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

共 89770 条记录，以下是231-240 订阅

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Dynamic Load Scheduling Optimization Utilizing the Rao Algorithm for Improved System Performance 1

Dynamic Load Scheduling Optimization Utilizing the Rao Algor...

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1st IEEE international conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Basak, Sourav Bhattacharyya, Biplab Rajbhar, Suraj Kumar Dhanbad Department of Electrical Engineering Dhanbad India

ISBN: (纸本)9798331542108

This paper explores the optimal scheduling of conventional generating units for two dynamic test systems, and enhances the systems performance. The Rao algorithmic approach, a parameter-free population-based optimization method, is utilized to improve the solutions through cooperative interactions on the basis of the best and worst candidate solutions in each iteration. Practical factors, such as ramp-rate limits, valve point effects and transmission loss, were incorporated to reflect real-world operational challenges. The Rao algorithm achieved high-quality solutions in terms of generation cost and reduced pollutant emission. These results highlight the effectiveness of the algorithm in solving complex nonlinear optimization problems and improving the efficiency of dynamic load scheduling. © 2025 IEEE.

关键词： Scheduling algorithms

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Predicting Stock Prices with K-Nearest Neighbors using Moving Average Techniques 1

Predicting Stock Prices with K-Nearest Neighbors using Movin...

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

作者： Yuvaraj, S. Chenni Kumaran, J. Kajendran, K. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research analyzes the effectiveness of the K-Nearest Neighbors algorithm combined with moving average techniques, Five-day and Ten-day Exponential Moving Averages, specifically EMA-5 and EMA-10, to predict stock prices. The study will focus on the adjusted closing price to deal with the autocorrelation issue that is common in time series data. The performance of the models was evaluated based on four key metrics: mean absolute error, mean squared error, root mean square error, coefficient of determination, overfitting risk test and generalization capability test. It was shown from the results that KNN models integrated with EMA-5 and EMA-10 offered high accuracy, one of which, with an R2 coefficient indicating the capacity to capture stock price trends with an accuracy of up to 99%. The research pointed out how short-term EMAs, combined with machine-learning algorithms, can substantially improve stock price prediction accuracy. It gives useful insights into stock price behaviour, therefore providing a decisive precedent for future works in creating a viable method for selecting trading strategies using machine-learning tools and moving averages. © 2025 IEEE.

关键词： Time series

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Functional task decomposition in wireless sensor network 1st

Functional task decomposition in wireless sensor network

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Mishra, Arpana Tomar, Nandini Choudhary, Shilpa Priyadarshini, Rashmi Lal Azad, Murari Department of Electronics and Communication Engineering Dronacharya Group of Institutions Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering G. L. Bajaj Institute of Technology & Management Uttar Pradesh Greater Noida India Department of Electrical Electronics and Communication Engineering Sharda University Uttar Pradesh Greater Noida India Department of Electrical Electronics Engineering Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032874036

Wireless sensor network is highly focused field of researchers belonging to industries and academics. Several applications of wireless sensor network exist from several past years. In wireless sensor network a complex task subdivision are key methodologies to upgrade the performance and life extension. It is very difficult to select Clusterisation and routing techniques while network overloaded with complex tasks. Decomposition or subdivisions of huge tasks into easy work are the key procedures to optimize the sensor network lifetime. However, such subdivision of tasks are greatly intensive, various field projects, making the decomposition selections are more complicated in WSN than other ordinary systems. We consider the load feasibility and functional structure of network for subdivision of complex task. Task decomposition basis on the systems engineering where need to apply. Superiority and ledgering properties of clustered nodes deciding the scheme for de composition by considering the several attributes like feasibility, probable risk and task integration complexity. Development of military surveillance systems can be used to verify this research contribution routing and computing algorithms. To format your abstract, use the Microsoft Word template style: © 2025 the Author(s).

关键词： Large scale systems

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Optimizing Intrusion Detection systems Using Deep Learning and Genetic algorithms for Network Traffic Analysis: A Survey 8th

Optimizing Intrusion Detection Systems Using Deep Learning a...

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8th international conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Akula, RadhaRani Kumar, G. S. Naveen Malla Reddy University Hyderabad India Department of CSE Malla Reddy Engineering College for Women Hyderabad India Malla Reddy University Hyderabad India

ISBN: (纸本)9789819757855

In the context of network traffic analysis, this review study offers a thorough examination of the integration of deep learning and evolutionary algorithms for intrusion detection system (IDS) optimization. The importance of network security and the shortcomings of traditional intrusion detection systems are examined. The study emphasizes the possible advantages of integrating evolutionary algorithms for feature selection and input layout optimization with deep learning methods like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The goals and research gaps are described, as well as the suggested technique. The anticipated outcomes—better accuracy, lower false alarm rates, better zero-day attack detection, and useful recommendations for network security experts—are given. This survey paper aims to contribute to the field of network security by providing a comprehensive overview of the state-of-the-art approaches and identifying future research directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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A Review of Machine Learning algorithms in Diabetes Management 4

A Review of Machine Learning Algorithms in Diabetes Manageme...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

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Challenges and Opportunities in Deploying Explainable AI for Financial Risk Assessment

Challenges and Opportunities in Deploying Explainable AI for...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Akkalkot, Afsha Imran Kulshrestha, Nitin Sharma, Geeti Singh Sidhu, Kawerinder Palimkar, Sneha S. Nethravathi, K. College of Engineering Pune Technological University Computer Science Engineering Maharashtra Pune India Christ Deemed to Be University Commerce Finance and Accountancy Uttar Pradesh Ghaziabad India Bengaluru India Uttaranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India Computer Engineering and Technology Coep Technological University Maharashtra Pune India University Bangalore India

ISBN: (纸本)9798331508685

Artificial intelligence (AI) has been used more and more in financial decision-making recently, raising questions about the accountability and transparency of these complex systems. The current study investigates the way Explained Artificial Intelligence (XAI) methods might alleviate these concerns and improve the openness of financial decision-making procedures. Nowadays machine learning algorithms are easier to use than ever before, but creating and deploying systems that facilitate real-world banking services has proved challenging. This is mostly due to the fact that algorithms for machine learning are neither transparent or explainable, two attributes that are essential to creating reliable technology. What sets this study unique is the construction of an explainable artificial intelligence (XAI) model that addresses these accessibility concerns while also serving as an instrument for the establishment of credit risk control policies. This work proposes an explainable artificial intelligence model for financing risk control to measure the risks associated with credit financing via peer-to-peer financing networks. The framework uses Shapley parameters to provide AI forecasts according to significant factors that explain. The Support Vector Machine (SVM) and gradient boosting methods had the greatest accuracy scores, 92.4 and 97.6, accordingly. The accuracy of the model was evaluated on a bigger database, and the findings demonstrated that it regularly achieved high levels of accuracy. The SVM and GBM models achieved accuracies of 94.8 and 97.6, respectively. © 2025 IEEE.

关键词： Decentralized finance

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Beluga Whale Optimization for Effective Energy Management in Microgrids with Electric Vehicle Charging in Smart Distribution Networks

Beluga Whale Optimization for Effective Energy Management in...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Pothula, Narendra Sunkara, Nagasrisaihari Vanitha, R. Thiyagesan, M. Aancy, H. Mickle Mallala, Balasubbareddy Vnr Vignana Jyothi Institute of Engineering & Technology Department of Mechanical Engineering Telangana Hyderabad500090 India Aarupadai Veedu Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai603104 India R.M.K Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai601206 India Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telanagana Hyderabad500075 India

ISBN: (纸本)9798331509828

EM in MGs with EV charging refers to the special mainstream of providing electricity and continuously managing and balancing energy supply, load demand and integrating prosumer elements to maintain proper grid stability and efficiency. There are several disadvantages in the incorporation of EVs in to MGs and smart distribution networks. They are the high initial investment costs which accompany infrastructure improvements like updating charging stations and smart grid technologies may hinder implementation. In this paper, we present a new approach of Beluga Whale Optimization (BWO) incorporating electric vehicle charging stations (EVCS) for EM in MGs. The primary task that the given approach addresses is the optimization of system utilization and LCOE reduction. The BWO is applied for the performance enhancement of load scheduling of MGs with incorporated EV charging in smart distribution grids. The structure of BWO approach is also established on MATLAB platform, and their performance is compared with other algorithms including Artificial Neural Network (ANN), Shell Game Optimization (SGO), Multi-Objective Improved arithmetic Optimization Algorithm (MOIAOA) and Young's double Slit-Experiment Optimizer (YDSEO). The presented BWO method obtains LCOE equal to 22,173/kWh to prove its efficiency over existing methodologies for planning and designing energy systems. © 2025 IEEE.

关键词： Charging stations

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Muthukumar, D. Surendiran Chowdary, Bommineni Sreekar Kumar, D. Senthil Jagadeesan, P. Mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qiang Soleymani, Mahdi Mahdavifar, Hessam National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Halıcıoğlu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9798350368741

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements. © 2025 IEEE.

关键词： Belief Propagation Boolean Matrix Compressed Sensing Matrix Completion

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Smart Antennas and Machine Learning: Enhancing Beamforming in Wireless Communications

Smart Antennas and Machine Learning: Enhancing Beamforming i...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sudhakar, A.V.V. Sharma, Upasana Visala, K. Kumar, K. Suresh Arora, Chirag Chouhan, Kuldeep Sr University School of Engineering Department of Eee Telangana Warangal India Abes Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India Vignan's Institute of Information Technology Computer Science Engineering Andhra Pradesh Vishakapatnam India Panimalar Engineering College Mba Department Varadarajapuram Chennai600123 India Kiet Group of Institutions Department of Electronics and Communication Engineering U.P Ghaziabad India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

One of the most effective and widely used technological advancements for increasing capacity, enhancing quality, and expanding coverage in wireless communications systems is the smart antenna. Smart antennas are essential for increasing the effectiveness and dependability of wireless communication systems because they make use of cutting-edge signal processing techniques. Smart antennas' beamforming capability, which concentrates signal transmission and reception in particular directions, increases signal strength, lowers interference, and expands network capacity. As wireless networks become more complicated, machine learning (ML) has become a potent tool for beamforming optimisation. ML algorithms can proactively adjust antenna characteristics by using real-time data, learning from fluctuations in the environment to optimise performance. In order to improve beamforming capabilities, this research investigates the incorporation of ML with smart antenna systems. We go over several ML techniques, such as supervised and reinforcement learning, for resource allocation, user localisation, and signal-to-noise ratio (SNR) optimisation. Additionally, we draw attention to how deep learning might enhance spatial filtering, facilitating more effective communication in 5G and beyond. The potential direction for next-generation wireless communication networks is presented by the convergence of ML and smart antennas. © 2025 IEEE.

关键词： Smart antennas

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