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

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TAO-Based data Governance in Intelligent Transportation systems 26

TAO-Based Data Governance in Intelligent Transportation Syst...

引用

ieee 26th international conference on Intelligent Transportation systems (ITSC)

作者： Liang, Xiaolong Li, Juanjuan Qin, Rui Ding, Wenwen Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Collaborat Lab Intelligent Sci & Syst Beijing 100190 Peoples R China

ISBN: (纸本)9798350399462

Intelligent Transportation systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

关键词： Intelligent Transportation systems True Autonomous Organization Parallel Intelligent Mechanism Design Parallel Governance

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Automated Waste Classification using Convolutional Neural Network 14

Automated Waste Classification using Convolutional Neural Ne...

引用

14th international conference on Software Technology and Engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Guru Saran, P. INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Efficient waste management relies on precise waste classification, which facilitates recycling, composting, and appropriate disposal. Conventional waste classification methods often involve manual sorting or simple rule-based systems, which are labor intensive and prone to errors. This study investigates the application of Convolutional Neural Networks (CNNs) for automated waste classification using image data. CNNs have shown significant success in various image classification tasks due to their ability to learn hierarchical features directly from pixel values. The methodology outlined covers data collection, preprocessing, model architecture design, and training procedures for the CNN model. Experimental results demonstrate the effectiveness of the CNN approach in accurately categorizing various waste types. Additionally, we discuss the practical implications of CNN-based waste classification systems for real-world waste management applications. By leveraging CNNs, waste management processes can be optimized, leading to better resource utilization and environmental sustainability. The authors also highlight potential avenues for future research, including exploring advanced CNN architectures and integrating emerging technologies like Internet of Things (IoT) sensors for improved waste management solutions. Overall, this study emphasizes the potential of CNNs to transform waste classification practices and enhance the efficiency of waste management systems. © 2024 ieee.

关键词： Convolutional neural networks

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An Energy-Efficient Analog-to-Pulse Encoding for Short-Range High-Density Wireless Telemetry 67

An Energy-Efficient Analog-to-Pulse Encoding for Short-Range...

引用

67th ieee international Midwest Symposium on Circuits and systems (MWSCAS)

作者： Haider, Mohammad R. Farr, Adam Singha, Showmik Islam, Syed K. Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA

ISBN: (纸本)9798350387186;9798350387179

The prevalence of sensors and sensor networks has resulted in the Internet of Things (IoT), transforming modern lifestyles. However, resource-constrained IoT edge devices require inventive sensing, computing, and wireless telemetry strategies to support the massive number of devices in an IoT network. Traditional telemetry and data encoding schemes for short-range sensor networks are limited by network bandwidth, data processing capability, and power constraints imposed by the battery energy density. To mitigate these challenges, an energy-efficient architecture is presented for analog orthogonal pulse (AOP) generation and AOP-based data encoding for high-density spectrum-efficient wireless telemetry. Unlike conventional digital pulse-based encoding, higher-order analog orthogonal pulses are used for spectrum-efficient data encoding. The orthogonal pulse generator contained a reduced number of functional blocks for energy efficiency. The MATLAB-Simulink package is used to design and simulate the proposed encoding architecture and, finally, embedded into a microcontroller unit. Test results show the successful generation of analog orthogonal pulses and pulse-based signal encoding.

关键词： analog orthogonal pulse data telemetry internet-of-things pulse encoding

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Maintaining Cache Consistency in RDMA-based Distributed Key-Value storage System 7

Maintaining Cache Consistency in RDMA-based Distributed Key-...

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7th international conference on data science and Information Technology, DSIT 2024

作者： Hou, Zhuohan Zheng, Qianyi Song, Yuanfeng Nie, Xiaowen University of Electronic Science and Technology of China School of ISE Chengdu China University of Electronic Science and Technology of China School of CSE Chengdu China University of Electronic Science and Technology of China Information Center Chengdu China

ISBN: (纸本)9798350384093

With the development of big data and artificial intelligence, the proliferation of data brings new challenges to the key-value storage systems. The Remote Direct Memory Access (RDMA) can bring clear performance benefits to the key-value storage systems. This research addresses the optimization cache consistency of ScaleStore. We propose a novel method, Cache consistency algorithm, R-scale(Reoptimization ScaleStore). It adopt a new paradigm, which is the separation of storage and compute, develop a novel write-back buffer approach and the asynchronous remote invocation. Compared to ScaleStore's cache consistency, the R-scale's write performance is improved by 20%. © 2024 ieee.

关键词： cache consistency algorithm distributed shared memory in-memory key-value storage system remote direct memory access

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Improving CLIP for Biomedical Retrieval: A Multi-Margin Contrastive Loss Approach

Improving CLIP for Biomedical Retrieval: A Multi-Margin Cont...

引用

2024 ieee international conference on Big data, Bigdata 2024

作者： Shakya, Ejan Rayakota, Haritha Prasad Huang, Pei-Chi University of Nebraska at Omaha Department of Computer Science Nebraska Omaha United States

ISBN: (纸本)9798350362480

Biomedical documents contain a wealth of multimodal data, including visual evidence and natural language descriptions. Effectively navigating this vast and expanding data source poses significant challenges, requiring labor-intensive manual curation processes at scale. Multimodal learning methodologies offer a promising approach to mitigate these bottle-necks in domain-specific information retrieval tasks, especially in biomedical sciences. This research investigates the potential of multimodal learning methodologies for various downstream tasks, such as information retrieval and classification based on paired multimodal image-text datasets. The proposed solution employs state-of-the-art transformer-based deep learning techniques and a contrastive learning approach to design a foundational model capable of adapting to biomedical vision-language processing. Through experiments and comparative studies, the effectiveness of our model was validated, demonstrating superior performance compared to existing approaches across diverse standard datasets. This study emphasizes the challenges in domain-specific environments and underscores the critical role of high-quality multimodal data in advancing biomedical AI applications, fostering innovation and progress in healthcare research and practice. © 2024 ieee.

关键词： Bottles

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Navigating the data Deluge: A Comprehensive Examination of Recommendation systems in Real-World Applications 14

Navigating the Data Deluge: A Comprehensive Examination of R...

引用

14th international conference on Cloud Computing, data science and Engineering, Confluence 2024

作者： Sharma, Shweta Sharma, Vikas Tata Consultancy Services Delhi India Delhi Technological University Department of Information Technology Delhi India

ISBN: (纸本)9798350344837

This research paper comprehensively explores recommendation systems, elucidating their functionality within real-world contexts. As the volume of data continues to burgeon daily, harnessing its potential has become imperative. The quest for pertinent information within constrained timeframes poses a formidable challenge. In contemporary times, web applications such as Amazon, Netflix, Flipkart, and Spotify have effectively addressed this challenge by implementing sophisticated recommendation systems. These systems adeptly mine user preferences, employing data extraction techniques to furnish tailored and personalized experiences. The paper delves into the intricacies of recommendation algorithms, shedding light on their mechanisms and contributions to enhancing user satisfaction across diverse digital platforms. Through an in-depth analysis, the study underscores the pivotal role recommendation systems play in navigating the information deluge, fostering efficient decision-making, and elevating user engagement. © 2024 ieee.

关键词： Recommender systems

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Towards Real-world Deployment of Deep Learning Solutions for Global Road Damage Detection and Classification

Towards Real-world Deployment of Deep Learning Solutions for...

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2024 ieee international conference on Big data, Bigdata 2024

作者： Yoo, Jooyoung Shtaiwi, Reem Emad Yasin, Mohammad Trivedi, Dweep Alfarrarjeh, Abdullah Jabal, Amani Abu Kim, Seon Ho University of Southern California Integrated Media Systems Center Los AngelesCA United States German Jordanian University Department of Computer Science Amman Jordan

ISBN: (纸本)9798350362480

Effective monitoring of essential road infrastructure is critical to ensure economic growth and community connectivity, as deterioration from climate change and intensive use can compromise safety and hamper emergency services. To address these critical issues, we propose optimized approaches for road damage detection using YOLOv5, YOLOv8, and YOLOv10 models, with fine-tuning and hyperparameter optimization to improve detection accuracy. Our approaches achieve good overall accuracy (in terms of F1-score) by combining models of different sizes and frameworks using ensemble methods. Moreover, to enhance the speed of inference, parallel processing techniques are used. In order to obtain fast detection performance, small models, such as YOLOv5-small and YOLOv8-nano were given priority in modeling to balance between detection accuracy and inference speed. In the context of the 2024 ieee Bigdata Optimized Road Damage Detection Challenge (ORDDC'2024), our approach achieved an F1 score greater than 0.7 with an inference speed of 0.0432 seconds per image, meeting the ORDDC's accuracy and inference speed goal. © 2024 ieee.

关键词： Change detection

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Hot and Cold data Migration Strategy Based on Newton's Law of Cooling 7

Hot and Cold Data Migration Strategy Based on Newton's Law o...

引用

7th international conference on data science and Information Technology, DSIT 2024

作者： Wei, Shuangjian Lin, Hongpeng Tan, Aidi China Institute of Marine Technology & Economy Beijing China Beijing Institute of Technology School of Computer Science & Technology Beijing China

ISBN: (纸本)9798350384093

We present a novel temperature-based data migration strategy for tiered storage systems that addresses the limitations of existing Newton's Law of Cooling approaches. While current methods treat temperature increase from data access as a constant, our approach introduces a logistic function-based model that dynamically adjusts temperature increases based on access patterns. This enhancement prevents artificial temperature inflation during burst accesses and better reflects real-world data access behaviors. Experimental results show our Temperature Migration Strategy (TMS) achieves superior identification effectiveness compared to LRU-2 and ARC across various configurations, with particular resilience to sudden access pattern changes. The proposed solution demonstrates superior stability under extreme workloads, with only a 3.4% average performance degradation compared to 31.2% in traditional approaches. Our work provides a robust foundation for efficient hot/cold data management in modern storage hierarchies, particularly beneficial for OLTP systems with skewed access patterns. © 2024 ieee.

关键词： data Migration Hot/Cold data Newton's Law of Cooling Tiered Storage

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Analysis of Purchasing Trends and Product Recommendations of Customer Clusters for Each Product Using Bayesian Network 9

Analysis of Purchasing Trends and Product Recommendations of...

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9th ieee/ACIS international conference on Big data, Cloud Computing, and data science, BCD 2024

作者： Muramatsu, Kohta Takeda, Yoshiyuki Kato, Kazuhiko Graduate School of Social Systems Science Chiba Institute of Technology Chiba Japan

ISBN: (纸本)9798350394191

In micromarketing, data mining is actively conducted using large-scale data to understand customers' detailed needs. Customer Relationship Management aims to improve customer satisfaction and increase revenue using analytical techniques such as RFM analysis. In this study, to segment customers and improve the accuracy of RFM analysis, we use a Bayesian network to analyze the causal relationships between purchasing trends for each customer cluster and product category. For products in categories where causal relationships have been confirmed, a Bayesian network is used to clarify detailed causal relationships between products. Based on sales history data, the top 20% customers with regard to purchase frequency and purchase amount were classified using cluster analysis, and the causal relationships were analyzed. The results revealed differences in the products sold together in each cluster, suggesting the potential for detailed analysis of purchasing trends through customer segmentation. © 2024 ieee.

关键词： Sales

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data-Driven Decision Making in Smart Health and Emergency Management

Data-Driven Decision Making in Smart Health and Emergency Ma...

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

作者： Sudharson, K. Selvi, K. Ramu, V. Monika, V. Sureshkumar, A. Nagarajan, S. R.M.D. Engineering College Department of Aiml Chennai India S.A. Engineering College Department of Mba Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mba Chennai India Vel Tech Ranga Sanku Arts College Department of Mba Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mba India

ISBN: (纸本)9798331529833

Phenomena such as smart health and emergency management systems when embedded with data-driven decision-making have become a game-changer in the field of healthcare and an emergency response. In this paper, we investigate the use of advanced data analytics, machine learning algorithms and real-time data processing in improving decision-making activities in these domains. Using data from a diverse set of large datasets such as EHRs, wearables, emergency response systems etc., we hope to build predictive models and decision support systems which can help save lives by predicting the epidemic before it peaks saving valuable time when every minute counts. The research also deals with issues such as data privacy, interoperability and real-time data integration. Early findings suggest large efficiencies and effectiveness gains in health and emergency management, showing the game-changing potential of datadriven solutions in their domains. © 2025 ieee.

关键词： Electronic health record

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