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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 j of stored energy,and energy is consumed at 0.01 j per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet delivery Ratio(PdR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(dE) nodes throughput lifetime

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Review Based on Blockchain and Financial Transactions Related to Cryptocurrencies Across Globe 11

Review Based on Blockchain and Financial Transactions Relate...

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Sonekar, S.V. Bejjaniwar, Saikiran dandekar, Shejal Pal, Sarthak Karwade, Pratik J D College of Engineering and Management Department of Computer Science and Engineering Nagpur India J D College of Engineering and Management Department of Information Technology Nagpur India

ISBN: (纸本)9798350348422

Blockchain is one of the most interesting and exciting field of technology today it provides a decentralized network for anyone who is willing to store data on it as blockchain is relatively new there is still experiments going on it to reveal its true potential. One of the applications of blockchain which has gone incredibly popular is cryptocurrency and also blockchain payment system. In today's blockchain payment system there are few components which are required for a successful transaction such as nodes, ledger, wallet, nonce and hash. Payment gateways are one of the first blockchain application that provide a decentralized way to do transactions we will also discuss some of the ledger applications of blockchain. We carefully selected the most recent research papers in this review paper. We will be reviewing various research paper and they take on blockchain technology. © 2023 IEEE.

关键词： Smart contract

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FAMMNPB: design of an Enhanced Forensic Analysis Model with Multidomain Feature Extraction and Advanced Neural Architecture for Permissioned Blockchains 9

FAMMNPB: Design of an Enhanced Forensic Analysis Model with ...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Mate, Ashwini Y. Khandare, Kiran S. Hassan, Mohammad Bode, Kiran Nikesh Bode, Nikesh S. Missal, Karishma K. Nagpur India Nagpur India J D College of Engineering And Technology Department of Electronics And Telecommunication Nagpur India J D College of Engineering And Technology Department of Computer Science And Engineering Nagpur India Suryodaya College of Engineering And Technology Department of Computer Science And Engineering Nagpur India J D College of Engineering And Technology Department of Information Technology Nagpur India

ISBN: (纸本)9798350394474

This work introduces a novel approach that leverages multidomain feature extraction coupled with a 1d Convolutional Neural Network (CNN) for robust anomaly detection. To address the challenge of transaction delay predictions, we employ a Bi-directional Gated Recurrent Unit (BiGRU) RNN, for its capability to capture temporal sequences with high precision. For the task of chain splitting, the Teaching-Learning-based Optimization (TLbO) technique is used to ensure the efficient creation of sidechains. Further, we integrated deepSHAP, in tandem with 1d CNN and BiGRU, to enhance the performance of our models, fostering better trust in the results. Our proposed methodology yields notable improvements over existing techniques. As we observed enhancements in the precision of anomaly detection by 4.9%, accuracy in detecting anomalous events by 3.5%, Recall by 4.5% and AUC by 3.9%. These advancements fortify the security posture of permissioned blockchains and optimize their operational efficacy, thus marking a significant improvement in blockchain forensics. © 2024 IEEE.

关键词： Feature extraction

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Survey: An IOT Based Approach for Hybrid Security management in Health Care for Secure Cloud Storage 11

Survey: An IOT Based Approach for Hybrid Security Management...

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11th International Conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Gogte, Purva Purve, Shravankumar Bhave, Roshni Pethe, dipali H.chandankhede, Pankaj Thakre, Bhakti P. Computer Science & Engineering Shri.Ramdeobaba College of Engineering and Management Nagpur India Computer Science & Engineering Yeshwantrao Chavan College of Engineering Nagpur India Computer Science & Engineering J D College of Engineering & Management Nagpur India Electronics and Telecommunication Engineering G.H Raisoni College of Engineering Nagpur India Rashtrasant Tukadoji Maharaj Nagpur University Department of Information Security Nagpur India

ISBN: (纸本)9798350348422

In some Emergency Situations, Patients must be monitored continuously and treated for. However, going to the hospital to perform such work is challenging due to time restraints. A virtual care monitoring system can help with this. With the rising use of cloud computing, the suggested system presents an efficient data science technique for IoT enabled healthcare monitoring systems that improve the productivity of data processing and the use of data in the cloud. IoT sensors in plenty are used to gather health care data. For the purposes of science processing, these data are stored in the cloud. An altered data science technique is initially introduced in the Healthcare Monitoring - data science Technique (HM-dST). The Upgraded Pigeon Optimization (IPO) technique is used to arrange the data that is kept in the cloud, which helps to increase prediction accuracy. The best method for selecting features is then illustrated for the removal and features selection. In order to analyze human healthcare, a backtracking search-based deep neural network (BS-dNN) is used. The performance of our system is then assessed using various real-time healthcare datasets, and changes are observed using the current smart healthcare monitoring systems. To assist the medical community in early detection of a patient's primary organs' health status, allowing for proper treatment, as well as to securely store patient health data in the cloud. The goal of the project is to demonstrate how the sensory system can be used to track a patient's movement invisibly. © 2023 IEEE.

关键词： Internet of things

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Combining Fuzzy Logic and Reinforcement Learning for Resource management in Edge Computing 5

Combining Fuzzy Logic and Reinforcement Learning for Resourc...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Prasath, S. Ram Selvi, M. Krishnan, R. Santhana Nithila, Ezhil E. Malar, P. Stella Rose Sankar, G. Ram Scad College of Engineering and Technology Computer Science and Engineering Cheranmahadevi India Rajarajeswari College of Engineering Computer Science and Engineering Bangalore India Scad College of Engineering and Technology Electronics and Communication Engineering Chranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Electronics and Communication Engineering Chennai Avadi India J.P. College of Engineering Electronics and Communication Engineering Thenkasi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350379945

With the wide spread of Internet of Things (loT) devices, edge computing plays a decisive part in meeting the computational demands of loT applications. Efficient resource allocation in such networks is essential to ensure optimal utilization of resources and meet the quality of service (QoS) necessities of loT applications. This research addresses the challenge of dynamic resource allocation in edge computing networks for loT applications through a novel hybrid AI approach, 'FledegeRL.' By integrating fuzzy logic and reinforcement learning, FledegeRL aims to optimize resource utilization and enhance system performance. The proposed framework is evaluated through simulation analysis and compared with existing algorithms, including Q-Iearning, Fuzzy Logic Control, and Ant Colony Optimization, using metrics such as resource allocation accuracy, average throughput, and network overhead. The results demonstrate that FledegeRL consistently outperforms these algorithms, achieving higher accuracy, greater throughput, and lower network overhead. This highlights the potential of FledegeRL as an effective solution for dynamic resource allocation in edge computing environments. © 2024 IEEE.

关键词： Quality of service

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Onion Crop Cultivation & Prediction of Yields by Machine Learning 2

Onion Crop Cultivation & Prediction of Yields by Machine Lea...

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

作者： Chaudhari, dipti dhaygude, Rakhi Mulani, Umar Rane, Priyanka Khalate, Yogesh Borate, Vishal Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Pune India Department of Computer Engineering SVPM College of Engineering Maharashtra Malegoan India Department of Computer Engineering K J College of Engineering & Management Research Pune India Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Sion Mumbai India Department of Computer Engineering SVPMs College of Engineering Pune Malegaon Bk India Department of Computer Engineering Dr. D. Y. Patil College of Engineering and Innovation Pune Talegaon India

ISBN: (纸本)9798331541217

The rising worldwide populace prompts the Indian government to upgrade food supply the executives for a long time to come. Reasonable farming practices are critical for guaranteeing steady food creation while protecting the climate against unsafe synthetics, accomplished through mechanical progressions in administration. This study presents a complex multi-phantom imaging framework customized for accuracy farming. Including a RGB camera and Pi NoIR camera worked by a Raspberry Pi installed a robot;this framework catches ethereal pictures for investigation. The pictures are communicated to a java application for handling, including honing and resizing. Ongoing information is utilized to register the Standardized Contrast Vegetation Record (NdVI), working with crop wellbeing appraisal. Utilizing Profound Learning (dL) strategies, the framework successfully distinguishes onion crop development stages from the gained dataset. The detail the execution of a dynamic profound brain network model for this reason. The framework exhibits a great presentation precision of 97.63% for a cluster size of 24 and 98.45% for a bunch size of 56. © 2024 IEEE.

关键词： Vegetables

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol dattatray dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

deep learning has recently become a viable approach for classifying Alzheimer's disease(Ad)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3d network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCId:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying Ad,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

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Multimedia Tools and Applications 2024年 1-19页

作者： j, jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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An Efficient Attendance management with deep Learning

An Efficient Attendance Management with Deep Learning

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Shivanna Hema, H.P. janardhana, d.R. Srinivas, P.M. Sahyadri College of Engineering and Management Department of Computer Science and Engineering Mangaluru India D S Dinakar Polytechnic College Department of Electronics and Communication Engineering Shivamogga India Nitte Meenakshi Institute of Technology Department of Computer Scince and Engineering Bengaluru India

ISBN: (纸本)9798331517892

Accurate attendance recording is critical in educational institutions to ensure students' and teachers' consistency and discipline. Traditional manual attendance systems are frequently inefficient and prone to errors, such as proxy attendance and missed entries, which may impact the accuracy of attendance records. To address these challenges, an automatic attendance system that uses camera-based facial recognition technology is proposed. The latest technology records accurate attendance by taking visuals of students as soon as the class starts and comparing them to an already-existing database. Face recognition happens through human detection. The concept used here is deep learning. For students, this automated solution streamlines attendance management by ensuring that records are accurate and up to date without the need for manual entry. The system efficiently keeps track of the presence of teachers, notifying the Head of department (HOd) right away in the case of an excused absence, therefore preventing disruptions during scheduled lessons. This technology significantly increases the reliability and efficiency of attendance tracking by automating the process. Enhancing record accuracy and accountability leads to a more regulated and effective learning environment that benefits teachers and students alike. This technological method offers greater precision and oversight, leading to improved institutional administration and educational outcomes. © 2024 IEEE.

关键词： Students

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Anomaly detection using deep learning approach for IoT smart city applications

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Multimedia Tools and Applications 2025年第17期84卷 17929-17949页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined deep Q-Learning (CdQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CdQL detects anomalies accurately. In addition, the CdQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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