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Smart Grid Management with AI Driven Analytics in Solar Energy 2

Smart Grid Management with AI Driven Analytics in Solar Ener...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Timande, Devanshi Agade, Nishant Gudadhe, Amit Faculty of Engineering & Technology Department of Artificial Intelligence and Data Science Wardha India Faculty of Engineering & Technology Wardha India

ISBN: (纸本)9798331505745

This paper presents an AI-driven framework for real-time smart grid optimization tailored specifically for solar energy integration. The proposed system features a multi-layered architecture that integrates data acquisition from solar irradiance sensors, smart meters, and meteorological sources with advanced pre-processing techniques to ensure data quality and fusion. Using advanced deep learning architectures, such as Long Short-Term Memory networks for forecasting solar irradiance and regression methodologies for predicting load, our proposed framework dynamically adjusts energy dispatch and demand response measures to address the inherent variability associated with solar power. Additionally, a combined strategy that incorporates reinforcement learning is implemented to improve real-time grid stability and operational efficiency, and explainable artificial intelligence methods are used to provide clarity in decision-making processes. Simulation studies conducted on an enhanced ieee 24-bus test system have shown to reduce forecast error by up to 30% and prevent the occurrence of grid instability events by about 40% in contrast with traditional methods. These results are promising for AI-based analytics since it promises to enhance smart grid functionality and enable more sustainable use of renewable resources. Further research will develop this model with additional renewable energy sources and increase cybersecurity. © 2025 ieee.

关键词： Reinforcement learning

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Decentralization of Cloud Environment Using DES Technique in Medical Image 2

Decentralization of Cloud Environment Using DES Technique in...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Roselinkiruba, R. Deepan, S. Sharmila, L. Poonguzhali, S. Jude Moses Anto Devakanth, J. Kavitha, J. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India S.A.College of Arts & Science India Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331505745

Cloud security is an important aspect in centralized data management in public resources. By increasing data processing and service access level, the integrity level be deformed due to data leakage, spoofing, and key leakages because of user behavioral approach to access the data is malpractice to own data. So the owner policy become unauthenticated to access the data rapidly increasing by non-authenticated users. To resolve this problem, propose a Service Level Integrity Proofing (SLIP) Verified data Security. Based on Decentralized ChainLink Provable Advanced Encryption Standard (DCLP-AES) in Cloud Environment, this proposed system verifies each service access policy of user which is relatively posed to aggregate the owner certificate signing at chain-link verification. The verification can be time- stamped after the transaction is recorded into integrity weight. This proposed system improve the security levels based on integrity security signing level to protect the data compared to the other system as well high performance in security. © 2025 ieee.

关键词： Information leakage

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

引用

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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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 ieee.

关键词： Convolutional neural networks

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C4ECC: data Compression for Bandwidth Efficiency Under ECC Protection in GPUs

C4ECC: Data Compression for Bandwidth Efficiency Under ECC P...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Park, Sanghyun Choi, Boyeol Kim, Jungrae Sungkyunkwan University Semiconductor Convergence Engineering Suwon Korea Republic of Semiconductor Systems Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331510756

Modern GPUs using Graphics DDR DRAM rely on in-band Error Correcting Code (ECC) for memory protection, but this reduces effective bandwidth and increases latency. This paper introduces data Compression for Bandwidth Efficiency under ECC Protection in GPUs, called C4ECC. C4ECC compresses 32B blocks to fit both data and redundancy within a single transfer between the GPU's Last Level Cache and device memory. Our evaluation shows that while traditional in-band ECC increases memory traffic by 68%, C4ECC reduces this overhead to an average of just 1.4 %. This reduction significantly lowers the performance overhead caused by in-band ECC from 34% to 3%, particularly in memory-intensive workloads. © 2025 ieee.

关键词： Bandwidth compression

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Adaptive Kernel Optimization for Probabilistic Learning: Integrating Support Vector Machines with Gaussian Process Frameworks 1

Adaptive Kernel Optimization for Probabilistic Learning: Int...

引用

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

作者： Kanth, Tatiraju V. Rajani Singh, Balveer Singh, Yaspal Bhutada, Sunil Yamaganti, Rohita P.K. University Department of Computer Science Engineering Shivpuri India P.K. University Department of Computer Science Engineering Shivpuri India P.K. University Department of Information Science Department of Computer Science Engineering Shivpuri India

ISBN: (纸本)9798331529246

With its robust capabilities for non-linear regression and classification, kernel-based learning has emerged as a fundamental component of state-of-the-art machine learning approaches. In order to improve probabilistic learning, this study investigates Adaptive Kernel Optimization (AKO), a new method that combines the best features of the Support Vector Machine (SVM) and the Gaussian Process (GP) frameworks. Achieving better flexibility in modeling complicated data distributions while keeping computational efficiency is achieved by employing adaptive kernel functions in the suggested strategy. Quantifying uncertainty in addition to deterministic SVM classifications is made possible with the incorporation of GP kernels, which offer probabilistic insights. The suggested approach guarantees resilience across varied and high-dimensional datasets by dynamically adjusting kernel parameters according to data properties. Extensive testing on benchmark datasets shows that, in comparison to conventional SVM and GP approaches, our model generalizability, classification accuracy, and interpretability are much improved. Autonomous systems, healthcare diagnostics, and financial sectors can all benefit from the scalable, adaptive, and probabilistic learning models that this study establishes. © 2025 ieee.

关键词： Adversarial machine learning

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Application of Bidirectional Gated Recurrent Unit and Stacked Gated Recurrent Unit Algorithms for Stock Price Prediction

Application of Bidirectional Gated Recurrent Unit and Stacke...

引用

2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Melyani, Nasya Amirah Mustakim Angraini Afdal, M. Sultan Syarif Kasim State Islamic University of Riau Faculty of Science and Technology Department of Information Systems Pekanbaru Indonesia State Islamic University of Sultan Syarif Kasim Riau Faculty of Science and Technology Department of Information System Pekanbaru Indonesia

ISBN: (纸本)9798331513320

Stocks are financial instruments that provide ownership rights and profits to their holders in public companies, which attract investors because of the potential for high returns despite the risks. Because stock price volatility is difficult to predict, accurate analysis techniques are needed to help investors make the right decisions, especially in the banking sector such as Bank Rakyat Indonesia. This study predicts the stock price of Bank Rakyat Indonesia (BBRI) using deep learning techniques, namely Bidirectional Gated Recurrent Unit (Bi-GRU) and Stacked Gated Recurrent Unit (Stacked GRU), which are chosen because of their ability to capture complex time series data patterns. BBRI stock data from 2018 to 2024 and from 2023 to 2024 were collected, processed, and divided into training and testing data with a ratio of 80:20. The Bi-GRU and Stacked GRU algorithms were optimized with Nadam, Adamax, AdamW, and SGDM, and evaluated using MSE, RMSE, and MAPE. As a result, 7-year data outperform 2-year data with the best algorithm Bi-GRU with Nadam optimization, batch size 8, and learning rate 0.001 giving the best performance with MSE 7283.1848, RMSE 85.3416, MAE 65.0879, and MAPE 1.2421%. The best prediction shows a decrease in BBRI stock prices in November 2024 which indicates a decrease towards the end of the year, thus providing valuable information for companies and investors in anticipating market behavior and making the right investment decisions. © 2025 ieee.

关键词： Time series

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Intelligent Tutoring systems Powered by Generative AI: Advancing Personalized Education and Overcoming Challenges 1

Intelligent Tutoring Systems Powered by Generative AI: Advan...

引用

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

作者： Balakrishnan, P. Anny Leema, A. Jothiaruna, N. Sangaiah, Arun Kumar Scope Vellore Institue of Technology Vellore India Scseai Vellore Institue of Technology Bhopal India National Yunlin University of Science and Technology International Graduate School of Ai Douliu Taiwan

ISBN: (纸本)9798331529246

Brain signals analysis can be applied to students to identify their subject- or topic-specific struggles while they are listening to lecture videos. This re- search work proposes a threshold-based anomaly detection algorithm to identify the topics that cause students the most distress by analysing a dataset available at https://***/datasets/wanghaohan/confused-eeg/data. Firstly, it identifies the index and timestamp of distress data points of students' brain signals, extracts the respective portion of transcribed textual content, given as prompt to ChatGPT or Bard via application programming interface (API) calls, get back the answers, and display it in a pop up window to the students. The proposed generative artificial intelligence (Gen AI)-powered intelligent tutoring system (ITS) captures almost 99% of confusion points and source confusion topic in brain signals as it uses context- aware algorithm thereby improving leaners' clarity on the source confusion topic after reading the pop-up window content generated by Gen AI. © 2025 ieee.

关键词： Adversarial machine learning

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Generative Diffusion Model-based Energy Management in Networked Energy systems

Generative Diffusion Model-based Energy Management in Networ...

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

作者： Lu, Xinyu Feng, Zhanbo Sun, Jiawei Lou, Jiong Wu, Chentao Bao, Wugedele Li, Jie Shanghai Jiao Tong University China Yancheng Blockchain Research Institute China Shanghai Key Laboratory of Trusted Data Circulation Governance and Web3 China Hohhot Minzu College China

ISBN: (纸本)9798350368741

In recent years, the proliferation of renewable energy sources has heightened the focus on networked energy systems. These systems face significant challenges due to the unpredictable nature of energy generation and consumption, as well as the complexity of managing numerous components and parameters. To address the challenges associated with the time-consuming nature of optimization problems and the expansive solution space, we propose an innovative energy management method based on a generative diffusion model applicable to general networked energy systems. This approach aims to balance energy supply and demand while minimizing transmission costs. The efficacy of this method is validated through evaluations on real-world datasets and simulations, demonstrating a 26.6% cost reduction compared to the state-of-the-art model and a 62.8% decrease in execution time compared to existing optimizers. This research highlights the potential of generative diffusion techniques in networked energy management. Code: https://***/gale13/GEM. © 2025 ieee.

关键词： energy management Generative diffusion model renewable energy sources scheduling

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Digital Touchpoints: Generating Synthetic data for Elderly Smartphone Interactions 11

Digital Touchpoints: Generating Synthetic Data for Elderly S...

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11th international conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Maqbool, Bilal Herold, Sebastian Department of Mathematics and Computer Science Faculty of Health Science and Technology Karlstad University Karlstad Sweden

ISBN: (纸本)9789897587436

Context: Ensuring smartphone interfaces are usable and accessible is essential for elderly users, particularly those with motor impairments, who face challenges with touchscreen interactions. Problem: Hand tremors and limited motor control can hinder touchscreen accuracy and efficiency. Meanwhile, recruiting elderly participants for usability studies can be challenging, often resulting in limited interaction data. Objectives: This study aimed to investigate elderly users’ smartphone interaction patterns, identify key challenges, and generate synthetic data to address data scarcity for usability research. Method: A custom-designed mobile app collected interaction data from 51 elderly participants performing tapping, dragging, and tracing tasks. Hand steadiness was assessed using accelerometer data. Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) models were used to generate synthetic datasets replicating user interaction patterns. Results: Users with shaky hands struggled with precision tasks, especially involving smaller GUI elements, while larger elements improved performance. Continuous control was also found to be challenging in tracing tasks. Synthetic datasets successfully replicated spatial, temporal, and distributional metrics, demonstrating potential utility in future usability evaluation research. Conclusions: Inclusive GUI designs and adaptive features can improve accessibility for the elderly with limited motor control. Synthetic data can offer a potential solution for further usability evaluation research in building AI-driven design evaluation tools, reducing reliance on resource-intensive participant recruitment in earlier prototypes. Future work should examine diverse tasks and scenarios and involve people with severe motor impairments. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Usability engineering

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