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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231-240 订阅

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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...

引用

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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Adversarial Encoder-Driven Filter for Targeted Image Tagging: A Novel Approach to Visual Content Manipulation 6

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

引用

6th IEEE international conference on Image Processing, Applications and systems, IPAS 2025

作者： Mckee, Cole Flowers, Dominic Wood, Jesse Shafer, Ethan United States Military Academy Department of Electrical Engineering and Computer Science West PointNY10996 United States

ISBN: (纸本)9798331506520

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon's Rekognition image tagging algorithm using a black box approach. © 2025 IEEE.

关键词： Adversarial machine learning

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A Review of Various Optimization Techniques for the Selection of Insulating Fluids in Power and Distribution Transformer Applications

A Review of Various Optimization Techniques for the Selectio...

引用

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

作者： Sarathkumar, D. Raj, Raymon Antony Geethalakshmi, M. Jayakumar, T. Manivel, Murugesan Ponnusamy, Sabarish Kongu Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638 060 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu 621215 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Department of Electrical and Electronics Engineering Tamil Nadu Samayapuram621112 India

ISBN: (纸本)9798331529833

The selection of insulating fluids is critical for the reliable and efficient operation of power and distribution transformers. This paper provides a comprehensive review of various optimization techniques employed to select the most suitable insulating fluids for these applications. The focus is on evaluating the performance, environmental impact, and economic feasibility of different fluids, including mineral oils, synthetic esters, and natural esters. We analyze optimization methods such as genetic algorithms, particle swarm optimization, and multi-criteria decision-making approaches, highlighting their advantages and limitations. Additionally, this review addresses recent advancements and future directions in the field, aiming to guide researchers and engineers in making informed decisions for transformer insulation. By synthesizing existing studies, this paper contributes to the development of more efficient, sustainable, and cost-effective transformer systems. © 2025 IEEE.

关键词： Distribution transformers

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Challenges and Opportunities in Deploying Explainable AI for Financial Risk Assessment

Challenges and Opportunities in Deploying Explainable AI for...

引用

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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Sentiment Analysis and Forecasting for Improved Business Performance in E-Commerce using Machine Learning algorithms 3

Sentiment Analysis and Forecasting for Improved Business Per...

引用

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

作者： Tiwari, Naveen Kumar Arora, Himanshu Arya College of Engineering Department of Cse Rajasthan Jaipur India

ISBN: (纸本)9798331509675

The study explores how sentiment analysis and forecasting can improve the business performance, especially for e-commerce platforms like Amazon to analyze customer feedback for enhancing future work. This study focuses on integrating machine learning models, including SVM, Random Forest and Naive Bayes to categorize product reviews into positive, negative or neutral sentiment analysis, achieving accuracy rates of 0.85, 0.87 and 0.85 based on more than 11,000 datasets. The process of analysis includes Data Acquisition, Data Pre-processing, Feature Extraction, Model Training and using a GUI that allows users to write their reviews for sentiment prediction. Additionally, the study also involves ARIMA model to train the model for likes forecasting analysis on historical data, that helps many businesses sectores with valuable and meaningful insights to optimize the engagement of the customer and their decision-making. © 2025 IEEE.

关键词： Adversarial machine learning

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An IoT-Enabled Intelligent Traffic Management System with CNN-Based Accident Detection and Dynamic Rerouting Using K-Means and Dijkstra's Algorithm

An IoT-Enabled Intelligent Traffic Management System with CN...

引用

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

作者： Sneha, S. Sriranjini, S. Thupakula, Himasai Balaji, M. Sri Sivasubramaniya Nadar College of Engineering Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331509828

Traffic congestion and accidents are significant challenges in modern urban environments, leading to economic losses, delays, and compromised public safety. This paper proposes an IoT and Machine Learning-based Traffic Congestion Management and Accident Detection System to address these issues efficiently. The system integrates IoT-enabled sensors for real-time traffic density monitoring and Convolutional Neural Networks (CNN) for accident detection from live camera feeds. An edge-computing approach is employed to process data locally, ensuring minimal latency and faster decision-making. Dynamic traffic rerouting is implemented using shortest path algorithms, such as Dijkstra's Algorithm, to optimize vehicle movement in response to congestion and accidents. Alerts and notifications are transmitted to relevant authorities via a cloud-based platform, enhancing emergency response time. The proposed solution is validated using real-world and simulated datasets, demonstrating improvements in accuracy, response speed, and system reliability. Future enhancements include integrating autonomous vehicle data for predictive traffic analysis. © 2025 IEEE.

关键词： Decision making

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Dual-band Image Sequence Registration based on Description of Velocity and Curvature Distribution 16

Dual-band Image Sequence Registration based on Description o...

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Zhao, Bingqing Tang, Shanjun Li, Chuchen Yang, Beier Yang, Qingyu Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

The composite seeker based on multi-band imaging detection is the development focus of future precision-guided weapons. A certain seeker adopts a recognition and tracking strategy based on the fusion of long-wave infrared and visible light images. The quality of the fused image is closely related to the image registration accuracy. In order to solve the problem that the feature description of infrared and visible dual-band images is not identical due to large difference in grayscale texture information, we propose a registration algorithm based on description of velocity and curvature distribution. In particular, we first extract the infrared and visible light target foreground, and use the created velocity distribution description to complete the FAST corner point matching on the foreground contour. All the matched point pairs are used to achieve coarse contour registration. Then, we use the created curvature distribution description to complete the CSS corner point matching of the target foreground. Finally, a real-time updated matching reservoir is built to calculate the fine registration matrix. The experimental results of multiple sets of actual infrared/visible light image sequences show that the proposed algorithm can achieve better feature description identity and better registration accuracy than the other algorithms. © 2025 SPIE.

关键词： Image fusion

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Prediction of Heart Disease Diagnosis using Machine Learning algorithms 3

Prediction of Heart Disease Diagnosis using Machine Learning...

引用

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

作者： Balvir, Parul Katore, Khushi Gourshettiwar, Palash Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331509675

The prevalence of heart disease is rising globally, with the World Health Organization reporting that cardiovascular diseases account for approximately 17.9 million deaths annually. Early diagnosis and treatment are critical to mitigating risks, as studies indicate that timely intervention can reduce mortality rates by up to 30%. This study presents a predictive numerical model leveraging machine learning classifiers - Random Forest, K-Nearest Neighbours (KNN), and Logistic Regression - to enhance diagnostic accuracy. The dataset utilized is sourced from the University of California, Irvine (UCI), comprising crucial patient characteristics. A comparative analysis of classifiers is performed, evaluating their efficacy in heart disease prediction. The study provides insights into feature selection, classification performance, and the impact of different modeling approaches, contributing to improved diagnostic frameworks in medical applications. Additionally, a detailed comparison of existing methods and their limitations has been incorporated. © 2025 IEEE.

关键词： Adversarial machine learning

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AI-Enabled Epileptic Seizure Detection and Prediction System using Random Forest and LightGBM in Internet of Healthcare Things (IoHT) 1

AI-Enabled Epileptic Seizure Detection and Prediction System...

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

作者： Bharathy, P. Arunkumar, M. Libina, M. Ifet College of Engineering Department of Information Technology Villupuram India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Epileptic seizures are a critical health issue affecting millions globally. With the rise of the Internet of Healthcare Things (IoHT), there is an opportunity to enhance seizure detection and prevention using advanced machine learning techniques. This project proposes an AI-enabled Epileptic Seizure Detection and Prevention System that leverages IoHT to monitor patients in real-time, collecting vital physiological data. Random Forest and LightGBM algorithms are utilized to classify seizure events and predict potential occurrences, enabling timely medical intervention. Experimental results demonstrate the system's high accuracy, achieving 97.3% detection accuracy and a recall rate of 96.7%, significantly outperforming traditional methods. The system also reduces false alarms while offering real-time alerts, ensuring immediate medical responses. This AI-driven approach enhances patient safety and fosters a proactive epilepsy management strategy. © 2025 IEEE.

关键词： Electronic health record

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ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction 4

ML for Sustainable Solutions: Applications in Renewable Ener...

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

作者： Awachat, Arya Dube, Ananya Chaudhri, Shivnath Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

This paper delves into the transformative potential of ML in addressing some of the world's most pressing challenges in optimizing renewable energy and mitigating climate change. How ML algorithms work to improve efficiency within solar and wind energy systems, smart grids, and energy storage technologies is revealed by large data sets and predictive analytics. As ML improves the forecasting of energy, minimizes waste, and allows for better grid integration, its contribution to cutting carbon emissions significantly is immense. In addition, it facilitates climate adaptation by accurately predicting extreme weather occurrences and climate variability. Challenges facing the approach include data paucity, computational cost, and integration with older systems. Interdisciplinary collaboration and newer ML models tackle these challenges well. The study recognizes the significant role of machine learning in creating a sustainable environment and notes its ability to actually reconcile environmental, economic, and social objectives toward a robust and greener future. © 2025 IEEE.

关键词： Clean energy

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