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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89784 条记录，以下是171-180 订阅

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DAFLB: Dynamic Adaptive Fuzzy Based Load Balancing with Genetic Optimization for Enhanced Cloud Performance 17

DAFLB: Dynamic Adaptive Fuzzy Based Load Balancing with Gene...

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Khatri, Madhav Ahmed, Faisal Ahmed, Mushtaq Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur India Microland Limited Bengaluru India Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9798331531195

Cloud computing requires efficient usage of resource allocation and optimal response times in load balancing. This paper introduces an extended version of our previous work, presenting the Dynamic Adaptive Fuzzy based Load Balancing model, which now considers additional attributes such as network latency and security. The proposed model utilizes memory, bandwidth, disk space, network latency, and security as fuzzy variables and incorporates a variety of membership functions. To enhance the model flexibility and efficiency a nature inspired genetic algorithm is incorporated. The load balancing algorithm employs a random numbers allocation scheme and introduces a set of new fuzzy rules for assigning tasks to virtual devices. In experimental evaluations, the Dynamic Adaptive Fuzzy-based load-balancing model is compared against existing load balancing algorithms, demonstrating an approximately improvement of 21.06% over SJF and 25.66% over FCFS and 21.78% over Max Min. These enhancements signify the adaptability and effectiveness of the Dynamic Adaptive Fuzzy-based Load Balancing model in handling diverse attributes and improving overall performance in cloud environments. © 2025 IEEE.

关键词： Genetic algorithms

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Image Contrast Enhancement using Fuzzy Logic with Metaheuristics 24

Image Contrast Enhancement using Fuzzy Logic with Metaheuris...

引用

11th international conference on Networking, systems and Security, NSysS 2024

作者： Kabir, Mohimenul Mobin, Jaiaid Hassanat, Ahmad Sohel Rahman, M. Department of Computer Science Bangladesh University of Engineering and Technology Dhaka Bangladesh Rochester Institute of Technology Rochester United States Faculty of Information Technology Mutah University Karak Mutah Jordan Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400711589

In this work, we present a method to enhance image contrast using a fuzzy logic-based transformation function. Our goal is to develop an image-specific transformation by tuning the fuzzy system. We employ Hill Climbing and Genetic algorithms in various configurations to evolve the fuzzy system, conducting multiple experiments. Different variants of our method are tested on several images, and the two variants with the highest fitness scores are selected. Additionally, we compare against existing contrast enhancement technique and conduct a survey to evaluate the visual improvements made by these two variants. The survey results indicate that one method effectively enhances image contrast visually. © 2024 Copyright held by the owner/author(s).

关键词： Computer circuits

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Road Lane Detection using Region-based Convolutional Neural Network (RCNN) 1

Road Lane Detection using Region-based Convolutional Neural ...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Thondralnayagi, A. Nandhakumar, T. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Road lane markings are one of the main factors that cause traffic accidents and jeopardize the safety of both drivers and pedestrians. Both computer vision systems find it challenging to recognize road lanes. The irregular maintenance, insufficient drainage, and traffic patterns. It negatively affects driving comfort, road safety, and vehicle condition, and it could also lead to a lot of accidents. Recently, several machine learning algorithms have been used, although their accuracy and efficiency have not improved significantly. We suggest applying Region-Based Convolutional Neural Networks (RCNN) for path identification to tackle this issue. RCNNs are another kind of neural network that is capable of extracting significant information from picture and time series data. This makes it highly helpful for tasks requiring images, such as shape identification, object classification, and recognition. Image from a dataset with noise removed using a Wiener filter. The next step is to extract features from images using Linear Discriminant Analysis (LDA). Determine which lane line in the RCNN is efficient and provides accuracy, precision, recall, and F1-score as 95.6%, 94.05%, 94.58%, and 94.23%, respectively. © 2025 IEEE.

关键词： Convolutional neural networks

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A survey on feature selection algorithms for machine learning

A survey on feature selection algorithms for machine learnin...

引用

international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Almesleh, Ziad Chkirbene, Zina Hamila, Ridha College of Engineering Qatar University Qatar

ISBN: (纸本)9781032803722

Wireless communication is one of the most substantial types of communication nowadays, leading to the IoT revolution and increasing demand for IoT networks. On the other hand, this demand also leads to an increase in the number of resource constrained devices, which creates a new problem related to vulnerabilities to attacks. The handling of attacks on IoT devices must be closely monitored due to resource limitations for these devices, which makes research in this field important. Many intrusion detection systems have been improved by using machine learning models that classify data as normal or abnormal and use evaluation parameters such as accuracy, f1-score, and precision to assess their performance. One of the most important steps in building a good intrusion detection system is feature selection, which can help improve the reasonable classification of data. This paper discusses the latest research on using machine learning techniques for intrusion detection systems. It examines various prominent algorithms and provides further analysis on their appropriateness. CICIDS2017 dataset is used to evaluate the robustness and determinism of intrusion detection system models. © 2025 The Author(s).

关键词： Network intrusion

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Comprehensive Analysis of Attention Deficit/Hyperactivity Disorder using Machine Learning and Deep Learning algorithms 1

Comprehensive Analysis of Attention Deficit/Hyperactivity Di...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Sindhu, T.N. Sujatha, S.R. Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798331529246

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects children from childhood to adulthood. The symptoms of ADHD include attention deficit, impulsive behaviour, hyperactivity, and early diagnosis and treatment are imperative, especially for children as it continues into adulthood. ADHD detection is generally carried out using the existing methods such as Machine Learning (ML) and Deep Learning (DL) methods. The ML methods such as Linear Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminative Analysis (LDA), Linear Regression (LR), Random Forest (RF), and Least Square SVM (LS-SVM) are used for the detection and analysis of ADHD. The existing DL methods such as Deep Convolutional Neural Network (DCNN), Graph Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Bidirectional LSTM are also employed for the detection of ADHD. The ML and DL algorithms enhance ADHD detection and diagnosis by automatically determining large patterns in data, thereby offering higher accuracy. The performance of the existing techniques is evaluated in terms of evaluation metrics: accuracy, precision, sensitivity, and F1-score to assess their effectiveness. © 2025 IEEE.

关键词： Adversarial machine learning

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Spam Detection in YouTube Comments: A Machine Learning Approach 1

Spam Detection in YouTube Comments: A Machine Learning Appro...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Yadav, Shristy Jena, Junali Jasmine Prakash Singh, Jay Gourisaria, Mahendra Kumar Jain, Sonal Kumar, Vivek Kiit Deemed University School of Computer Engineering Bhubaneswar India Manipal University Jaipur Department of Computer Science & Engineering Jaipur India Kiit Deemed to be University School of Economics and Commerce Bhubaneshwar India Manipal University Jaipur Dept of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798331529246

Due to the broad spectrum of content and evolving tactics of spammers, identifying spam in YouTube comments has become a challenging task. If it goes undetected it may hamper the security and integrity of the application. Automated spam detection techniques have found profound application in this field. This study offers an extensive approach for enhancing spam classification through the integration of cutting-edge machine learning algorithms. A multi-pronged paradigm is investigated, including SMOTE to address class imbalance, and TF-IDF vectorization for feature extraction and used for spam classification. A wide range of models is scrutinized in the study such as Gradient Boosting, AdaBoost, XGBoost, etc. The best-performing models, based on cross-validation scores, were the Support Vector Classifier (SVC) with an average accuracy of 96.42%, followed by Gradient Boosting, Voting Classifier and LightGBM, achieving an average accuracy of 94.63%, followed by Logistic Regression and Random Forest achieving an accuracy of 94.37%. © 2025 IEEE.

关键词： Decision trees

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Laboratory Management and Computer Network Security Maintenance Strategies in the Era of Artificial Intelligence 24

Laboratory Management and Computer Network Security Maintena...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Lai, Zhenhuan Wu, Qixing School of Mechanics and Construction Engineering Jinan University Guangdong Guangzhou China

ISBN: (纸本)9798400709999

In the era of artificial intelligence, the integration of AI technology into network security has transformed how threats are detected and *** paper explores the application of AI in maintaining network security, with a specific focus on laboratory management *** enhances real-time monitoring, automates intrusion detection, and facilitates dynamic defense strategies in both general networks and laboratory *** management systems, which handle sensitive data and complex equipment networks, particularly benefit from AI-driven security *** study outlines how AI can improve the efficiency of security systems while minimizing human *** also discusses the challenges related to resource consumption, false positives, and privacy concerns in both network security and laboratory *** directions for AI development in network security are proposed, with an emphasis on optimizing algorithms and balancing security needs with resource management. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Resource allocation

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Research on Point Cloud Registration Method Based on Improved Harmony Search Algorithm 24

Research on Point Cloud Registration Method Based on Improve...

引用

8th international conference on algorithms, Computing and systems, ICACS 2024

作者： Liang, Yuchong Mao, Yunsheng Guo, Rong School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9798400718304

When measuring and inspecting hull surfaces using a 3D laser scanner, the large volume and complex surface features of the hull necessitate multi-station scanning. Complete hull surface data is obtained by registering point cloud data from multiple stations. However, the registration process accumulates errors, resulting in the registration not meeting inspection accuracy requirements. To address the issue of cumulative errors in point cloud registration for large-scale laser scanning, a multi-station point cloud data registration error correction algorithm based on global control targets is proposed. This algorithm leverages the long-distance measurement and high accuracy of total stations to compensate for the large measurement errors of the scanner at long distances. By applying the coordinate information of global control targets measured by the total station to the point cloud data registration, cumulative registration errors are effectively avoided, thereby improving measurement accuracy. Experimental results show that the proposed algorithm reduces the cumulative error of the first and last point clouds to 16% of that of traditional registration, effectively eliminating registration errors and meeting inspection requirements. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Laser applications

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Personalized Recommendations System for Mental Health Support using NLP 3

Personalized Recommendations System for Mental Health Suppor...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Akash, G. Gopinath, S. Mohamed Zachariah, S.M. Mithuna, S. K S Rangasamy College of Technology Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331509675

The prevalence of mental health conditions is increasing so much that detecting them early will make interventions more effective. I introduce a recommendation system which benefits from Natural Language Processing combined with machine learning approaches to analyze users' mental health conditions through their written and spoken content. Users can transmit writing or speech data directly to the system for processing. Among all available algorithms Machine learning algorithms including Logistic Regression, Random Forest, Decision Tree and Multinomial Naive Bayes exhibit the best performance. The system performs user classification based on their diagnosed mental disorder which enables it to present appropriate mental health video content specifically designed to support their needs. Their goal was to create a system which provides real-time feedback while raising accessibility to mental health care and starting interventions early. Integration of advanced technology into personal mental health experiences enters a critical phase because of these developments. © 2025 IEEE.

关键词： Electronic health record

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Data-Driven Decision Making in Smart Health and Emergency Management

Data-Driven Decision Making in Smart Health and Emergency Ma...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sudharson, K. Selvi, K. Ramu, V. Monika, V. Sureshkumar, A. Nagarajan, S. R.M.D. Engineering College Department of Aiml Chennai India S.A. Engineering College Department of Mba Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mba Chennai India Vel Tech Ranga Sanku Arts College Department of Mba Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mba India

ISBN: (纸本)9798331529833

Phenomena such as smart health and emergency management systems when embedded with data-driven decision-making have become a game-changer in the field of healthcare and an emergency response. In this paper, we investigate the use of advanced data analytics, machine learning algorithms and real-time data processing in improving decision-making activities in these domains. Using data from a diverse set of large datasets such as EHRs, wearables, emergency response systems etc., we hope to build predictive models and decision support systems which can help save lives by predicting the epidemic before it peaks saving valuable time when every minute counts. The research also deals with issues such as data privacy, interoperability and real-time data integration. Early findings suggest large efficiencies and effectiveness gains in health and emergency management, showing the game-changing potential of datadriven solutions in their domains. © 2025 IEEE.

关键词： Electronic health record

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