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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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Analyzing Cyberbullying Word Distribution Across Social Media to Develop Lexicon Dictionary 2

Analyzing Cyberbullying Word Distribution Across Social Medi...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Sahana, V. Anil Kumar, K.M. Jss Academy of Technical Education Dept. of Information Science and Engineering Bengaluru560060 India Jss Science and Technology University Dept. of Computer Science and Engineering Mysuru570006 India

ISBN: (纸本)9798350372892

Social media platforms have become essential tools for communication, collaboration, and exchanging information, ideas, and knowledge among users worldwide. Despite their benefits, the anonymity offered by these platforms has unfortunately led to a rise in hate speech and cyberbullying, causing concern across the globe. This issue has drawn the attention of researchers and scholars now focused on devising methods for automatically detecting cyberaggression and hate speech. The goal is to mitigate these harmful behaviors and create safer online environments [1]. This study specifically aims to identify and analyze the usage of cyberbullying language within the English Tweet corpus, focusing on distinguishing between bullying and non-bullying language. This research contributes to developing more effective cyberbullying detection models by examining the frequency and patterns of cyberbullying words. These findings are vital for enhancing the efficacy of cyberbullying detection on social networks in future research efforts. The conclusions of the experiments indicate that the SVM achieved the highest average accuracy, approximately 84 %. © 2024 IEEE.

关键词： Anonymity

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Audiovisual speech recognition based on a deep convolutional neural network

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Data Science and Management 2024年第1期7卷 25-34页

作者： Shashidhar Rudregowda Sudarshan Patilkulkarni Vinayakumar Ravi Gururaj H.L. Moez Krichen Department of Electronics and Communication Engineering JSS Science and Technology UniversityMysuru570006India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobar34754Saudi Arabia Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal560064India Department of Information Technology Faculty of Computer Science and Information Technology(FCSIT)Al-Baha UniversityAlaqiq65779-7738Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Audiovisual speech recognition is an emerging research *** is the recognition of what someone is saying using visual information,primarily lip *** this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual *** features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural *** feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural ***,integration was performed using a deep convolutional *** audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 *** training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 *** integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

关键词： Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)

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Developing a Convolutional Neural Model for Segmentation of Medical Images 1

Developing a Convolutional Neural Model for Segmentation of ...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Arunachalam, M. Kumar, P. Mohan Selvam, L. Kumar, S. Senthil Arunkumar, B. Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India Hindusthan Institute of Technology & Sciences Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Sethu Institute of Technology Department of Computer Science and Engineering Kariapatti India

ISBN: (纸本)9798350359756

Medical image segmentation is a new biomedical image processing method that has made a significant contribution to sustainable health care. It has now become a major study area in the realm of computer vision research. Medical image processing based on deep convolutional neural networks has become a research hotspot due to the fast development of deep learning techniques. An additional cascade component is included to the architecture in this article to better replicate spatial closure tags' direct dependences. It is also utilised for post-segmentation processing, resolving the conflict between classification performance and network intensity and number of mixing times in a typical convolutional net. Numerous state-of-the-art deep recognition models, and a multi-atlas segmentation methodology, are outperformed by our methodology. © 2023 IEEE.

关键词： Image segmentation

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Forest Fire Prediction and Management using AI (Artificial Intelligence), ML (Machine Learning) and Deep Learning Techniques 8

Forest Fire Prediction and Management using AI (Artificial I...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Devi, Kavuluri Leela Sai Rasagna Kumar, Garnepudi Narasimha Narayana, Potturi Ashok Ramana, Kakani Venkata Amarendra, K. Gullipalli, Tirupathi Rao Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram522502 India Aditya Engineering College Dept of Cse Surampalem India

ISBN: (纸本)9798350386578

Forest fires pose significant threats to public safety and the environment, releasing hazardous pollutants and spreading rapidly through vegetated areas. Early detection is critical to prevent forest fires from becoming catastrophic, but the dynamic nature of weather conditions complicates this process. This study addresses the challenge by employing advanced Deep Learning (DL) techniques, utilizing algorithms such as Support Vector Machines (SVM), Logistic Regression, and Convolutional Neural Networks (CNN) to analyze images captured by satellites, drones, and webcams. Developing a neural model involves constructing, training, and fine-tuning sophisticated algorithms, optimizing accuracy by adjusting parameters such as dense layers and hidden layers. Data preprocessing techniques, including data augmentation, are used to enhance the input and improve model performance. The integration of new technologies and approaches, such as deep learning and data augmentation, aims to mitigate the effects of wildfires and protect both human lives and the environment. The ultimate goal of this research study is to enhance early detection systems and reduce response times, thereby minimizing the detrimental impact of forest fires on ecosystems and communities. © 2024 IEEE.

关键词： Convolutional neural networks

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An Intersection-Based Traffic Awareness Routing Protocol in VANETs Using Deep Reinforcement Learning

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Wireless Personal Communications 2024年第1期138卷 659-683页

作者： Song, Ya-Jing Yen, Chin-En Hsieh, Yu-Hsuan Kuo, Chunghui Chang, Ing-Chau Department of Computer Science and Information Engineering National Changhua University of Education No. 1 Jinde Rd. Changhua County Changhua City Taiwan Department of Early Childhood Development and Education Chaoyang University of Technology 168 Jifeng E. Rd. Wufeng District Taichung Taiwan Raycers Technology Fairport New York14450 United States

In Vehicular Ad Hoc Networks, reliable information transmission relies on an effective routing strategy. Most existing reinforcement learning-based routing methods are ineffective in dynamic network environments and cannot prevent inefficient network routing. Efficient network routing can be controlled by network traffic management, so this paper proposes an intelligent routing strategy based on Deep Reinforcement Learning to enhance routing performance. By integrating intersection forwarding and traffic awareness capabilities, this paper addresses the problem of local optimality and utilizes the Deep Q Network to make intersection forwarding decisions. The state space of this strategy consists of intersection nodes, road information between intersections, and forwarding packet information. When a vehicle node carrying a packet approaches an intersection based on the state space, the intersection node uses a neural network to select the optimal next-hop relay intersection from past learning experiences. It generates appropriate vehicle routing decisions based on information from the current and candidate relay intersections. Finally, we use the real taxi trajectory data of Beijing City to conduct extensive simulation experiments. Simulation results and analysis demonstrate that the proposed strategy outperforms related research regarding higher average packet delivery ratio, shorter average end-to-end delay, and lower average overhead ratio in dense and sparse traffic periods under real road environments. Consequently, this strategy provides efficient and reliable message transmission services for Vehicular Ad Hoc Networks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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Exploring the Potential of Underwater Fibre Optic Networks for Bandwidth Optimization

Exploring the Potential of Underwater Fibre Optic Networks f...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sasirekha, R. Dhabliya, Dharmesh Yadav, Dhyan Tiwari, Mohit Mandal, Sanjeev Kumar Ravivarman, G. Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India

ISBN: (纸本)9798350329773

underwater fiber optic networks offer an enormous capacity for optimizing worldwide conversation. As a result of their dramatically decreased transmission latency, they may be now broadly taken into consideration because the best way for imparting comfortable, fast and dependable bandwidth for an expansion of programs. This paper examines the capability of underwater fiber networks for bandwidth optimization, focusing on 4 principal elements: topology, routing, modulation and signal transmission. By way of exploring existing research in every of these regions, this paper develops a holistic assessment of the capacity of underwater networks for improving bandwidth efficiency. The paper additionally highlights the key demanding situations and considerations that have to be taken into account while considering the deployment of underwater fiber networks in phrases of value, complexity, reliability and scalability. Sooner or later, the paper affords insights and tips at the functions that need to be taken into consideration for the successful deployment of such networks for bandwidth optimization. © 2023 IEEE.

关键词： Bandwidth

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High-performance self-powered ultraviolet to near-infrared photodetector based on WS_(2)/InSe van der Waals heterostructure

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Nano Research 2023年第5期16卷 7851-7857页

作者： Jinping Chen Zhen Zhang Yi Ma Jiying Feng Xiaoyu Xie Xiaoxuan Wang Aoqun Jian Yuanzheng Li Zhuxin Li Heng Guo Yizhi Zhu Qiannan Cui Zengliang Shi Chunxiang Xu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Micro Nano System Research Center Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education&College of Information Engineering and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education Northeast Normal UniversityChangchun 130024China

van der Waals heterostructures(vdWHs)based on two-dimensional(2D)materials without the crystal lattice matching constraint have great potential for high-performance optoelectronic ***,a WS_(2)/InSe vdWH photodiode is proposed and fabricated by precisely stacking InSe and WS_(2)flakes through an all-dry transfer *** WS_(2)/InSe vdWH forms an n–n heterojunction with strong built-in electric field due to their intrinsic n-type semiconductor characteristics and energy-band alignments with a large Fermi level offset between WS_(2)and *** a result,the device displays excellent photovoltaic behavior with a large open voltage of 0.47 V and a short-circuit current of 11.7 nA under 520 nm light ***,a fast rising/decay time of 63/76μs,a large light on/off ratio of 105,a responsivity of 61 mA/W,a high detectivity of 2.5×10^(11) Jones,and a broadband photoresponse ranging from ultraviolet to near-infrared(325–980 nm)are achieved at zero *** study provides a strategy for developing high-performance self-powered broadband photodetectors based on 2D materials.

关键词： two-dimensional materials van der Waals heterostructure self-powered photodetector photocurrent

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A Review on Alzheimer's Disease Detection using Machine Learning 2

A Review on Alzheimer's Disease Detection using Machine Lear...

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

作者： Moorthy, Deepthi K Nagaraj, P. Subhashini, S.J. Mar Baselios College of Engineering and Technology Department of Computer Science and Engineering Nalanchira Trivandrum India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Srm Madurai College of Engineering and Technology Department of Information Technology Sivagangai India

ISBN: (纸本)9798350325799

Alzheimer's disease is a progressive and irreversible brain disorder affecting millions worldwide. The timely detection of Alzheimer's Disease (AD) is essential for the effective management and treatment of the disease. Machine learning (ML) approaches have shown promising results in detecting AD at an early stage. This review article provides an overview of the recent developments in the application of ML techniques for AD detection. Also discusses the application of certain image processing, feature extraction, and ML techniques for detecting AD. The article covers various ML algorithms and their performance in detecting AD with different data sources, such as MRI, PET, etc. with their advantages and limitations. Overall, this research study demonstrates the potential of ML approaches in improving the accuracy and efficiency of Alzheimer's disease detection, which could lead to better patient outcomes and disease management. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Application of Ensemble Machine Learning Techniques in Yield Predictions of Major and Commercial Crops 4th

Application of Ensemble Machine Learning Techniques in Yield...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Jayashree, T.R. Subba Reddy, N.V. Dinesh Acharya, U. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819920990

Farming activities and crop productivity has been greatly affected by climate change, soil fertility, and the availability of planting areas in recent decades. Early estimation of yields and their quality are the prime requirements in the global food market which depend on several input parameters. Predicting crop yields gives the farmers an insight to decide on the crop production rate and cultivate suitable crops for the given climatic conditions. The present work aims at estimating yields of major and cash crops of Karnataka using climate and crop-related data through four ensemble regression approaches. The feature importance concept is incorporated that describes which features contribute most to the prediction results in all the models that help in better data interpretation. The performance metrics such as MSE, MAE, and R2 were adapted to measure the accuracy. The extreme gradient boosting regressor was found to deliver the best performance (R2 of 0.999) while incurring the lowest computational cost among the four ensemble regressor models. The results showed that ensemble regression methods can be effectively used for yield predictions when there is a medium-sized dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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