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检索条件"任意字段=14th International Conference on Computer Systems and Technologies, CompSysTech 2013"

共 2000 条记录，以下是41-50 订阅

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A Hybrid Reinforcement Learning Framework for Optimizing Node Deployment in WSNs 14

A Hybrid Reinforcement Learning Framework for Optimizing Nod...

引用

14th IEEE international conference on Communication systems and Network technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Gupta, Akash Kumar Ranjan, Rakesh Verma, Amit Teja, Panduranga Ravi Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India Dept of ECE BIT Mesra Deoghar India School of Computer Science UPES Dehradun India

ISBN: (纸本)9798331531935

Efficient node deployment in Wireless Sensor Networks (WSNs) is critical for maximizing network coverage, enhancing energy efficiency, and prolonging network lifetime. However, achieving optimal node placement is challenging due to the limited energy resources and dynamic environmental conditions in WSNs. this paper presents a novel hybrid approach that combines Reinforcement Learning (RL) with the Differential Evolution (DE) algorithm to optimize node deployment in WSNs. In this method, RL dynamically adapts node positions based on local network states, while DE performs global optimization to refine these placements and improve coverage. Simulation results demonstrate that the proposed RL-DE hybrid approach significantly outperforms traditional deployment methods, standalone RL, and DE in terms of coverage, energy conservation, and network longevity. the results suggest that the RL-DE approach provides an adaptive and robust solution for node deployment in WSNs, with potential applications in real-world sensor network deployments. © 2025 IEEE.

关键词： Energy efficiency

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks 14

A Bayesian Optimization Framework for Adaptive Node Deployme...

引用

14th IEEE international conference on Communication systems and Network technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Vishwakarma, Anish Kumar Dhengre, Nikhil Ranjan, Rakesh Verma, Amit Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Uttarakhand Dehradun India Department of ECE IIIT Maharashtra Nagpur India

ISBN: (纸本)9798331531935

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. this paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. the results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. these findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations. © 2025 IEEE.

关键词： Bayesian Optimization Coverage Ratio Energy Efficiency Node Deployment Wireless Sensor Networks

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Optimal Frame Extraction for Event Detection in Surveillance Videos 16

Optimal Frame Extraction for Event Detection in Surveillance...

引用

16th international conference on ENTERprise Information systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information systems and technologies, HCist 2024

作者： Ranganath, Venkataraman Padmavathi, S. Aarthi, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641112 India

this paper addresses the efficient identification of candidate keyframes in long and untrimmed surveillance videos. Existing fixed-length segmentation methods for violence detection suffer from computational inefficiency and fragmented short-Term event repre-sentation. To overcome these limitations, we propose an Optimal Keyframe Extraction framework utilizing shot boundary detection to segment videos based on significant visual changes. Candidate Keyframe segments are extracted from the shots, focusing on potential anomalous segments. Keyframes are selected based on the highest motion changes, accurately representing short term dynamic events. this dynamic approach aims to minimize the processing time of long and untrimmed surveillance videos by reducing the number of segments required for analysis of the event. Results show that the complexity of analyzing the event ef-fectively can be done by extracting an optimal set of keyframes within 2-3% of total number frames for each video. this research improves violence detection by efficiently analyzing large video datasets and accurately identifying short-Term dynamic events, thereby enhancing public safety and security applications. © 2025 the Author(s).

关键词： Deep learning

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Intelligent Anti-theft System with Automated Spray Control using ESP32 WROOM 14

Intelligent Anti-Theft System with Automated Spray Control u...

引用

14th IEEE international conference on Communication systems and Network technologies, CSNT 2025

作者： Shanmugam, Geetha Balusamy, Deepa Elamparithi, S. Vetriselvan, R. Praamoth, Mourin S. Department of Computer Science and Engineering Nandha Engineering College Erode India

ISBN: (纸本)9798331531935

this project demonstrates a low-cost anti-theft solution built around the ESP32-WROOM microcontroller, with integrated chemical spray for security. the system is designed to detect unauthorized entry and respond with spraying disinfectant chemicals such as pepper spray, thus ensuring security as well as being effective against attackers. the ESP32-WROOM microcontroller is known for its Wi-Fi and Bluetooth capabilities, which plays a key role in providing real-time monitoring and control. Users can also access the system via mobile or web applications. In case of any abnormal situation, the user will be notified immediately. this hospitality provides a living environment for its users. therefore, they can move with peace of mind and even safeguard their valuable things from anywhere. this system also has a modular design that saves energy for easy installation and is suitable for residential, small business and commercial environments. © 2025 IEEE.

关键词： Disinfectants

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Hybrid Fake News Detection Model: Bagging and Logistic Regression Approach 14

Hybrid Fake News Detection Model: Bagging and Logistic Regre...

引用

14th IEEE international conference on Communication systems and Network technologies, CSNT 2025

作者： Kumar, Shivam Tiwari, Naveen Kumar Bajpai, Abhishek Yadav, Shashank Dept. of Computer Science and Engineering Rajkiya Engineering College Kannauj UP Kannauj India

ISBN: (纸本)9798331531935

In Today’s world or from Past Scenario’s Fake News has a Huge Impact on Everyone’s Lives including Politics, Sports, Education, External Foreign Affairs, Defense, etc. this project aims to develop a model for identifying and detecting fake news. In this model, we implement an ensemble method that leverages the TF IDF vectorization technique to extract meaningful textual features and employ a Bagging Classifier with Logistic Regression as the base estimator to enhance classification accuracy. To build this classifier, we utilized the scikit learn library. Our work was implemented using Jupyter Notebook, an online platform. For feature extraction, we employed a well-known vectorizer called Term Frequency Inverse Document Frequency (TF-IDF). We split our data into training sets (75%) and test sets (25%). the proposed model achieves an accuracy of 99.15% on the test dataset, with a precision of 99.45%, a recall of 99%, and an F1-score of 99.17%. It outperforms standalone Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting classifiers. © 2025 IEEE.

关键词： Logistic regression

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Dynamic Forensic Analysis of CryptBot Malware 14

Dynamic Forensic Analysis of CryptBot Malware

引用

14th IEEE international conference on Communication systems and Network technologies, CSNT 2025

作者： Rahman, Rizwan Ur Acharya, Abanteeka Deb, Sanjipan Panchal, Priyanshi Vellore Institute of Technology Bhopal Madhya Pradesh Bhopal India

ISBN: (纸本)9798331531935

CryptBot, an infostealer malware first identified in 2019, poses a serious threat by stealing sensitive data, including login credentials and cryptocurrency wallet details, and transmitting them to command-and-control servers. It spreads through phishing emails, malicious ads, and pirated software, using advanced evasion techniques to remain undetected. this study employs dynamic malware analysis to observe CryptBot’s real-time behavior, including its data exfiltration methods, persistence mechanisms, and evasion strategies. Key findings reveal tactics such as subprocess creation, process renaming, and temporary file usage to facilitate data theft. To mitigate such threats, a multi-layered security approach is essential, combining dynamic, static, and hybrid analysis methods with security measures like intrusion detection, encryption, and multi-factor authentication. Additionally, user education on phishing and software piracy is crucial in preventing infections. this research enhances understanding of CryptBot’s operations and proposes detection and mitigation strategies. Future work includes developing advanced sandbox environments and integrating machine learning to improve threat detection and response. © 2025 IEEE.

关键词： computer viruses

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Exploration of computer Linguistics Model Algorithms Based on Deep Learning 14

Exploration of Computer Linguistics Model Algorithms Based o...

引用

14th IEEE international conference on Communication systems and Network technologies, CSNT 2025

作者： Zhong, Ying School of Foreign Languages and Trade Guangdong Vocational Institute of Public Administration Guangzhou China

ISBN: (纸本)9798331531935

How to build an effective language model in NLP field to capture the statistical laws of language, improve the performance of machine translation, and enable machines to understand and generate texts with complex semantic structures is still a problem that cannot be ignored. Firstly, word segmentation, cleaning, etc., and word embedding using Word2Vec pre-training model are carried out. then the Transformer model is constructed, and the whole sequence is processed simultaneously through the self-attention mechanism. In this paper, the learning rate, the size of hidden layer and the number of layers are adjusted by grid search, and the dropout technology is used to prevent over-fitting. Transformer model performs well in machine translation tasks, with accuracy exceeding 95%, and the METEOR value is generally higher than that of LSTM model, which is stable at a high level. In 20 different text generation tasks, the BERT score of the model is between 0.83 and 0.99, which shows that the model can understand and generate complex semantic structures. these results prove the effectiveness of Transformer model in dealing with natural language tasks, and provide new directions and ideas for future research and application. © 2025 IEEE.

关键词： Deep learning

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Plant Disease Detection using Image Processing and Generative Adversarial Network 14

Plant Disease Detection using Image Processing and Generativ...

引用

14th IEEE international conference on Communication systems and Network technologies, CSNT 2025

作者： Teresha, Y. Shaji, Nikhil John Jarial, Manav Mirjankar, Ved Vishal Patil, Swetha Computer Science Engineering Dept. PES University Bengaluru India

ISBN: (纸本)9798331531935

Agriculture is a key contributor to the GDP of our country, India. Plants play a very important role in the ecosystem and plant diseases can negatively impact the agricultural industry by causing huge losses to the food supply. Plant leaves are prone to a variety of diseases, including blights, leaf spots, and other infections. therefore, identifying diseases efficiently is crucial to pave the way for sustainable agriculture. In the field of agriculture, traditional and manual methods for detecting diseases in plants have sometimes been found to be inefficient and error prone. We therefore present a novel solution to these issues by introducing a method to automate plant disease detection in the Tomato plant leaf by using advanced Convolutional Neural Network (CNN) frameworks, image processing libraries and Generative Adversarial Networks (GANs). the pipeline of our work involves preprocessing plant image datasets, generating synthetic images using GANs, segmenting region of interests via a U-Net model, and classifying diseases with a CNN-based classifier. A deployed model ensures accurate disease identification from user input. © 2025 IEEE.

关键词： Photointerpretation

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Discussion on Recursive Algorithm and Genetic Algorithm Based on computer Algorithm Dynamic System 14th

Discussion on Recursive Algorithm and Genetic Algorithm Base...

引用

14th international conference on Frontier Computing, FC 2024

作者： Zeng, Xin Heilongjiang International University Harbin150025 China

ISBN: (纸本)9789819623907

With the continuous development of computer information technology, its influence in various fields is increasing, especially in algorithmic dynamic systems, special computer algorithms can be used to improve calculation accuracy and efficiency, and improve working methods. With the rapid development of science and technology, especially the rapid development of various network technologies, the shortest path problem is increasingly used in various technologies, such as urban transportation planning, network routing, project planning, artificial intelligence games, etc. Nowadays, many new algorithms are gradually developed, the representatives of these new algorithms are genetic algorithms and recursive algorithms. In order to prove the superiority of genetic algorithms in solving the shortest path problem in dynamic weight systems, this article analyzes recursive algorithms and genetic algorithms based on dynamic systems based on computational algorithms, studies two cases of bus planning model based on multi-objective optimization and genetic algorithm and aircraft height optimization based on recursive algorithm. the research results show that the bus dispatch model based on multi-objective optimization and genetic algorithm can find the optimal bus dispatch plan through the algorithm in only 32 s;the recursive algorithm can optimize the green trajectory of aircraft high-altitude flight, reducing the total cost by 9.08%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Optimized Scheduling Model of Integrated Energy System in Industrial Park Based on Improved MPA 14

Optimized Scheduling Model of Integrated Energy System in In...

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14th IEEE international conference on Communication systems and Network technologies, CSNT 2025

作者： Liang, Shuang Jiang, Qingxuan School of Computer Science Northeast Electric Power University Jilin China

ISBN: (纸本)9798331531935

In the context of the global energy shortage and the increasing emphasis on sustainable development goals, optimizing the scheduling of integrated energy systems in industrial parks has become a critical issue. this paper presents an optimal scheduling model and an improved solution method to enhance energy efficiency in industrial parks. Firstly, we develop an energy efficiency optimization model for the integrated energy system in an industrial park. the model aims to maximize system energy efficiency and minimize operational costs while considering load balance constraints, equipment capacity, operational limits, and renewable energy generation constraints. Secondly, we introduce an enhanced Marine Predators Algorithm (MPA) incorporating an adaptive step-size strategy and a multi-subgroup approach. these improvements enable adaptive adjustment of the iteration step size, accelerating convergence and improving solution accuracy, thus facilitating the efficient solving of the model. then, the effectiveness of the proposed solution method is validated through standard test functions. Finally, we test the model through simulations. the results show that, when considering both energy efficiency and economic factors, our proposed scheduling model effectively improves energy efficiency. It can theoretically reduce energy costs by 3.13% and save 23.683 tons of standard coal annually, all while maintaining the same output level. © 2025 IEEE.

关键词： Scheduling algorithms

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