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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171-180 订阅

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Comparative Analysis of Ensemble Techniques for Bone Fracture Detection from X-Ray Images Using HOG Features

Comparative Analysis of Ensemble Techniques for Bone Fractur...

引用

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

作者： Muzakki, Abyan Farrel Prasetyo, Simeon Yuda Purwanto, Eko Setyo School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Musculoskeletal conditions encompass 150 varying conditions and approximately affect 1.71 billion people worldwide, according to the World Health Organization. Bone fractures are among the musculoskeletal conditions that significantly impact individuals, families, societies, and healthcare systems, as they can lead to work absences, decreased productivity, disability, and a host of other life-limiting conditions. This study aims to evaluate the performance of superior-performing machine learning (ML) models that are fed with histograms of oriented gradients (HOG). The bestperforming ML models were chosen and implemented toward ensemble models in aims to reduce overfitting and address resource-limited settings. The results indicate that by using KNearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF), which yield the highest scores, the proposed ensemble models were able to increase the score, with the stacking ensemble model achieving accuracy score of 99.30%. Future research could focus on integrating advanced machine learning techniques, hybrid feature extraction methods, and larger datasets to enhance diagnostic accuracy and applicability in clinical environments. © 2025 ieee.

关键词： Musculoskeletal system

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CellRejuvo: Rescuing the Aging of 3D NAND Flash Cells with Dense-Sparse Cell Reprogramming 24

CellRejuvo: Rescuing the Aging of 3D NAND Flash Cells with D...

引用

43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Liao, Han-Yu Chen, Yi-Shen Hsieh, Jen-Wei Chang, Yuan-Hao Chen, Hung-Pin National Taiwan University of Science and Technology Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Innodisk Corporation New Taipei Taiwan

ISBN: (纸本)9798400710773

3D NAND flash memory is one of the most important storage technologies in modern computer systems because of its non-volatile nature and excellent data access performance. However, it suffers from aging and reliability issues due to its inherent property. In contrast to the previous research that tried to recover the data with additional encoding techniques, we propose a novel reprogramming technique, called CellRejuvo, to improve the reliability of NAND flash cells. To the best of our knowledge, CellRejuvo is the pioneer for data recovery technique that cleverly leverages reprogramming to alleviate cell aging, extending the lifespan of solid-state drives. We implement CellRejuvo on a real 3D NAND flash-based SSD and evaluate its capability on various realistic workloads. The extensive experimental results show that CellRejuvo successfully reduces the error rate of SSD by an average of 38.28% under various retention times. © 2024 Copyright is held by the owner/author(s).

关键词： Physical addresses

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Open Electronic Health Record Mechanism for Interoperable Cloud Performance and Confidentiality 3

Open Electronic Health Record Mechanism for Interoperable Cl...

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

作者： Reddy, Sadhula Srihari Vutukuru, Murali Mohan Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798331509675

The popularity of interoperable cloud environments can be attributed to the growing need for secure and effective administration of healthcare data. In order to guarantee interoperability, performance and privacy, OpenEHR - an open standard specification in health informatics is essential. This survey research uses OpenEHR technologies to investigate performance and privacy management issues in interoperable cloud environments. The main features of OpenEHR are covered, along with how it can be used in cloud contexts and how it affects the administration of medical data. Along with outlining the advantages and difficulties of utilizing OpenEHR in healthcare data management, the article also covers recent developments and research in this area. The study also addresses how OpenEHR 's standardized data models and access control methods might support effective data interchange while protecting patient privacy. Four EHR systems are compared in this study: Cleveland Clinic's Epic, HL7 FHIR, OPENEHR, and CDA. Although its implementation complexity is minimal, OpenEHR boasts significant levels of interoperability, data reuse, and customization possibilities. Although HL7 FHIR has modest implementation complexity and expense, it allows real-time data access and flexibility. Although CDA offers strong regulatory compliance, it is less flexible and requires more work when creating templates. Cleveland Clinic's Epic system has a high degree of maturity, strong community support, and complete regulatory compliance;but, it also has a high cost, restricted data reuse, and severe vendor lock-in. © 2025 ieee.

关键词： Electronic health record

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LiTformer: Efficient Modeling and Analysis of High-Speed Link Transmitters Using Non-Autoregressive Transformer 24

LiTformer: Efficient Modeling and Analysis of High-Speed Lin...

引用

43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Sun, Songyu Dong, Xiao Sha, Yanliang Chen, Quan Zhuo, Cheng Zhejiang University Hangzhou China Southern University of Science and Technology Shenzhen China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (纸本)9798400710773

High-speed serial links are fundamental to energy-efficient and high-performance computing systems such as artificial intelligence, 5G mobile and automotive, enabling low-latency and high-bandwidth communication. Transmitters (TXs) within these links are key to signal quality, while their modeling presents challenges due to nonlinear behavior and dynamic interactions with links. In this paper, we propose LiTformer: a Transformer-based model for high-speed link TXs, with a non-sequential encoder and a Transformer decoder to incorporate link parameters and capture long-range dependencies of output signals. We employ a non-autoregressive mechanism in model training and inference for parallel prediction of the signal sequence. LiTformer achieves precise TX modeling considering link impacts including crosstalk from multiple links, and provides fast prediction for various long-sequence signals with high data rates. Experimental results show that LiTformer achieves 148-456× speedup for 2-link TXs and 404-944× speedup for 16-link with mean relative errors of 0.68-1.25%, supporting 4-bit signals at Gbps data rates of single-ended and differential TXs, as well as PAM4 TXs. © 2024 Copyright is held by the owner/author(s).

关键词： Bandwidth

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Advancements in Public Access to Satellite Weather data: Signal Reception And data Interpretation Using NOAA 18 And NOAA 19 Satellites

Advancements in Public Access to Satellite Weather Data: Sig...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Ahamed, S Wajeed Pasha, Md M Venkatanarayana, M. Akhil, S Md Anjitha, G. Lavanya, G. Kumari, G Muni Ksrm College of Engineering Department of Ece Andhra Pradesh Kadapa India Ksrm College of Engineering Electronics and Communication Engineering Andhra Pradesh Kadapa India

ISBN: (纸本)9798331529833

This paper aims to demonstrate the importance of satellite imagery on modern weather forecasting by focusing specifically on NOAA-18 and 19 satellites. These satellites in the 137 MHz band supply critical information on significant weather events, like hurricanes. Here, the focus is on the progress which has made satellite data increasingly available via online tools, contrasting it with the use of satellite beams, which remain a challenge for direct satellite data reception. The technical details of satellite communication are discussed, with a focus on how NOAA (National Oceanic and Atmospheric Administration) satellites transmit data, and which specialized equipment is needed to receive it directly: using antennas, RF electronics, and Software Defined Radio setup. The paper describes how to build a simple and low-cost system. Devised following an analysis of orbit conditions and predicted parameters of satellite signals and ground reception techniques, which are collected and discussed throughout this paper on the signal transmission protocols and reception systems;aiming to provide an overview of the used methods along with practical steps that allowed the building of the system which is capable of receiving and decoding weather images transmitted by NOAA-18 and NOAA-19 satellites. The results indicate the possibility of precise weather monitoring in real-time, along with challenges encountered for signal reception like interference and atmospheric environment. Finally, we conclude this paper and suggest enhancements to come that would allow better quality images than the current ones, as well as easier and more affordable accessibility of satellite data for use in the weather forecast. © 2025 ieee.

关键词： Geostationary satellites

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mBERT: A Query Refinement Model for Marathi Word Sense Disambiguation

mBERT: A Query Refinement Model for Marathi Word Sense Disam...

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2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Manwar, Vivek A. Manwar, A.B. Sant Gadge Baba Amravati University Department of Computer Science Maharashtra Amravati India

ISBN: (纸本)9798331529833

Word Sense Disambiguation involves determining the appropriate meaning of a word within a given context. Natural language processing continues to face challenges related to ambiguity. Reducing these ambiguities for applications such as machine translation, data extraction, information retrieval, query response systems. In this paper, the proposed mBERT model for word disambiguation in Marathi query refinement show good performance with accuracy of 92.5%. This accuracy shows the model's proficiency in correctly interpret the intended meanings of ambiguous words within Marathi queries. With a precision of 91.2%, the model effectively minimizes false positives, ensuring that the refined queries remain relevant and meaningful and recall score is 90.8%, shows its capability to retrieve the most relevant information from the query context. The model's balanced performance is further highlighted by a high F1 score of 91.0%, which reflects a harmonious blend of precision and recall. © 2025 ieee.

关键词： Structured Query Language

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DocQueue: A Scheduling Algorithm for Optimizing Healthcare Resources

DocQueue: A Scheduling Algorithm for Optimizing Healthcare R...

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2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Joji, Jeff Johnson, Sobin Angayarkanni, V. Rama, P. Kalaivani, J. Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331529833

DocQueue is a cloud-based system that optimizes patient scheduling in order to maximize the delivery of healthcare by prioritizing cases (dynamically) based on real-time information and availability of resources. It uses a decision-tree algorithm to analyze symptoms in real time, and then flags high-priority cases immediately, but uses a round-robin scheduling mechanism to manage low-priority cases so things are fair. And the AWS based architecture of the system allows for scalable deployment throughout the healthcare continuum. In simulations, these changes have shown to drastically cut down patient waiting time and make better use of resources, and therefore provide better patient care and more efficient operations. DocQueue is different from the usual scheduling systems because it is flexible and data-driven, it can change its schedule based on the real time needs of health care. © 2025 ieee.

关键词： Scheduling algorithms

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Advancing HRM with AI Powered Recruitment Performance Management and Employee Engagement Using One Class SVMs 3

Advancing HRM with AI Powered Recruitment Performance Manage...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Banu, Rafiya Tripathi, Ruchi Sharma, Saumya Chenniappan, Loganathan Chandrasekaran, K. Umanesan, R. B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Commerce Chennai India SRM-IST Faculty of Management Studies Ghaziabad India The Northcap University Department of Management & Commerce Gurugram India Business Administration IBRI Oman K.S.R College of Engineering Department of Mechanical Engineering Tiruchengode India School of Computing Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

AI is making labour-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI technology in HRM could change several sectors. Through efficient applicant sourcing, screening, and selection, AI-driven solutions simplify hiring processes and ensure fair and accurate picks. Performance management using AI-powered solutions provides data-driven feedback and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement by personalizing experiences and meeting needs. The method focuses on preprocessing and model training. data are cleansed, ordinal and categorical variables encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured before processing. For model training, the simple and robust one-class SVM technique is used. One-class SVM model outperformed others with 95.28%. This method worked best compared to GAN and GRU models. AI could improve HRM by improving decision-making and offering data-driven solutions. The findings show that the proposed model outperforms current options, providing the framework for HRM AI applications. © 2025 ieee.

关键词： Human resource management

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MF-BERT: A Siamese Pre-training Framework for Motion Forecasting

MF-BERT: A Siamese Pre-training Framework for Motion Forecas...

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

作者： Shi, Jianxin Chen, Jinhao Chen, Xiaolong Ma, Jun Wo, Tianyu School of Computer Science and Engineering Beihang University Beijing China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China

ISBN: (纸本)9798350368741

Accurately predicting the future motions of traffic agents is essential for autonomous systems. Despite the significant success of existing motion forecasting methods based on supervised learning, they still exhibit two main limitations. First, when annotated data for a scene is limited, these methods often fail to achieve the expected accuracy. Second, they typically rely on complex architectures and extensive prior knowledge to improve performance. To overcome these challenges, we propose MF-BERT, a novel framework that adapts the concept of BERT to motion forecasting, inspired by advancements in the self-supervised pre-training paradigm. During pre-training, we design a siamese sequence modeling task with an asymmetric mask strategy to capture complex behavior patterns of agents. During fine-tuning, the pre-trained representation module initializes the feature encoder of the motion forecasting model, and a multimodal trajectory decoder generates all possible predictions. Experimental results demonstrate the superiority of MF-BERT over state-of-the-art methods. © 2025 ieee.

关键词： Autonomous Motion Forecasting Self-supervised Learning Time Series Analysis

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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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