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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86325 条记录，以下是181-190 订阅

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

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

引用

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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Federated Learning for Kidney Disease Prediction using Combined LSTM and MLP Models 14

Federated Learning for Kidney Disease Prediction using Combi...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Pathak, Anurag Pandey, Anshuman GLA University Uttar Pradesh Mathura India Computer Science GLA University Uttar Pradesh Mathura India

ISBN: (纸本)9798331531935

Kidney disease (KD) is a prevalent global health issue, and early prediction is critical for improving patient outcomes. In this study, we propose a federated learning approach that combines Long Short-Term Memory (LSTM) networks with Multi-Layer Perceptron (MLP) models for kidney disease prediction. The LSTM component captures temporal dependencies in the data, while the MLP enhances the model’s ability to classify based on learned features. The federated learning framework ensures data privacy by keeping patient information decentralized across multiple hospitals. Experimental results show promising accuracy, precision, recall, and F1 score, demonstrating the potential of combining LSTM and MLP for healthcare applications. © 2025 ieee.

关键词： Federated learning

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

引用

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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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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Anomaly Detection in Social Networks using Improved Local Outlier Factor 1

Anomaly Detection in Social Networks using Improved Local Ou...

引用

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

作者： Rana Veer Samara Sihman Bharattej, R. Mhsnhasan, Muntader Sowmya, M. Prabhakaran, M.V. Ghamya, K. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively. © 2025 ieee.

关键词： Principal component analysis

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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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EPI-Mamba: State Space Model for Semantic Segmentation from Light Fields

EPI-Mamba: State Space Model for Semantic Segmentation from ...

引用

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

作者： Li, Yan Chen, Jianan Wang, Qiong Xu, Jinshan College of Computer Science & Technology Zhejiang University of Technology Hangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Global contextual dependency is of significance for semantic segmentation from light fields. However, previous works mostly exploit attention mechanisms to model spatial context dependency and angular context dependency separately, since a light field capture is very data-intensive. Considering that self-attention is resource-consuming, existing methods only use it to capture spatial contextual dependency for the central view of light fields, and employ resource-efficient attention mechanisms to model angular view dependencies instead. Inspired by the recent success of State Space Sequence model (SSM), we present a novel SSM-based encoder for light field semantic segmentation, which models spatio-angular contextual dependency together. The proposed method is evaluated on the existing light field semantic segmentation datasets, and experimental results show that the proposed method significantly outperforms previous state-of-the-art methods. The code and models used in this work are available in https://***/YanWQ/EPI-Mamba. © 2025 ieee.

关键词： Light field semantic segmentation Mamba State space model

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Do Multimodal Language Models Really Understand Direction? A Benchmark for Compass Direction Reasoning

Do Multimodal Language Models Really Understand Direction? A...

引用

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

作者： Yin, Hang Lin, Zhifeng Liu, Xin Sun, Bin Li, Kan Department of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350368741

Direction reasoning is essential for intelligent systems to understand the real world. While existing work focuses primarily on spatial reasoning, compass direction reasoning remains underexplored. To address this, we propose the Compass Direction Reasoning (CDR) benchmark, designed to evaluate the direction reasoning capabilities of multimodal language models (MLMs). CDR includes three types images to test spatial (up, down, left, right) and compass (north, south, east, west) directions. Our evaluation reveals that most MLMs struggle with direction reasoning, often performing at random guessing levels. Experiments show that training directly with CDR data yields limited improvements, as it requires an understanding of real-world physical rules. We explore the impact of mixdata and CoT fine-tuning methods, which significantly enhance MLM performance in compass direction reasoning by incorporating diverse data and step-by-step reasoning, improving the model's ability to understand direction relationships. © 2025 ieee.

关键词： Benchmark dataset Compass direction reasoning Multimodal language model

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The Missing data Imputation Method for Medical data Classification Using Most Probable Value 14

The Missing Data Imputation Method for Medical Data Classifi...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Tarle, Balasaheb Tidake, Vaishali Tajanpure, Rupali Litoriya, Ratnesh Department of Computer Engineering MVP Samaj's KBTCOE Nashik India Department of Computer Science and Engineering Medi-Caps University Indore India

ISBN: (纸本)9798331531935

In medical data classification, improving the performance of classification is essential when the dataset's size is small, and some attributes' values need to be included. Classification performance can suffer if the training dataset starts with insufficient data samples. The challenges like overfitting, high variance, inability to find real performance of model, biased models, sensitivity to noise, improper evaluation occurs with small dataset. The proposed algorithm uses domain-based multiple imputations to create additional data tuples and appends these tuples to the originally available training data. This improves the classification power of the classifier. This proposed technique uses a set of domain values to impute missing attribute values. The accuracy of imputed tuples is verified using classifier filters. The proposed technique significantly improves the classification performance of the classifier. This technique is suitable for small to medium-sized data sets. data imputation helps develop improved and more accurate classifiers. The proposed approach offers improved classification performance. The proposed method achieves a 4.01% improvement in the classification accuracy of fuzzy neural network FBNFC. We performed two hypothesis tests, the t-test, and the Wilcoxon rank sum test. It is observed that the alternative hypothesis is true. © 2025 ieee.

关键词： Labeled data

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Personalized Dementia Prediction Using Machine Learning Techniques

Personalized Dementia Prediction Using Machine Learning Tech...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Jayashree, S. Livedha, S. Ragupathi, T. Hariharan, Kumaran Sanjana, R. Arora, Ginni Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9798331508685

Early-stage dementia detection is a significant challenge in the healthcare field. Many individuals remain unaware of their cognitive decline until the condition progresses. This research aims to develop a personalized approach for predicting dementia. Multiple machine learning algorithms were applied to the dataset. Impressively, Random Forest model achieved a perfect accuracy of 98.75%. © 2025 ieee.

关键词： Support vector regression

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