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

作者： Nithila, Ezhil E. Bibin Christopher, V. Raj, J. Relin Francis Srinivasan, A. Soundiraraj, N. Krishnan, R. Santhana Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350379945

'DensePneumoNct' is a specialized deep learning algorithm designed for accurate and efficient detection of pneumonia from CT chest images. Leveraging the DenseNet architecture within TensorFlow, this algorithm demonstrates superior performance across critical metrics including accuracy, recall, precision, and F1 score. The implementation utilizes the ImageNet dataset, preprocessed to ensure standardization and resizing, optimizing input uniformity for robust model training. The DenseNet model architecture features dense blocks and transition layers, fostering strong feature propagation and effective downsampling of feature maps. Hyperparameter tuning, including a learning rate of 0.001, a batch size of 64, and training over 100 epochs, enhances model convergence and performance refinement. Training enhancements such as early stopping and learning rate scheduling further stabilize model training and prevent overfitting. Evaluation on a validation set showcases 'DensePneumoNet''s capability to distinguish COVID-19 pneumonia, viral pneumonia, and healthy lung tissues with unparalleled accuracy. Comparative analysis against ResNet-50, Inception-v3, SqueezeNet, and LeNet confirms DensePneumoNet's superiority in all evaluated parameters, affirming its efficacy for critical medical imaging tasks. 'DensePneumoNet' represents a significant advancement in pneumonia detection, poised to enhance diagnostic accuracy and clinical decision-making in medical settings. © 2024 IEEE.

关键词： Medical imaging

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Quantum-Blockchain Healthcare System for Invasive and No-Invasive-IoMT Data 20th

Quantum-Blockchain Healthcare System for Invasive and No-I...

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20th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

作者： Grønli, Tor Morten Lakhan, Abdullah Younas, Muhammad School of Economics Innovations and Technology Kristiania University College Oslo Norway School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom

ISBN: (纸本)9783031680045

These days, the Internet of Medical Things (IoMT) is a new paradigm where different bio-sensors, such as invasive and non-invasive, generate data to monitor the healthcare of the human body. However, the IoMT application consisted of sensor data (e.g., pacemaker heartbeat detection, blood pressure monitoring, and ingestible gas). These sensor data must be connected to the remote server for decision-making and analysis. Therefore, security and processing time are key issues for IoMT applications in distributed fog cloud networks. This paper presents the Quantum-Blockchain Healthcare System for Invasive and Non-Invasive IoMT Data. The paper has dual objectives: to minimize the processing time and security risk of IoMT data in distributed fog cloud networks and to design a novel quantum blockchain for IoMT application. In this paper, we present quantum key distribution blockchain scheduling (QKDBS), which consists of local scheduling of sensor data and remote scheduling with the quantum blockchain key distribution in the system. Simulations show that QKDBS works better than any other blockchain or quantum method for running IoMT applications on network nodes that are spread out. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Pacemakers

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Cyber-Syndrome:Concept,Theoretical Characterization,and Control Mechanism

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Tsinghua Science and technology 2024年第3期29卷 721-735页

作者： Feifei Shi Huansheng Ning Liming Chen Sahraoui Dhelim School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Computing Ulster UniversityNewtownabbeyBT370QBUK School of Computer Science University College DublinDublinD04 V1W8Ireland

The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a raft of new behaviors within CPST,which affects users’physical,social,and mental *** this paper,we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST *** we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs,from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology,formation,symptoms,and ***,we propose an entropy-based Cyber-Syndrome control mechanism for its computation and *** goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.

关键词： Cyber-Syndrome cyber-physical systems Cyber-Physical-Social-Thinking(CPST)space Maslow’sHierarchy of Needs entropy

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Weighted Fuzzy System for Identifying DNA N4-Methylcytosine Sites With Kernel Entropy Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 895-903页

作者： Wang, Leyao Tiwari, Prayag Ding, Yijie Guo, Fei College of Intelligence and Computing Tianjin University Tianjin300350 China School of Information Technology Halmstad University Halmstad30118 Sweden University of Electronic Science and Technology of China Quzhou324000 China School of Computer Science and Engineering Central South University Changsha410083 China

N4-methylcytosine (4mC) is a common DNA methylation that has been implicated in epigenetic regulation and host defense. Accurate prediction of 4mC sites in DNA sequences will help to better explore the biological processes and mechanisms. For this problem, computational methods based on machine learning and deep learning are faster, less complex, and less expensive than experimental detection methods. However, the existing computational methods are still unsatisfactory in terms of prediction accuracy, so we propose a new method with better performance. In this work, we propose a weighted fuzzy system for identifying DNA 4mC sites by kernel entropy component analysis (KECA). We named it as W-TSK-FS-KECA. This model is improved based on the Takagi-Sugeuo-Kang fuzzy system (TSK-FS). We use position-specific trinucleotide propensity to construct feature vectors on representative benchmark datasets. Then we use KECA to get the reconstruct error. Finally, we put the calculated reconstruction error add to the regular term of TSK-FS as the weights to enhance the model performance. Comparative experiments with other methods show that it has good classification perfor- mance. © 2023 IEEE.

关键词： Entropy

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A Data-Driven Approach in predicting Heart Disease Detection in earlier stage 2

A Data-Driven Approach in predicting Heart Disease Detection...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Nagarjuna, N. Kavitha, S. Lakshmi, H.N. SRM Institute of Science and Technology School of Computing Dept. of Computing Tamilnadu Kattankulathur India CVR College of Engineering Dept. of CSIT Hyderabad India CVR College of Engineering Dept. of Emerging Technologies Hyderabad India

ISBN: (纸本)9798350365269

In the healthcare field accumulation of vast amounts of data, known as big data, that contains valuable insights important for decision-making is available. The Data-driven approaches consistently proves their effectiveness by highlighting the pivotal roles of data analysis in error detection, valuable information identification, healthcare variables assumption and validation, and correlation establishment among health-related System. The proposed system Heart-Risk predict (HRP) serves as a data analysis tool by using statistical modeling techniques. The proposed novelistic model focuses on prediction of heart disease and risk factors using the k-means grouping algorithm by employing data analytics and visualization tools, which is further tested on publicly available datasets. Some of basic health related attributes like patient age, chest pain category, blood pressure, glucose levels and heart rate aid in early-stage chest pain type of determination. Through preprocessing methods and classifier-based evaluations, the current study showcases heart disease prediction accuracy at a higher level. Furthermore, the proposed system integrates the Tabu search mechanism and the K-means algorithm in addressing traditional method limitations. This integration enhances efficacy in handling large datasets by reducing computational complexity and give improvement in prediction accuracy. Thorough experimental validation depict system's efficacy and superiority over existing methods by offering a promising implications for early heart disease detection and proactive management, thus improvising public health outcomes. © 2024 IEEE.

关键词： Data visualization

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Satellite Image Classification using CNN with Particle Swarm Optimization Classifier 5

Satellite Image Classification using CNN with Particle Swarm...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Vidhya, S. Balaji, M. Kamaraj, V. Dept. of Computing Technologires School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Federated Learning Enabled Green Edge computing System for IIoT Applications 20th

Federated Learning Enabled Green Edge Computing System for ...

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20th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

ISBN: (纸本)9783031680045

These days, the usage of industrial Internet of Things (IIoT), such as industrial robotics, smart homes, and electric, has been growing progressively. The IIoT applications consisted of different sensors and generated huge amounts of data. Due to resource issues of local devices, generally, IIoT workloads offload to a centralized cloud for execution. However, centralized power consumption is much higher, which is not optimal in a green environment. Green edge nodes are an extended version of cloud computing that brings computing devices and infrastructure to the network’s edge. They are designed to be environmentally friendly and energy-efficient. However, edge node-enabled green systems still have many issues. This paper presents a novel conceptual federated Learning Enabled Green Edge computing system for IIoT Applications. The paper aims to reduce the power consumption of computing nodes such as local devices, edge nodes, and cloud nodes during data training and analysis of IIoT applications. The proposed system offers green data training and analysis services at the edge nodes and offloads the data to the cloud for storage. The paper presents energy-green efficient federated learning deep convolutional neural network scheduling (FL-DCNNS) algorithm methodology to schedule all workloads of IIoT applications with minimum power consumption. Simulation results show that FL-DCNNS meets the requirements of a green computing environment on edge nodes during the execution of IIoT applications as compared to existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Federated learning

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A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

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Intelligent Automation & Soft computing 2023年第6期36卷 2685-2702页

作者： Mercy Sharon Devadas N.Bhalaji Xiao-Zhi Gao Department of Information Technology SSN College of EngineeringKalavakkamChennai603110Tamil NaduIndia School of Computing University of Eastern FinlandYliopistonranta70210KuopioFinland

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet *** this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the *** MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network *** further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling *** adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting *** statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.

关键词： Packet scheduling firefly algorithm ad hoc networks fuzzy scheduler opnet simulator

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Website Traffic Time Series Forecasting Using Regression Machine Learning 12

Website Traffic Time Series Forecasting Using Regression Mac...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Sikka, Dhruv Vinoth Kumar, C.N.S. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9781665462617

The term ''Web analytics'' pertains to the act of monitoring, analysing, and creating reports regarding the use of a website, such as its web pages, images, and videos. By utilising web analytics, businesses can retain their customers, increase their website traffic, and attract more visitors, which ultimately leads to higher revenue. A study has been conducted on statistical forecasting teaching notes of a website, which involved analysing five years of daily time series data for various traffic measures. These measures included the daily counts of page views, unique visitors, first-time visitors, and returning visitors for academic teaching notes on the website. This work presents a single unit of multiple regression machine learning models that can predict new user and returning user web traffic amounts over some time that can be expected over a certain period or on a specific time interval. The voting Regression model combines algorithms like Decision Tree Regression, Multi Linear Regression and Support Vector Machine Regression into a single unit that can predict expected traffic volume over a period with 99.96% accuracy and 0.24% of absolute error. © 2023 IEEE.

关键词： Websites

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A Graph Theory Based Self-Learning Honeypot to Detect Persistent Threats

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Intelligent Automation & Soft computing 2023年第3期35卷 3331-3348页

作者： R.T.Pavendan K.Sankar K.A.Varun Kumar Department of Mathematics College of EngineeringAnna UniversityChennai600025India Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathur602203India

Attacks on the cyber space is getting exponential in recent *** penetrations and breaches are real threats to the individuals and *** security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats(APTs)they *** APTs are targeted,more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing ***,there is a need for an effective defense system that can achieve a complete reliance of *** address the above-mentioned issues,this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT *** key idea of honeypot leverages the concepts of graph theory to detect such targeted *** proposed honey-pot is self-realizing,strategic assisted which withholds the APTs actionable tech-niques and observes the behavior for analysis and *** proposed graph theory based self learning honeypot using the resultsγ(C(n,1)),γc(C(n,1)),γsc(C(n,1))outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.

关键词： Graph theory Domination Connected Domination Secure Connected Domination honeypot self learning ransomware

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