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2nd IEEE International Conference on Integrated Circuits and communication systems, ICICACS 2024

作者： Masfequier Rahman Swapno, S.M. Nuruzzaman Nobel, S.M. Ramachandra, A.C. Islam, Md Babul Haque, Rezaul Rahman, Mohammad Mominur Bangladesh University of Business and Technology Dept of CSE Dhaka Bangladesh Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India UNICAL Dept. of Computer Modeling Electronic and System Engineering Rende Italy East West University Dept of CSE Dhaka1212 Bangladesh Hamad Bin Khalifa University College of Science and Engineering Qatar

ISBN: (纸本)9798350317558

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people's general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability. © 2024 IEEE.

关键词： Action Deep Q Learning GS Model Q Value Rewards RS State Traffic signal vehicles Waiting Time

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A Novel approach for Data mining Classification using J48DT Classifier for Intrusion Detection system 11

A Novel approach for Data mining Classification using J48DT ...

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11th IEEE International Conference on communication systems and Network Technologies, CSNT 2022

作者： Manimegalai, T. Ravishankar, T. Nadana Kannagi, L. Kannan, K. Anitha, G. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Data Science and Business System Srm Institute of Science and Technology Chennai Kattankulathur India Department of Computer and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Ece R.M.K. College of Engineering and Technology Puduvoyal Chennai India Department of Ece Saveetha School of Engineering Simats Chennai India

ISBN: (纸本)9781665480383

Because of the growing quantity of network traffic that is being transmitted across the network in today's modern era, network attack detection has become a necessity. The technique of data mining is extremely important in the hunt for network assaults and anomalies. These strategies aid in the selection and refinement of meaningful and relevant knowledge from enormous data sets, which is beneficial in many situations. The process of data mining aids in the categorisation of important information for Intrusion Detection systems (IDS). The IDS provides warnings for the network traffic informing it of any foreign intrusions into the network. We show how different data mining methods may be used for intrusion detection to offer a safe network environment in the research article. The J48DT Classifier is utilised for classification in this case. Comparisons based on characteristics such as data accuracy, the discovery of valuable patterns, and the extraction of useful information between approaches are made. © 2022 IEEE.

关键词： Intrusion detection

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Adaptive Intrusion Detection in Cyber-Physical systems Using Reinforcement Learning-Based Autoencoders

Adaptive Intrusion Detection in Cyber-Physical Systems Using...

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Integrated Intelligence and communication systems (ICIICS), International Conference on

作者： N. Rajathi Divya. S s. Anitha Elavarasi G. Saritha V. Jeya Ramya Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The increasing integration of Cyber-Physical systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection system (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments.

关键词： Adaptation models Adaptive systems Accuracy Autoencoders Intrusion detection Reinforcement learning Cyber-physical systems Real-time systems Critical infrastructure Anomaly detection

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Novel orange-red glaze based on in-doped CaFe2O4 red chromophore: Synthesis, properties and characterizations

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Ceramics International 2025年

作者： Qi, Menghang Tian, Liangsheng Wang, Qiuying Xiao, Ruoxiu Hu, Nan Shi, Lin Siritanon, Theeranun Jiang, Peng Department of Inorganic Nonmetallic Materials School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin300072 China School of Chemistry Institute of Science Suranaree University of Technology 111 University Avenue Muang Nakhon Ratchasima30000 Thailand

The CaFe2-xInxO4 (0 ≤ x ≤ 1) pigment with red shade was successfully synthesized through solid state reactions. The crystal structure, color development mechanism, morphology and its application in the glaze were studied. XPS analysis confirmed Fe resides in +3 oxidation state. Combined with UV–Vis–NIR analysis, it is determined that the formation of red shade of the pigment is due to the 6A1(S) → 4T2(D), 6A1(S) → 4A1, 4E(G) and 6A1(S) → 4T2(G) transitions of Fe3+ in the visible light region. The pigment was applied to the lead-free low temperature glaze to evaluate its potential in preparing new glaze color. The as-synthesized glaze showed orange-red shade. With increased In3+ doping concentration, the glaze demonstrates a stronger red tone. The glaze also shows high NIR solar reflectance. Inferred from SEM-EDS, the pigment particles are evenly scattered in the glaze. Therefore, CaFe2-xInxO4 (0 ≤ x ≤ 1) red pigment is a qualified candidate for red shade glaze preparation. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Crystal structure

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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

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Recent Advances in Electrical, Electronics, Ubiquitous communication, and Computational Intelligence (RAEEUCCI), International Conference on

作者： T Thamaraimanalan C Venkatesan M Ramkumar A Sivaramakrishnan M Marimuthu Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Business System Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra pradesh India Department of Computer Science and Engineering Sona College of Technology Salem India

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection.

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Retraction Note: Enhancing low latency and high performance in clustered-based wireless sensor network routing using genetic algorithm for 5G networks

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Banu, Firdouse Udhayamoorthi, M. Ramana Gupta Nallagattla, Venkata Phaneendra Maguluri, Lakshmana Mahesh Kumar, N. B. Jayachitra, S. Department of Applied Information System Applied College King Khalid University Khamis Mushaiyt Saudi Arabia Department of Computer Science and Design Karpagam College of Engineering (Autonomous) Coimbatore India Department of CSE Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

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Investigation of Traffic Radar Coverage Efficiency under Different Placement Strategies

Investigation of Traffic Radar Coverage Efficiency under Dif...

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International Conference on Internet of Things 2024, ICIoT 2024

作者： Yao, Shun Zhou, Jin Chieng, David Kwong, Chiew Foong Lee, Boon Giin Li, Jianqiang Chen, Yusen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computer Science University of Nottingham Ningbo China Ningbo China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Zhejiang Communication Investment Group Co. Ltd. Zhejiang China

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang communications Investment Group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring. © The Institution of Engineering & Technology 2024.

关键词： Multiobjective optimization

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STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting 27th

STMAE: Spatial Temporal Masked Auto-Encoder for Traffic For...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Cai, Chengyou Wang, Xiaoxiao Wang, Jianjia Yao, Junfeng Qian, Quan Song, Jun School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Shentong Metro Co. Ltd. Shanghai China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou China CSSC Seago System Technology Co. Ltd. Shanghai China Department of Geography Hong Kong Baptist University Hong Kong

ISBN: (纸本)9783031781681

The advancement of intelligent transportation systems underscores the importance of data-driven approaches in traffic forecasting, which plays a crucial role in tasks such as traffic signal control and route guidance, among others. However, the inherent uncertainty stemming from regional traffic dynamics, coupled with intricate spatio-temporal correlations, poses formidable challenges to accurate traffic prediction. Moreover, the complexities inherent in sequence forecasting across varying scales further exacerbate the accuracy dilemma. Recognizing the need for integrating information across spatial and temporal dimensions to enhance forecasting precision, a novel solution termed Spatial Temporal Masked Autoencoder (STMAE) is introduced. The STMAE framework addresses these challenges through a two-stage learning process. In the pre-training phase, an autoencoder architecture is employed to extract spatio-temporal features from the data. In the fine-tuning phase, the pre-trained encoder of the STMAE model undergoes further refinement to specifically target traffic forecasting tasks. Extensive evaluations validate the effectiveness of the proposed STMAE model. Notably, STMAE demonstrates competitive performance, achieving 3.32 Vehs MAE for long-term (60 min) traffic forecasting while operating within a reduced computational budget. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Leveraging Data Analytics to Enhance Smart Home Efficiency and Security

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Karthi Vivekanandan B. Kaleeswari P L. Natchiammai Arun Anthonisamy R. Vijayabharathi Manjunathan Alagarsamy Department of Electrical and Electronics Engineering Builders Engineering College Tirupur India Department of Electronics and Communication Engineering Easwari Engineering College (Autonomous) Chennai India Department of Electronics and Communication Engineering Mookambigai College of Engineering Pudukkottai India Department of Computer Science and Business System Panimalar Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices’ energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security.

关键词： Performance evaluation Energy consumption Data analysis Smart homes Machine learning Market research Energy efficiency Security Object recognition Optimization

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Detection of Student Sleepiness Using U-Net and Conv-LSTM Methods to Improve Learning Quality

Detection of Student Sleepiness Using U-Net and Conv-LSTM Me...

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Technology, Engineering, and Computing Applications (ICTECA), International Conference on

作者： Ben Rahman Septi Andryana Aris Gunaryati Teddy Mantoro Department of Information System Faculty of Information and Communication Technology Universitas Nasional Jakarta Indonesia Department of Information Technology Faculty of Information and Communication Technology Universitas Nasional Jakarta Indonesia Department of Computer Science Faculty of Science and Technology Sampoerna University Jakarta Selatan Jakarta Indonesia

In the context of monitoring student engagement during learning sessions, the ability to accurately detect sleepiness can be a valuable asset. Early detection of sleepiness is critical to evaluate the need for intervention to ensure effective learning outcomes. Prolonged student sleepiness can negatively impact learning effectiveness and may result in academic challenges. This research used a semantic segmentation network to monitor and detect the level of sleepiness in students during learning sessions by modifying the U-Net architecture, which is enhanced with Convolutional Long Short-Term Memory (Conv-LSTM) blocks. Conv-LSTM blocks enable efficient feature sharing between network layers and improve accuracy in detecting sleepiness. In this study, experiments were carried out and the proposed method for monitoring students in the classroom was evaluated. The results of this research showed that the percentage of students who made movements detected in the video and extracted in image detection was 92% accurate and not sleepy.

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