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Attack-Resilient Distributed Cooperative Control of Virtually Coupled High-Speed Trains via Topology Reconfiguration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1066-1068页

作者： Shunyuan Xiao Xiaohua Ge Qing Wu the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the School of Engineering and Technology Central Queensland UniversityRockhampton QLD 4701Australia

Dear Editor,This letter addresses the resilient distributed cooperative control problem of a virtually coupled train convoy under stochastic disturbances and cyber *** main purpose is to achieve distributed coordinati... 详细信息

关键词： prescribed Speed trains

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AI-Enhanced Learning Assistant Platform 7

AI-Enhanced Learning Assistant Platform

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sumanth, N. Sai Priya, S. Vishnu Sankari, M. Kamatchi, K.S. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Sist Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question and answer generation, answer evaluation, identification of weak areas, recursive testing, an integrated query forum, and expert chat support. This platform makes use of artificial intelligence (AI) technology to try to satisfy the many needs that students and teachers have. Using natural language processing and machine learning, the platform's question and answer generating feature generates relevant questions on its own from the provided content. This encourages participation and in-depth subject understanding. The answer evaluation section provides quick feedback for improvement by utilizing AI algorithms to assess the accuracy and caliber of student responses. One of this platform's key advantages is its capacity to identify students' areas of weakness. Through the analysis of performance patterns and root causes, the system can generate customized recommendations and learning materials to help overcome those constraints. Through repeated practice, the program gradually pushes students to increase their understanding of the material by creating adaptive exams. Through the integrated query forum, students can collaborate and ask for assistance from others by asking questions and receiving answers from teachers and their peers. Furthermore, by enabling real-time communication between users and subject matter experts, the expert chat support tool fosters an engaging and motivating learning environment. © 2024 IEEE.

关键词： Students

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Analyzing Brain Damages in Children with Image Processing Using KNN and FCM Algorithms

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SN computer science 2024年第1期5卷 62页

作者： Kayathri, B. Prasanna, S. School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

In current scenario, children are addicted towards mobile communication devices such as mobile phone, tablet and various wireless devices. Exploring more radio frequencies with these devices causes brain damage in children aged 5–10 years. This paper uses an original calculation for the examination of the cerebral image. Typically, an MRI scan or CT scan will show the Cerebrum’s living structures. The brain of children is scanned in MRI and analyzed through image processing to know whether the children put their lives in danger or not, parents should be aware of their kids’ true health. The analysis of training data selected from MRI scan pictures yields 96.3% accuracy in determining the outcomes through examination of the children’s brains. Hence, the findings of brain analyses on various children are examined. Thus, the existing methods to analysis on finding brain damage which is not enough to sort out the problem still, so proposed method is needed to find Brain Damage Findings (BDF) which has to work prominently compared with the existing methods of FCM and KNN with unique test and trained data of brain. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Communication devices CT (computerized Tomography) Damage FCM (Fuzzy C-Means) KNN (K-Nearest Neighbour) MRI (Magnetic Resonance Imaging)

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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ESOA-Based 1D-CNN Classification for Amazon Textual Review Sentiment Analysis of Mobile Phones

ESOA-Based 1D-CNN Classification for Amazon Textual Review S...

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International Conference on computing and Machine Learning, CML 2024

作者： Kosala, N. Nirmalrani, V. School of Computing Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9789819775484

A trend toward the use of digital platforms for product marketing and profit margin enhancement is evident in the contemporary corporate environment. As a result, using deep learning methods to evaluate consumer sentiment in tweets based on product reviews becomes essential. The study's proposal is to use a big dataset and a one-dimensional convolutional neural network (1D-CNN) for the classification process in order to extract lexical characteristics for sentiment analysis. In order to do this, analysis is done on customer review datasets of Amazon items with an emphasis on reviews pertaining to mobile phones. To improve the performance and accuracy of the classifier, data preprocessing is first done. A lexical method is then used to efficiently compress the text via feature extraction. For the purpose of correctly assessing review emotions, efficient classification is essential. So, classification is done using the 1D-CNN method. The Egret Swarm Optimization Algorithm is used to adjust the 1D-CNN model's hyperparameters (ESOA). After this procedure, the research evaluates the 1D-CNN method's performance in relation to other techniques currently in use. The research concludes that the suggested method performs well overall, achieving 98.77% accuracy, 97.37% precision, 97.42% sensitivity, and 97.21% F-measure, respectively. These performance measures outperform those of current algorithms created in earlier research. The sentiment analysis of mobile phone evaluations inside the Amazon e-commerce system shows improved accuracy and efficiency when using the suggested 1D-CNN neural network approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Anomaly Detection in Surveillance Videos Using Deep Learning and SVM Based Data Reduction Method 5

Anomaly Detection in Surveillance Videos Using Deep Learning...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Hareesh Narayan, B.K. Chowdhury, Abishi Shukla, Pankaj School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Advanced Science Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350383522

Anomaly detection in video data is a crucial subject that has gained a significant research implication in computer vision with several real-time applications. The researchers worldwide developed innovative solutions in this context in the diverse areas of research specific practical applications. This paper emphasizes over the importance of anomaly detection in a public environment along with its applications for the same. A brief survey of prominent existing techniques with a comparative analysis has been presented in this paper in order to highlight the relevant techniques, available datasets, considered metrics for analysis, and future scope of improvements. In this context, deep learning (DL) approaches like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are prominently used for anomaly detection. In this paper, Support Vector Machine (SVM) along with convolution 2D LSTM based data reduction approach is presented which is used to keep only the important data over incremental addition of the data. The experimental analysis, performed on benchmark dataset DCSASS, shows the efficacy of the proposed approach when compared with state-of-the-art approaches as it maintains significant accuracy while ensuring effective data reduction. Furthermore, the proposed approach demonstrates the training and validation loss analysis. It reduces the errors as time progresses which is directly proportional to the incremental addition of data. © 2024 IEEE.

关键词： Accuracy Anomaly Detection Dataset Deep Learning Long Short-Term Memory Support Vector Machine

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Distributed Nash Equilibrium Seeking for General Networked Games With Bounded Disturbances

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 376-387页

作者： Maojiao Ye Danhu Li Qing-Long Han Lei Ding IEEE the School of Automation Nanjing University of Science and TechnologyNanjing 210094China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial ***,reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with firstorder and second-order players,*** the developed algorithms,the observed disturbance values are included in control signals to eliminate the influence of disturbances,based on which a gradient-like optimization method is implemented for each ***,a signum function based distributed algorithm is proposed to attenuate disturbances for games with secondorder integrator-type *** be more specific,a signum function is involved in the proposed seeking strategy to dominate disturbances,based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players'objective *** Lyapunov stability analysis,it is proven that the players'actions can approach a small region around the Nash equilibrium by utilizing disturbance observerbased strategies with appropriate control ***,exponential(asymptotic)convergence can be achieved when the signum function based control strategy(with an adaptive control gain)is *** performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform(V-REP)and MATLAB.

关键词： Distributed networks disturbance rejection Nash equilibrium networked games

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Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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computer Systems science & engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia Department of Information Technology MLR Institute of TechnologyHyderabad500043TelanganaIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various *** same can be adapted to the problem of brain tumor ***,several deep learning models are presented earlier,but they need better classification *** efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this *** method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple *** noise-removed image has been equalized for its quality by using histogram ***,the features like white mass,grey mass,texture,and shape are extracted from the *** features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality *** texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution *** neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of *** on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor

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Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

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International Journal of Cognitive computing in engineering 2024年第1期5卷 58-65页

作者： Pradeep C, Antony Amali D, Geraldine Bessie Noel, Mathew Mithra Ghalib, Muhammad Rukunuddin Subramaniam, Prabhakar Rontala Venugopal, Chitra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computing De Montfort University Dubai United Arab Emirates Information Systems and Technology Westville South Africa Electrical Engineering & Renewable Energy Oregon Institute of Technology United States

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection Systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems. © 2024

关键词： Generative adversarial networks

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Delay-Sensitive Task Offloading With Edge Caching Through Martingale-Based Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 6225-6242页

作者： Dong, Chongwu Li, Weidong Zhou, Zhi Chen, Xu Tian, Zhihong Wen, Wushao Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China

In the forthcoming era of 6G networks, delay-sensitive applications for Internet of Things (IoT) are poised to become the prevailing services with ultra-reliable and low-latency (URLLC) requirements. Unlike traditional video caching, IoT-based edge caching faces unique challenges due to diverse data types, update frequencies, and computational needs, requiring integrated storage and computational resource management. To support the more stringent requirements for these innovative applications, mobile edge computing (MEC) is introduced to enhance the service reliability of delay-sensitive applications in the 6G era. However, task offloading, as an indispensable procedure in MEC, would encounter many challenges, such as network jitter and resource insufficiency, possibly leading to unpredictable queuing delays and other negative issues. To ensure reliable services in a dynamical MEC environment, the caching-enabled MEC network has emerged as a novel architecture, placing computing and storage resources in the edge network. In this paper, we investigate the caching-enabled MEC to support reliable task offloading for delay-sensitive applications, with a focus on IoT scenarios. In our system model, we formulate the task process as a two-hop tandem queuing system with limited capacity, including task transmission and computation queues. The Martingale theory is leveraged to analyze the delay violation probability in this system, demonstrating how the offloading and caching decisions affect the end-to-end (E2E) delay. Besides, task offloading and resource allocation policies are integrated to reduce high system costs, including energy consumption and cache resource rental costs. Based on the delay analysis of martingale theory, we propose an advanced deep reinforcement learning (DRL) algorithm called Dynamic Request Aware Soft Actor-Critic (DRA-SAC) algorithm to achieve minimal system costs by obtaining the optimal task offloading and resource allocation policies, includin

关键词： Delay-sensitive applications

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