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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sharma, Puneet Shyamala devi, J. Manikandan, K. Ani Minisha, r. Harini, r. Karthik, S. Sr University School of Cs & Ai Telangana Warangal India Rajalakshmi Institute of Technology Chennai India Tamilnadu Vellore632014 India R.M. K. Engineering College Department of Artificial Intelligence and Data Science India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Avadi Tamilnadu Chennai600062 India

ISBN: (纸本)9798350350067

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Paddy Plant disease detection using Hybrid deep Learning Algorithms 8

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Suguna, r. deepa, S. Kanimozhi, T. Chandru, M. Arun, G. Harshini, M. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Tamilnadu Erode638401 India Kongu Engineering College Perundurai Department of Computer Technology - Pg Tamilnadu Erode638060 India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641202 India College Department of Computer Science Erode India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Kongu Engineering College Tamilnadu Perundurai Erode638060 India

ISBN: (纸本)9798350367904

Agriculture is the backbone of India's economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and resNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity. © 2024 IEEE.

关键词： Crops

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Spectrum Aware Unequal Clustering with route Selection Protocol for Energy Efficient Cognitive radio Wireless Sensor Networks 2

Spectrum Aware Unequal Clustering with Route Selection Proto...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Arthi, A. Nayak, Chinmaya Kumar Subbaiyan, Arunmurugan Muthalakshmi, M. Srimathi, S. Venkatesh, P. Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sri Sri University Sri Sri Vihar Faculty of Emerging Technologies Odisha 754006 India Periyar Maniammai Institute of Science & Technology Ece Tamilnadu Thanjavur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Ramco Institute of Technology Department of Ece Rajapalayam India

ISBN: (纸本)9798350325799

Spectrum awareness with energy conservation is a main prerequisite in Cognitive radio Sensor Networks (CrSN) as the sensor nodes (SNs) operate in physically unattended areas and are energy constrained. Cluster based routing combined with channel allocation accomplishes energy effective path selection as a countermeasure to preserve energy. It is required to perform reliable routing with cluster based channel distribution contributing to development of network efficiency. Therefore, this study introduces a new Spectrum Aware metaheuristic Unequal Clustering with route Selection Protocol (SAMUC-rSP) technique for Energy Efficient CrSN. The focus of the SAMUC-rSP technique lies in the effective selection of CHs and routes in the CrSN. For accomplishing this, the SAMUC-rSP technique designs fruit fly optimization algorithm based unequal clustering (FFOA-UC) technique to produce unequal clusters in the CrSN. Besides, moth search optimization based routing (MSOr) approach is used to produce an optimal set of routes in the CrSN. The simulation results of the SAMUC-rSP technique are examined in terms of different measures in python environment. Extensive comparison analysis reported the superior results of the SAMUC-rSP method over other existing techniques in the CrSN. © 2023 IEEE.

关键词： Cognitive radio

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud technology 10

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Prabakaran, T. Babu, A. ramesh Hemalatha, S. ramasamy, Jayabharathi Kovarasan, rajesh Kambattan Elango, B. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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Enhancing Alzheimer's disease diagnosis Using Multi-relation Graph Convolutional Networks and Structural MrI data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (Ad) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MrI data and Multi-relation Graph Convolutional Networks (Mr-GCN) to improve Ad diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MrI features. Using a large dataset, the resusts are evaluated and contrasted in Mr-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of Ad. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how Ad progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Image Analysis of MrI-based Brain Tumor Classification and Segmentation using BSA and rELM Networks 2

Image Analysis of MRI-based Brain Tumor Classification and S...

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2nd International Conference on Automation, Computing and renewable Systems, ICACrS 2023

作者： Saranya, r. Pushpakumar, r. Srinivasan, P. Aparna, N. Karthika, K. Chauhan, Amit Department of Computer science with Data Analytics PSG College of Arts and Science Coimbatore India Department of Information Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of ECE Sona College of Technology Tamilnadu Salem India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Tamil Nadu Veerappanur India Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Karnataka Bengaluru India

ISBN: (纸本)9798350340235

Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. due to the sheer volume of MrI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-rELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-rELM is used to train the models with the input features. The proposed technique outperforms BSA and rELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. © 2023 IEEE.

关键词： Brain Tumor Segmentation Central Nervous System (CNS) FuzzyClustering Method (FCM)

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Attack and defense of deep Learning Models in the Field of Web Attack detection

arXiv

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arXiv 2024年

作者： Shi, Lijia dong, Shihao R&D Department of Security Technology Platform Chinatelecom Research Institute Shanghai201315 China School of Data Science and Engineering East China Normal University Shanghai200062 China

The challenge of WAd (web attack detection) is growing as hackers continuously refine their methods to evade traditional detection. deep learning models excel in handling complex unknown attacks due to their strong generalization and adaptability. However, they are vulnerable to backdoor attacks, where contextually irrelevant fragments are inserted into requests, compromising model stability. While backdoor attacks are well studied in image recognition, they are largely unexplored in WAd. This paper introduces backdoor attacks in WAd, proposing five methods and corresponding defenses. Testing on textCNN, biLSTM, and tinybert models shows an attack success rate over 87%, reducible through fine-tuning. Future research should focus on backdoor defenses in *** the code and data of this paper can be obtained at https://***/r/attackdefenceindL-7E05 Copyright © 2024, The Authors. All rights reserved.

关键词： Image recognition

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Autoregressive diffusion Networks for Trustworthy Node Identification and Optimized routing in Wireless Sensor Networks

Autoregressive Diffusion Networks for Trustworthy Node Ident...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： M. Satheesh Kumar P. Ganesh Kumar S. Amudha T. Kavitha P. ravikumar M. raghini Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of IT K.L.N. College of Engineering Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Instrumentation K.L.N. College of Engineering Department of Artificial Intelligence and Data Science Velammal College of Engineering and Technology

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Finding and eliminating unwanted nodes is a foremost challenge in wireless sensor networks (WSNs). This paper proposes an Autoregressive diffusion Network (AdN) to find and categorize the nodes in the network as affected or trustworthy. Here, the proposed AdN provides a strong foundation for finding uncommon or insecure actions by effectively recording the characteristics of the sensor over a period of time. Thus, using the capability to capture successive data. In addition, an improved routing mechanism is developed using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols. Then, the Pelican Optimisation Algorithm (POA) is used to optimize the cluster heads optimally. To further improve the routing mechanism using fitness proportional selection technique to transfer the data safely and effectively. Simulation results show that the proposed technique enhances the performance by reducing energy consumption and increasing the detection accuracy of affected nodes. The proposed LEACH + POA + AdN model shows the improved detection accuracy of 77%, false positive rate of 7.50%, average energy consumption of 8.02 J, network lifetime of 92.94 rounds, and packet delivery ratio of 92.97%, which is high compared to other benchmark models.

关键词： Wireless sensor networks Energy consumption Accuracy Protocols Simulation Clustering algorithms routing Energy efficiency Security reliability

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Advanced dual Module Weapon detection for Public Safety and Surveillance System 4

Advanced Dual Module Weapon Detection for Public Safety and ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sirigineedi, Manikanta Sowmiya, M. Hemavath, I.U. Bharathi, V.C. Karmode, Sayali S. Sathya, r. Vishnu Institute of Technology Department of Information Technology Bhimavaram Andhra Pradesh Vishnupur India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering Avadi600062 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Mahatma Gandhi Mission's College of Engineering & Technology Kamothe Maharashtra Navi Mumbai India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798331540364

This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments. © 2024 IEEE.

关键词： Convolutional neural networks

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