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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Senthilnathan Chidambaranathan Vanitha C.S. Sasireka Naduvathezhath Nessariose Jose K. Madhusudhana Rao T. Lakshmi Devi Architecture and Design Virtusa Department of Computer Science and Engineering Sri Sairam Institute of Technolgoy Department of Computer Science and Engineering SRM Institute of Science and Technology Regent Strategic Solutions Rochester USA KKR&KSR Institute Of Technology & Science Guntur A.P Department of Data Science Siddhartha Institute of Engineering and Technology (SIET) (A UGC Autonomous Institute) Hyderabad Telangana India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

In the era of expanding wireless sensor network (WSN) applications, safeguarding these networks against a spectrum of threats, including Denial-of-Service (DoS) attacks, is paramount. The innate resource limitations of sensor nodes often render conventional security measures ineffective. This study introduces a pioneering approach integrating minimalist machine learning techniques to combat DoS attacks within WSNs. Our methodology adeptly leverages machine learning capabilities while strategically curbing computational and resource demands. By distilling essential features from network traffic patterns, our model adeptly identifies aberrations signaling DoS attacks, all while preserving the efficiency of resource-limited sensor nodes. Its minimalist framework seamlessly integrates with existing WSN architectures, negating the need for substantial hardware upgrades and maintaining network performance. Results underscore the superior detection accuracy and efficiency of our minimalist machine learning strategy compared to traditional methods. Moreover, its scalability and adaptability render it ideal for deployment across varied WSN setups, fortifying them against DoS threats while safeguarding core network *** mitigating DoS attack impacts without burdening computational resources, our approach marks a significant stride towards bolstering the security and dependability of wireless sensor networks amid evolving threat landscapes.

关键词： Wireless sensor networks Renewable energy sources Scalability Machine learning Telecommunication traffic Network security Denial-of-service attack

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Discrepancies of remote techno-tolerance due to COVID-19 pandemic within Arab middle-east countries

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Journal of Umm Al-Qura University for Engineering and architecture 2023年第3期14卷 151-165页

作者： Abu-Hashem, Muhannad A. Gutub, Adnan Salem, Osama Shambour, Mohd Khaled Shambour, Qusai Shehab, Mohammad Izzat, Ahmad Alrawashdeh, Mufda J. Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia Computer Engineering Department College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Educational Technology and E-Learning Department College of Education Umm Al-Qura University Makkah Saudi Arabia The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Software Engineering Department Faculty of IT Al-Ahliyya Amman University Amman Jordan Artificial Intelligence & Software Engineering Departments Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Faculty of Engineering and Information Technology Israa University Gaza Palestine Department of Mathematics College of Sciences and Arts Qassim University Al-Ras Saudi Arabia

The coronavirus disease (COVID-19) changed the world’s lifestyle switching many techno-services to be provided remotely instead of direct usual physical interactions between people. This study focused on university students’ perceptions of this virtual technology-engineering change as discrepancies to be analyzed. The research surveyed 777 different students from four Arab middle-east neighboring countries, with related demographics and specifications, expressing full remarkable experiences of e-technology in virtual as well as traditional performances helping reveal overall tolerance possibilities. The study adopted examination technology via ANOVA to test discrepancies within students' perceptions for overall multi-factors deep analysis classification. The exploration highlighted an interesting range of pros and cons remarks including feminism and economic effect as well as other cultural and engineering interaction issues, raising signals to essentially consider and mutually benefit from adequate future generation e-techno adaptation within the region. © The Author(s) 2023.

关键词： ANOVA COVID-19 E-techno Students’ perceptions Traditional and virtual classes

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Edge Computing in Smart Cities: Enhancing Real-Time Data Processing 2

Edge Computing in Smart Cities: Enhancing Real-Time Data Pro...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Rajakumari, S. Natarajan, C. Alone, Vinod N. Senthilkumar, P. Gayathri, R. John, Godwin J. Department of Mathematics R.M.D. Engineering College Chennai India Department of CSE P. S. R Engineering College Tamilnadu Sivakasi India Computer Engineering Department VPPCOE &VA Sion Maharashtra Mumbai India School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of ECE Rajalakshmi Engineering College Tamilnadu Chennai India Department of Mechanical Engineering Rajalakshmi Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331541217

One of the most crucial issues that smart cities have to keep addressing as they grow is urban mobility improvement. Maximizing the efficiency of transportation, so lowering congestion, and so optimizing traffic flow can help to solve the growing urbanization and increasing vehicle numbers. This research aims to develop and implement a deep learning method based on DenseNet in order to reach the target of raising the effectiveness of urban traffic management. The unique selling proposition that distinguishes this sector from others is the introduction of modern technologies into a sector that has always depended on conventional methods. The main goal of the work was to apply DenseNet architecture to analyze traffic patterns. Considering the two other strategies, the proposed option has a lot of possibilities. From evaluation criteria-which comprised recall, precision, and F1-score, the proposed solution outperformed the approaches regarded to be state-of- the-art. © 2024 IEEE.

关键词： Deep Learning DenseNet Smart Cities Traffic Management

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Gamefication of Youth's Career Guidance Self-Identification

Gamefication of Youth's Career Guidance Self-Identification

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Poliakov, Mykyta Mezzane, Daoud Terenchuk, Svitlana Riabchun, Yuliia Rusnak, Patrik Biloshchytska, Svitlana Kyiv National University Of Construction And Architecture Department Of Information Technology Design And Applied Mathematics Kyiv Ukraine Inovative Materials Energy And Sustainable Devellopment Laboratpry Cadi Ayyad University Marrakech Morocco Kyiv National University Of Construction And Architecture Department Of Information Technology Kyiv Ukraine University Of Zilina Faculty Of Management Science And Informatics Zilina Slovakia Astana It University Department Of Computer Technology And Data Nur-Sultan Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The work is devoted to solving the youth's professional orientation problem in modern conditions of limited offline communication. The urgency of development of new methods and tools of adolescents' professional orientation, which are based on modern information technologies, is shown. The research's focus is aimed at the introduction of computer game technologies in the work of career guidance centers, pre-university departments of colleges and higher education institutions. The expediency and effectiveness of the existing professional orientation computer game tasks usage in career guidance work with youth who need decision support at the stage of choosing a future profession are substantiated. It is shown that in the conditions of virtual reality the estimate reliability of personal qualities for compliance with a certain professional activity depends on the selection of game tasks. A clear criterion for automatic measurement of activity interest in professional activity, which depends on the task selection time, is proposed. There are fragments of computer game tasks that allow a person to self-estimate their ability to perform activities in different situations and professional relationships without communicating with career counselors and psychologists. © 2022 IEEE.

关键词： computer games

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A Rapid GBDT-BiLSTM Based Damage Detection Technique in Bridges using Accurate Parameters

A Rapid GBDT-BiLSTM Based Damage Detection Technique in Brid...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Babu, Uppara Raghu Patil, Deepak T. Qasim, Jameela Soni, Mukesh Sharma, Neelam Bhoopathy, V. Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India Bharati Vidyapeeth College of Architecture University of Mumbai Maharashtra Navi Mumbai India Department of Bridge Engineering School of Civil Engineering Tongji University Shanghai China Department of Research and Development Lovely Professional University Phagwara India Physical Education Department Lovely Professional University Punjab India Department of Computer Science and Engineering Sree Rama Engineering College Tirupathi India

ISBN: (纸本)9798350374933

Conventional methods for diagnosing bridge damage have centred on tracking changes in modal-based Damage Sensitive Features (DSFs), which are strongly related to structural stiffness. Environmental and operational variations, inefficient use of machine learning approaches for damage detection, and an overall reliance on modal-based DSFs are some of the limits and hurdles that the system still faces, despite substantial progress towards practical deployment. According to the proposed method, the initial three steps are data preparation, feature extraction, and model training. In order to identify damage during the preprocessing step, the suggested approach utilises anomaly scores. Features in the time-frequency domain allow for parallel processing of signals in the signal-to-feature extraction pipeline. After the features have been extracted, the data is used to train GBDT-BiLSTM models. The proposed approach achieves better results than the well-known alternatives, GBDT and BiLSTM. © 2024 IEEE.

关键词： Bridges

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Critical assessment of opal mining techniques, tools, market system, environmental impact challenges, and prospects — A systematic review

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Arabian Journal of Geosciences 2025年第6期18卷 1-14页

作者： Adamu, Anteneh Yayeh Eneyew, Demessie Mulu Ejigu, Assamen Ayalew Dubiniewicz, Andrzej Kajetan Endalew, Sisay Awoke Nigussie, Asmare Belay Amognehegn, Asnake Enawgaw School of Civil and Water Resource Engineering & Architecture Kombolcha Institute of Technology Kombolcha Ethiopia Department of Electrical and Computer Engineering Kombolcha Institute of Technology Kombolcha Ethiopia Faculty of Earth Sciences and Spatial Management Nicolaus Copernicus University Torun Poland Department of Chemistry College of Natural Science Wollo University Dessie Ethiopia

The study presents the existing challenges in opal mining in the Delanta area (mining methods, tools, market system, and environmental impact) and provides possible recommendations to overcome these challenges. Literature review, data collection, field survey, and data analysis were used as methods for this study. Environmental impact assessment also applied in the study area. Based on the study, the opal mining system in the Delanta area is performed in traditional ways and this method is not benefiting the local miners as well as the country. In the area, due to roof collapse of mining sites, three to eight artesian small-scale miners are dying each year. Opal quality deterioration (wastes locally called Ajara) because of cracking is the other main challenge in the study area. Moreover, environmental problems such as deforestation, concealed underground channels, landslides, the loss of numerous species’ habitats, annihilation of flora, land degradation, soil erosion and pollution, water pollution, air pollution, downstream sedimentation, and the covering of topsoil on agricultural land, which affects the farming system are the main findings of this study. In the study area, only 3% of the opal is processed, while 97% is rough, and this results in loss of income for the country. Finally, the study indicated the possible solutions in opal exploration and mining methods, as well as market system for the safe and sustainable extraction of Delanta opal resources. The study recommended for future work to design a safe mining method, a ventilation shaft, develop a smartphone app for quality grading and branding for selling, developing a low-cost opal cutting and polishing machine for value addition, and establishing training centers. In addition, the sector in the area also needs solar systems planting for lighting of mining sites, design and development of waste soil removal technology/conveyors and hammers for safe mining, sustainable development and management.

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Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network 3

Reliable Multihop Routing and Congestion Aware Chicken Swarm...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Anitha, Cuddapah Al-Tarazi, Dalia Vignesh, G.D. Vasuki, A. Vinotha Giri, Jayant Dinesh, M. School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Computer Science and Engineering Tirupati India Zarqa University Faculty of Engineering Department of Architecture Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia St. Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India J.P. College of Engineering Department of Computer Science and Engineering Tenkasi India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798331508456

Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security. © 2025 IEEE.

关键词： Deep reinforcement learning

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Identifying Challenges and Opportunities for Intelligent Data-Driven Health Interfaces to Support Ongoing Care

Identifying Challenges and Opportunities for Intelligent Dat...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Knoche, Hendrik Abdul-Rahman, Alfie Clark, Leigh Curcin, Vasa Huo, Zhiqiang Iwaya, Leonardo Horn Lemon, Oliver Mikulík, Robert Neate, Timothy Roper, Abi Skovfoged, Milo Marsfeldt Verdezoto, Nervo Wilson, Stephanie Ziadeh, Hamzah Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark Department of Informatics King's College London London United Kingdom Bold Insight London United Kingdom School of Population Health and Environmental Sciences King's College London London United Kingdom Department of Population Health Science King's College London London United Kingdom Research Group Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Department of Computer Science Heriot-Watt University Edinburgh United Kingdom Neurology Department The St. Anne's University Hospital Brno Brno Czech Republic School of Health and Psychology City University of London London United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science City University London Londond United Kingdom

ISBN: (纸本)9781450394222

This workshop will explore future work in the area of intelligent, conversational, data-driven health interfaces both from patients' and health care professionals' perspectives. We aim to bring together a diverse set of experts and stakeholders to jointly discuss the opportunities and challenges at the intersection of public health care provisioning, patient and caretaker empowerment, monitoring provisioning of health care and its quality. This will require AI-supported, conversational decision-making interfaces that adhere to ethical and privacy standards and address issues around agency, control, engagement, motivation, and accessibility. The goal of the workshop is to create a community around intelligent data-driven interfaces and create a road map for their future research. © 2023 Owner/Author.

关键词： User interfaces

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DAC-MD: A privacy preserving drone-access control scheme for last mile delivery

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Transactions on Emerging Telecommunications Technologies 2024年第3期35卷

作者： Irshad, Azeem Farooq, Muhammad Mahmood, Khalid Mallah, Ghulam Ali Chaudhry, Shehzad Ashraf Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Mathematics and Statistics College of Arts and Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Science Shah Abdul Latif University Sindh Khairpur Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey

The service delivery has lately witnessed strides in the form of paradigm shifts from conventional logistics to drone-oriented supply chain to conserve ecosystem. The use of drones or unmanned aerial vehicles (UAVs) for package deliveries offer enhanced accessibility, flexibility, efficiency, speed, safety, and even few environmental gains. However, the drones can come across various security hazards while communicating with ground installations on open channels including physical capture attack, privacy infringement, and so on. Thus, earnest endeavors are needed to fix those security challenges in applications related to drone communications. To address these problems, an efficient authenticated key agreement technique, drone access control scheme for last mile delivery (DAC-MD) is suggested for user-drone interactions in IoD environment that help the participating entities in developing a mutually agreed session key for securing subsequent communication interactions. The proposed scheme is proved using automated ProVerif tool as well as analyzed by using formal analysis based on RoR random oracle model. The comparative analysis against recently presented known studies reveals that our scheme not only carries comparable properties in terms of security and privacy, but also low computational and communication costs. © 2024 John Wiley & Sons, Ltd.

关键词： Drones

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Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第9期47卷 51-62页

作者： Rawat, Romil Oki, Olukayode A. Chakrawarti, Rajesh Kumar Adekunle, Temitope Samson Lukose, Jose Manappattukunnel Ajagbe, Sunday Adeola Department of Computer Architecture and Communications University of Extremadura Badajoz06006 Spain Department of Information Technology East London Walter Sisulu University South Africa Department of Computer Science and Engineering Sushila Devi Bansal College Bansal Group of Institutions Indore India Colorado State University United States Department of Information Technology Walter Sisulu University South Africa Department of Computer & Industrial Production Engineering First Technical University Ibadan200255 Nigeria

Artificial Intelligence (AI) influenced technical aspects of research for generating automated intelligent behaviors covering divergent domains but has shown appreciable results when used in cyber forensic technology for crime analysis and detection. AI experts warned about possible security risks associated with algorithms and training data, as AI inherits computing features dealing with IoT-based Smart applications and autonomous transportation, and may be found to be susceptible to vulnerability and threats. The present work discusses models for analyzing terrorists related information and categorizing malicious events by covering the literature review on security risks and AI-related criminality by presenting a taxonomy of criminal behavior and signatures covering tools and criminal targets used in fraudulent activities using AI features by digital forensic techniques. We've also shown how AI may make existing crimes more potent, and that new sorts of crimes could emerge that haven't been identified previously. This study has presented a systematic structure for AI crime and dealing strategies. Furthermore, we have proposed AI forensics, a unique strategy for combating AI crime. We discovered that several concepts of DF are still not preferred in AI-based forensics after conducting a comparative examination of forensics. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Artificial intelligence

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