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检索条件"任意字段=International Conference on Advances in Computer Science and Engineering"

共 542647 条记录，以下是221-230 订阅

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Adaptive Electric Vehicle Charge Controllers Using Deep Reinforcement Learning for Real-Time Optimization 1

Adaptive Electric Vehicle Charge Controllers Using Deep Rein...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kavitha, N. Bdair, Mohammad Chandrasekar, P. Veerakumar, S. Kumar, M. Hema Kumar, Vinay Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of EEE Tamil Nadu Chennai India School of Architecture Computing & Engineering University of East London London United Kingdom Bannari Amman Institute of Technology Department of EEE Tamil Nadu Erode India Sona College of Technology Department of ECE Tamil Nadu Salem India B N M Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331518578

The increasing adoption of electric vehicles necessitates intelligent charging systems to address challenges such as grid overload, renewable energy integration, and user prioritization. This paper presents a new framework in optimizing the efficiency of electric car charging: Hierarchical Multi-Agent Deep Reinforcement Learning. In this architecture, a global coordinator relies on Proximal Policy Optimization to coordinate across stations, and the local agents follow Deep Q-Networks to manage their resources at the station level. Test results: A comparison of the test results with regular methods shows an average waiting time reduced by 30%, a 15% increase in the usage of renewable energy, and a reduction of 22% in the system latency. For grid loads, consistent results from the operation of this architecture ensure that minimal unserved energy demand leads to the balanced serving of loads. This method, therefore, aligns well with sustainable energy goals through simultaneous improvement in effectiveness of charging infrastructure and on the flexible scale of solution. © 2025 IEEE.

关键词： Deep reinforcement learning

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Time Series Forecasting for COVID-19 Confirmed Cases Using Transformer Based Stacked LSTM Model 8th

Time Series Forecasting for COVID-19 Confirmed Cases Using T...

引用

8th IFIP TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Penumadi, Sri Naga Jathin Nambiar, Vaishakh Vignesh, G.S. Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Perak Malaysia

ISBN: (纸本)9783031736162

The worldwide impact of COVID-19 necessitates accurate forecasting for informed decision-making by governments and health entities. This study proposes neural network architectures for time series forecasting of confirmed COVID-19 cases based on historical data. The models, including variants of LSTM networks, CNN-GRU hybrids, RBMs, DBNs, and self-attention mechanisms, are trained on comprehensive datasets of confirmed cases, deaths, recoveries, and other variables across various countries. Comparative analysis indicates that CNN-GRU and LSTM models exhibit superior performance in forecasting accuracy, surpassing RBM, DBN, and self-attention models across metrics like Mean Absolute Error and Root Mean Squared Error. This research emphasizes the viability of employing advanced neural networks for precise COVID-19 spread predictions, pivotal in shaping public health policies during global crises. © IFIP international Federation for Information Processing 2025.

关键词： Time series

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Evaluation of Transfer Learning for Visual Multiple Target Tracking 1

Evaluation of Transfer Learning for Visual Multiple Target T...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Jadhav, Anisha Anil California State University Department of Computer Science Monterey Bay Seaside United States

ISBN: (纸本)9798331518578

Deep learning has revolutionized high-level image processing tasks, notably image classification and segmentation, by effectively handling multi-dimensional features in image space. This report investigates the application of transfer learning, utilizing features extracted from a pre-trained VGG deep neural network to visual multiple-target tracking (MTT). Traditional feature detectors like the Lucas-Kanade Tracker and Good Features to Track (GFTT) algorithm are computationally efficient but limited in dynamic, cluttered environments, such as those encountered by unmanned aerial systems (UAS). Replacing GFTT with deep feature detectors from VGG is proposed to assess tracking accuracy and improve computational efficiency. Our experiments reveal that, though promising, deep feature extraction results in lower frame rates and less smooth trajectories than traditional methods. This study highlights the potential and current limitations of integrating deep learning features into real-time visual tracking systems. Plans include optimizing the deep learning model for real-time processing, automating filter selection for feature extraction, and exploring additional pre-trained networks to improve tracking performance and efficiency. © 2025 IEEE.

关键词： Image segmentation

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Smart Manufacturing: Integrating IoT and AI for Agile Production Management 1

Smart Manufacturing: Integrating IoT and AI for Agile Produc...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Annavarapu, Babu Jagadeesh Bastray, Tejaswini Rusho, Maher Ali Long, Choi Sang Channi, Arvinder Singh Kumar, Vishwa Vijay Hyderabad India St Joseph's University Department of Management India University of Colorado Lockheed Martin Engineering Management BoulderCO United States Raffles University Malaysia Guru Kashi University Talwandi Sabo Punjab Bathinda India Stuart School of Business Illinois Institute of Technology ChicagoIL United States

ISBN: (纸本)9798331518578

Production management is being revolutionised by smart manufacturing, which is fuelled by the combination of artificial intelligence (AI) and the Internet of Things (IoT) and allows for quick, data-driven decision-making. Through this interface, producers may use cutting-edge machine learning models and real-time sensor data for process optimisation, quality assurance, and predictive maintenance. Model performance is evaluated using evaluation metrics like accuracy, recall, ROC curves, and AUC in conjunction with machine learning approaches including classification, regression, and time series forecasting. AI-driven systems can enhance production scheduling, anticipate equipment problems, and optimise resource allocation by utilising IoT data. This makes it possible to create agile manufacturing environments that can minimise downtime and react swiftly to changes in demand. The robustness and dependability of these AI models are further improved by the addition of machine learning assessment techniques including learning curves, feature importance, and cross-validation. In the end, this IoT and AI synergy enables manufacturers to attain increased productivity, cost reductions, and adaptability in dynamic production environments. © 2025 IEEE.

关键词： Smart manufacturing

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AI-Powered Performance Management: Machine Learning Approaches for Employee Evaluation and Development 1

AI-Powered Performance Management: Machine Learning Approach...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Krishna, C. Balarama Reddy, K.Nagi Kumar.R, Sathish Sidhu, Kawerinder Singh School of CS & AI SR University Department of Mathematics Telangana Warangal506371 India Lords Institute of Engineering and Technology Department of Information Technology Hyderabad500091 India Karnataka Bengaluru562112 India Uttaranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India

ISBN: (纸本)9798331518578

This study explores how AI, primarily, the axiomatic realms of machine learning algorithms, is revolutionizing performance management systems. In most traditional systems, there are issues of bias, and poor performance and even the methodologies used are dated. The above-mentioned challenges are resolved in AI-powered solutions by improving the inherent objectivity of thinking. The real-time insights provided;the capacity to provide predictions. Supervised, as well as unsupervised learning enhances the method of employee assessment and reinforcement learning helps in enhancing self-development plans. This analysis shows that Random Forest and XGBoost models yield the best performance and have higher prediction accuracy. Although data may be prejudiced and there are a lot of ethical issues, AI provides a great opportunity to optimize the employee's job performance and their satisfaction with results for the organization's management. © 2025 IEEE.

关键词： Self-supervised learning

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Development of Web Platform for Home Screening of Neurological Disorders Using Artificial Intelligence 1

Development of Web Platform for Home Screening of Neurologic...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Bama, S. Maroju, Praveen Kumar Banala, Subash Kumar Sehrawat, Sunil Kommineni, Mohanarajesh Kodi, Divya Velammal Engineering College Department of CSE Surapet Chennai India Clientserver Technology Solutions India Financial Services & Cloud Technologies India Bausch Health Companies Canada TEKsystems Global Services LLC Truist Financial

ISBN: (纸本)9798331518578

Millions of people worldwide suffer with Parkinson's disease (PD), one of the most prevalent neurological illnesses. The diagnosis of PD has changed significantly over the last 25 years;at first, autopsy and neurological exams were the only methods accessible. Impaired handwriting has been shown to be a significant early indicator for this disease's identification. The purpose of this study was to use machine learning techniques to provide a quick and easy method of differentiating PD patients from controls using handwriting activities. It developed a telemonitoring technology based on user-friendly drawing tablet software that gave the subject visual feedback on the screen and enabled us to gather data in real time about the location, pressure, and tilt of the digital pen throughout the procedure. This paper created a procedure that included writing and drawing exercises, including ones in Italian, and we gathered information from ten PD patients and twenty healthy participants. With a 93.4% accuracy rate, the current research developed a machine learning (ML) framework that automatically separates PD patients from healthy control people using signals and data from a previous database. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Intelligent Hybrid System with Bidirectional Energy Flow in PEMFC Powered BLDCM Drive and Batteries 1

Intelligent Hybrid System with Bidirectional Energy Flow in ...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Anand, Garima Ranjan Yadav, K.B. National Institute of Technology Department of Electrical Engineering Jamshedpur India

ISBN: (纸本)9798331518578

The promise of increased efficiency and dependability has drawn much attention to hybrid energy systems combining several energy sources. This study suggests integrating a fuel cell, a battery system through a bidirectional converter (BDC), and a brushless DC (BLDC) motor for electric vehicle applications. The battery system functions as a buffer, storing extra energy and supplying power during periods of high demand or sporadic circumstances, while the fuel cell (FC) serves as the main power source, generating energy continuously. A BDC effectively manages power flow between the fuel cell, battery, and load, allowing a smooth transition between charging and discharging modes. A BLDC motor is also used for mechanical work or propulsion since it is extremely efficient, requires little maintenance, and has accurate speed control. By combining these elements, a flexible hybrid energy system can be created that maximizes energy efficiency, improves system resilience, and has a smaller overall environmental effect. This work highlights the potential of the suggested system for multiple applications in both fixed and mobile environments by discussing its design considerations, control methodologies, and performance analysis. The integrated FC, battery, BDC, and BLDC motor systems are both effective and feasible through simulation results;this has opened the door for creating sustainable and efficient energy solutions for the modern world. The circuit is simulated on MATLAB Simulink software and the results are discussed. © 2025 IEEE.

关键词： MATLAB

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Filtering Methods, Assessment Criteria, and Prospects for Image Denoising

Filtering Methods, Assessment Criteria, and Prospects for Im...

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Wandile, Piyush S Singh Kirar, Bhupendra Kirar, Seema Indian Institute of Information Technology Computer Science and Engineering Bhopal India Indian Institute of Information Technology Electronics and Communications Engineering Bhopal India Bansal Institute of Science and Technology Electronics and Communications Engineering Bhopal India

ISBN: (纸本)9798331529833

Image denoising is a crucial step in image acquisition and processing that helps improve the image quality by removing the unwanted noise. In this paper Gaussian and median filters are used as denoiser and performance is evaluated using some of the key metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index Measure (FSIM), Structural Similarity Index Measure (SSIM), Mean Absolute Error (MAE), and Correlation Coefficient. A denoiser with low MSE, high PSNR, values of 1 for SSIM, FSIM, and Correlation Coefficient. Reported results are better in comparison to existing for better image quality. © 2025 IEEE.

关键词： Wiener filtering

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Performance Enhancement of 6T SRAM : A Comparative Study of Conventional and Stacking Techniques in 45nm Technology using cadence virtuoso

Performance Enhancement of 6T SRAM : A Comparative Study of ...

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sharma, Vaibhav Subbarao, Akkala Maulana Azad National Institute of Technology Department of Electronics and Communication Engineering Bhopal India

ISBN: (纸本)9798331529833

This paper presents an in-depth examination of 6T SRAM (Static Random Access Memory) architectures within the 45nm technology node, with a particular emphasis on optimizing power consumption and delay characteristics. Both traditional 6T SRAM and stacked 6T SRAM structures are evaluated through comprehensive simulations performed in Cadence Virtuoso, enabling a detailed comparison of average power and delay across varied operational parameters, like supply voltage (VDD). The results indicate that the stacked 6T SRAM configuration achieves notable gains in power efficiency and operational speed, especially at reduced VDD levels, underscoring its suitability for future low- power applications. This study not only advances the understanding of key trade-offs in SRAM design but also offers a valuable framework for further refinement of SRAM technologies. [1] © 2025 IEEE.

关键词： Static random access storage

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High-Frequency Stock Market Price Prediction Using Blockchain and Deep Learning 1

High-Frequency Stock Market Price Prediction Using Blockchai...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Govindasamy, P. Radhakrishnan, G.V. Shankar, Uma Adhiparasakthi College of Engineering Department of Management Studies Tamilnadu Vellore India Kalinga School of Management Bhubaneswar India Ramcharan School of Leadership MIT world Peace University Pune India

ISBN: (纸本)9798331518578

Researchers and financiers utilize statistics on stock market operation, including past returns, share values, accessible, large, poor, near, and transaction size, to identify trends in stock activity. Technological evaluation can provide financiers with contradictory indications. They focused on predicting the return and risk levels of stock market prices. As a result, the stock market requires an automatic technology that can anticipate the return rate of bitcoin. Deep learning (DL) technology can estimate share market trends employing technological evaluation. This research creates a deep learning-based smart return rate estimation method for blockchain-based stock market prices (RRE-DLBSP). The proposed RRE-DLBSP framework develops an LSTM (long short-term memory) paradigm for RRE assessment. Additionally, the LSTM algorithm's hyperparameters are effectively adjusted using the Adam optimization algorithm, improving prediction efficiency. The Ethereum return rate is selected as the goal and the model outcomes are examined in various ways to ensure the highest functioning of the RRE-DLBSP paradigm. The research results demonstrated the RRE-DLBSP method's superiority over the cutting-edge approaches based on a variety of performance metrics. The suggested RRE-DLBSP has an MSE of 0.0436 and 0.0656, while the mean for the evaluated techniques in training and evaluation is 0.725 and 0.6124, respectively. © 2025 IEEE.

关键词： Marketplaces

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