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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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Cyber Twin-Enabled Real-Time AI Orchestration for Advanced IoT Security and Dynamic Software Optimization 4

Cyber Twin-Enabled Real-Time AI Orchestration for Advanced I...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Sunkara, Krishna Chaitanya Barmola, Pareshwar Prasad Ravi, K.S. Shukla, Vishal Soni, Umang Sapatnekar, Amol Oracle Cloud Engineering Data Center Design & Modeling NC United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vidyavardhaka College of Engineering Department of Mechanical Engineering Mysuru India Ramdeobaba University Department of Mechanical Engineering Nagpur India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The rapid expansion of Internet of Things (IoT) ecosystems has increased the complexity and vulnerability of software systems, making them more susceptible to security breaches and performance inefficiencies. Traditional security and optimization techniques struggle to keep up with the dynamic nature of IoT networks. This calls for the development of advanced frameworks that can adapt in real-time to evolving threats and operational requirements. This paper introduces Cyber Twins, an AI-driven framework designed for real-time security and software optimization in IoT environments. Cyber Twins leverage artificial intelligence and digital twin technology to create dynamic cyber representations of physical IoT devices and software processes. The framework integrates reinforcement learning for adaptive optimization, convolutional neural networks (CNNs) for anomaly detection, and graph neural networks (GNNs) for real-time threat analysis. By continuously synchronizing with live IoT data, Cyber Twins enable proactive threat detection and autonomous system optimization, ensuring a secure and high-performing IoT ecosystem. The proposed framework combines Federated Learning (FL) for distributed intelligence and Reinforcement Learning (RL) for adaptive decision-making. Key advancements include (1) a 35% improvement in intrusion detection accuracy compared to traditional methods, (2) a 40% reduction in false positive rates, and (3) a 25% increase in dynamic software adaptation speed. Experiments conducted on a simulated IoT environment with 1,000 devices demonstrated scalability and resilience under varying network conditions. Additionally, the framework achieved a 20% decrease in latency during AI model updates and a 30% reduction in computational overhead, ensuring efficient resource utilization. © 2024 IEEE.

关键词： Federated learning

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Navigating Energy: Unveiling Power Consumption Patterns in Autonomous Vehicles 1

Navigating Energy: Unveiling Power Consumption Patterns in A...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Saif, Abdu Shah, Nor Shahida Mohd Ameen, Ali Alnoamani, Aiman Al-Qatf, Majjed Alsamhi, Saeed Hamood Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University P.O. Box 6803 Taiz Yemen Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer Science Lincoln University College Kuala Lumpur Malaysia Insight Center for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9798331533557

Energy consumption is critical in self-driving cars and is set to change how transportation works. In this paper, we address the challenges of vehicle energy consumption by examining how the location of self-driving cars affects their energy use over time, looking at two- and three-dimensional aspects. Using models and real-world data, we examine the energy usage patterns of vehicle systems within MATLAB to reveal insights into their design and operation. In this paper, the power dynamics of cars that significantly contribute to the discussions of practical and sustainable self-driving transportation are introduced. The main goal is to demonstrate how power management may be revolutionized for self-driving car systems, ensuring maximum performance and dependability in today’s dynamic mobility scene. The findings show that enhancing autonomous vehicles’ design and operational strategies ensures their reliability and optimizes performance in today’s rapidly evolving transportation landscape. © 2024 IEEE.

关键词： Autonomous vehicles

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Advanced Joint Monitoring system Utilizing Real-time Data and KNN Algorithm for Prediction of Joint Health 4

Advanced Joint Monitoring System Utilizing Real-time Data an...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Antony Joseph Rajan, D. Raman, Ramakrishnan Mookherjee, Ujal Kumar Vekariya, Daxa Ganeshbabu, T.R. Rajmohan, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Maharashtra Pune India Maharashtra Pune India Parul University Faculty of Engineering and Technology Parul Institute of Engineering and Technology Gujarat Vadodara India Department of Electronics and Communication Tamil Nadu Namakkal India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

This research proposes a novel strategy for the field of tele-orthopedics by incorporating a K-Nearest Neighbors (KNN) algorithm into a Raspberry Pi-based remote joint monitoring system. The KNN model can provide accurate predictions about the present condition of the joint health of orthopedic patients by using real-time data from sensors that capture joint movement measures. The technology makes it easier for medical experts to intervene at the appropriate moment, which in turn makes it possible to design treatment programs that are more individualized and data-driven. The solution enables patients and healthcare practitioners to communicate without interruptions by using a protected platform hosted in the cloud. The malleability and interpretability of the KNN algorithm contribute to a sophisticated framework for tele-orthopedics, which offers precise insights into the development of orthopedic therapies. This study lays a framework for future developments in remote patient monitoring for orthopedic treatment. It proves that it is possible to use machine learning in telemedicine, which is a significant contribution to the field. © 2024 IEEE.

关键词： Orthopedics

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Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

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computer Modeling in engineering & sciences 2024年第2期138卷 1385-1403页

作者： Muhammad Farman Ali Akgül Mir Sajjad Hashemi Liliana Guran Amelia Bucur Institue of Mathematics Khwaja Fareed University of Enginnering and Information TechnologyRaheem Yar KhanPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Mathematics Art and Science FacultySiirt UniversitySiirt56100Turkey Department of Mathematics Mathematics Research CenterNear East UniversityNicosiaTurkey Department of Computer Engineering Biruni UniversityIstanbul34010Turkey Department of Hospitality Services Babes-Bolyai UniversityCluj-Napoca400174Romania Department of Pharmaceutical Sciences Vasile Goldi竤Western University of AradArad310025Romania Department of Mathematics and Informatics Faculty of SciencesLucian Blaga University of SibiuSibiu550012Romania Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennaiTamilnaduIndia

New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal *** applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.

关键词： COVID-19 model fractal-fractional operator Ulam-Hyers stability existence and uniqueness numerical simulation

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT Data Analytics

Random Forest Predictive Model for Ventilator-Associated Pne...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Ramadevi, R. Kishoresonti, V.J.K. Jain Jacob, M. Vaidehi, V. Mohankumar, N. Rajmohan, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India Sathyabama Institute Of Science And Technology Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Management Tamil Nadu Chennai India Department Of Computer Science Tamil Nadu Salem India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

To identify and forecast ventilator-associated pneumonia (VAP) early on, this study investigates the use of random forest predictive models in combination with Internet of Things (IoT) data analytics. Patients on mechanical ventilation are particularly vulnerable to VAP, a serious nosocomial infection. It uses Random Forest algorithms to examine a wide range of factors, such as vital signs, patient demographics, and ventilator settings, by using the abundance of healthcare data supplied by the IoT. The goal is to create reliable prediction models that can identify people who are at risk of getting VAP before any symptoms appear. Using data gathered from ventilator-integrated real-time monitoring devices, it creates a complete dataset. The findings reveal that random forest models are reliable and very accurate in predicting VAP. Integrating analytics from the IoT improves prediction accuracy, which in turn helps healthcare providers make better, timelier preventative decisions. Improving patient outcomes and optimizing resource allocation in critical care units may be possible as a result of this study's contributions to the expanding body of work on using sophisticated data-driven methodologies for infectious illness prediction in clinical settings. © 2024 IEEE.

关键词： Internet of things

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Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

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Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial Network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial Network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial Networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring system

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computers, Materials & Continua 2022年第3期70卷 6141-6158页

作者： Bander Alzahrani Ahmed Barnawi Azeem Irshad Areej Alhothali Reem Alotaibi Muhammad Shafiq Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Software Engineering International Islamic University IslamabadPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial *** example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real ***,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management *** addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication ***,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD ***,the AKA scheme involves many vulnerabilities in terms of security and *** this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling *** scheme supports key security features,including anti-forgery attacks,and confirms user *** security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle *** simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.

关键词： IoT unmanned aerial vehicles authentication crowd monitoring

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A Comprehensive Study on Drones and Big Data for Supply Chain Optimization Using a Novel Approach 1

A Comprehensive Study on Drones and Big Data for Supply Chai...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Palanikkumar, D. Suganthi, N. Malleswari, A.S. Thapa, Sanjog Tanguturi, Rama Chaithanya Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Dr Ngp Institute of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Lamar University Department of Information System and Analysis BeaumontTX United States Woxsen University School of Business Hyderabad India

ISBN: (纸本)9798350367720

This research paper aims to provide qualitative and quantitative insights regarding the application of drones and big data techniques in the SCM field through a new scientific method. The proposed method exploits the opportunities of drones for real-time data capturing and big data technologies to enrich decision making in supply chain. The study is pertinent to modern tendencies in supply chain systems and proves the efficiency of the new method through the precise calculations and contrastive analysis. The following are some of the findings that suggest efficiency, cost decrease, and outstanding performance of the supply chain. The studies being offered in this research will prove beneficial for practitioners and researchers who endeavor to advance the concepts and practice of logistics and supply chain management. © 2024 IEEE.

关键词： Big Data Cost Reduction Data Analytics Decision-making Drones Efficiency Improvement Logistics Novel Approach Real-time Data Collection Supply Chain Optimization

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