International Journal of Automation and Smart Technology: Announcements
type: channel
description: 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
language: en
items: Notice: [2026.03] The International Journal of Automation and Smart Technology Monthly Summary
items: Notice: Welcome New Associate Editor, Prof. Mahtab Uddin (United International University, Bangladesh)!
items: Notice: [2026.02] The International Journal of Automation and Smart Technology Monthly Summary
items: Notice: Welcome New Associate Editor, Prof. Chen - Ting Wen (National Taiwan University of Science and Technology, Taiwan)!
items: Important: [2026.01] The International Journal of Automation and Smart Technology Monthly Summary
items: Notice: AUSMT 2026 Special Issue Proposal Google Form
items: Call for papers: Special Issue (25-04-01): Human-Computer Interaction in the Virtual Reality: Challenges and Solutions
items: Important: AUSMT 2025 Special Issue Proposal Google Form
items: Call for papers: Special Issue (24-30)Integration of sensor data with AI for personalised health interventions
items: Call for papers: Special Issue (24-29):Energy-Efficient Security Protocols for Wireless Sensor Networks in Harsh Environments
items: Call for papers: Special Issue(24-28): Evolution of Robotics: Autonomous Machines in Manufacturing, Healthcare, and Personal Assistance
items: Call for papers: Special Issue(24-24): Leveraging AI and Smart Technology for Enhanced Business Intelligence in International Journal of Automation and Smart Technology
items: Call for papers: Special Issue(24-23): Cloud Computing for Corporate Sustainability in Digital Era
items: Call for papers: Special Issue(24-21):Image Processing Techniques for Improved Medical Image Segmentation
items: Call for papers: Special Issue(24-20): Novel Machine Learning Methods for Anomaly Detection in Sensor Networks
items: Call for papers: Special Issue(24-19): Predictive AI Models for Patient Readmission Risk and Resource Allocation
items: Call for papers: Special Issue(24-08):Smart Grid Technologies for Demand Response and Grid Resilience
items: Call for papers: Special Issue(24-07):Simulation-based Modeling for Human Computer Interaction in Modern Video Games
items: Important: IMPORT Notice: Stop accepting new SI proposals in 2024.
items: Call for papers: Special Issue(24-18): Real-Time Face Detection Algorithms for Internet of Things (IoT) Applications
items: Call for papers: Special Issue(24-17): Image Processing for Next-Gen Robotics: Bridging the Physical and Digital
items: Call for papers: Special Issue(24-16): Human-Computer Interaction in Virtual Reality- Challenges and Solutions
items: Call for papers: Special Issue(24-15): Emerging advances in human robot interaction for large complex business marketing innovations
items: Call for papers: Special Issue(24-14): AI-Driven OCT Image Analysis for Automated Disease Detection and Classification.
items: Call for papers: Special Issue(24-13): Advances in Smart Robotics for Manufacturing
items: Call for papers: Special Issue(24-12):Comparative Study of Machine Learning Algorithms for Facial Recognition Systems
items: Call for papers: Special Issue(24-09):Deep Learning for 3D Object Recognition in Smart City Surveillance Systems
items: Call for papers: Special Issue(24-06):Utilising Unmanned Aerial Vehicles (UAVs) for Real-time Disaster Risk Assessment
items: Call for papers: Special Issue(24-05): Trends in Artificial Intelligence for Predictive Maintenance in Industrial IoT Applications
items: Call for papers: Special Issue(24-04): Ubiquitous Green Artificial Intelligence based Computing for Smart Transportation
items: Call for papers: Special Issue(24-03): Intelligent Systems for Smart Automation and Self-Optimization of Industry 4.0
items: Call for papers: Special Issue(24-02): Sensors in Machine Vision of Automated Systems for Intelligent Manufacturing

Notice: [2026.03] The International Journal of Automation and Smart Technology Monthly Summary
type: item
description: Welcome to the March, 2026 Summary of the International Journal of Automation and Smart Technology. We are deeply grateful for the dedication of our distinguished Researchers, Reviewers and Authors, as well as the interest, support of our valued readers. AUSMT aims to highlight key developments in the fields of Intelligent Automation Technology, Ambient Intelligence, Context Awareness, Sensor Networks, Human-Computer Interface, Optomechatronic Systems, and Robotics. AUMST is proud to present of the March, 2026 insights. 1. Monthly Total Views: Number of visitors: 2833 (+35.6% compared to last month) Articles Abstract Views: 5096 (+13.3% compared to last month) Articles File Views: 5954 (+21.9% compared to last month) Issue Views: 721 (+49.9% compared to last month) Issue Downloads: 52 2. Top-view Papers: Rising Stars of the Month: UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization. (2024). International Journal of Automation and Smart Technology, 14(1) by Chung-Hsien Kuo et al. (203 views). A Simple And Low-Cost Method For Fabrication Of Non-Planar PDMS Microfluidic Channel. (2025). International Journal of Automation and Smart Technology, 15(1) by Tuan Vo et al.(180 views). Internet of Things Assisted Improved Fuzzy Aggregation Data Management in Smart Manufacturing Enterprise. (2025). International Journal of Automation and Smart Technology, 15(1) by Mohanarangan Veerappermal Devarajan et al. (167 views). Most Viewed of Last Year (last 12 months): Design of smart and self-controlled Solar Grass Cutter with Lawn Coverage. (2024). International Journal of Automation and Smart Technology, 14(1) by Muhammad Raza. (1314 views) UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization. (2024). International Journal of Automation and Smart Technology, 14(1) by Chung-Hsien Kuo et al. (1069 views). Internet of Things Assisted Improved Fuzzy Aggregation Data Ma…
creator: International Journal of Automation and Smart Technology
date: 2026-04-04

Notice: Welcome New Associate Editor, Prof. Mahtab Uddin (United International University, Bangladesh)!
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description: Welcome New Associate Editor, Prof. Mahtab Uddin The International Journal of Automation and Smart Technology (AUSMT) is delighted to announce that Prof. Mahtab Uddin has joined the editorial board of AUSMT as our newest Associate Area Editor. Dr. Uddin is an Associate Professor of Mathematics at the Institute of Natural Sciences, United International University (UIU), Dhaka, Bangladesh. He earned his Ph.D. in Mathematics from Bangladesh University of Engineering and Technology (BUET). His research interests include control theory, numerical linear algebra, model-order reduction, large-scale sparse descriptor systems, matrix equation solvers, and computer-aided modeling and simulation. During his career, Dr. Uddin has served as an editorial board member for several international journals, including the International Journal of Electrical and Communication Engineering Technology, International Journal of Material and Mathematical Sciences, Iris Journal of Mathematics, Journal of Biomedical Science and Engineering, and Research & Reviews: Discrete Mathematical Structures and now the International Journal of Automation and Smart Technology (AUSMT). - Personal Information: https://ins.uiu.ac.bd/faculty/mahtab-uddin/ - Google Scholar: https://scholar.google.com/citations?user=UcuAYhkAAAAJ&hl=en With the goal to provide a worldwide forum for the dynamic exchange of ideas and findings from research in different disciplines, AUSMT believes that Prof. Mahtab Uddin's appoinment not only marks a milestone in our commitment to maintain the highest standard of our journal, based on his academic exellence, but also efforts to broadening the journal's international reach and strengthening the global academic impact. Please join us in warmly welcoming Prof. Mahtab Uddin to his new position. Editorial Board, AUSMT!
creator: International Journal of Automation and Smart Technology
date: 2026-03-18

Notice: [2026.02] The International Journal of Automation and Smart Technology Monthly Summary
type: item
description: Welcome to the February, 2026 Summary of the International Journal of Automation and Smart Technology. We are deeply grateful for the dedication of our distinguished Researchers, Reviewers and Authors, as well as the interest, support of our valued readers. AUSMT aims to highlight key developments in the fields of Intelligent Automation Technology, Ambient Intelligence, Context Awareness, Sensor Networks, Human-Computer Interface, Optomechatronic Systems, and Robotics. AUMST is proud to present of the February, 2026 insights. 1. Monthly Total Views: Number of visitors: 2089 (+15.7% compared to last month) Articles Abstract Views: 4496 (+12.1% compared to last month) Articles File Views: 4884 (+48.0% compared to last month) Issue Views: 481 (+7.8% compared to last month) Issue Downloads: 60 (+66.7% compared to last month) The number of monthly total views illustrate a remarkable increase traffic, compared to the January, 2026. 2. Top-view Papers: Trending This Month: A Simple And Low-Cost Method For Fabrication Of Non-Planar PDMS Microfluidic Channel. (2025). International Journal of Automation and Smart Technology, 15(1) by Tuan Vo et al. , (158 views). The Industrial Specialization and Economic Contribution of the Smartphone Supply Chain: The Case of Apple and HTC, (2012). International Journal of Automation and Smart Technology, 2(3) by Wen-Nan Tsan et al. (128 views). Internet of Things Assisted Improved Fuzzy Aggregation Data Management in Smart Manufacturing Enterprise. (2025). International Journal of Automation and Smart Technology, 15(1) by Mohanarangan Veerappermal Devarajan et al. (124 views). Most Viewed of Last Year (last 12 months): Design of smart and self-controlled Solar Grass Cutter with Lawn Coverage. (2024). International Journal of Automation and Smart Technology, 14(1) by Muhammad Raza. (1310 views) UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization. (2024). International Journal of Au…
creator: International Journal of Automation and Smart Technology
date: 2026-03-07

Notice: Welcome New Associate Editor, Prof. Chen - Ting Wen (National Taiwan University of Science and Technology, Taiwan)!
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description: Welcome New Associate Editor, Prof. Chen - Ting Wen! The International Journal of Automation and Smart Technology (AUSMT) is delighted to announce that Prof. Chen - Ting Wen has joined the editorial board of AUSMT as our newest Associate Area Editor. Prof. Wen is the Assistant Professor of the Department of Mechanical Engineering at National Taiwan University of Science and Technology (NTUST). He obtained his Ph.D degree from Tohoku University, Japan. His research interests include Physical Human-Robot Interaction, Tactile Servo, Machine Learning and Soft Robotics. Personal Information: https://www.me.ntust.edu.tw/p/404-1005-126838.php?Lang=en Google Scholar: https://scholar.google.com/citations?user=DgIJLU0AAAAJ&hl=en With the goal to provide a worldwide forum for the dynamic exchange of ideas and findings from research in different disciplines, AUSMT believes that Prof. Chen - Ting Wen's appointment marks a milestone in our commitment to maintain the highest standard of our journal, based on his academic excellence and review experience. Please join us in warmly welcoming Prof. Wen to his new position. Editorial Board, AUSMT!
creator: International Journal of Automation and Smart Technology
date: 2026-02-28

Important: [2026.01] The International Journal of Automation and Smart Technology Monthly Summary
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description: Welcome to the January, 2026 Summary of the International Journal of Automation and Smart Technology. We are deeply grateful for the dedication of our Distinguished Researchers, Reviewers and Authors, as well as the interest, support of our valued readers. In that spirit, we are proud to introduce this Monthly Summary as part of our commitment to bringing AUSMT closer to the domestic and international research community. We now aim to highlight key developments in the fields of Intelligent Automation Technology, Ambient Intelligence, Context Awareness, Sensor Networks, Human-Computer Interface, Optomechatronic Systems, and Robotics. AUMST is proud to present this month's insights. 1. Monthly Total Views and Downloads: - Number of visitors: 1806 - Articles Abstract Views: 4011 - Articles File Views: 3300 - Issue Views: 449 - Issue Downloads: 36 2. Top-view Papers: - Design of smart and self-controlled Solar Grass Cutter with Lawn Coverage by Muhammad Raza (178 views). - The Industrial Specialization and Economic Contribution of the Smartphone Supply Chain: The Case of Apple and HTC by Wen-Nan Tsan et al. (108 views). - Securing Cloud-Edge Collaborative Computing with AI, GANs, TEEs, Micro Data Centers, and Cloud-Oriented DevOps for Privacy Protection by Rama Krishna Mani Kanta Yalla et al. (103 views). 3. Editorial Activity: - Submissions Received: 20 - Submissions Accepted: 1 - Submissions Declined: 18 - Days to First Editorial Decision: 7 4. Up to date CiteScore 2025: 1.4 AUMST hopes to continue receiving support from the research community as we steadily become a reliable venue for your innovative and creative work. Editorial Office, AUMST.
creator: International Journal of Automation and Smart Technology
date: 2026-02-07

Notice: AUSMT 2026 Special Issue Proposal Google Form
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description: 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 =================================================================================== AUSMT kindly requests our valued researchers to read carefully below policies before submitting your proposal. To maintain AUSMT standard peer-review protocols, and ensure the highest quality of control. For that reason, the journal's policy requires the reviewing process remain entirely centralized within our Editorial Board. Editorial Board will carefully review proper submitted, potential proposals and contact the Guest Editors via provided institutional emails. The Editor-in-Chief (EIC) and Co-Editor-in…
creator: International Journal of Automation and Smart Technology
date: 2025-12-04

Call for papers: Special Issue (25-04-01): Human-Computer Interaction in the Virtual Reality: Challenges and Solutions
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description: 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 Intellige…
creator: International Journal of Automation and Smart Technology
date: 2025-03-27

Important: AUSMT 2025 Special Issue Proposal Google Form
type: item
description: 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-ba…
creator: International Journal of Automation and Smart Technology
date: 2025-03-05

Call for papers: Special Issue (24-30)Integration of sensor data with AI for personalised health interventions
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description: 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, pre…
creator: International Journal of Automation and Smart Technology
date: 2024-08-27

Call for papers: Special Issue (24-29):Energy-Efficient Security Protocols for Wireless Sensor Networks in Harsh Environments
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description: 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, wormho…
creator: International Journal of Automation and Smart Technology
date: 2024-08-27

Call for papers: Special Issue(24-28): Evolution of Robotics: Autonomous Machines in Manufacturing, Healthcare, and Personal Assistance
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description: 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. Bec…
creator: International Journal of Automation and Smart Technology
date: 2024-08-27

Call for papers: Special Issue(24-24): Leveraging AI and Smart Technology for Enhanced Business Intelligence in International Journal of Automation and Smart Technology
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description: 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 …
creator: International Journal of Automation and Smart Technology
date: 2024-08-27

Call for papers: Special Issue(24-23): Cloud Computing for Corporate Sustainability in Digital Era
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description: 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 technolog…
creator: International Journal of Automation and Smart Technology
date: 2024-08-27

Call for papers: Special Issue(24-21):Image Processing Techniques for Improved Medical Image Segmentation
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description: 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 t…
creator: International Journal of Automation and Smart Technology
date: 2024-08-27

Call for papers: Special Issue(24-20): Novel Machine Learning Methods for Anomaly Detection in Sensor Networks
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description: 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 base…
creator: International Journal of Automation and Smart Technology
date: 2024-08-27

Call for papers: Special Issue(24-19): Predictive AI Models for Patient Readmission Risk and Resource Allocation
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description: 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 sig…
creator: International Journal of Automation and Smart Technology
date: 2024-08-09

Call for papers: Special Issue(24-08):Smart Grid Technologies for Demand Response and Grid Resilience
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description: 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, t…
creator: International Journal of Automation and Smart Technology
date: 2024-08-09

Call for papers: Special Issue(24-07):Simulation-based Modeling for Human Computer Interaction in Modern Video Games
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description: 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 verisim…
creator: International Journal of Automation and Smart Technology
date: 2024-08-09

Important: IMPORT Notice: Stop accepting new SI proposals in 2024.
type: item
description: 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
creator: International Journal of Automation and Smart Technology
date: 2024-08-07

Call for papers: Special Issue(24-18): Real-Time Face Detection Algorithms for Internet of Things (IoT) Applications
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description: 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 int…
creator: International Journal of Automation and Smart Technology
date: 2024-08-07

Call for papers: Special Issue(24-17): Image Processing for Next-Gen Robotics: Bridging the Physical and Digital
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description: 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 …
creator: International Journal of Automation and Smart Technology
date: 2024-08-07

Call for papers: Special Issue(24-16): Human-Computer Interaction in Virtual Reality- Challenges and Solutions
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description: 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 pr…
creator: International Journal of Automation and Smart Technology
date: 2024-08-07

Call for papers: Special Issue(24-15): Emerging advances in human robot interaction for large complex business marketing innovations
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description: 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…
creator: International Journal of Automation and Smart Technology
date: 2024-08-07

Call for papers: Special Issue(24-14): AI-Driven OCT Image Analysis for Automated Disease Detection and Classification.
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description: 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 inci…
creator: International Journal of Automation and Smart Technology
date: 2024-08-07

Call for papers: Special Issue(24-13): Advances in Smart Robotics for Manufacturing
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description: 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 the…
creator: International Journal of Automation and Smart Technology
date: 2024-08-07

Call for papers: Special Issue(24-12):Comparative Study of Machine Learning Algorithms for Facial Recognition Systems
type: item
description: 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 la…
creator: International Journal of Automation and Smart Technology
date: 2024-08-07

Call for papers: Special Issue(24-09):Deep Learning for 3D Object Recognition in Smart City Surveillance Systems
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description: 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 …
creator: International Journal of Automation and Smart Technology
date: 2024-08-02

Call for papers: Special Issue(24-06):Utilising Unmanned Aerial Vehicles (UAVs) for Real-time Disaster Risk Assessment
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description: 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 a…
creator: International Journal of Automation and Smart Technology
date: 2024-08-02

Important: IMPORT Notice: Stop accepting new SI proposals in 2024.
type: item
description: 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)
creator: International Journal of Automation and Smart Technology
date: 2024-07-30

Call for papers: Special Issue(24-05): Trends in Artificial Intelligence for Predictive Maintenance in Industrial IoT Applications
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description: 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 patt…
creator: International Journal of Automation and Smart Technology
date: 2024-07-25

Call for papers: Special Issue(24-04): Ubiquitous Green Artificial Intelligence based Computing for Smart Transportation
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description: 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…
creator: International Journal of Automation and Smart Technology
date: 2024-07-13

Call for papers: Special Issue(24-03): Intelligent Systems for Smart Automation and Self-Optimization of Industry 4.0
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description: 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…
creator: International Journal of Automation and Smart Technology
date: 2024-07-13

Call for papers: Special Issue(24-02): Sensors in Machine Vision of Automated Systems for Intelligent Manufacturing
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description: 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…
creator: International Journal of Automation and Smart Technology
date: 2024-07-13