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International Conference on computer and Communications Technologies (ICCCT)

作者： Sathya T Tharun A K Arun S Laith Aravind P Y Saraswathy S Department of Computer Science and Business System Sri Sairam Engineering college Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Road safety is a significant concern worldwide, with a high number of fatalities occurring due to the negligence of helmet usage by two-wheeler riders. According to statistics, thousands of lives are lost annually due to non-compliance with helmet regulations. This paper proposes an automated helmet detection system using image processing techniques to enforce safety measures. The system captures real-time images of the rider using a camera and processes them with a pretrained machine learning model on a Raspberry Pi. If a helmet is detected, the vehicle's ignition is enabled; otherwise, it remains disabled. This method ensures compliance with safety regulations and contributes to reducing road accidents. The model achieves 95% accuracy in helmet detection, making it a reliable and practical solution for real-world deployment. Experimental results validate the effectiveness of the proposed system, highlighting its potential in improving road safety and reducing fatalities.

关键词： Head Road accidents Image processing Machine learning Ignition Road safety Regulation Real-time systems Safety Reliability

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An Integrated Demand Side Management Framework Utilizing Smart Grid Technologies and IoT for Enhanced Residential Energy Efficiency

An Integrated Demand Side Management Framework Utilizing Sma...

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2024 IEEE International Women in Engineering (WIE) Conference on Electrical and computer Engineering, WIECON-ECE 2024

作者： Venkatesan, P. Gopinath, C. Prabhavathy, P. Department of Electrical and Electronics Engineering India Center for Integrated Smart Waste Management System Department of Electrical and Electronics Engineering Sriperumbudur India Anna University Department of Computer Science and Engineering College of Engineering Guindy Chennai India

ISBN: (纸本)9798331535476

Increasing energy demands and the need for efficient management necessitate innovative solutions. This study introduces an integrated Demand Side Management (DSM) framework tailored for residential buildings within smart grid systems. The proposed methodology employs Earth Worm Optimization Algorithm (EWOA) and Virulence Optimization Algorithm (VOA) to optimize scheduling for household appliance using integration of Renewable Energy Storage (RES) and Renewable Energy Resources (RERs). Real-Time Electricity Scheduling (RTES) is implemented using IoT technologies to enable adaptive energy management. This system demonstrates significant performance improvements, achieving a 76.19% reduction in the Peak-to-Average Ratio (PAR) using VOA and a 73.8% reduction with EWOA when RES is included. Comparative analyses highlight substantial cost reductions, enhanced grid stability, and improved energy efficiency. This work not only showcases the novelty of integrating advanced optimization algorithms with IoT but also provides a robust approach to achieve sustainable and cost-effective energy management. © 2024 IEEE.

关键词： Smart power grids

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning system, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Intelligent Software-Defined Network for Cognitive Routing Optimization Using Deep Extreme Learning Machine Approach

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computers, Materials & Continua 2021年第4期67卷 1269-1285页

作者： Fahd Alhaidari Sultan H.Almotiri Mohammed A.Al Ghamdi Muhammad Adnan Khan Abdur Rehman Sagheer Abbas Khalid Masood Khan Atta-ur-Rahman Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science Umm Al-Qura UniversityMakkah City715Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54792Pakistan School of Computer Science NCBA&ELahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

In recent years,the infrastructure,instruments,and resources of network systems are becoming more complex and heterogeneous,with the rapid development of current internet and mobile communication *** order to efficaciously prepare,control,hold and optimize networking systems,greater intelligence needs to be ***,due to the inherently dispensed characteristic of conventional networks,Machine Learning(ML)techniques are hard to implement and deployed to govern and operate ***-Defined Networking(SDN)brings us new possibilities to offer intelligence in the ***’s characteristics(e.g.,logically centralized control,global network view,software-based site visitor analysis,and dynamic updating of forwarding rules)make it simpler to apply machine learning *** perspectives of fiber-optic communications including fiber nonlinearity coverage,optical performance checking,cognitive shortcoming detection/anticipation,and arranging and improvement of softwaredefined networks are examined in Machine Learning(ML)*** research paper has presented an imaginative framework concept called Intelligent Software Defined Network(ISDN)for Cognitive Routing Optimization(CRO)using Deep Extreme Learning Machine(DELM)approach(ISDN-CRO-DELM)in light of the new challenges in the development and operation of communication systems,and capturing motivation from how living creatures deal with difficulty and *** proposed methodology develops around the planned applications of progressive DELM methods and,specifically,probabilistic generative models for framework wide learning,demonstrating,improvement,and information ***,ISDN-CRO-DELM,suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system *** 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.

关键词： SDN DELM machine learning cognition

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Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

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computers, Materials & Continua 2023年第5期75卷 2575-2591页

作者： Badriyya BAl-onazi Abdulkhaleq Q.A.Hassan Mohamed K.Nour Mesfer Al Duhayyim Abdullah Mohamed Amgad Atta Abdelmageed Ishfaq Yaseen Gouse Pasha Mohammed Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24231Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the *** SA technique is specifically applied to the data collected from social media *** research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction *** this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in ***,the data pre-processing is performed to convert the raw tweets into a useful ***,the word2vec model is applied to generate the feature *** Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the ***,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU *** proposed QPSODL-SAAT model was experimentally validated using the standard *** extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

关键词： Sentiment analysis Arabic tweets quantum particle swarm optimization deep learning word embedding

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An Intranet Defense Capability Assessment Method for Network Security Solutions Based on ATT&CK Framework 2nd

An Intranet Defense Capability Assessment Method for Network...

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2nd International Conference on Network Simulation and Evaluation, NSE 2023

作者： An, Qi Zhu, Zifeng Li, Shudong Han, Weihong Wu, Xiaobo Li, Shumei Cyberspace Institute of Advanced Technology GuangzhouUniversity Guangzhou510006 China Department of New Networks Peng Cheng Laboratory Shenzhen518000 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Computer College Jilin Normal University Siping136000 China

ISBN: (纸本)9789819745180

Faced with the growing threat of intranet attacks and numerous network security solutions, the defensive capabilities of network security solutions need to be evaluated in order to reasonably select a network security solution. However, the existing assessment of the defense capability of network security solutions mainly focuses on anti-DDoS devices, and lacks the assessment of the defense capability of the intranet, and it is complicated to obtain the values of assessment indicators during the assessment process. In addition, it is also difficult to obtain ideas for defense improvement of network security solutions. In the face of this situation, we propose an intranet defense capability assessment method for network security solutions based on the ATT&CK framework from an attack perspective. This method introduces the ATT&CK framework into the assessment of the intranet defense capability of network security solutions, constructs an indicator system with comprehensiveness and good scalability, and involves tactics, techniques and sub-technologies in the enterprise framework used by 138 APT organizations in the ATT&CK framework as objective data for indicator assignment, balancing the influence of attackers’ subjective experiences and preferences on the assessment results, and this method can easily obtain ideas to improve the intranet defense capability of network security solutions. The effectiveness of the proposed evaluation method is verified by conducting simulation experiments at a network range. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intranets

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning 27

Video2Reward: Generating Reward Function from Videos for Leg...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781643685489

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. © 2024 The Authors.

关键词： Robots

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Fundamental Limits of Multiple-Access Integrated Sensing and Communication systems

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IEEE Transactions on Information Theory 2025年第6期71卷 4317-4341页

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： Signal distortion

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Geometric behavior of the generalized Hadid-Luchko convolution operator in the open unit ball

Geometric behavior of the generalized Hadid-Luchko convoluti...

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2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023

作者： Hadid, Samir B. Ibrahim, Rabha W. Obaiys, Suzan J. Nadia M. G, Al-Saidi Ajman University Department of Mathematics and Sciences College of Humanities and Sciences Ajman United Arab Emirates Ajman United Arab Emirates Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon University of Malaya Faculty of Computer Science and Information Technology Department of Computer System & Technology Kuala Lumpur Malaysia University of Technology Department of Applied Sciences Iraq

ISBN: (纸本)9798350321685

In this effort, by consuming the k-symbol calculus, we generalize the Hdid-Luchko function (the Mittag-Leffler function of several variables). Some inequalities are established containing the normalized type of this function. In addition, we illustrate a list of conditions on the function to recognize some special geometric properties in a unit ball. Our theory is based on the differential subordination of several variables. © 2023 IEEE.

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Arabic Sign Language Detection Using Deep Learning

Arabic Sign Language Detection Using Deep Learning

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2022 International Conference on Intelligent Technology, system and Service for Internet of Everything, ITSS-IoE 2022

作者： Dabwan, Basel A. Jadhav, Mukti E. ALBaydha University Department of Information System ALBaydha Yemen Shri Shivaji Science & Art College Department of Computer Science Chikhali India

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

Deaf or hard of hearing person number roughly 466 million people globally. Individuals in this situation must interact with people, gain new skills, and participate in activities. They use sign language to communicate with the rest of the world. The development of an autonomous system to detect gesture language has various difficulties, particularly for Arabic sign language. We built models for Arabic Sign Language to accomplish this. We used the Arabic Alphabets Sign Language (ArSL) Dataset, which contains 32 categories of sign characters, A total of 40 people of various ages participated in these Arabic sign and letter classes. There are 506 images in each category, for a total of 506 ∗ 32 = 16192 images. 80 percent of the dataset was used for training, while 20 percent was used for testing. The results of the proposed system were 100 percent accurate. © 2022 IEEE.

关键词： Statistical tests

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