications in the healthcare industry is provided in this special issue. The technologies under assessment are an outcome of a partnership between academics and the healthcare sector, and they highlight the kind of testing and development required to create service robots before they are used in practical applications. We welcome articles exploring topics including, but not limited to: Robotics using artificial intelligence: Moving from automation to self-governing systems Development progression of autonomous systems, artificial intelligence, and robotics Autonomous robots and artificial intelligence: Implications, repercussions, and conundrums for human care Realistic prospects for artificial intelligence, machine learning, and the advancement of healthcare Health care automation: a qualitative evaluation and potential paths Intelligent technology and robotics in the workplace: revolutionising industries Powered by the Internet of Robotic Things, smart robotics applications and capabilities Service robots in healthcare using cognitive technology and wireless connections edge Robotics and intelligent machines as the engines of contemporary society In modern robotics, computer vision, algorithmic learning, and deep learning Industrial cyber-physical systems: Meeting user needs and robotics obstacles Lead Guest Editor: Dr. Kashif Hussain Memon, Associate Professor, Department of Computer Systems Engineering, The Islamia University of Bahawalpur, Bahawalpur, 63100, Punjab, Pakistan Email id: kashif.hussain@iub.edu.pk, kashifhmemon@outlook.com Google Scholar: https://scholar.google.co.in/citations?user=Ni_0_5sAAAAJ&hl=en Biography: Kashif Hussain Memon is an Associate Professor in the Department of Computer Systems Engineering at The Islamia University of Bahawalpur, Punjab, Pakistan. He specializes in Fuzzy Logic, Computer Vision, and Machine Learning, focusing on image, video, and 3D processing and analysis. He earned his PhD in Fuzzy Logic and Computational Vision from Hanyang University, and both his M.Eng. in Communication Systems & Networks and B.E. in Computer Software Engineering from Mehran University of Engineering and Technology (MUET) Jamshoro. His work has been widely recognized in the academic community, as reflected in his Google Scholar profile, showcasing his impactful research contributions. Co-Guest Editor 01: Dr. Dost Muhammad Saqib Bhatti School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea Email id: saqib@ssu.ac.kr Google Scholar: https://scholar.google.co.kr/citations?user=EBsTUi0AAAAJ&hl=en Biography: Dost Muhammad Saqib Bhatti (Senior Member, IEEE) received the Bachelor of Engineering degree from the Mehran University of Engineering and Technology, Jamshoro, Pakistan, and the M.S. and Ph.D. degrees in electronics and communication engineering from Hanyang University, Seoul, South Korea. His career in academia commenced as an Assistant Professor with the Department of Telecommunication Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan, where he was the Head of Department. Additionally, he was a Research Fellow with Hanyang University. He is currently employed with Soongsil University, Seoul. His research interests include artificial intelligence, deep learning, federated learning, 6G communication, multiaccess edge computing, and heterogeneous networks. Co-Guest Editor 02: Dr. Sajid Khan, Associate Professor, Department of Computer Science, School of Engineering, Central Asian University, Tashkent, Uzbekistan. Email id: s.khan@centralasian.uz Google Scholar: https://scholar.google.com/citations?user=q9_uvM0AAAAJ&hl=en&oi=sra Biography: Sajid Khan is an Associate Professor in the Department of Computer Science at Central Asian University in Tashkent, Uzbekistan. His research interests span Image segmentation, Image super-resolution, Semantic Segmentation, Object detection, Image classification, and Biomedical Image Processing. He is actively involved in advancing these areas through research and academic leadership at Central Asian University. His professional experience includes 10 months as a Software Engineer, two years as a Teaching Assistant at Hanyang University, and four and a half years as a Teaching experience. Deadline: Submission Deadline : 10th December, 2024 Author Notification : 20th February, 2025 Revision and Resubmission Deadline : 25th May, 2025 Paper Acceptance : 05th August, 2025
Call for papers: Special Issue(24-24): Leveraging AI and Smart Technology for Enhanced Business Intelligence in International Journal of Automation and Smart Technology
Leveraging AI and Smart Technology for Enhanced Business Intelligence Businesses can now forecast future trends and understand historical performance by integrating AI algorithms with BI platforms, which encourages proactive initiatives. AI-driven data analytics is at the centre of this revolution. A subset of artificial intelligence called machine learning algorithms is capable of processing enormous volumes of data at previously unheard-of rates, revealing previously hidden patterns and connections. During the past few decades, business intelligence has experienced considerable development. In the past, BI systems produced insights through manual study of historical data. But real-time data processing, predictive analytics, and automated decision-making have all been made possible by the emergence of AI and smart technologies. Predictive analytics models, for example, have the ability to predict consumer behaviour, market trends, and operational inefficiencies. This allows firms to make highly accurate data-driven decisions. This capacity is especially useful in sectors where prompt and accurate insights can have a big influence on results, like banking, healthcare, and retail. Improving customer insights is one of the most interesting uses of AI and smart technologies in business intelligence. Natural language processing (NLP) and sentiment analysis allow organisations to examine client feedback from a variety of sources, such as surveys, social media, and reviews. This makes it possible to comprehend client preferences, problems, and new trends on a deeper level. AI-driven chatbots and virtual assistants, for instance, can offer customised client interactions, enhancing pleasure and loyalty while also collecting important data for business intelligence. Smart technology, which includes edge computing and the Internet of Things (IoT), is essential for streamlining corporate processes. Real-time data is gathered by IoT devices from a variety of sources, including environmental sensors, supply chain operations, and manufacturing equipment. After that, this data is processed closer to the source, at the edge, which lowers latency and speeds up decision-making. For example, AI-powered predictive maintenance in manufacturing can detect possible equipment breakdowns before they happen, limiting downtime and cutting expenses. Another area where AI and smart technology are having a big impact is supply chain management. Supply chain data may be analysed by AI algorithms to improve logistical efficiency, estimate demand, and manage inventory levels. Real-time visibility into the flow of commodities is made possible by smart sensors and RFID technology, which improves coordination and lowers the possibility of disruptions. Businesses can build supply chains that are more flexible and resilient, able to adjust to shifting market conditions, by utilising artificial intelligence and smart technology. Not only are AI and smart technologies useful for data analysis, but they also significantly facilitate strategic decision-making. Decision-makers may access actionable insights, data visualisations, and intuitive dashboards using advanced BI solutions with AI capabilities. These platforms have the ability to highlight key performance indicators (KPIs), create automated reports, and provide advice based on data analysis. Consequently, managers and executives are able to promptly make well-informed judgments and match their strategies to the demands of the market and the company's objectives. The business intelligence landscape is changing as a result of the integration of AI and smart technologies, which presents hitherto unseen chances for innovation and competitive advantage. We welcome contributions from a variety of fields and viewpoints, such as but not limited to: Leveraging AI and Smart Technology for Enhanced Business Intelligence. Potential topics include but are not limited to the following: Utilising Reciprocal Symmetry and AI to Gain Business Intelligence. Leveraging business intelligence to achieve strategic expansion. Business intelligence trends and possibilities related to machine learning and artificial intelligence. Applying Business Intelligence to Promote Innovation in Cyber Security. Optimising Business Intelligence Systems to Increase Corporate Competitiveness. Data analysis and business intelligence using artificial intelligence. A comparison of big data analytics with artificial intelligence and business intelligence. Artificial intelligence and big data analytics are used to create value based on data. Applying deep learning to improve organisational management's business intelligence. Business intelligence and the utilisation of information in healthcare enterprises from a managerial perspective. Employing artificial intelligence to improve supply chain efficiency. Increasing corporate intelligence through big data analytics services. Guest Editor Detailed Information: Dr. Norshakirah Aziz Associate Professor, Department of Computer and Information Sciences, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia Research Link: https://scholar.google.com.my/citations?user=7ydYY4MAAAAJ Email: norshakirah.aziz@utp.edu.my, norshakiirah.my@gmail.com Dr. Hiroyuki Iida Professor Information Science, Human Information Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1292 Japan Research Link: https://scholar.google.com/citations?user=zVSgcLMAAAAJ Email: iida@jaist.ac.jp Dr. Abdullahi Abubakar Imam School of Digital Science, Universiti Brunei Darussalam, Gadong BE1410, Brunei Research Link: https://scholar.google.com.my/citations?user=6XPY-XoAAAAJ Email: abdullahi.imam@ubd.edu.bn Important Dates: Submissions Due: 05.01.2025 Preliminary Notification: 10.03.2025 Revisions Due: 15.05.2025 Final Notification: 20.07.2025 Publication date will be based on journal decision
Call for papers: Special Issue(24-23): Cloud Computing for Corporate Sustainability in Digital Era
Cloud Computing for Corporate Sustainability in Digital Era Cloud computing has emerged as one of the most powerful and disruptive technologies transforming IT. Clouds are a large group of computers known as servers connected to the internet. Cloud computing is a model for enabling convenient, on-demand access to shared pools of data, computational power, and storage in which the underlying capacity is provided as a service. The cloud ensures that the data and applications are available to users anywhere, anytime across any device, along with strong security features to ensure that it remains private. Cloud computing can be for several reasons: to save space on your hard drive, using advanced software solutions, or to keep files saved and backed up in a protected location. Cloud computing plays a significant role in corporate sustainability as it promotes the essential idea of a shared economy. It incorporates the life-cycle analysis and assesses the environmental impacts at all stages, right from manufacturing to transportation, installation, usage, and recovery or disposal. Cloud computing is considered responsible for reducing carbon emissions; it allows customers to access their services at any time while reducing their power consumption. With cloud computing, companies can share their servers and disk space virtualization technologies that allow several virtual machines to run on a single physical server to optimize resource utilization. Data centers' efficiency increases with cloud computing as customers primarily create most of the data themselves. Cloud Computing is set to make a huge impact on the business world. It does not only help in the reduction of costs, but it is also creating entirely new ways of communicating and collaborating for business organizations. Cloud computing brings the concept of green information technology by recycling its resources completely from one area to another. Cloud computing is uniquely suited to be the disruptive technology that helps bring down emissions and save energy in IT. It pools vast amounts of distributed computing power, storage, applications, and other resources on-demand—matching them to user needs as they arrive online. In addition, it also helps in the conservation of energy by providing smart work systems. By providing a single “big pipe” with shared resources, cloud computing also eliminates the wastefulness of widely dispersed stand-alone server rooms. Cloud computing offers a new way to operationalize IT green, sustainable way. Shared infrastructure has been shown to reduce electricity and carbon emissions, with the unit economics of cloud computing further driving sustainability. Cloud computing also accelerates enterprise innovation, again by providing the ability to use shared infrastructure rather than trying to construct everything from scratch. This reduces time-to-value for enterprises developing their next-generation business models. Modern cloud computing solutions have enabled corporate entities to achieve unprecedented networked productivity, efficiency, and communication levels. However, despite their many benefits, cloud-based services incur substantial risks to the security and integrity of corporate data. There are significant cost savings in individual energy efficiency, the cost-sharing model of cloud computing can lead to more energy consumption. Another challenge stems from the fact that even though certain benefits are gained, there is also a need to invest significantly in cloud infrastructure. As a result, there is an overall net increase in carbon emissions. Submissions are welcome for articles that examine scientific advice from various academic perspectives, including science and technology studies. Insights from practitioners and case studies are also welcome. List of topics: Challenges of cloud computing for cooperate sustainability. An initial approach for handling these challenges in implementing cloud computing employing a maturity model. Study on the role of cloud computing for uniting sustainability. Study on the provide information security and privacy in cloud computing. Study on legal, ethical, social, and commercial challenges in cloud computing. Cloud technologies to improve business sustainability in today’s digital era A significant change in the data storage and management market using cloud computing. Cloud computing for corporate sustainability reduces a company's IT hardware and infrastructure use. Bosting business operations and efficiency, decreasing carbon emissions, energy consumption, and reducing environmental costs. Study the modern business world for high interconnection and dependence on cloud computing. Study on relations between cloud computing adoption to improve an organization's sustainability. Identifying, analyzing, and discussing the elements of cloud computing with emphasis on corporate sustainability. Timeline for this Special Issue: → Submission Deadline: 08.12.2024 → Notification to Author: 16.02.2025 → Revised Version Submission: 08.05.2025 → Final Acceptance: 16.07.2025 Guest Editor Information: Dr. Joseph Bamidele Awotunde Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin 240003, Kwara State, Nigeria → Email: awotunde.jb@unilorin.edu.org, awotunde.joba@gmail.com → Google scholar: https://scholar.google.com/citations?user=3hiPMRAAAAAJ&hl=en → ORCiD: https://orcid.org/0000-0002-1020-4432 Dr.Akash Kumar Bhoi Directorate of Research, Sikkim Manipal University, Gangtok, Sikkim, 737102, India → Email: akash.b@smit.smu.edu.in → Google scholar: https://scholar.google.com/citations?user=1pFjkMsAAAAJ&hl=en → ORCiD: https://orcid.org/0000-0003-2759-3224 Dr.Paolo Barsocchi Institute of Information Science and Technologies, National Research Council, 56124, Pisa, Italy → Email: paolo.barsocchi@isti.cnr.it → Google scholar: https://scholar.google.com/citations?user=3oaaXWUAAAAJ&hl=en → ORCiD: https://orcid.org/0000-0002-6862-7593
Call for papers: Special Issue(24-21):Image Processing Techniques for Improved Medical Image Segmentation
Image Processing Techniques for Improved Medical Image Segmentation The process of extracting the desired object (organ) from a medical image (2D or 3D) can be done manually, semi-automatically, or totally automatically. Medical image segmentation is fundamentally the same as natural image segmentation. Medical image processing starts with obtaining the raw data from CT or MRI scans and reconstructing them into a format that can be utilised by the appropriate software. An image processing usual input is a 3D bitmap or grayscale intensities with a grid of voxels (3D pixels). Picture segmentation is the process of breaking down a picture into a group of pixel-rich sections that can be represented by a labelled image or a mask. Image segmentation is the process of breaking down a picture into a group of pixel-rich sections that can be represented by a labelled image or a mask. By separating a picture into segments, one can process only the critical segments of the image instead of processing the complete image. Images with distinct boundaries between objects and backdrop can benefit from this technique. Medical image processing commonly uses threshold-based segmentation to detect anomalies like cancer. It is also used in traffic analysis for vehicle detection and tracking. There are four main segments in the worldwide radiology market: services, type, end-user, and region. The global market is divided into segments based on services: computed tomography, positron emission tomography (PET), ultrasound, nuclear medicine, X-rays, fusion imaging, and magnetic resonance imaging (MRI). To identify the ideal target population, markets can be segmented in a variety of ways. Market segmentation can be done in five different ways: psychographic, behavioural, geographic, and firmographic. Watersheds, region expanding, k-means clustering, thresholding, and histogram-based bundling are examples of previous techniques. More sophisticated algorithms, however, rely on sparsity-based techniques, conditional and Markov random fields, graph cuts, and active contours. Semantic and instance segmentation are the two main categories of image segmentation. While related items in instance segmentation receive separate labels, all objects of the same kind are recognized using a single class label in semantic segmentation. Image segmentation has several benefits, such as multiple copy systems, faster processing, and ease of data storage. The necessity for great precision and findings that are prone to error are drawbacks. In the realm of computer vision, segmenting an image into meaningful and coherent parts is a key problem. Numerous applications, including object recognition, image manipulation, and scene interpretation, depend on this vital activity. Regions of interest (ROIs) are extracted from 3D image data, such as those from CT or MRI scans, as part of the process of medical image segmentation. Every imaging technology has a different use. For instance, radiography is frequently used to take pictures of bone structures in order to check for fractures. The brain and other interior tissues are frequently imaged with magnetic resonance imaging (MRI) scanners, especially when high-resolution images are required. Faster, more sophisticated processing of pictures is made possible by image segmentation, which divides an image's complex visual data into precisely formed segments. Articles are invited that explore Image Processing Techniques for Improved Medical Image Segmentation. Case studies and practitioner perspectives are also welcome. Potential topics include but are not limited to the following: The fundamental concepts and overview of segmenting medical images. An overview of methods for segmenting medical images. Techniques, algorithms, and applications for segmenting medical images. Deep learning-based medical image categorization. A modified Otsu method-based medical picture segmentation algorithm. Segmentation of medical images using enhanced watershed method and k-means clustering. A revolutionary method for enhancing medical image segmentation. better FCM for medical picture segmentation. An overview of segmenting medical images interactively. Current medical image segmentation investigation. An overview of techniques for segmenting medical images using deep learning. Enhanced watershed transform with previous knowledge for medical picture segmentation. A multi-dimensional statistical feature-based approach to segment medical images. A manual for processing and analysing medical images. Our outlined tentative timeline schedule for the special issue: Call for Papers: [10.08.2024] Manuscript Submission Deadline: [20.12.2024] Peer Review Process: [30.02.2025] Final Manuscript Submission Deadline: [10.05.2025] Expected Publication Date: [20.07.2025] Guest Editor Details: Dr. Rabbia Mahum Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan --------------------------------------------------------------- Email id: rabbia.mahum@uettaxila.edu.pk, rabbia.mahum@outlook.com IEEE Website: https://ieeexplore.ieee.org/author/37088878359 Google Scholar Website: https://scholar.google.com/citations?user=gKQtYFcAAAAJ&hl=en Orcid Website: https://orcid.org/0000-0003-1983-8201 --------------------------------------------------------------- Dr. Haseeb Hassan College of Big Data and Internet, Shenzhen Technology University (SZTU), Shenzhen 518118, China. --------------------------------------------------------------- Email id: haseeb87@yahoo.com IEEE Website: https://ieeexplore.ieee.org/author/37086929062 Google Scholar Website: https://scholar.google.com/citations?user=CES_SQIAAAAJ&hl=en ResearchGate Website: https://www.researchgate.net/profile/Haseeb-Hassan-5 --------------------------------------------------------------- Dr. Muhammad Awais College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China --------------------------------------------------------------- Email id: dr.muhammad.awais@henau.edu.cn Google Scholar Website: https://scholar.google.com/citations?user=TC9Z6c0AAAAJ&hl=en Scopus Website: https://www.scopus.com/authid/detail.uri?authorId=58089012000 --------------------------------------------------------------- Dr. Mohammed A. Elmeligy Advanced Manufacturing Institute, King Saud University, Riyadh, Saudi Arabia --------------------------------------------------------------- Email.id: melmeligy@ksu.edu.sa IEEE Website: https://ieeexplore.ieee.org/author/37086320498 Orcid Website: https://orcid.org/0000-0001-7967-4259 ---------------------------------------------------------------
Call for papers: Special Issue(24-20): Novel Machine Learning Methods for Anomaly Detection in Sensor Networks
Novel Machine Learning Methods for Anomaly Detection in Sensor Networks Machine learning has become a potential tool for anomaly identification. Without the requirement for manual rule development, machine learning algorithms are able to learn from sensor data and identify patterns that indicate both normal and abnormal behaviour. Along with more conventional methods like Isolation Forests and One Class SVMs, promising machine learning techniques for anomaly identification include deep learning techniques like autoencoders and convolutional neural networks. These techniques are excellent for identifying odd patterns in data from a variety of fields. This approach is a tool for anomaly detection in regression modelling or density based classifiers. One statistical method for determining the link between labelled and variable data is regression modelling. HMM and clustering are employed in an unsupervised learning system to identify abnormalities in network traffic. The advantages and disadvantages of each unsupervised approach are discussed in this paper along with the accuracy level. Categorical and continuous data are the primary applications of reinforcement learning. Anomalies are essentially singular, unexpected data items that occur inside a dataset. The words are frequently used synonymously with outliers. Novelties, on the other hand, are anomalies in data that are unique to each new instance. The original dataset does not contain them. It is a well known instance based non parametric method for identifying abnormalities. Finding irregularities in the data based on their distance from nearest neighbours is the main objective of this approach. An alternative approach would be to use a rigorous mathematical model to describe the definition of regular system usage and then mark any violation from this model as an attack. The term strict anomaly detection describes this. Additional ways for identifying abnormalities include artificial immune systems, grammar based algorithms, and data mining techniques. Anomaly detection is used in the manufacturing and industrial sector for equipment predictive maintenance. Machine sensors gather information on a range of characteristics, including vibration, sound, and temperature. Using machine learning algorithms, this data is analysed to find trends that might point to equipment breakdowns. Novelty detection makes it possible to spot data patterns that noticeably deviate from typical behaviour. It makes it possible to classify and identify items accurately that were unknown to the model throughout its learning phase. Unsupervised anomaly detection is most commonly implemented using artificial neural networks. Because of their layered, non linear learning, ANNs can be trained on vast, unlabeled datasets and are reliable in identifying complex patterns that help classify anomalies of a wide range. Unsupervised anomaly detection algorithms, which can recognize unusual or rare events automatically, may be used in these situations by data scientists. A cloud cost estimator, for instance, can search for odd increases in processing costs or data egress charges that might be the result of a badly constructed algorithm. Reducing human labour, enabling proactive countermeasures, and identifying unexpected events or irregularities in data are some advantages of anomaly based detection. The capacity to identify unknown attacks and adjust to novel attack patterns are two advantages of anomaly based detection. Articles are invited that explore Novel Machine Learning Methods for Anomaly Detection in Sensor Networks. Case studies and practitioner perspectives are also welcome. The topics relevant to this special issue include but are not limited to: Machine learning algorithms for anomaly detection in wireless sensor networks. Using multi parameterized edit distance and machine learning, wireless sensor networks can detect anomalies over the short and long term. An investigation on anomaly detection techniques in wireless sensor networks using machine learning. Machine learning anomaly detection to find IoT system sensor manipulation. Machine learning for sensor network anomaly assessment for aerospace NDT. A new adaptive machine learning approach for network anomaly detection. A hybrid machine learning approach for identifying anomalies in wireless sensor network unlabeled data. Enhanced transitory extreme learning system for anomaly detection in wireless sensor networks. A machine learning strategy based on measurement data for industrial control systems anomaly detection. Wireless sensor network anomaly detection with ensemble random forest. Machine learning techniques are being used to detect attacks and anomalies in IoT sensors at IoT locations. A novel method of employing support vector data description for anomaly detection in wireless sensor networks. Guest Editor Biographies: Dr. Md. Tarek Habib Assistant Professor Department of Computer Science and Engineering Independent University, Bangladesh Email: tarek@iub.edu.bd, mhd.tarekhabib@outlook.com Website : http://www.cse.iub.edu.bd/index.php/faculties/73 Biography: Md. Tarek Habib achieved his Ph.D. degree at the Department of Computer Science and Engineering at Jahangirnagar University in 2022. He obtained his B.Sc. degree in Computer Science from BRAC University in 2006. Then he got an M.S. degree in Computer Science and Engineering from North South University in 2009. He is an Assistant Professor at the Department of Computer Science and Engineering at Independent University, Bangladesh. Earlier, he was an Associate Professor at the Department of Computer Science and Engineering at Daffodil International University. He is much fond of research. He has published many articles in international journals and conference proceedings. His research interest is in Artificial Intelligence, especially Machine Learning, Artificial Neural Networks, Computer Vision, and Natural Language Processing. Recently, he has started working on IoT too. Dr. Abdullah Al-Mamun Assistant Professor School of Computer and Cyber Science, Augusta University, Augusta, GA 30912, United States. Email: aalmamun@augusta.edu Website: https://www.augusta.edu/faculty/directory/view.php?id=AALMAMUN Biography: Abdullah Al-Mamun is an assistant professor at the Computer and Cyber Sciences Department at Augusta University. His research interests encompass resource-efficient data management, distributed systems, and high-performance computing. He collaborates with many industries and academic partners to solve big and significant problems, ranging from HPC infrastructures to large-scale data-intensive computing systems. His research outcomes have been published in premiere venues such as SC, ICDE, IPDPS, NDSS, and ICPP and turned into software prototypes. Dr. Ismail Jabiullah Professor Department of Computer Science and Engineering, Daffodil International University, Birulia, Bangladesh Email: drismail.cse@diu.edu.bd Website: https://faculty.daffodilvarsity.edu.bd/profile/cse/jabiullah.html Biography: Md. Ismail Jabiullah is a distinguished academic with over 33 years of experience in teaching, research, and development in Computer Science and Engineering. He serves as a professor in the Department of Computer Science and Engineering at Daffodil International University, Birulia, Bangladesh, under the Faculty of Science and Information Technology. His teaching areas include Software Engineering, Cryptography and Network Security, and E- Commerce. Dr. Jabiullah has authored 27 books, published 78 journal papers, and presented 105 conference papers. His research interests span Network Security, Web Security, Software Security, Image Processing, Computer Vision, Wireless, Cellular, and Satellite Networks, Artificial Intelligence, Neural Networks, Software Testing, Machine Learning, and Deep Learning. Tentative Dates: Submission Deadline: Dec. 20, 2024 Authors Notification: March. 10, 2025 Revised Papers Deadline: Jun. 10, 2025 Final notification: Sep. 15, 2025
Call for papers: Special Issue(24-19): Predictive AI Models for Patient Readmission Risk and Resource Allocation
Predictive AI Models for Patient Readmission Risk and Resource Allocation One of the most expensive issue confronting healthcare systems is hospital readmissions, which are usually reliably predicted by traditional models. A lot of the approaches that are currently in use only employ expensive, dataset-specific manually-engineered parameters. The goal was to create and assess algorithms that use equipment for algorithms learning that autonomously derived characteristics from temporal data in order to anticipate hospital readmissions. Neural network approaches to acquire the vectors of features from everybody's cumulative healthcare record. These records span a minimum of several generations before the patient's initial hospitalisation and are sourced from a variety of sources. Each patient has a significantly different quantity of these information. By allocating time to the appropriate tasks, a team with a fair resource utilisation rate makes sure that the endeavour as a whole move ahead. Because it provides you with a more comprehensive view of your workers' burden, resource utilisation is particularly crucial if your staff members are engaged on numerous assignments. By starting to schedule individuals for the mission with utilisation understanding, you may prevent employee exhaustion and excessive utilisation by having a real-time insight of assignments, processes, and resource allocation. It is possible to effectively target healthcare efforts to lower the risk of readmission by predicting if an arriving patient is at high risk. In the era of massive data, we can identify the risk of rehabilitation by using machine learning to evaluate a cohort of factors. The lack of simplicity is one of the barriers preventing artificial intelligence from being widely used in healthcare. In addition to that, it offers many ways to comprehend the rationale behind the model's assumptions, ranging from the overall collection at global scale to the level of individual patient sightings. These justifications raise trust in the model's ability to make decisions. An algorithm for scheduling is a collection of guidelines that establishes that has to be done when. Numerous transmission rescheduling methods have been presented in the literature; however, their design is hindered by several factors such as technical intricacy, integrity, and the necessity to handle varying service levels. In communication networks, planning and resource allocation are crucial elements. The issue of predicting which users will be online within a specific time window is referred to as scheduling. The issue of distributing tangible layer resources, such lighting and bandwidth, among several active users is recognised as resource allocation. Papers are invited that consider, but are not limited to, the following themes: Challenges to developing a synthetic knowledge model for impulsive readmissions. Predicting readmissions using deeply contextualised clinical concept embedding. Probability of postoperative Intensive Care hospital readmission using neural networks. Immediate care predictive modelling: an analysis of machine learning techniques. Integrating electronic health information to enhance hospital readmission predictions using predictive analytics. Predicting ICU returning by data mining with clinical physiology on evacuation. Using several models to forecast patient readmissions in critical care units. Estimating patient readmissions in medical facilities using multiple models. Predicting readmissions to hospitals using persistent mobile data sources. Monitoring hospital readmissions with assistance vector machines powered by smart swarms. A modified multiple-phase classification approach to evaluate the risk of patient readmission. Advantages and obstacles in implementing predictive modelling in computerised health care. Guest Editor Information: Dr. Engr. Abdul Kadar Muhammad Masum Professor, Department of Software Engineering, Faculty of Science and Information Technology, Daffodil International University, Birulia, Savar, Dhaka –1216, Bangladesh Official Email: masum.swe@diu.edu.bd Personal Webpage: https://faculty.daffodilvarsity.edu.bd/profile/swe/kadar.html Scholar Webpage: https://scholar.google.com/citations?user=SOZ8uAEAAAAJ&hl=en Research Areas: Machine Learning, Business Informatics, Health Informatics, ICT applications in Business, Education and Governance Dr. Md Zia Uddin Senior Research Scientist, Sustainable Communication Technologies Department, SINTEF Digital, Oslo, Norway Official Email: zia.uddin@sintef.no Personal Webpage: https://sites.google.com/site/webpagezia/ Scholar Webpage: https://scholar.google.com/citations?user=YFu7iB8AAAAJ&hl=en Research Areas: Image Processing, Sensing, Robotics, Computer Vision, Smart Home Healthcare, Artificial Intelligence, Machine Learning, Pattern Recognition Dr. Md Abdus Samad Professor, Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, South Korea Official Email: masamad@yu.ac.kr Personal Webpage: https://www.drabdus.info/ Scholar Webpage: https://scholar.google.com/citations?user=NmUWfQIAAAAJ&hl=en Research Areas: Data Science, Wireless communication, RF propagation, Signal Processing, Machine Learning Tentative Timeline: Deadline for manuscript submissions will be February 20, 2025. (Revised) Expected publication date will be based on Journal decision
Call for papers: Special Issue(24-08):Smart Grid Technologies for Demand Response and Grid Resilience
Smart Grid Technologies for Demand Response and Grid Resilience Smart grids with demand response. DR models were developed in order to preserve the supply and demand trade-off in load management systems. One of the key tactics used by power companies to reduce customer energy use during peak hours is demand response (DR). In order to better match the supply and demand of power in real time, reduce costs, and preserve the stability and dependability of the grid, smart grids integrate digital technology, sensors, and software. Demand response is the process of persuading consumers to change their electricity use to periods when there is more availability of electricity or when there is less demand overall. This is usually done through price adjustments or other financial incentives. Resilience to network interruptions is referred to as grid resilience. Furthermore, a self-healing smart grid is one that can recover swiftly on its own without assistance from humans. In a Smart Grid (SG), Demand Side Management (DSM) refers to the intelligent management of load appliances. Customers can save money using DSM programs because they lower their electricity rates, minimise peak demand from the utility, and increase load factor. The two types of networks used by smart grids for communication are WAN and HAN. The smart metre and household appliances are connected via HAN. A home area network can be based on a variety of technologies, including Bluetooth, Wireless Ethernet, Wired Ethernet, and Zigbee. Demand response enhances the efficiency and dependability of the grid by assisting utilities in managing demand and optimising energy delivery. To reduce the impact on the business, GridPoint offers tailored load curtailment along with automatic demand response. Being paid to remain in a hotel while your flight is overbooked is another way to compare demand response to that. Instead of renting a new aircraft, the airline would prefer to pay for your hotel stay. In a similar vein, the grid operator would prefer to compensate you for using less than to construct a new power plant. Because its planners, designers, and operators anticipate, prepare for, and react to changing grid circumstances, a resilient power grid can withstand, respond to, and recover quickly from significant power outages. Demand response gives users the chance to actively participate in the functioning of the electrical grid by adjusting or lowering their peak-time electricity use in response to time-based pricing or other types of monetary incentives. A smart grid will collect data on prices and grid conditions, precisely limit electricity power to the residential level, network small-scale distributed energy generation and storage devices, communicate information about operating status and needs, and transition the grid from a centralised to a collaborative network. By balancing surges in electricity usage, V2G helps lessen grid overload during peak hours. For instance, to increase grid stability and optimise the advantages of renewable energy, V2G can transfer energy, that is, unused battery capacity back into the power grid from the battery of an electric car. The WAN connects numerous NANs and LDCs and operates over large geographic areas, acting as the backbone of the smart grid communication infrastructure. We encourage submissions from a variety of fields and viewpoints, such as but not limited to: Smart Grid Technologies for Demand Response and Grid Resilience. Potential topics include but are not limited to the following: In an effort to render the grid resilient to natural calamities, demand response is used to restore distribution services. An investigation of smart grid restoration to improve the resilience of cyber-physical systems. Innovative green energy solutions for a resilient and sustainable grid of the future. Microgrids to improve the resilience of the electrical grid under harsh circumstances. Combining emergency and preventive actions to improve the resilience of the electricity grid. Machine learning-based secure and robust demand side management engine for Internet of Things smart grid. Demand response management strategy for smart grid systems that is both scalable and resilient. Cyber-constrained optimal power flow model for enhancing smart grid resilience. Demand response program impact study on dynamic clustered distribution systems resilience. Adaptive demand and renewable energy system operations together in a smart grid. Enhancement of the distribution systems' resilience through demand response and operational resources. An unusual smart grid resilient demand response system based on microgrids. uest Editor Information: Dr. Emmanuel Kweinor Tetteh Assistant Professor, Durban University of Technology, Durban, South Africa Email-ID: Emmanuelk@dut.ac.za, Emmanuelktza@gmail.com Google Scholar: https://scholar.google.co.uk/citations?user=gyl5umYAAAAJ&hl=en Official Website: https://www.dut.ac.za/dr-emmanuel-kweinor-tetteh/ Dr. Ifeanyi Michael Smarte Anekwe School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa. Email-ID: Ifeanyi.anekwe@wits.ac.za Google Scholar: https://scholar.google.com/citations?user=XD0RansAAAAJ&hl=en Dr. Sherif Ishola Mustapha School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa. Email-ID: Sherif.mustapha@wits.ac.za Google Scholar: https://scholar.google.com/citations?user=Zz0Ve_oAAAAJ&hl=en Dr. Edward Kwaku Armah School of Chemical and Biochemical Sciences, K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana. Email-ID: Edwardkarmah@gmail.com Google Scholar: https://scholar.google.co.za/citations?user=iDu8Cv4AAAAJ&hl=en Manuscript Deadline: Submission Deadline : Mar 25, 2025 Authors Notification : Jun 15, 2025 Revised Papers Deadline : Aug 30, 2025 Final Notification : Nov 15, 2025
Call for papers: Special Issue(24-07):Simulation-based Modeling for Human Computer Interaction in Modern Video Games
Simulation-based Modeling for Human Computer Interaction in Modern Video Games The swift evolution of the computer gaming industry has led to real-time video gaming events and amusements that have attracted many people and have become more than just simple entertainment. Researchers and practitioners have classified video games as games for education and related activities, games for play therapy, games for counselling, games with simulation and modelling, and application-based games. Much attention has been paid recently to making and using games for the good of society. Game systems are made up of three main parts: input, process, and outcome. At a high level, each of these parts is important. System input is about how things outside the game system affect the game system. People's personalities and why they play games could be part of it, but it's not the only thing. The process talks about how the game works and what it's like to play. Including gameplay in the system outcome is a good thing. It is affirmative that all the end users have their role in all three parts. Modern Video games are an obvious subject of investigation in Human-Computer Interaction. HCI is the vital core component in creating video games because it allows us to interact with computers. In contrast to the conventional idea of HCI, video game interaction varies in how video games are played by people rather than used by them. HCI professionals can assist in creating and developing better games for the benefit of society by evaluating player characteristic features, acquaintances throughout the game, and behavioral implications of the gameplay. Recent video games based on Persuasive techniques, games with crowdsourcing patterns, games for healthcare, games for fitness and well being, games for neurological therapy, games based on virtual reality / augmented reality, games for enhancing psychological perspectives, Live Action Role Play games, games with hegemonic features, realism and verisimilitude games and games with an alternate reality are HCI aided games. HCI collaborates with the gaming industry to produce new standards, such as the visual user experience for games in structuring the screen's layout, creating pragmatic 3D visuals, true graphics, navigation, and selecting commands or buttons used in gameplay. Data analytics, Speculative game design approaches, customization and veracity capturing, blockchain, and monetization techniques in gaming applications are viewed in the principles of HCI. Hence, this special issue focuses on the Role of Human-Computer Interaction in Modelling and Simulation of Modern Video Games. The topics relevant to this special issue include but are not limited to: HCI-based persuasive video gaming models for differently-abled persons Advanced distributed cognition technique for HCI in Modelling and Simulation the Modern Video Games Novel crowdsourcing patterns for modern video gaming techniques AR/VR based modern gaming technique for advanced neurological therapy Advanced simulation model for creating pragmatic 3D visualization for education Advanced Cognitive psychology for HCI in Modelling and Simulation the Modern Video Games Enhanced blockchain-enabled monetization techniques for HCI-based PlayStations HCI based Data analytics algorithms in Modelling and Simulation 3D PlayStations Futuristic video gaming designs for customization and veracity capturing User Interface Design HCI based realism and verisimilitude 3D gaming platform for special children. Special Issue Timeline: Submission Deadline: March 30, 2025 Author Notification: May 21, 2025 Revised Submission: July 31, 2025 Final Acceptance: Nov 1, 2025 Guest Editor Information: Managing GE: Dr. Mehdi Gheisari Assistant Professor, Department of Computer Science, Islamic Azad University, Tehran, Iran. Email: mehdi.gheisari61@gmail.com, mehdigheisarics@outlook.com Google Scholar Link: https://scholar.google.com/citations?user=tmWQt9UAAAAJ&hl=en ORCID: https://orcid.org/0000-0002-5643-0021 Biography: Mehdi Gheisari is a Ph.D. holder in computer science. His research interests are IoT, Healthcare, Smart City, Big Data, Artificial Intelligence, Machine Learning, Remote Sensing Data, Distributed Systems, Autonomous Vehicles and Cyber Security. Furthermore, he has published papers in several domains in highly-ranked journals and ranked conferences. He has also been serving in academic works, such as reviewing papers from several well-known venues such as IEEE communication magazine, TPC of several conferences, etc. Beyond that, he served as an associate editor and Guest editorial member of numerous venues, such as IEEE JSTAR, Cryptography, JHCE, etc. First Co-GE: Dr. Subhendu Kumar Pani Professor, Krupajal Engineering College, Biju Patnaik University of Technology, India. Email: pani.cse@oec.ac.in Google Scholar: https://scholar.google.com/citations?user=Uyp4TWsAAAAJ&hl=en ORCID: https://orcid.org/0000-0001-9595-0567 Biography: Subhendu Kumar Pani received the Ph.D. degree from Utkal University, Odisha, India, in 2013. He has published 51 International Journal articles (25 Scopus index). His professional activities include roles as the Book Series Editor (CRC Press, Apple Academic Press, and Wiley-Scrivener), an associate editor, an editorial board member, and a reviewer of various international journals. He is an associate with number of conference societies. He has more than 150 international publications, five authored books, 15 edited and upcoming books, and 20 book chapters into his account. His research interests include data mining, big data analysis, Web data analytics, fuzzy decision making, and computational intelligence. He is a fellow in SSARS Canada and a Life Member in IE, ISTE, ISCA, OBA.OMS, SMIACSIT, SMUACEE, and CSI. He was a recipient of five researcher awards. Second Co-GE: Dr. Kamalakanta Muduli Professor, Department of Mechanical Engineering, The Papua New Guinea University of Technology, Papua New Guinea. Email: kamalakanta.muduli@pnguot.ac.pg Google Scholar: https://scholar.google.co.in/citations?user=LdMwYiIAAAAJ&hl=en Linked-In: https://in.linkedin.com/in/kamalakanta-muduli-98119076 Biography: Dr. Kamalakanta Muduli obtained PhD from the School of Mechanical Sciences, IIT Bhubaneswar, Orissa, India. He has published 75 papers in peer-reviewed international journals, most of which are indexed in Clarivate Analytics, Scopus, and listed in ABDC, and more than 34 papers in national and international conferences. He has also been guest editing a few special issues in journals and books approved for publication by Springer, Taylor and Francis, MDPI, CRC Press, Wiley Scrivener, and Apple Academic Press. Dr. Muduli has also guided three PhD students. His current research interests include materials science, manufacturing, sustainable supply chain management, and Industry 4.0 applications in operations and supply chain management. Dr. Muduli is a fellow of the Institution of Engineers of India. He is also a senior member of the Indian Institution of Industrial Engineering and a member of ASME.
Important: IMPORT Notice: Stop accepting new SI proposals in 2024.
IMPORT Notice: The number of Special Issues reached a limited number in 2024. From 30 July 2024, we will stop accepting new SI proposals. We apologize for any inconvenience. Best regards, Editorial Office, AUSMT. 30 July 2024 Taipei Time 19:30 (GMT +8) S
Call for papers: Special Issue(24-18): Real-Time Face Detection Algorithms for Internet of Things (IoT) Applications
Real-Time Face Detection Algorithms for Internet of Things (IoT) Applications Personalized customer interactions in stores and improved security in smart homes are just two examples of the many Internet of Things (IoT) applications that depend heavily on real-time facial identification. This technique utilizes the computing power of Internet of Things (IoT) devices for seamless data collecting and analysis. It includes the real-time detection and accurate location of human faces inside photos or video streams. Real-time face detection improves Internet of Things applications by enabling personalized user interactions, improving security by instantly recognizing individuals, automating smart home and office automation by identifying residents or staff, helps to monitor patient conditions and emotions in the healthcare industry, and enhancing customer analytics and personalized marketing in the retail sector. In real-time face detection, cameras or other sensors are used to capture images or video streams. Noise reduction, normalization, and resizing are then applied to improve image quality. Many techniques and tools are available for real-time face detection in Internet of Things applications such as Haar Cascade, which uses the AdaBoost learning algorithm for efficient and fast face detection. The gradient orientation histograms are computed via Histogram of Oriented Gradients (HOG) in order to extract features. Applications like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) assist significantly from the high accuracy and speed of convolutional neural networks (CNNs). MTCNN (Multi-Task Cascaded Convolutional Networks) integrates many tasks to improve detection performance. Current developments in real-time face recognition for the Internet of Things involve edge computing, which internally processes data to reduce latency and consumption of bandwidth. Using facial recognition combined with other biometric data for increased security and user interaction, employing cloud-based solutions for scalable and strong face detection, and merging AI and machine learning with advanced neural networks for increased accuracy. Future directions for real-time face detection in IoT include creating more sophisticated algorithms for improved accuracy even in challenging conditions, optimizing algorithms for low-power consumption critical for battery-powered devices, addressing data privacy and security concerns through techniques like federated learning and on-device processing, and incorporating face detection with augmented and virtual reality for immersive user experiences. Despite its potential, it has several kinds of difficulties, such as low computing power, inconsistent illumination and surroundings factors, concerns about data privacy, and scaling problems. To overcome these challenges, edge computing and lightweight models are utilized and optimized for IoT devices, integrating robust pre-processing techniques and adaptive algorithms, employing on-device processing and encryption to protect data, and using scalable cloud infrastructure with efficient data management practices. However, for Internet of Things applications, real-time face identification is a game-changing technology that provides better automation, personalized experiences, and increased security. The topics of the issue include but not limited to the following: Enhancing IoT Applications with Real-Time Facial Recognition. Role of Real-Time Face Detection in Smart Homes. Employing Real-Time Face Detection for Personalized Retail Experiences. Challenges and Solutions in Real-Time Face Detection for IoT Devices. Optimizing Real-Time Face Detection for Low-Power IoT Devices. Ensuring Data Privacy and Security in Real-Time Face Detection for IoT Applications. Advanced Algorithms for Accurate Real-Time Face Detection in IoT Systems. Integrating Biometric Data for Enhanced Security and User Interaction in IoT. Future of Real-Time Face Detection in IoT. Utilization of Machine Learning Models for Real-Time Face Detection in IoT. Guest Editor: Dr.Rahmat Widia Sembiring Department of Computer and Informatic, Politeknik Negeri Medan, North Sumatra 20155, Indonesia Email id: rahmatws@polmed.ac.id, dr.rahmatws@gmail.com Official Page: http://itec.pkb.edu.my/v4/rahmat_widia_sembiring.jsp Scholar Page: https://scholar.google.com/citations?user=hg_mkGwAAAAJ&hl=en Dr.Jasni Mohammad Zain Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia Email id: jasni67@uitm.edu.my Official Page: https://ibdaai.uitm.edu.my/index.php/corporate/director-s-message Scholar Page: https://scholar.google.com.my/citations?user=WePAGgkAAAAJ&hl=en Dr.Tao Hai School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China. Email id: haitao@sgmtu.edu.cn Scopus page: https://www.scopus.com/authid/detail.uri?authorId=36350315600 Scholar Page: https://scholar.google.com/citations?user=1u2fgf4AAAAJ&hl=en Manuscript Deadline: Manuscript submissions: 30.11.2024 First round of reviews: 30.01.2025 Revised manuscripts: 15.04.2025 Second round of reviews: 30.06.2025
Call for papers: Special Issue(24-17): Image Processing for Next-Gen Robotics: Bridging the Physical and Digital
Image Processing for Next-Gen Robotics: Bridging the Physical and Digital The main image processing methods used in robotics are segmentation, object recognition, picture filtering, and feature extraction. Filtering improves the quality of the image; segmentation separates items; feature extraction finds important components; and object recognition finds and classifies objects. Digital cameras can be added to a robot to act as its "eyes" and generate photos. The robot could utilise an object if it could identify it in these pictures. For example, in a manufacturing setting, the robot might assemble things with a screwdriver. It is possible to build next-generation robots that are more accurate and productive than humans at certain tasks. Increased productivity and efficiency may result from this, especially in industries like manufacturing where robots may operate continuously without the need for breaks. Robotics is about to take a giant step ahead thanks to the introduction of artificial intelligence (AI) of the next generation. It is hoped that this potent combination would give robots enhanced cognitive capabilities, making it harder to distinguish between intelligent support and automation. The following three steps are essentially included in image processing: Using image acquisition tools to import the image; Analysing and modifying the image; and producing an output that may include a changed image or a report derived from image analysis. A program called Digital Image Processing (DIP) is used to alter digital images on a computer system. Additionally, it is employed to improve the pictures and extract some significant data from them. Take MATLAB and Adobe Photoshop, for instance. An image can undergo one of three forms of processing in general. These include the processing types that are explained below: low-level, intermediate-level, and high-level. Digital Image Processing (DIP) Areas. Enhanced visual quality: Digital image processing methods can enhance an image's clarity, sharpness, and informativeness. Automated image-based tasks: A variety of image-based jobs, including object recognition, pattern recognition, and measurement, can be automated by digital image processing. Future automation and robotics could be used as teachers, explorers, assistants, colleagues, and surgeons. Robotics' capabilities will only grow as engineers and scientists work to develop and expand this technology. Robots are already a commonplace sight in many aspects of daily life. Robotics are employed in material handling sectors to choose, sort, package, and palletize finished products that are ready for distribution. Robotics is also widely used in the education, agricultural, and clinical lab sectors. Ameca is one of the world's most sophisticated robots. The business released a video in August in which Ameca could be seen sketching a cat. Soon after, the video became viral. Ameca was the first robot to sketch on her own when the company gave her the ability to do so utilising Stable Diffusion. Image processing can be done in two ways: Analog Image Processing involves enhancing hard copies of pictures, printouts, and other images. Computer algorithms are used in digital image processing to improve or recreate images. DIP is mostly concerned with creating a computer system that can process images. A digital image is the system's input. It processes the image using effective algorithms and outputs an image as the result. The most often used illustration is Adobe Photoshop. Articles are invited that explore Image Processing for Next-Gen Robotics: Bridging the Physical and Digital. Case studies and practitioner perspectives are also welcome. Potential topics include but are not limited to the following: Machine learning and next-generation cell phones are employed in a digital twin framework for smart greenhouse management. Increasing AEC capabilities through human computer collaboration and robotic fabrication. An industrialised robotic swarm testing facility in open spaces. An investigation of the obstacles, patterns, opportunities, and enabling technology. Vulnerable mixes on-demand adjustment using explainable and abnormal machine learning. An innovative paradigm for AI-driven urban development and architecture. Intelligent machines to further science and investigation of the moon and planets. Tentative assessment of an initial sophisticated virtual exhibition driven by robots, XR, and AI. Employing smartphone technology to improve sensory and cognitive function. Digital medical treatment, artificial intelligence, and sophisticated wireless combined. Concepts, applications, and anticipated developments of extended reality in Internet of Things settings. Edge improved binocular meta-lens depth perception. Timeline: Submission deadline: December 25, 2024 Author notification: February 25, 2025 Revised papers due: April 30, 2025 Final notification: June 30, 2025 The Publication of the special issue will as per the policy of journal Credentials of Guest Editor team: Dr. Emmanuel Gbenga Dada Associate Professor, Department of Computer Science, University of Maiduguri, Maiduguri, Nigeria. Email id: gbengadada@unimaid.edu.ng, gbengang@outlook.com Profile Links: https://scholar.google.com/citations?user=b5k7P-MAAAAJ&hl=en https://www.researchgate.net/profile/Emmanuel-Dada Dr. Stephen Joseph Bassi Assistant Professor Department of Computer Engineering, University of Maiduguri, Maiduguri, Nigeria. Email id: sjbassi74@unimaid.edu.ng Profile Links: https://scholar.google.com.my/citations?user=NFZXVnUAAAAJ&hl=en https://www.researchgate.net/profile/Stephen-Joseph-4 Dr. Ayodele Lasisi Assistant Professor Department of Computer Science, King Khalid University, Abha, Saudi Arabia. Email id: alasisi@kku.edu.sa Profile Links: https://scholar.google.com/citations?user=OhAYVBQAAAAJ&hl=en https://www.researchgate.net/profile/Ayodele-Lasisi
Call for papers: Special Issue(24-16): Human-Computer Interaction in Virtual Reality- Challenges and Solutions
HUMAN-COMPUTER INTERACTION IN VIRTUAL REALITY: CHALLENGES AND SOLUTIONS Virtual Reality has become increasingly advanced recently in various industries such as gaming, healthcare, education, and training. The interaction between humans and computers in VR environments is an important aspect that determines the effectiveness and immersion of the experience. Effective HCI in VR can lead to more intuitive, immersive, and engaging experiences. While poor HCI can result in user frustration, minimized productivity, and even physical discomfort. In virtual reality, traditional input devices like keyboards and mouse are not effective. In result, specialized equipment like brain-computer interfaces, motion controllers, and haptic gloves are becoming popular. The infrared sensors, cameras, and inertial measurement devices (IMUs) enable accurate tracking of movements throughout the head, hands, and body. It is necessary to switch from traditional 2D user interface designs to 3D interfaces, gaze-based controls, and spatial audio in VR are used to promote more immersive experiences. Action reinforcement and increased involvement depend on feedback mechanisms, which include haptics, visual signals, and audio. Massive technologies in VR include Mixed Reality and Augmented Reality, which combines real and virtual environments for more interactive and engaging experiences. Social VR enables multiple users to interact in the same virtual space, enhancing social experiences and collaboration. AI and machine learning are incorporated to create smarter, more responsive virtual environments that adapt to user behaviors. VR is utilized for surgical training, therapy, and rehabilitation within controlled, safe, and customizable environments. In education and training, it provides immersive learning experiences ranging from virtual classrooms to realistic simulations. In gaming and entertainment, Virtual reality continues to push boundaries of interactive narrative and gameplay, boosting progress in industrial technology. Future developments in virtual reality (VR) include more accurate tracking systems, lighter, more comfortable head protection with a wider field of vision and greater resolutions, and advanced haptic feedback for an immersive experience. Furthermore, initiatives have started to improve accessibility, bringing up VR to a wider range of users, including people with disabilities. Despite its potential, VR has several challenges including motion sickness, which multiple users experience due to discomfort and nausea from prolonged use, solutions involve improving frame rates, reducing latency, and utilizing more natural movement controls. Interaction design should create intuitive and efficient paradigms to ignore overwhelming or confusing users. High processing requirements and the requirement for advanced hardware are further technological barriers to wider adoption. To overcome these obstacles, research and development expenditures must be endured in order to resolve technical problems and improve user experience. User participation in interface design is necessary to ensure user-friendly interfaces and cross-disciplinary cooperation between computer scientists, psychologists, and ergonomics experts is welcomed to produce comprehensive solutions. While there are critical challenges to address, however, that are paving the way for more immersive and effective VR experiences. POTENTIAL TOPICS INCLUDE BUT ARE NOT LIMITED TO THE FOLLOWING: Enhancing human-computer interaction in virtual reality environments. Applications of tracking technology in virtual reality to understand the role of sensors and IMUS. Exploring the role of spatial audio in virtual reality experiences. Advancements in motion controllers and haptic gloves for revolutionizing VR interaction. Utilization of gaze-based controls for enhanced user engagement in virtual environments. Analysis of feedback mechanisms in virtual reality. Contribution of AI and machine learning in virtual environments. Employing virtual reality in architecture and urban planning to design cities for the future. Strategies to overcome challenges in VR. Ethical considerations in VR development and implementation. TENTATIVE TIMELINE: Paper Submission Deadline : 25.10.2024 Notification to Author : 25.01.2025 Revised Papers Submission : 05.04.2025 Final Acceptance : 05.06.2025 GUEST EDITOR INFORMATION: Dr. Ajay Kumar G Assistant Professor, Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, Korea. Email id: ajay@cau.ac.kr, ajaykumarg20@outlook.com Google Scholar: https://scholar.google.co.kr/citations?user=5oAea44AAAAJ&hl=en Research Gate: https://www.researchgate.net/profile/G-Ajay-Kumar Dr. Rohit Singh Assistant Professor, Electronics and Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India. Email id: rohits@nitj.ac.in Google Scholar: https://scholar.google.com/citations?user=sKTZXpQAAAAJ&hl=en Research Gate: https://www.researchgate.net/profile/Rohit-Singh-5 Dr. Nishant Gupta Assistant Professor, Department of Electrical Engineering, Linköping University, Linköping, Sweden. Email id: nishant.gupta@liu.se Google Scholar: https://scholar.google.co.in/citations?user=YUlsom8AAAAJ&hl=en Institutional Webpage: https://liu.se/en/employee/nisgu99 Dr. Ashok Kumar Patil Associate Professor, Computer Science and Engineering, PES University, India. Email id: ashokkumarp@pes.edu Google Scholar: https://scholar.google.com/citations?user=I18gfJQAAAAJ&hl=en Institutional Webpage: https://staff.pes.edu/nm1611/ Dr. Sameer Kumar Singh Assistant Professor, Department of Electrical Engineering, IIT Ropar, Punjab, India. Email id: sameer.20eez0020@iitrpr.ac.in Google Scholar: https://scholar.google.com/citations?user=SfbcKDIAAAAJ&hl=en Institutional Webpage: https://sites.google.com/iitrpr.ac.in/personal-web-page/
Call for papers: Special Issue(24-15): Emerging advances in human robot interaction for large complex business marketing innovations
Emerging advances in human robot interaction for large complex business marketing innovations Technological trends paved the way to revolutionize various sectors for the easy functioning of society. The evolution of advanced techniques is found to set a generative platform for developing business marketing strategies. The incorporation of robotic technologies emphasizes effective marketing management. The advanced techniques of human-robot interaction for business marketing are a great and essential endeavour aiming for progressive global changes. Advanced innovative approaches enable robots capable of functioning effective interaction expertizing the marketing sector than the previous practices. Innovations in sensor technology result in sophisticated functions that replace humans' functionalities serving as an ideal choice for enhancing the physical interaction between humans and robots. Human-robot interactions are becoming increasingly important in the expanding market due to its significant features of personalized services. The effective HRI (Human-Robot Interaction) plays a vital role in the marketing field in performing complex tasks in the business environment to increase the sale of a product. Researchers envisaged the unique attributes of human-robot interactions like consumer-oriented service, computer-based marketing, and automotive marketing. Adopting human-robot interaction promotes operational assistance for the controller to execute and interrogate automated self-operating robotic technology into the consumer assistance services. Additionally, the intervention of entertainment robots and interactions are found to be the emerging trends used in business marketing. The successful progressions of technologies in developing the effectual interaction connecting humans and robots enhance complex tasks by solving multifaceted problems in business marketing. Despite many reliable trends in human-robot interaction for complex business marketing, it also lags with certain downsides. Researchers and practitioners are invited to present a theoretical research framework regarding this context. The special issue provides various opportunities to discuss the downsides to tackling and expertise in the system. List of Topics include but not limited to the following, New frontiers in business marketing strategies for the economic development Limitations and challenges in implying human-robot interaction for business marketing Human-robot interaction for businesses: trends and opportunities Advanced innovative approaches through robots for business marketing Effective approaches in the field of marketing for the enhancement of businesses Risk correlated with the data mishandling and lack of technical knowledge with the interference of robots for businesses The new paradigm of business marketing innovations for future development of businesses Future perspectives of human-robot interaction for the effective marketing strategies Role of robots for the emerging business trends for the future era Insights of human-robot interaction for the marketing industries Need for new sustainable policies and legislations for the enhancement of business enterprise Innovative business marketing strategies: pros and cons Important Dates: Submission deadline : Nov 29th 2024 Author notification : Feb 15th 2025 Revised papers due : April 30th 2025 Final notification : June 05th 2025 GE Information: Dr. Zohaib Mushtaq Assistant Professor, Department of Electrical Engineering, College of Engineering and Technology, University of Sargodha, Sargodha, 40100, Pakistan Email ID: zohaib.mushtaq@uos.edu.pk, zohaibmushtaq908@gmail.com Official Page: https://www.su.edu.pk/facultyprofile/10297 Google Scholar Page: https://scholar.google.com/citations?user=CqfA2GEAAAAJ&hl=en Research Interests: Artificial Intelligence, Machine and Deep Learning, Data Science, Biomedical Engineering, Feature Engineering Profile Summary: Dr. Zohaib Mushtaq has completed his PhD in Electrical Engineering from the National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan, and his MS in Electrical Engineering from the Government College University (GCU), Lahore, Pakistan. He earned his PhD degree on a fully funded university scholarship from NTUST. During his doctorate studies, he also received the Research Excellence Award among International Students in Taiwan for the year 2020, organized and sponsored by CTCI. He has vast teaching and research experience and has served as an assistant professor in electrical engineering at Riphah International University Islamabad, a research associate (RA) at NTUST, Taipei, and a lecturer at the Electrical Engineering Dept., University of Management & Technology, Sialkot. Dr.Wahyu Rahmaniar Assistant Professor, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan. Email ID: rahmaniar.w.aa@m.titech.ac.jp Personal Website: https://wahyurahmaniar.github.io/ Google Scholar Page: https://scholar.google.com/citations?user=V81MER8AAAAJ&hl=en Research Interests: Real-Time Computer Vision, Artificial Intelligence, Medical Imaging, Robotics, and Control Systems Profile Summary: Dr.Wahyu Rahmaniar is an assistant professor (specially appointed) at the Tokyo Institute of Technology, Japan. She received the B.S. degree in Electronics and Instrumentation from Universitas Gadjah Mada (UGM), Yogyakarta, Indonesia, in 2009 and the Ph.D. degree in Electrical Engineering from National Central University (NCU), Taiwan, in 2020. She has been a reviewer in several journals, such as Circuit World, Sensor Review, Journal of Robotics and Control, Journal of Electronic Imaging, Sensors (MDPI), BDCC (MDPI), and Scientific Reports (Nature). Dr.Qazi Mazhar ul Haq Assistant Professor, Department of Computer Science and Engineering and IBPI, Yuan Ze University, Taoyuan, Taiwan. Email ID: qazi@saturn.yzu.edu.tw Personal Website: https://dr-qazi.github.io/ Google Scholar Page: https://scholar.google.com/citations?user=AeJN-sYAAAAJ&hl=en Research Interests: Machine Learning, Artificial Intelligence, Medical Image Processing, Self-driving Cars, Object Detection Profile Summary: Dr. Qazi Mazhar ul Haq is currently working as an assistant professor in the Department of Computer Science and Engineering and IBPI at Yuan Ze University, Taiwan. He holds a Ph.D. in Electronics and Computer Engineering from the National Taiwan University of Science and Technology. His research interests include object detection, incremental learning, anomaly detection, image processing, deep learning, and 3D object detection.
Call for papers: Special Issue(24-14): AI-Driven OCT Image Analysis for Automated Disease Detection and Classification.
SPECIAL ISSUE PROPOSAL AI-Driven OCT Image Analysis for Automated Disease Detection and Classification The evaluation and interpretation of optics coherence tomography in retinal science has greatly progressed to advances in computer vision, which is defined as the field of expertise that teaches computers to understand digital images using both artificial intelligence (AI) and traditional techniques. The field of deep learning in computational neuroscience has a prospect for improving diagnosis, classification, and management in personal and population settings. Deep learning is predicated on the ability to recognise images using pre-existing information. Diabetic retinopathy is a slowly developing health issue that frequently progresses silently without obvious symptoms. Damage to the retinal blood vessels is a major factor in this condition. Optical coherence tomography, or OCT, has become the preferred noninvasive imaging modality for quickly and precisely identifying diabetic retinopathy. Still, these techniques have three main drawbacks: first, their black box attributes make it difficult for medical professionals to fully trust the results they produce instantly; second, their lack of exactness makes it difficult to apply those techniques to complex and clinical cases; and third, the size and complexity of the models and works that are currently available for OCT classification make them more difficult to use in immediate applications by requiring a significant amount of memory and processing power. A process for improving the features that can be recovered from optical coherence tomography images using a cognitive network by evaluating the images using image processing technologies. The algorithms already in use that rely on fundus photography and optical coherent tomography imaging have already surpassed human evaluators in diagnosis accuracy. More exploration of artificial intelligence (AI) in ophthalmology is being conducted as a result of the rising incidence and burden of eye illnesses worldwide, as well as the increasing usage of sophisticated imaging technologies for ophthalmic inspection, such as fundus cameras and optical coherence tomography (OCT). Numerous research groups have created automated analytic algorithms for the purpose of identifying and diagnosing eye disorders. In this context, the integration of AI with medical image analysis represents a significant accomplishment, providing a deep understanding of an individual's anatomy and physiography using the complex interpretation of visual data. The combination of AI and medical imaging has accelerated the creation of advanced methods of great importance for disease diagnosis, prognosis, and treatment planning. AI-enabled medical imaging techniques have proven to be exceptionally effective at automating processes that were previously susceptible to personality and variations. This special issue of computer vision, which is identified as the branch of science that enables computers to interpret digital images using both traditional and artificial intelligence (AI) methods, has made great strides in the processing and interpretation of optical coherence tomography (OCT) in retinal imaging. TOPICS INCLUDE, BUT ARE NOT LIMITED TO: Transfer learning-powered artificial intelligence model for image-based medical diagnostics Assessment of Intelligent Systems in OCT Image-Based Retinal Screening Artificial learning and image processing in computerised images and optical coherence tomography Possible uses of artificial intelligence for corneal disease image interpretation Harnessing Artificial Intelligence to Help Doctors Interpret Medical Images An Overview Deterioration with special image preprocessing utilising optical coherence tomography Intelligent technology utilisation for corneal disease diagnosis and treatment AI's prognostic promise in ophthalmology: using retinal imaging to predict systemic diseases Image analysis using artificial intelligence in dermatology: present advancements and potential directions Unauthorised fundus image registration using AI-driven generalised exponential transformation methods Deep learning applications for the detection, categorisation, and management of degeneration GUEST EDITORS Dr.Abdul Aziz Universidad de Zaragoza, Zaragoza, Spain. Email ID: abdul.aziz@unizar.es, unizar.abdul@gmail.com Research Link: https://scholar.google.com/citations?user=BB0FCKwAAAAJ&hl=en Official Webpage: https://i3a.unizar.es/es/investigadores/abdul-aziz Professional Summary: Dr.Abdul Aziz received his Bachelor’s degree in computer science from the COMSATS Institute of Information Technology, Lahore, Pakistan, in 2013, and Master’s degree in computer science from the National University of Computer and Emerging Sciences, Karachi, Pakistan, in 2018. He is currently working at the University of Zaragoza, within the Advanced Information Systems Laboratory (IAAA) of the Aragon Institute of Engineering Research (I3A). Research Interests: Open (Linked) Data, Open Data Portals, Open Government Data, Data Science, Information Retrieval Dr.Moiz Khan Sherwani University of Copenhagen, Denmark Email ID: moiz.sherwani@sund.ku.dk Research Link: https://scholar.google.it/citations?user=uDlA1JgAAAAJ&hl=en Professional Summary: Dr.Moiz Khan Sherwani received my PhD in Computer Science (Artificial Intelligence in Medicine) from University of Calabria. Currently, he is working at University of Copenhagen. His previous research scope was based on the image synthesis, diagnosis, infection segmentation in medical images (MRI, CT, and other image modalities). He is currently working on machine learning approaches applied over multi-omics data. His main activities include data analysis of genomics data and applying ML models to predict, classify and implement algorithms to solve the biological questions within the context. Research Interests: Machine Learning, Natural Language Processing, Data Mining and Knowledge Discovery, Text Analysis, Data Science, Medical Image Analysis, Medical Imaging, Medical and Biomedical Image Processing, Deep learning in Biomedical data, Machine Learning on Multi-omics data, Graph databases, Artificial Intelligence Dr.Waseem Akram Khalifa University, UAE Email ID: waseem.akram@ku.ac.ae Research Link: https://www.researchgate.net/profile/Waseem-Akram Professional Summary: Waseem Akram has expertise and experience in both software development as well as in research. He is working on the control and guidance of autonomous underwater vehicles for the visual inspection of underwater assets. Apart from this, he is also working on agent-based modelling, complex networks modelling, machine learning, computer vision and deep learning-based research projects. Research Interests: Complex Networks, Agent-Based Modeling, Smart Grid, Marine vehicles, Fault Tolerance, Underwater Acoustics, Complex Adaptive Systems, Marine Inspection, Marine Aquaculture. TENTATIVE DATES Submission Deadline: 31th December, 2024 Authors Notification: 25th March, 2025 Revised Version Submission: 25th May, 2025 Final Decision Notification: 05th Aug, 2025
Call for papers: Special Issue(24-13): Advances in Smart Robotics for Manufacturing
SPECIAL ISSE TITLE Advances in smart robotics for manufacturing Summary: The emergence of the second industrial revolution marks the beginning of the robotic workforce for various industrial purposes. Robots were placed in various sectors of manufacturing which reduces human intervention significantly and with enhanced production. However, the involvement of the robotic workforce is restricted in deciding due to the lack of context awareness and intelligent thinking. However, the arrival of artificial intelligence and machine learning confers robots the ability to think & interpret a given situation and allows them to make intelligent decisions whenever needed. The performance of these intelligent robots (also called Smart robots) is far better than traditional robotics. However, the usage of smart robotics for manufacturing is limited due to their inadequacies. Limited improvement in precise force sensing resulted in a delayed response of robots which reduces their adaptation in accomplishing delicate tasks. For designing the training module of smart robotics enormous datasets are required which is narrow for many industrial processes. Moreover, the intelligent decisions made by smart robots need to be quality controlled to ensure the correctness of the decision and to further improve the decision-making process. The interaction between humans and robots in the workplace is still limited due to the risk of injuries. In addition, wired robots still need costly equipment and infrastructure to deploy and run. Smart robots capable of having enhanced interaction with robots would be better assistants in the manufacturing process and enhance productivity. Advances in fabrication materials and methodologies of robotics, with novel AI algorithms capable of reasoning, had a transformative impact in industrial manufacturing. Reinforcing robotic parts with soft materials (like silicon polymers, artificial muscles) confer them a delicacy in performance which also increases their precise force sensing and thus proved to increase their response time to nanoseconds. Moreover, robots reinforced with soft parts can do delicate tasks with intricate details like humans. A combination of wireless technology with solid-state battery powering in smart robots would further extend their applicability by enhancing their versatility. Moreover, the development of nature-inspired deep learning algorithms increases the appropriateness of decisions by smartly analyzing the given scenario which has far-reaching applications in the industrial manufacturing process. Moreover, adaptive deep learning modules enhance the context-awareness of smart robots (during handling machines) by analyzing various physical parameters (like vibrations, the heat emitted, friction, etc). Technologies like this would reduce the need for large data sets for designing a training module and also reduce human intervention during automated processes. Robotic interaction inspired by human social interaction (also called CoBots), can truly interact with humans in a human-like fashion and mitigated potential downfalls in human-robot interaction. Thus innovations in deep learning methodologies would create sophisticated smart robots capable of working faster and smarter will certainly boost the progression of Industry 4.0. Therefore this special issue forms an ideal platform for researchers, entrepreneurs, and programmers to present their unpublished works, reviews, and perspectives on recent innovations in development of smart robots for manufacturing processes in various industrial sectors. List of Interested topics include, but are not limited to, the following: Trends in digitization and learning applications in the process of industrial manufacturing. Innovations in robotic intelligence for planning and execution of industrial process. Research in robotic sensors for perception and context analysis. Advances in big data analytics for robotic functions. Emerging advances in fault detection and predictive maintenance of industrial robots. Innovations in the construction of soft robots for intricate jobs. Advancements in computer vision for detection and recognition of human face. Trend in speech and gesture recognition in work place for improved interaction with humans. Advances in bio-inspired algorithms for mimicking human social interaction. Research in construction of wireless smart robots for enhanced performance. Trends in identification of sustainable alternative power source for complete robotic automation. Innovations in swarm technology for cooperative robotic automation. Trends in smart robots for enhancing the accuracy of adaptive decision-making processes. Special Issue Guest Editors: Dr. Mahmud Iwan Solihin Associate Professor at Faculty of Engineering, UCSI University, Malaysia. Email id: mahmudis@ucsiuniversity.edu.my, mahmud.iwan@proton.me Google Scholar Link: https://scholar.google.com.my/citations?user=BlBZJPUAAAAJ Dr. Lin Guoping Professor of Department of Engineering, Department of Industrial Engineering and Enterprise Information, Tunghai University, Taiwan Email id: kplin@thu.edu.tw Google Scholar Link: https://scholar.google.co.uk/citations?user=OyWUYLAAAAAJ Dr. Slamet Riyadi Associate Professor, Department of Information Technology, Universitas Muhammadiyah Yogyakarta, Indonesia. Email id: riyadi@umy.ac.id Google Scholar Link: https://scholar.google.com/citations?user=bl1BHx8AAAAJ&hl=en Deadlines: Article Submission Deadline - [10.08.2024] Authors Notification Date - [20.10.2024] Revised Papers Due Date - [25.12.2024] Final notification Date - [20.02.2025]
Call for papers: Special Issue(24-12):Comparative Study of Machine Learning Algorithms for Facial Recognition Systems
Comparative Study of Machine Learning Algorithms for Facial Recognition Systems Convolutional Neural Networks are thought to be the finest algorithm for facial recognition because of their superior capacity to extract features and recognize faces in photos. The individual is predicted using a different CNN by using the computed angles and ratios. Facial biometrics are employed to accurately identify the individual. The face is recognized using the kilometre nearest neighbour algorithm in order to do this. The core of contemporary facial recognition systems is artificial intelligence, namely machine learning and deep learning algorithms. The system can learn from enormous volumes of data and get better over time because of these algorithms. It functions by recognizing and quantifying face features in a picture. A machine learning model accuracy is dependent on a number of variables, such as the type and volume of data, the algorithm selected, and the proper adjustment of the model hyperparameters. There is not a single optimal algorithm that consistently performs more accurately than the rest. The programming language Python became the most widely used programming language for facial recognition, probably as a result of its user-friendly interface and flexibility. It is a great option for designing and prototyping facial recognition applications due to its popularity among developers. No additional training is needed because the OpenCV built in Haar Cascade classifier has already been trained on a sizable dataset of human faces. The capacity of CNNs to create an internal representation of a two-dimensional image is a benefit. As a result, the model is able to determine the scale and location of faces in an image. Following training, CNN can identify a face in an image. Convolutional neural networks are useful for processing image data. Convolutional neural networks are among the most popular and effective machine learning techniques for image recognition. Multiple layers of artificial neurons make up CNNs, which are designed to extract characteristics from images, including objects, edges, forms, and colours. CNNs are the most widely used and efficient classifier technique in machine learning because of these complementing phases. Within the domain of biometric security is facial recognition. Voice, fingerprint, and iris or retinal recognition software are examples of additional biometric software. Though interest in other applications is growing, security and law enforcement are the primary uses of the technology. A machine may learn, anticipate, identify patterns, or classify data by being shown a vast amount of data. This process is referred to as machine learning. Supervised, unsupervised, and reinforcement learning are the three categories of machine learning. For deep learning, multilayer perceptron’s are the optimal algorithm. It is among the earliest deep learning methods that Instagram and Meta, among other social media platforms, use. In order to identify human faces, facial recognition technology and biometrics, usually using AI, are used. It maps the features of the face from an image or video, then looks for a match by comparing the data with a database of recognized faces. Face recognition software uses machine learning techniques to recognize facial traits in an image or video in order to detect faces. Contributions are invited from a range of disciplines and perspectives, including, but not restricted to: Comparative Study of Machine Learning Algorithms for Facial Recognition Systems. Suggested research and application topics of interest include but not limited to: A comparative analysis of facial recognition machine learning methods. A comparison of facial recognition algorithms using deep learning and machine learning. A comparison of face recognition algorithms in terms of illumination and expression. A comparison of face detection and recognition methods based on machine learning. Comparative comparison of face recognition using various machine learning techniques. An analysis comparing facial biometric recognition using deep learning and conventional machine learning methods. An analysis comparing facial emotion recognition techniques using deep learning and machine learning. A comparison of facial expression recognition algorithms and techniques. An Analysis of Machine Learning Classifiers' Performance in Combining Feature Extraction with Face Recognition. A comparison of machine learning techniques for crime detection and privacy protection using picture recognition. A comparison of several cutting-edge face recognition methods with different expressions on the face. A comparison of machine learning techniques for human identification based on lip prints. Tentative Timeline: Submission Deadline : 30.12.2024 Authors Notification : 25.02.2025 Revised Papers Deadline : 25.04.2025 Final Notification : 30.05.2025 Guest Editor Team: Dr. Sabari Nathan Senior AI Engineer, Couger Inc., Tokyo 150-0001, Japan. Email ID: sabari@couger.co.jp, prof.sabarinathan@gmail.com Google Scholar: https://scholar.google.com/citations?user=3pySUPQAAAAJ&hl=en Dr. Sasithradevi A Associate Professor, Centre for Advanced Data Science, Vellore Institute of Technology, Tamil Nadu, India. Email ID: sasithradevi.a@vit.ac.in Google Scholar: https://scholar.google.com/citations?user=16J6vSwAAAAJ&hl=en Dr. Adeline Sneha J Senior Lecturer, Asia Pacific University of Technology and Innovation, Kualalumpur, Malaysia. Email ID: adeline.john@apu.edu.my Google Scholar: https://scholar.google.co.in/citations?user=XJBcDIUAAAAJ&hl=en
Call for papers: Special Issue(24-09):Deep Learning for 3D Object Recognition in Smart City Surveillance Systems
Technology is the only way to deal with this predicted increase in demand, and privacy and urbanisation challenges are predicted to develop in order to maximise current resources. The goal of the smart city is to seamlessly integrate ICT (information and communication technology) with cutting-edge developments, such as networked homes and appliances. A smart city's efficient architecture and improved security improve the quality of life for its citizens. When carried out over an extended period of time, surveillance is a repetitive and tedious task that diminishes the effectiveness of natural defenders. Although numerous sites have adopted video surveillance for 3D object recognition, it is now necessary to continuously watch an individual camera operator across several cameras in order to monitor anomalous behaviour. This is a laborious operation. It is challenging to recognise various types of firearms and knives in real-time settings on multidimensional devices and to classify them apart from other video surveillance items. The majority of detecting cameras are low-resource, computationally-capable sensors. Surveillance is a crucial component of smart cities, both to ensure public safety and to discourage criminal activity. Thus, frameworks for intelligent video surveillance (IVS) are becoming more and more well-known in safety applications overall. The most common methods for improving the quality of images and videos for smart city surveillance are provided, along with the key functions and statistics. Smart city applications include things like transportation, social security, and service-related smart monitoring. They also go over the most important recently developed deep learning methods for monitoring and managing smart cities. The rise of the smart city has led to an increasing demand for adaptable deep learning techniques that assess human behaviour and infrastructure usage to simulate social behaviours pertinent to flexible and ecological buildings. In order to better understand the way humans utilise facilities, particularly social structures, the cyber-physical system (CPS) frameworks that are used for tracking and automating facilities in smart cities can be expanded to detect people. In order to create a cyber-physical social system (CPSS) for smart cities, it uses convolutional neural network (CNN) topologies to automate 3D object recognition and topographical tracking of individuals using camera data. In the present day, dynamic surveillance systems aim for high-speed streaming, and object recognition in real-time visual data has proven difficult to do within a tolerable latency. The special issue is categorised according to smart city design, methodologies, software, and historical data, and this allows the article to provide a thorough review of object recognition deep learning topologies. List of Interested Topics Deep Learning-Based IoT-Based Smart City Management Network Leveraging 3D object recognition for Proactive Surveillance in Smart Cities AI and deep learning-based object recognition for surveillance footage systems An effective foundation for IoT-based smart city monitoring utilising object recognition Real-time anomalous object recognition for smart city surveillance cameras Utilising deep learning to implement region-based video monitoring in smart cities Deep learning-driven computer vision for monitoring in its Assessment of cutting-edge techniques Regarding public street contexts, quick item detection utilising dimensions based characteristics Deep learning applications in smart cities: current state, classification, and obstacles Spatiotemporal Deep Learning for Object Recognition of Anomalies in Intelligent and Smart Cities An ideal deep learning-based intelligent surveillance system for object recognition Guest Editor Information Dr.Muhammad Asim Saleem Assistant Professor, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand. Email: muhammadasim.s@chula.ac.th, muhammadasim.sa@hotmail.com Research Link: https://scholar.google.com.pk/citations?user=uTJ-_NQAAAAJ&hl=en Dr. Uzair Aslam Bhatti School of Information and Communication Engineering, Hainan University, Haikou 570100, China. Email: uzair@hainanu.edu.cn uzairbhatti86@hotmail.com Google Scolar: https://scholar.google.se/citations?user=aQSMyzAAAAAJ&hl=en Dr.Zahid Ullah Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan,Italy Email: zahid.ullah@polimi.it Research Link: https://scholar.google.com.pk/citations?user=Yya54h4AAAAJ&hl=en Important Dates Deadline for submissions will be due on Jul 30, 2025 The preliminary notification will be due on Jun 30, 2025 Revision will be due on Aug 30, 2025 Final notification will be due on Nov 30, 2025 The final publication process will be followed based on journal direction
Call for papers: Special Issue(24-06):Utilising Unmanned Aerial Vehicles (UAVs) for Real-time Disaster Risk Assessment
UAVs can serve as early warning systems (EWSs) by gathering data and evaluating it for forecasts. The applications of UAVs in disaster management have developed along with the technology. UAVs can enhance situational awareness in impacted areas, collect data, and supply communications infrastructure. Drones deliver thorough and fact-based damage reports by quickly taking overhead photos and data, providing a detailed picture of the most damaged areas and inaccessible places. Due to restrictions on the physical load size and weight onboard, UAVs are only able to deliver a certain amount of food, medicine, and other supplies. weather-related catastrophes like storms and excessive rain. Rescuers can locate missing persons more rapidly when they use an emergency drone. It aids in the hunt for lost persons or animals in difficult-to-reach areas like summits, destroyed buildings, etc. The capacity of multispectral sensors to detect body heat makes them indispensable in search and rescue operations. Compared to manned aerial vehicles, drones can be deployed quickly and easily, making them very effective as first responders. Drone delivery of emergency medical supplies and kits can accelerate the response time when life-saving vaccines, biological samples, transfusion plasma, or organs are needed in a variety of humanitarian situations. Unmanned aerial vehicles, or drones, are capable of quickly mapping sizable regions before, during, and following floods. They enable prompt identification of any safe locations. Inspectors may assess the damage from a safe distance with drones, reducing the chance of accidents. Drones with high-resolution cameras and sensors are able to collect detailed data and photographs without endangering human inspectors. By identifying hazard zones related to flood plains, coastal inundation and erosion, and active faults, remote sensing can support risk reduction programs. Some regions may be too risky or inaccessible for human responders to enter after an earthquake because of debris, unstable structures, or other risks. These difficult-to-reach places can be accessed by drones without endangering human life. Videos showing the massive size of a camp for people displaced by disaster or aerial shots of a vast flooded area are examples of how useful drone images are in helping the public understand the scope and breadth of a specific tragedy. The creation and upkeep of a medical drone network, regulations, safety concerns, flying conditions, privacy issues, and other difficulties provide challenges to the use of drones in emergency management. It is necessary to address the risks of malicious use and cybersecurity breaches associated with drones. UAVs are utilized in drone journalism, asset protection, disaster response, search and rescue, firefighting, communications relay, healthcare, and agriculture in addition to surveillance and delivery applications. law enforcement and surveillance for border control. Hurricane and tornado tracking and forecasting. Due to the numerous investments that are being made daily into this potential business, hundreds of new drone applications are currently being developed. Drones have been used by terrorists to target civilian centers, diplomatic missions, energy infrastructure, international trade, and state military assets. Topics of interest for the special issue include, but are not limited to, the following: Unmanned aerial system traffic control framework for real-time risk assessment Utilizing aerial drones for assessing potential flood risks. Image processing and geocomputing using UAVs to track hazards and disaster risk. Unmanned aerial machines in disaster-related uses. Integrating unmanned aerial vehicles in rescue initiatives. Deep neural networks and unmanned aircraft for detection of disasters. safety evaluation and analysis of risk, both qualitative and quantitative, of drone activities performed on the internet. Utilizing unmanned aerial vehicles to assess and reduce the risk of disasters at factories. Unmanned aerial aircraft technology for disaster management and monitoring. Unmanned aerial vehicle security analysis and risk assessment for national airspace system operation. Obstacles related to catastrophe mitigation with drones and virtual/augmented environments. Disaster management methods utilizing aerial drones. GUEST EDITOR CREDENTIALS: Dr. Shakeel Mahmood Department of Geography, Government College University, Lahore, Pakistan Email - shakeelmahmoodkhan@gmail.com , shakeelmahmoodkhan@hotmail.com Scholar Page - https://scholar.google.com/citations?user=bdE80HMAAAAJ&hl=en&oi=sra Dr. Sofiane Bensefia Department of Agricultural Science, University Mohamed El Bachir El Ibrahimi, Bordj Bou Arreridj, Algeria. Email - sofiane.bensefia@univ-bba.dz Scholar Page - https://scholar.google.com/citations?user=YJ0YTHwAAAAJ&hl=en&oi=ao Dr. Helen Muhammad Abdul Hussein Al-Badiri College of Education for Girls, University of Kufa, Najaf, Iraq. Email - helenm.abdulhussein@uokfa.edu.iq Dr.Muhammad Irfan Ahamad College of Geography and Environmental Science, Henan University, Kaifeng 475004, China Email - mirfan230@hotmail.com Scholar Page - https://scholar.google.com/citations?user=m5AISGQAAAAJ&hl=en&oi=ao SI TIMELINE: Submission Deadline 05, Nov 2024 Authors Notification 30, Jan 2025 Revised Papers Due 20, Mar 2025 Final notification 15, May 2025
IMPORT Notice: The number of Special Issues reached a limited number in 2024. From 30 July 2024, we will stop accepting new SI proposals. We apologize for any inconvenience. Best regards, Editorial Office, AUSMT. 30 July 2024 Taipei Time 19:30 (GMT +8)
Call for papers: Special Issue(24-05): Trends in Artificial Intelligence for Predictive Maintenance in Industrial IoT Applications
The core of predictive maintenance in the industrial Internet of things is machine learning algorithms driven by AI. These algorithms are able to anticipate possible equipment breakdowns by identifying early warning indicators because they are continuously learning from the data. In order to enhance efficiency, forecast or identify equipment breakdowns, and increase the usable life of asset components, data analysis and machine learning are used. The introduction of AI poses issues that need to be properly addressed, including transparency, reliability, prices, and job displacement. The increasing sophistication of artificial intelligence and machine learning algorithms makes it possible to predict failures with greater accuracy. With edge computing, decision-making can happen faster by analysing sensor data in real-time. IoT predictive maintenance is evaluating machinery, assets, or equipment and better forecasting possible failures or outages using data collected via IoT technology. The PdM systems' brains are AI algorithms, which include machine learning and deep learning methods. The algorithms examine the information to determine the key elements associated with potential failures. By analysing past data, they are able to forecast RUL, abnormalities, and equipment failures. Several advantages can be obtained by implementing predictive maintenance in a connected product environment and smart factory use case. These include fewer unplanned breakdowns, increased asset uptime and improved asset reliability, lower operational costs by only performing maintenance or field service when necessary, increased production hours, enhanced safety, more efficient maintenance, lower spare part inventory costs, and more. To assist maintenance personnel in maintaining ideal inventory levels, AI systems can evaluate equipment, inventory, and purchase data in both the past and the present. When it comes to recommending when to refill parts, these systems are able to recognize patterns in the usage and purchasing history associated with the parts. Artificial intelligence (AI), predictive analytics, and the Internet of Things (IoT) are some of the technologies that predictive maintenance depends on. With the help of data analysis, predictive maintenance (PdM) can spot possible equipment flaws and operating irregularities in advance of failures, allowing for prompt repairs. By reducing the frequency of maintenance, it seeks to reduce unplanned outages and needless preventive maintenance expenses. By anticipating when maintenance is necessary, AI-powered predictive maintenance can stop equipment breakdowns. Whereas ML-based systems employ sophisticated modelling to anticipate failure, rule-based systems rely on hard-coded thresholds. Generative artificial intelligence (AI) can assist in spotting patterns and abnormalities that might point to imminent equipment failures by learning from enormous volumes of operational data. With the help of sensors, analytics, and machine learning algorithms, IoT-based predictive maintenance may anticipate when a machine or other piece of equipment will need maintenance, saving businesses money by minimising downtime as well as maintenance expenses. The implementation of reliability best practices to maximise equipment uptime in a manufacturing context is known as industrial maintenance, or plant maintenance. Manufacturers can steer clear of obstacles during the supply chain's production phase when their equipment is operating as planned. This technique aids in predicting equipment failures. It also reduces maintenance costs, lengthens the lifespan of machinery, and minimises unplanned downtime. Articles are invited that explore Trends in Artificial Intelligence for Predictive Maintenance in Industrial IoT Applications. Case studies and practitioner perspectives are also welcome. LIST OF TOPICS • Predictive maintenance: connecting IoT and artificial intelligence. • AI-Driven Proactive Maintenance for Industrial Internet of Things Devices. • AI for industrial systems predictive maintenance. • Industrial Internet of Things predictive maintenance facilitated by deep learning. • IoT Device Predictive Maintenance utilising deep learning and time series analysis. • Application of artificial intelligence tools to anticipate repair principles. • Smart factories with sophisticated sensors and predictive maintenance. • Deep learning-based quick predictive maintenance for the industrial internet of things. • Predictive maintenance using IoT in the manufacturing industry. • Predictive maintenance applications using artificial intelligence. • A framework for autonomous monitoring of industrial IoT systems using predictive maintenance. • Predictive maintenance using an industrial internet of things platform. • Predictive Maintenance using Deep Learning for Industrial Internet of Things Applications. SUGGESTED TIMELINE • Manuscript submissions due April 15, 2025 • First round of reviews completed June 15, 2025 • Revised manuscripts due Aug 15, 2025 • Second round of reviews completed Oct 15, 2025 • Final manuscripts due Dec15, 2025 GUEST EDITORS • Dr. Jawad Khan Assistant Professor Gachon University, Seongnam, South Korea Email: jkhanbk1@gachon.ac.kr, prof.jawadkhan@gmail.com Google Scholar: https://scholar.google.com/citations?user=BWpBBh0AAAAJ&hl=en Research Gate: https://www.researchgate.net/profile/Jawad-Khan-21 ORCID: https://orcid.org/0000-0001-8263-7213 • Dr. Muhammad Hameed Siddiqi Associate Professor Jouf University, Sakaka, Aljouf, Saudi Arabia Email: mhsiddiqi@ju.edu.sa Google Scholar: https://scholar.google.co.uk/citations?user=PktU0eEAAAAJ&hl=en Research Gate Link: https://www.researchgate.net/profile/Muhammad-Siddiqi-7 ORCID: https://orcid.org/0000-0002-4370-8012 • Dr. Tariq Rahim Lecturer Kingston University, Kingston, England Email: t.rahim@kingston.ac.uk Google Scholar: https://scholar.google.com/citations?user=fr4C9ogAAAAJ&hl=en ResearchGate: https://www.researchgate.net/profile/Tariq-Rahim-2 ORCID: https://orcid.org/0000-0001-7817-9715 • Dr. Shah Khalid Assistant Professor National University of Sciences & Technology, Islamabad, Pakistan Email: shah.khalid@seecs.edu.pk Google Scholar: https://scholar.google.com/citations?user=Sff9RyoAAAAJ&hl=en ResearchGate: https://www.researchgate.net/profile/Shah-Khalid-13 ORCID: https://orcid.org/0000-0001-5735-5863
Call for papers: Special Issue(24-04): Ubiquitous Green Artificial Intelligence based Computing for Smart Transportation
Speical Issue: Ubiquitous Green Artificial Intelligence based Computing for Smart Transportation Using advanced mobile and ubiquitous computing in smart transportation stands as a forefront in smart city environment. Since, the future of transportation lies in Green AI-based computing shifting towards enhanced efficiency and productivity, it facilitates seamless people mobility and fosters a higher quality of life. Its significance deepens as it becomes instrumental in constructing intelligent systems, particularly within the area of intelligent transportation. In addition, the core objective of intelligent transportation systems is to provide a safer, cleaner, affordable and efficient mode of transportation. This route to smart cities necessitates the integration of cutting-edge transportation systems like autonomous vehicles, connected roads and intelligent parking solutions. This surge in smart transportation technologies demands heightened memory bandwidth, energy efficiency, accelerated computing speeds and robust edge processing capabilities. AI-based computing extends many advantages to transportation consisting streamlined traffic flow, diminished emissions, heightened customer satisfaction and strengthen safety measures. The importance of this progress is green machine intelligence-based computing for Smart Transportation. In recent years, the role of intelligent applications in transportation has expanded by Ubiquitous Green AI-based Computing for Smart Transportation in the Future Mobile Internet. As the transportation industry undergoes a radical transformation to mitigate environmental impact, Green Machine Intelligence-based Computing emerges as a pivotal force, ensuring the long-term sustainability of land, air and sea transportation. Further, with an increasing array of sensors and communication technologies in vehicles, the scope for intelligent transportation systems to enhance safety, efficiency and sustainability is vast. AI-based green computing will be instrumental in shaping real-time intelligent transportation applications and also ensures adaptation and seamless interaction with their environment. Also, in a landscape where automated driving, connected services and shared mobility models redefine transportation dynamics, smart cities must prioritize reducing their carbon footprint. This Special Issue aims to understand and analyze the current state of Ubiquitous Green Artificial Intelligence in Smart Transportation while fostering discussions on future directions. It also aims to contribute to the development of sustainable and intelligent transportation infrastructures for smart cities List of topics of interest includes, but are not limited to the following: · AI-driven Traffic Management Strategies for Urban Sustainability · Integration of Electric and Autonomous Vehicles in Smart Transportation · Privacy-Preserving Data Analytics in UGAI for Transportation · Interconnected Smart Infrastructure for Seamless Transportation Systems · Energy-Efficient Routing Algorithms for Green Transportation · Human-Centric Design and User Experience in Smart Transportation Apps · Carbon Footprint Reduction through AI-Optimized Logistics · Cross-Domain Collaboration for Standardized Communication Protocols · Resilience and Robustness of UGAI Systems in Adverse Conditions · Social and Economic Impacts of UGAI in Smart Transportation · Green Fleet Management: AI Applications in Vehicle Maintenance · Autonomous Public Transportation: Challenges and Opportunities Guest Editor Details of this Special Issue: Dr. Kamalakanta Muduli (Lead GE) Associate Professor, Mechanical Engineering Department, Papua New Guinea University of Technology, Morobe Province, Papua New Guinea. Email-ID: kamalakanta.muduli@pnguot.ac.pg, dr.kamalakantamuduli@gmail.com Google Scholar: https://scholar.google.co.in/citations?user=LdMwYiIAAAAJ&hl=en Linked-In: https://in.linkedin.com/in/kamalakanta-muduli-98119076 Research Interest: Operations Management, Industry 4.0, Health Care, Waste Management, Decision Making, Machining Dr. Jiaqi Ruan (Co-GE) Research Associate, Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong. Email-ID: jiaqi.ruan@polyu.edu.hk Google Scholar: https://scholar.google.com/citations?user=XL1OiAUAAAAJ&hl=en Linked-In: https://cn.linkedin.com/in/jiaqi-ruan-803a05260 Research Interest: Smart Grid, Cyber-Physical Security, Deep Learning, Demand Response, Electricity Market, Renewable Energy Dr. Karthik Chandran (Co-GE) Professor, Department of Robotics and Automation, Jyothi Engineering College, Thrissur, Kerala. Email-ID: karthikc@jecc.ac.in Google-Scholar: https://scholar.google.com/citations?user=lGmG0hQAAAAJ&hl=en Linked-In: https://in.linkedin.com/in/dr-karthik-chandran-98aa1050 Research Interest: Process Control design and Automation, Medical Image Processing, Machine learning Important Dates (Tentative): Manuscript Submission Deadline : Mar 31, 2025 Authors Notification : Jun 30, 2025 Revised Papers Due : Jul 31, 2025 Final notification : Sep 15, 2025
Call for papers: Special Issue(24-03): Intelligent Systems for Smart Automation and Self-Optimization of Industry 4.0
Call for Papers: Special Issue: Intelligent Systems for Smart Automation and Self-Optimization of Industry 4.0 SI: Intelligent Systems for Smart Automation and Self-Optimization of Industry 4.0 Today Industry 4.0 is expected to increase productivity, customisation, automation, flexibility and quality of products. We will witness more computerised and digital tools using intelligent systems widely used to achieve the intended results. Primarily, intelligent system tools are used in decision-making processes in a complex work environment. Intelligent tools and software’s are applied in different domains of almost every sector of Industry 4.0. The wide use of intelligent systems is due to the wide availability of technologies like AI, IoT, and cloud computing. In the manufacturing industries, intelligent systems are applied in intelligent manufacturing, which involves producing goods using smart sensors, intelligent devices and advanced decision-making models. These devices process the data with analytical tools and optimise production and transaction. One of the advantages of intelligent systems is the integration of human-machine interaction with which various levels of operating an industry can be combined. The real-time data collection feature of intelligent systems enables data collection and transfer between tools, industrial workers and materials. They also permit the flow of information regarding the production, transport of products and transaction details to ensure visibility and traceability. Intelligent systems also play a role in enterprise-resource planning systems. Other applications of intelligent systems are automation in production, industrial connectivity, adaptive manufacturing systems and wireless communication. In service-oriented industries, intelligent systems are used in chatbots, cyber security and interconnected devices. Cyber-physical systems developed with intelligent algorithms operate to integrate machines, electronics, software and other devices, thus enabling machine-to-machine interaction quickly. The continuous assistance given to the machines through artificial and intelligent control systems reduces potential risks to people and property in the industries. Many countries worldwide have already adopted intelligent systems for smart designing, smart monitoring and smart production of goods in the industry 4.0 transitions. As these systems use data for intelligent decision-making, the possibility for errors is significantly minimised. More attention to industry 4.0 increases the opportunity for intelligent systems for manufacturing and supply chain management and other managerial tasks in the industrial sector. Collaboration of new and advanced technologies is transforming traditional industrial practices. LIST OF TOPICS AREAS INCLUDE, BUT ARE NOT LIMITED TO: Implications of Cyber-physical system and architecture on Intelligence in Industry 4.0 Role of intelligent technologies for enhancement of Industry 4.0 Intelligent manufacturing techniques with cloud computing technology for industry 4.0 Role of Artificial intelligence in transforming traditional industrial processes Intelligent systems for supply chain management for industries Value creation and designing for industries using technologies Intelligent systems for enterprise management and manufacturing Wireless connectivity and its contribution to industrial transitions AI-based semantic web-based frameworks for service industries Future of applications using intelligent agents in agro-industries GUEST EDITOR INFORMATION: Name: Dr. Faheem Khan Affiliation: Assistant Professor, Department of Computer Engineering, Gachon University, Seongnam, South Korea Email: faheem@gachon.ac.kr, hamzahooram0915@gmail.com G Scholar: https://scholar.google.com.pk/citations?user=eIC_cSYAAAAJ&hl=en Short Bio: Faheem Khan received the Ph.D. degree in computer science from the University of Malakand, KPK, Pakistan. He served four years in Pakistan as an Assistant Professor and supervised many papers and students. Since April 2021, he has been an Assistant Professor with the Department of Computer Engineering, Gachon University, South Korea. His field of interest is Computer Networking, Wireless Networking, MANET, IoT, AI, Machine Learning, etc., Name: Dr. Umme Laila Affiliation: Assistant Professor, Computer Engineering Department, Sir Syed University of Engineering & Technology, Karachi, Pakistan. Email: ulaila@ssuet.edu.pk G Scholar: https://scholar.google.com.pk/citations?user=wib1gQkAAAAJ&hl=en Short Bio: Dr. Umme Laila received her Ph.D. in Computer Science and Information Technology, NED University of Engineering and Technology, Karachi, Pakistan (2016~2021). Since May 2008, she has been an Assistant Professor with the Computer Engineering Department, Sir Syed University of Engineering & Technology, Karachi, Pakistan. Her area of interest includes Open-source software, Object oriented programming, Human Computer Interaction and Android. Name: Dr. Muhammad Adnan Khan Affiliation: Associate Professor, Riphah International University, Pakistan. Email: khanadnan.khan@riphah.edu.pk G Scholar: https://scholar.google.com/citations?user=0v2xyVUAAAAJ&hl=en&oi=ao Short Bio: Dr. Muhammad Adnan Khan received the B.S. and M.S. degrees from the International Islamic University, Islamabad, Pakistan, by obtaining the Scholarship Award from the Punjab Information and Technology Board, Government of Punjab, Pakistan, and the Ph.D. degree from ISRA University, Islamabad, by obtaining the Scholarship Award from the Higher Education Commission, Islamabad, in 2016. He is currently working as an Associate Professor in Riphah International University, Pakistan. He has been teaching graduate and undergraduate students in computer science and engineering for the past 12 years. He is guiding five Ph.D. scholars and six M.Phil. scholars. He has published more than 200 research articles with a Cumulative JCR-IF and more than 450 in reputed international journals as well as international conferences. His research interests primarily include machine learning, MUD, image processing and medical diagnosis, and channel estimation in multi-carrier communication systems using soft computing. MANUSCRIPT SUBMISSION – KEY DATES: Submission Deadline: April 05, 2025 Author Notification: June 20, 2025 Revision and Resubmission Deadline: July 25, 2025 Paper Acceptance: Sept 28, 2025
Call for papers: Special Issue(24-02): Sensors in Machine Vision of Automated Systems for Intelligent Manufacturing
Special Issue: Sensors in Machine Vision of Automated Systems for Intelligent Manufacturing Machine vision is a disruptive technology that is transforming manufacturing and production lines across the globe. Over the years machine vision has assisted in building seamless quality control systems, particularly in the manufacturing sector. A lot of progress has been made ever since to select and develop an ideal and customised approach for every application. Through its integration with technologies like embedded vision, image processing systems, data transmission systems, deep learning, machine learning, CMOS sensors etc., machine vision can be leveraged to optimise the manufacturing processes at various levels. Industry 4.0 focuses on intelligent manufacturing systems. To meet this rising level of complexity in industrial automated systems, machine vision technologies are being upgraded and innovated. For instance, the chemical composition of the materials can be easily obtained with the help of hyperspectral imaging. Conventional imaging systems cannot reveal specific details. However computational imaging can be used to combine a series of images in different ways to provide information on various details that cannot be obtained through conventional methods. Overall sensors in machine vision can greatly enhance the intelligent manufacturing process in terms of automation, performance, and integration. Machine vision cameras and sensors can be used to aid in manual assembly, add vision to the production line, use vision-guided robots, and enable embedded vision. Machine vision can be used to prevent errors in assembling objects, A stored image will be used by the machine vision system to analyse and check the assembled products for correctness. Errors or deviations will be notified to the operator immediately. The data collected can aid in traceability and further analysis. With the help of either single point, self-contained smart cameras or personal computer-based machine vision systems, the existing lines can be retrofitted or new ones can be built to meet the level of sophistication required. Vision-guided robots can effectively be used for pick and drop/place operations in an intelligent manufacturing unit. In addition, vision systems can be embedded into various equipment and manufacturing processes. This is possible with the help of ARM architecture and the availability of minute embedded processing boards. List of topics to be covered in this special issue: Novel multiresolution techniques for machine vision in a smart manufacturing facility. Design and development of a biomimetic machine vision for automated systems in intelligent manufacturing. Machine vision systems for smart orbital operations in space. Innovative techniques to improve the predictive maintenance in intelligent manufacturing using machine vision. Machine vision-based systems and solutions for bar code scanning. New methods for enhancing the track and trace operations in an intelligent manufacturing unit with the help of machine vision. Automated problem identification in assembly lines using sensors in machine vision. Automated product classification system using machine vision for smartphone manufacturing. Development of a scalable image coding system methodology for intelligent manufacturing. A machine vision algorithm for quality control of 3D printed products. Tentative Timeline for this special issue: Manuscript Submission Date - April 1, 2025 Author Notification Date - Jun 25, 2025 Revised paper submission Date - Aug 20, 2025 Final acceptance Date - Sep 25, 2025 Guest Editor Credentials: Dr. Muhammad Zakarya (Lead GE), Assistant Professor, Faculty of Computing and Information Technology, Sohar University, Oman. E-mail Id: MZakarya@su.edu.om, muha.zakarya@gmail.com Official Website: https://awkum.edu.pk/ IEEE Link: https://ieeexplore.ieee.org/author/37086383516 Google Scholar Link: https://scholar.google.com/citations?user=8Jc6d3IAAAAJ&hl=en LinkedIn: https://www.linkedin.com/in/muhammad-zakarya-ph-d-936672158/ Dr. Santosh Tirunagari, Assistant Professor, Department of Computer Science, School of Science and Technology, Middlesex University, UK E-mail Id: s.tirunagari@mdx.ac.uk Official Website: https://www.mdx.ac.uk/ IEEE Link: https://ieeexplore.ieee.org/author/37085475496 Google Scholar Link: https://scholar.google.co.uk/citations?user=EPfhRvUAAAAJ&hl=en LinkedIn: https://www.linkedin.com/in/santosh-tirunagari/ Prof. Jinguang Han, Professor, School of Cyber Science and Engineering, Southeast University, China E-mail Id: jghan@seu.edu.cn Official Website: https://www.seu.edu.cn/english/ IEEE Link: https://ieeexplore.ieee.org/author/37535076600 Google Scholar Link: https://scholar.google.com/citations?user=euLvHOkAAAAJ&hl=en Dr. Peiying Zhang, Associate Professor, China University of Petroleum (East China), China E-mail Id: zhangpeiying@upc.edu.cn Official Website: https://www.upc.edu.cn/ IEEE Link: https://ieeexplore.ieee.org/author/37085896900 Google Scholar Link: https://scholar.google.com/citations?user=pdrBAbIAAAAJ&hl=en