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7th International Conference on Inventive Computation technologies, ICICT 2024

作者： Senthilkumar, R. Yasotha, S. manochithra, P.m. Senthil, J. Sivakumar, G. Shree Venkateswara Hi Tech Engineering College Computer Science And Engineering Tamilnadu Erode India Sri Eshwar College of Engineering Computer Science And Engineering Tamilnadu Coimbatore India Sri Krishna College of Engineering And Technology M.Tech Computer Science And Engineering Tamilnadu Coimbatore India Erode Sengunthar Engineering College Computer Science And Engineering Tamilnadu Erode India

ISBN: (纸本)9798350359299

Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network systems susceptible, attackers have increased their determinations. Improving the security of such network systems is very crucial. Using Deep Learning (DL) methods with an Intrusion Detection System (IDS) framework is put into action in this research. The Gated Recurrent Unit (GRU) as well as Support Vector machine (SVm) are used in this study for attack detection. Therefore, the implementation of Softmax in the GRU model's last output layer, this study introduces a linear SVm. The NSL-KDD dataset is taken into account to evaluate the proposed IDS framework's performance. In addition, when the number of features increases, current IDSs have poor test accuracy ratings when it comes to identifying new attacks. Each dataset's feature space was reduced using an XGBoost (XGB)-based feature selection approach in this research. After that, 22 detailed characteristics were selected from the NSL-KDD dataset using XGB. The results show that, the proposed IDS paradigm outperformed other approaches. © 2024 IEEE.

关键词： Support vector machines

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Prognostic Pattern: A Hybrid methodology For Predictivity in medical Imaging Using VGG-16 4

Prognostic Pattern: A Hybrid Methodology For Predictivity in...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Parmar, Uttkarsh Dubey, Anil Kumar M.Tech Abes Enginnering College Computer Science and Enginnering Department Ghaziabad India Abes Enginnering College Faculty of Computer Science and Enginnering Department Ghaziabad India

ISBN: (纸本)9798350364729

At present, deep learning is being used to compute data related to medical images with the purpose of improving disease-related research. Deep learning based medical image analysis includes various important tasks such as medical image reformation, quality enhancement in different types of images, segmentation and classification of images (computer-Aided Detection) CAD, registration to distinguish between normal cells and tumor cells. This article focuses on the hybrid approach for analyzing medical images that makes use of both clustering and classification via k-mean, VGG, RNN) techniques on different diseases datasets like cardio-vascular diseases, breast cancer and brain disease dataset. This paper utilizes predictive analytics in medical image data which is spread across a combination of clustering and classification. The objective of this study is to demonstrate that integrating these two methods will help improve accuracy as well as save time in diagnosing illnesses from image data by avoiding the process where labeling the data for feature detection takes place. Furthermore, this paper aims at presenting an overview of the methodology employed during investigation along with results emanating from this analysis highlighting how predictive analytics can be applied in healthcare. © 2024 IEEE.

关键词： Deep learning

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EPDTNet + -Em: Advanced Transfer Learning and SubNet Architecture for medical Image Diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini m, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable model (EPDTNet + -Em), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including mRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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A Brief Review on Accuracy Level of Smart and micro Grid Systems Use of mL and DL Algorithms Through Hybrid Integration

A Brief Review on Accuracy Level of Smart and Micro Grid Sys...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Adhikari, Pitamber Prakash, Febin Sharma, Brajehdra Noida Institute of Engineering and Technology Department of Information Technology & M.Tech Integrated Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Bangalore India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350357769

Forecasting of energy is a crucial component in overcoming the challenges of smart grid mechanisms, which include functions such as demand-side management, load reduction, and desirable allocation. The most significant obstacle battling today's grid has to do with efficient forecasting management for restricted predictive inconsistencies. This paper provides a comprehensive and application-focused review of advanced prediction strategies for smart grid systems, as well as the latest advances in the use of machine Learning (mL) and Deep Learning (DL) approaches. It also discusses the significance of hybridized techniques in improving the accuracy of forecasts. A study of comparison using an hourly energy consumption database has been carried out to evaluate the effectiveness of mL and DL techniques. The analysis' findings highlight hybrid methods' superior efficiency when compared to individual mL, DL, and standard statistical methods. © 2023 IEEE.

关键词： deep learning Energy forecasting machine learning power consumption Smart grid systems

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AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience 8

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Chokkanathan, K. Karpagavalli, S.m. Priyanka, G. Vanitha, K. Anitha, K. Shenbagavalli, P. Madanapalle Institute of Technology & Science Department of Artificial Intelligence India Erode Sengunthar Engineering College Department of M.Tech Computer Science and Engineering Erode India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Csi College of Engineering Department of Information Technology Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331505462

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm, powered by AI, provides a strong answer by removing the concept of inherent trust within a network and demanding ongoing verification for each access request. This article examines the combination of artificial intelligence (AI) with Zero Trust principles to improve the resilience of cyber-security. This study demonstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in comparison to conventional security models. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contemporary digital infrastructures. It provides robust protection against emerging cyber threats as well as addressing challenges related to data privacy and system integration. The paper also explores potential areas for future study and the practical consequences of implementing AI-driven Zero Trust Architecture (ZTA) in different organizational settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1287-1301页

作者： Takiddin, Abdulrahman Ismail, muhammad Atat, Rachad Davis, Katherine R. Serpedin, Erchin Department of Electrical and Computer Engineering Texas A and M University College StationTX77843 United States Department of Computer Science Tennessee Tech University CookevilleTN38505 United States Department of Electrical and Computer Engineering Program Texas A and M University Qatar Doha23874 Qatar

machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d

关键词： Cybersecurity

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Securing Smart microgrids: A Cybersecurity Survey

Securing Smart Microgrids: A Cybersecurity Survey

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Nair, Prabha Shreeraj mandal, Sanjeev Kumar Sharma, Neeraj Noida Institute of Engineering & Technology Department of It and M.Tech Integrated Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350357769

The cybersecurity concerns of intelligent microgrids are thoroughly investigated in this scholarly work. The intricate link that exists between the cyber grid and smart grid operating processes is examined, exposing the cyber system's susceptibility to different types of attacks. The research highlights the potentially catastrophic consequences, both materially and financially, that might result from hacks that jeopardize the operation of these smart microgrids. Particularly in islanded modes with little inertia, hackers are more likely to affect power electronics-intensive smart small power plants and seriously jeopardize their operational stability. The study starts with a brief synopsis of digital and physical systems in wise micro-grids before delving further into cyberattacks that aim to compromise data availability, confidentiality, and integrity. The research categorizes defensive methods into two groups in order to counter FDI attacks: protection plans, which include safeguarding certain meter readings, and surveillance/mitigation strategies, which comprise perhaps static or dynamic detection techniques. Examples of how to create, detect, and mitigate FDI attacks in smart microgrids are provided. The paper also includes a list of critical cybersecurity guidelines pertaining to smart electricity networks and notable instances of ongoing cybersecurity projects carried out globally. The essay concludes with a discussion of future directions in smart microgrid cybersecurity. © 2023 IEEE.

关键词： AC-DC microgrids Communication Network Cyber-Physical Systems Cyberattacks Cybersecurity Defense Strategies Detection Strategies Fraudulent Data Injection (FDI) Power Electronics Converters Smart Grid Cybersecurity and Ongoing Cybersecurity Projects Smart microgrids

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Comparative analysis of various web crawler algorithms

arXiv

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arXiv 2023年

作者： Nithin, T.K. Chandana, S. Barani, G. Dharani, Chavva Karishma, m.S. Integrated M.Tech Computer Science and Engineering VIT Vellore India

This paper describes and evaluates five different crawling algorithms that we have implemented within our evaluation framework: Shark search, Priority Based Focused Crawler,Naive Bayes,Breadth-First, Depth-First and c... 详细信息

关键词： Web crawler

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Developments In Hybrid Electric Vehicles: Investigating Plans, management techniques, and Power Resources for Sustainable Travel

Developments In Hybrid Electric Vehicles: Investigating Plan...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： mattoo, Aditee Bhuvana, J. Pandey, Vijay Kumar Noida Institute of Engineering & Technology Department of It and M.Tech Integrated Uttar Pradesh Greater Noida India Department of Computer Science Bangalore India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350357769

technological developments in energy production and use are crucial to the shift to a future with fewer emissions and better health. Vehicle technology is impacted by a number of variables, including pollution-induced climate change, stricter environmental rules for autos, depletion of petrol and diesel supplies, and price volatility for transit systems. One of the most notable possibilities for clean automobile technology is the hybrid electric vehicle (HEV). This research explores several HEV designs and approaches, clarifying development guidelines and ways to enhance control algorithms. We also look at the various battery-powered drives that HEVs employ. Performance is the main area of study since control methods are essential to improving the fuel economy and lowering pollutants of hybrid engine systems. © 2023 IEEE.

关键词： Electric Vehicle FLC and Fuzzy-Based management technique Fuel Cell HEV Hybrid Vehicles ICE Engines IGBT Im motor Size SRm

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The Brief Assessment of Performances of Electrical Components Grids

The Brief Assessment of Performances of Electrical Component...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Rastogi, Ritesh Kannagi, A. Srivastava, manish Noida Institute of Engineering & Technology Department of It and M.Tech Integrated Uttar Pradesh Greater Noida India Department of Computer Science Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350357769

In this study, we explore the vital importance of electrical measurements in the smart grid setting, especially for data-driven applications. We highlight the close connection between data quality and smart grid data mining and provide a novel fuzzy evaluation technique for multivariate electrical measurement quality evaluation in smart grids. This approach entails determining relevant quality dimensions for smart grid data and providing novel membership functions designed to assess data quality according to certain desired attributes linked to each dimension. Using actual power and current data from the IEEE 14-bus power flow analysis, we perform assessments to verify the effectiveness of the suggested membership functions. Especially, our method is adaptable as it can assess the integrity of a single smart grid dataset including electrical readings of different power system parameters, including binary parameters like the state of the interrupter. It distinguishes itself from previous techniques by assessing measurement consistency. Additionally, our proposed method demonstrates sensitivity regarding the false data distribution in a given database. © 2023 IEEE.

关键词： Data Quality Data Quality Evaluation Duplicate Data Electrical measurements Fuzzy Theory Gaussian Noise and Fuzzy Inference Systems Inter-variable Consistency measurement Consistency Smart Grids

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