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Feasibility Analysis Study on Constructing a grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd international conference on Security and Information Technologies with AI, Internet computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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AI-Powered Healthcare with Advanced Graph Convolutional Neural Networks and SVM for Diagnosis 3

AI-Powered Healthcare with Advanced Graph Convolutional Neur...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mahendran, A. Nalla, Sridhar Babu Trivedi, Lalit Mohan Mohankumar, S. Williams, D.R. Mohan Renuka, Manne Sona College of Arts and Science Department of Computer Science Salem India Sr University Department of Computer Science and Engineering Hyderabad India Moradabad Institute of Technology Department of Ash Mathematics Moradabad India Erode Sengunthar Engineering College Department of Computer Science and Engineering Perundurai India College of Computing and Information Sciences University of Technology and Applied Science-Sohar Department of Information Technology Sohar Oman Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798331508456

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGe on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data. © 2025 ieee.

关键词： Patient treatment

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Minimizing Average Job Completion Time for Multi-resource Queuing computing Systems Using Drift-Plus-Penalty 3rd

Minimizing Average Job Completion Time for Multi-resource Q...

引用

3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Chen, Chuanxu Li, Ruiting Guan, Quansheng Shi, Zhan Zeng, Ying Zheng, Jianping School of Electronic and Information Engineering South China University of Technology Guangzhou China Electric Power Dispatch and Control Center Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (纸本)9789819616978

The Lyapunov drift-based scheduling algorithms control the queue length to stabilize the system and maximize the throughput for multi-resource multi-queue cloud/edge systems. However, these algorithms often ignore the heterogeneity in job length, thus leading to a long delay for job processing. To this end, this paper takes the job completion time (JCT) as a penalty in the drift-plus-penalty expression to minimize the average job competition time, and proposes a Lyapunov drift-plus-time (LDPT) based scheduling algorithm for multi-resource queuing computing systems. The optimality of LDPT with respect to average JCT and queue length is analyzed. By using the Amazon eC2 to simulate, the results show that LDPT can improve average JCT and average queuing time fairness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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Comparing the Pre-Trained Models with LSTM Combination to Improve Image Captioning Technology 3

Comparing the Pre-Trained Models with LSTM Combination to Im...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mukil, L.D. Singh, Tripty Sharma, Mansi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Graphic Era University Department of Computer Science and Engineering India

ISBN: (纸本)9798331508456

Image captioning, integrating computer vision and natural language processing, has become one of the critical areas of work for better accessibility and advancing technology. In this study, the performance of pre-trained convolutional neural networks-DenseNet, VGG-16, ResNet, and Xception-has been evaluated when coupled with LSTM units for generating captions on Flickr8k. These models have been evaluated on the basis of BLeU and ROUGe scores. The best scores were produced by ResNet-LSTM, which indicates the model's stronger ability to produce captions that are accurate and contextual. VGG-16 with LSTM ranked second;it was almost on par. Xception with LSTM gave results that were moderate. DenseNet with LSTM had the worst score, indicating that the feature extraction mechanism of this model is weak. The following results provide evidence for the practical use of ResNet-LSTM to facilitate access: from assistive technology for visually impaired users to automatically generated content. The comparative study would further indicate which one has more merits or drawbacks compared with others and thereby enable a more informed selection of the models suitable for each particular application. © 2025 ieee.

关键词： Convolutional neural networks

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Securing Networks: Advanced Detection and Prevention of Address Resolution Protocol Cache Spoofing Attack 3

Securing Networks: Advanced Detection and Prevention of Addr...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sriharsha, V.V.S. Daule, Viraj Kisan Surya, R. Thangam, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

As the technology advances, an increase in the attacks to manipulate or extract data illegally has also risen. This also includes one such attack known as Address Resolution protocol cache spoofing attack. The Address Resolution protocol Spoofer conducts an in-depth exploration into one of the most significant security vulnerabilities identified in local networks, namely Address resolution protocol cache poisoning. The study implements a system which uses a Python based tool which is able to simulate Address Resolution protocol spoofing attacks on the network traffic of two hosts through a Python library known as the Scapy library. This tool allows the execution of Man In The Middle attacks by forging Address Resolution protocol replies and changing the stream of traffic without setting off any alarm by the user. The tool also has a detection mechanism that alerts the users as soon as it detects attempted spoofing;which hence identifies both the offender and the defensive capabilities in Address Resolution protocol spoofing. Additionally, another layer of security is added by user-defined encryption and decryption of the message being delivered. The study has resulted in a secure network, protecting the data from ARP spoofing. © 2025 ieee.

关键词： HTTP

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Legal Contract Analysis and Risk Assessment Using Pre-Trained Legal-T5 and Law-GPT 3

Legal Contract Analysis and Risk Assessment Using Pre-Traine...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Poornima, A. Nagaraja, K.V. Venugopalan, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Bengaluru India

ISBN: (纸本)9798331508456

The need for effective solutions in reviewing, risk identification, and compliance management of complex legal contracts has remained a critical challenge. This research explores how modern machine learning models like Legal T5, and Law GPT can automate contract analysis and improve review accuracy. Legal T5 summarizes the complex legal documents while Law GPT captures the critical risks. K-Means clustering was used to improve the contract segmentation by grouping similar clauses for a structured risk assessment. Tested on the CUAD dataset, the system's text summarizing findings showed coherence and a strong semantic similarity of 0.78, ensuring that the final summaries retained significant legal importance. The model achieved an average precision of 0.85, recall of 0.77, and F1-score of 0.80 across various risk categories. The findings prove that Legal T5, along with clustering, improves contract analysis with the ability to provide a context and structure-aware summary with better identification of risks. © 2025 ieee.

关键词： Risk management

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Android Malware Detection via Hybrid Lion-Bee Optimization with Bi-Directional Recurrent Neural Network 3

Android Malware Detection via Hybrid Lion-Bee Optimization w...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Rajeyyagari, Sivaram elfaki, Mohamed Ahmed Rashid, Khan Asif Bala, B. Kiran Reyad, Omar College of Computing and Information Technology Shaqra University Department of Computer Science Shaqra Saudi Arabia K.Ramakrishnan College of Engineering Department of Ai and Ds Trichy India College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Faculty of Computers and Artificial Intelligence Sohag University Sohag Egypt

ISBN: (纸本)9798331508456

The number of malicious programs that target the Android OS has significantly expanded with the rise in mobile device usage. It is crucial to identify, prevent, and defeat Android malware assaults since they have long presented a severe threat to Android apps. The emergence of secure Android app ecosystem depends on recognizing and classifying harmful applications into groups are similar to another. Malware families can be categorized in order to recognize harmful activity and to systematically spot dangerous-patterns. Thus, the study suggests a hybrid strategy that combines the attributes discovered from doing static and dynamic malware assessment for enhanced identification and categorization of Android malware. This method more effectively addresses the issue of studying, identifying Android malware. The feature extracted from image sections utilizing the hybrid lion and artificial bee colony (HL-ABC) optimization. RNN were used to classify the retrieved features. each segment of a malware image file served as a test case for the categorization performance. This is contrasted with other current ways to show the effectiveness of the suggested strategy. Comparing the suggested model to other approaches, the study's findings demonstrate its effectiveness in identifying and classifying Android malware. © 2025 ieee.

关键词： Android malware

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Research on the Application of Support Vector Machine in Transformer Fault Diagnosis Based on Neighborhood Rough Set Theory and Gray Wolf Optimizer 3rd

Research on the Application of Support Vector Machine in Tra...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Wang, Guoyou Xu, Weijin Xu, Zhenyu Cheng, Xingguang Yuan, Zeyi Chen, Renhui State Grid Jilin Electric Power Co. Ltd. Changchun130012 China Changchun University of Technology Changchun130012 China

ISBN: (纸本)9789819616978

Dissolved gas analysis (DGA) in oil has some problems in reflecting and accurately diagnosing transformer faults. In order to solve these problems, We use the Neighborhood Rough Set technique to approximate a large number of transformer fault data ratios and reduce redundant data. This approximation technique resulted in a new set of ratios as our samples for fault diagnosis. In this study, a high-precision transformer fault diagnosis model combining the Gray Wolf Algorithm (GWO) and Support Vector Machine (SVM) has been successfully established. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Rough set theory

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AI Integrated Healthcare System for Disease Predictions and Virtual Consultations 3

AI Integrated Healthcare System for Disease Predictions and ...

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3rd international conference on electronics and Renewable Systems, ICeARS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331509675

AI-based Disease Prediction and Online Consultation System aims to redesign the way people experience healthcare. The deep growth of machine learning opened doors for new opportunities, such as this system that carefully inputs and validates patients' symptoms, and subsequently predicts possible diseases. In addition, it fosters remote consultation in a user-friendly manner over the internet and erases the barriers of distance and mobility. It is expected that this combination of data-driven forecasting and logic will enhance the treatment accuracy in terms of increased availability of healthcare services. AI-based Disease Prediction and Online Consultation System is a practical showcase on how to transform equity in healthcare. The increasing change in technology can give us possibilities for the utmost usage of this system. In this regard, the complex predictive analytics associated with the system may in effect change how HeIs (health education institutions) practice healthcare management. Patients will be offered a practice which supports the idea of delivering the right information on time and stress the universal right of access to healthcare. Its transformational power has far more prospects than it seems at first. At the core it sets the landscape and, indeed, the stage for the most equal, efficient and inclusive healthcare ecosystem that could ever exist. © 2025 ieee.

关键词： electronic health record

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Investigation on enhanced Detection of Parkinson's Disease in Brain Imaging Using ensemble Learning Approaches 3

Investigation on Enhanced Detection of Parkinson's Disease i...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sathishkumar, S. Santhoshkumar, S.P. Kumar, Satyam Banupriya, N. Hariharasudhan, S. Gajalakshmi, S. Hindustan Institute of Technology Department of Computer Science and Engineering Coimbatore India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi India School of Cs and It Mahatma Gandhi Central University Motihari India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331508456

Masses of people around the world are affected by Parkinson's disease (PD), a progressive neurological disorder. Timely and precise diagnosis is critical for effective management. Yet conventional diagnostic methods often lack compassion and specificity. Advances in machine learning and cerebral imaging offer promising approaches to improve PD diagnosis. This investigation investigates an ensemble machine learning move toward to enhance the identification of Parkinson's disease using cerebral imaging data. By integrating models such as decision trees (DT), support vector machines (SVM), and neural networks, the ensemble method combines their strengths to create a reliable diagnostic system. Feature extraction and preprocessing further enhance interpretability and performance. Compared to individual models, the ensemble approach significantly improves diagnostic accuracy, sensitivity, and specificity. Brain imaging aids differential diagnosis in Parkinsonism, but no Cerebral imaging methods is currently recommended for routine clinical practice despite their proven utility. Larger trial are needed to validate the medical value and long-standing cost-effectiveness of Cerebral imaging for managing patients within healthcare systems like the NHS. © 2025 ieee.

关键词： Neurodegenerative diseases

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