Procedia Computer Science最新文献_第3页

Developing Skeletal Activity Scheduler using Machine Learning 使用机器学习开发骨骼活动调度程序

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.03.054

Sagar Bhandari , Muhammad Ahsanul Habib

{"title":"Developing Skeletal Activity Scheduler using Machine Learning","authors":"Sagar Bhandari , Muhammad Ahsanul Habib","doi":"10.1016/j.procs.2025.03.054","DOIUrl":"10.1016/j.procs.2025.03.054","url":null,"abstract":"<div><div>Understanding human mobility patterns is crucial for sustainable urban planning. This study presents a novel approach for predicting daily activity sequences using machine learning techniques, specifically Long Short-Term Memory (LSTM) networks and Explainable Boosting Machines (EBM). Utilizing data from the 2022 Halifax Travel Activity (HaliTRAC) Survey, we train these models to predict sequences of activities based on individual and household characteristics, aiming to balance predictive performance with interpretability. The LSTM model effectively captures complex temporal dependencies, while EBM provides clear insights into the significance of individual features, addressing the \"black box\" nature of Machine Learning models. By simplifying activity sequences into five primary activity types, the refined LSTM and EBM models achieve accuracies of 70.25% and 73.73%, respectively. Key findings highlight employment status, age, and education level as major determinants of activity patterns, with household characteristics like size playing a secondary role. This research demonstrates the potential of utilizing advanced machine learning techniques in mobility analysis, offering both accurate predictions and actionable insights. The proposed framework provides a foundation for developing transparent and reliable tools to inform transportation policies and urban development strategies.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"257 ","pages":"Pages 412-419"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding the Drivers of Cryptocurrency Acceptance: An Empirical Study of Individual Adoption 了解接受加密货币的驱动因素：个人采用的实证研究

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.151

Máté Hidegföldi, Gergely Laszlo Csizmazia, Justina Karpavičė

引用次数: 0

Comparative Analysis of Simulated Annealing and Tabu Search for Parallel Machine Scheduling 模拟退火和塔布搜索在并行机调度中的比较分析

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.154

Alzira Mota , Paulo Ávila , João Bastos , Luís A.C. Roque , António Pires

引用次数: 0

Sovereignty in Automated Stroke Prediction and Recommendation System with Explanations and Semantic Reasoning 具有解释和语义推理的自动中风预测和推荐系统的主权

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.079

Ayan Chatterjee

{"title":"Sovereignty in Automated Stroke Prediction and Recommendation System with Explanations and Semantic Reasoning","authors":"Ayan Chatterjee","doi":"10.1016/j.procs.2025.02.079","DOIUrl":"10.1016/j.procs.2025.02.079","url":null,"abstract":"<div><div>Personalized approaches are required for stroke management due to the variability in symptoms, triggers, and patient characteristics. An innovative stroke recommendation system that integrates automatic predictive analysis with semantic knowledge to provide personalized recommendations for stroke management is proposed by this paper. Stroke exacerbation are predicted and the recommendations are enhanced by the system, which leverages automatic Tree-based Pipeline Optimization Tool <strong>(TPOT)</strong> and semantic knowledge represented in an <strong>OWL Ontology (StrokeOnto). Digital sovereignty</strong> is addressed by ensuring the secure and autonomous control over patient data, supporting data sovereignty and compliance with jurisdictional data privacy laws. Furthermore, classifications are explained with Local Interpretable Model-Agnostic Explanations <strong>(LIME)</strong> to identify feature importance. Tailored interventions based on individual patient profiles are provided by this conceptual model, aiming to improve stroke management. The proposed model has been verified using public stroke dataset, and the same dataset has been utilized to support ontology development and verification. In TPOT, the best <strong>Variance Threshold + DecisionTree Classifier</strong> pipeline has outperformed other supervised machine learning models with an accuracy of <strong>95.2%,</strong> for the used datasets. The Variance Threshold method reduces feature dimensionality with variance below a specified threshold of 0.1 to enhance predictive accuracy. To implement and evaluate the proposed model in clinical settings, further development and validation with more diverse and robust datasets are required.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"254 ","pages":"Pages 201-210"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neoteric Threat Intelligence Ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems 通过机器学习注入的NEXT-G和超越生态系统的主动防御分析，确保数字主权和信任的现代威胁情报

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.062

Sudhakar Kumar , Sunil K. Singh , Rakesh Kumar , Chandra Kumari Subba , Kwok Tai Chui , Brij B. Gupta

{"title":"Neoteric Threat Intelligence Ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems","authors":"Sudhakar Kumar , Sunil K. Singh , Rakesh Kumar , Chandra Kumari Subba , Kwok Tai Chui , Brij B. Gupta","doi":"10.1016/j.procs.2025.02.062","DOIUrl":"10.1016/j.procs.2025.02.062","url":null,"abstract":"<div><div>In the domain of Cyber-Physical Systems (CPS) and the Internet of Things (IoT), this research presents a novel approach to Neoteric Threat Intelligence ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems. As Sixth Generation and Beyond (6G and B) wireless networks undergo rapid evolution, our framework is designed to proactively anticipate and counter security incidents by utilizing advanced machine learning algorithms. This approach effectively addresses the shortcomings of conventional models, ensuring that digital assets and communications remain secure, trustworthy, and under rightful control. The study delves into the theoretical integration of this paradigm within the NextG network architecture, reinforcing digital sovereignty through a dynamic and adaptable defense mechanism. In-depth technical examinations include advanced machine learning algorithms, adaptive defenses, and scalability considerations. By critically analyzing and comparing existing security approaches, this study significantly advances technical knowledge and practical applications for wireless network security, supporting defenses against the evolving and complex threats characteristic of the 6G and Beyond era.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"254 ","pages":"Pages 39-47"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sovereignty-Aware Intrusion Detection on Streaming Data: Automatic Machine Learning Pipeline and Semantic Reasoning 流数据的主权感知入侵检测：自动机器学习管道和语义推理

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.066

Ayan Chatterjee , Sundar Gopalakrishnan , Ayan Mondal

{"title":"Sovereignty-Aware Intrusion Detection on Streaming Data: Automatic Machine Learning Pipeline and Semantic Reasoning","authors":"Ayan Chatterjee , Sundar Gopalakrishnan , Ayan Mondal","doi":"10.1016/j.procs.2025.02.066","DOIUrl":"10.1016/j.procs.2025.02.066","url":null,"abstract":"<div><div>Intrusion Detection Systems (IDS) are critical in safeguarding network infrastructures against malicious attacks. Traditional IDSs often struggle with knowledge representation, real-time detection, and accuracy, especially when dealing with high-throughput data. This paper proposes a novel IDS framework that leverages machine learning models, streaming data, and semantic knowledge representation to enhance intrusion detection accuracy and scalability. Additionally, the study incorporates the concept of Digital Sovereignty, ensuring that data control, security, and privacy are maintained according to national and regional regulations. The proposed system integrates Apache Kafka for real-time data processing, an automatic machine learning pipeline (e.g., Tree-based Pipeline Optimization Tool (TPOT)) for classifying network traffic, and OWL-based semantic reasoning for advanced threat detection. The proposed system, evaluated on NSL-KDD and CIC-IDS-2017 datasets, demonstrated qualitative outcomes such as local compliance, reduced data storage needs due to real-time processing, and improved adaptability to local data laws. Experimental results reveal significant improvements in detection accuracy, processing efficiency, and Sovereignty alignment.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"254 ","pages":"Pages 78-87"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensuring Digital Sovereignty in Cross-chain EHR Sharing: A Relay-as-a-Service Approach for Secure Healthcare Interoperability 确保跨链EHR共享中的数字主权：用于安全医疗互操作性的中继即服务方法

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.02.063

Dharavath Ramesh , Thakur Santosh , Munesh Chandra Trivedi , Chi Hieu Le

{"title":"Ensuring Digital Sovereignty in Cross-chain EHR Sharing: A Relay-as-a-Service Approach for Secure Healthcare Interoperability","authors":"Dharavath Ramesh , Thakur Santosh , Munesh Chandra Trivedi , Chi Hieu Le","doi":"10.1016/j.procs.2025.02.063","DOIUrl":"10.1016/j.procs.2025.02.063","url":null,"abstract":"<div><div>Electronic Health Records (EHRs) stored in cloud environments often face privacy challenges in healthcare data management due to the divide between patient ownership and institutional control. Blockchain technology offers a promising solution with its features of immutability and traceability. However, existing blockchain-based approaches for EHR privacy preservation are limited to single institutions and fail to address the critical need for cross-chain compatibility and digital sovereignty. To bridge this gap, we propose two novel strategies: Polkadot-based Cross-chain for EHR-preserving Blockchain (PCEB) and Relay-as-a-Service-based Cross-chain for EHR-preserving Blockchain (RaSCEB). PCEB utilizes Polkadot's relay communication to securely share EHR data across multiple healthcare networks while preserving patient privacy and ensuring digital sovereignty. RaSCEB introduces Relay-as-a-Service (RaaS) to enable seamless EHR sharing across blockchain ecosystems, empowering patients with control over their data while maintaining regulatory compliance and sovereignty over their digital health records. Both approaches are validated through comprehensive security analysis and performance evaluations. We also present an interoperability framework tailored for permissioned blockchain networks, emphasizing trust derived from consensus mechanisms. Our work addresses the urgent need for cross-chain compatibility in EHR privacy preservation and advances interoperability solutions while safeguarding digital sovereignty in healthcare and beyond.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"254 ","pages":"Pages 48-57"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Company perspectives of generative artificial intelligence in industrial work 工业生产中生成式人工智能的公司视角

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.085

Susanna Aromaa , Päivi Heikkilä , Marko Jurvansuu , Selen Pehlivan , Teijo Väärä , Marko Jurmu

{"title":"Company perspectives of generative artificial intelligence in industrial work","authors":"Susanna Aromaa , Päivi Heikkilä , Marko Jurvansuu , Selen Pehlivan , Teijo Väärä , Marko Jurmu","doi":"10.1016/j.procs.2025.01.085","DOIUrl":"10.1016/j.procs.2025.01.085","url":null,"abstract":"<div><div>The use of artificial intelligence (AI) technologies in the manufacturing industry is rapidly increasing. During this transformation, it can be difficult to understand how AI will change the way work is done. This study explores how generative AI could change manufacturing work. Data collection was conducted using interviews and a questionnaire with seven representatives from three industrial companies. They identified several application areas for GenAI in the industrial work context, such as design, planning, training, problem solving, coding and data management. They also expressed positive attitudes but raised concerns about trust, safety, acceptability and interoperability. Changes in work were identified as being more related to cognitive aspects such as changing the way of thinking and altering the interaction with people and machines. Therefore, human-AI design efforts should focus especially on cognitive ergonomics. Findings from this study can be used in the manufacturing industry when adopting AI, as well as in identifying research topics in the human-AI research community.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 217-226"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Chipless RFID Technology to Provide Seamless Data Interoperability for Textile Industry Circularity 集成无芯片RFID技术为纺织工业循环提供无缝数据互操作性

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.101

Maximilian Scholz, Omid Fatahi Valilai

{"title":"Integrating Chipless RFID Technology to Provide Seamless Data Interoperability for Textile Industry Circularity","authors":"Maximilian Scholz, Omid Fatahi Valilai","doi":"10.1016/j.procs.2025.01.101","DOIUrl":"10.1016/j.procs.2025.01.101","url":null,"abstract":"<div><div>The textile industry faces tremendous challenges when it comes to waste management and recycling. The current methods for textile companies and drop-off centres for sorting the textiles for recycling is largely through manual labour, which is inefficient and involves high costs. The bottleneck due to slow process for visual inspection creates bottlenecks for effective sorting. One idea to solve this problem is to use an embedded data mechanism in textile tags via radio frequency identification (RFID) chips. Considering the requirements of recycling processes, there is an essential need for RFID technologies which are compatible with recyclability of textile processes. Therefore, the need and demand for a sustainable solution for traceability and recycling via chipless RFID technologies is highly motivated. Moreover, the technology should be economically viable for industries for adoption. This study explores a new technological concept that offers a solution for the current problem of creating a circular economy in the textile industry with traceability of data. So, the study focuses on analysing how chipless RFID technology may be integrated into textiles with 3D printing technology. The research investigates 3D printing technology for providing the ability to create a fast, inexpensive, and detailed chipless RFID labelling solution for textile materials. Finally, the paper investigates the consumer populations readiness to adopt the technology by identifying pain points and outlining the integration of this technology into the textile industry.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 393-402"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Material Fatigue Testing Strategies regarding Failure-Free Load Level of Steel Specimens using Bootstrapping and Statistical Models 基于自举模型和统计模型的钢试件无故障水平材料疲劳试验策略比较

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.095

Nikolaus Haselgruber , Gerhard Oertelt , Kristopher Boss

{"title":"Comparison of Material Fatigue Testing Strategies regarding Failure-Free Load Level of Steel Specimens using Bootstrapping and Statistical Models","authors":"Nikolaus Haselgruber , Gerhard Oertelt , Kristopher Boss","doi":"10.1016/j.procs.2025.01.095","DOIUrl":"10.1016/j.procs.2025.01.095","url":null,"abstract":"<div><div>The analysis of material fatigue data is an important step in the development of complex technical products to achieve a design which reliably withstands field load but avoids over-engineered and further unnecessary weight, energy consumption, and consequently, life cycle costs. The application of statistical methods helps to consider both, the variability of real-world load situations and the variability of material load capacity. However, to provide effective and accurate results, not only analysis methods but also data generation techniques should be selected with care. In this paper, we compare several material fatigue evaluation strategies, all consisting of a data generation/test part and an analysis part. E.g., stair-case, load-step and pearl-string as test procedures and Dixon-Mood analysis, lifetime-stress regression or the random fatigue limit model as analysis methods are investigated. The sensitivity on parameters which have to be set and the accuracy regarding load capacity as well as the required testing effort are compared. Load-step provides the most accurate estimation of the failure-free load level but is the most expensive method. Pearl-string and DoE provide similar results with much less effort and moderately higher uncertainty compared to load-step.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 323-335"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0