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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251-260 订阅

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Cloud-Enabled Machine Learning System for Real-Time Health Monitoring of Cardiac Patients

Cloud-Enabled Machine Learning System for Real-Time Health M...

引用

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

作者： Madamanchi, Venkata BhujangaRao Chandran, C.P. Madurai Kamaraju University Madurai625021 India Ayya Nadar Janaki Ammal College Department of Computer Science Sivakasi626124 India

ISBN: (纸本)9798331529833

Heart disease remains a leading cause of mortality worldwide, highlighting the need for advanced health monitoring systems for timely intervention. This paper presents PulseGuard, a cloud-enabled machine learning framework for real-time cardiac monitoring. Using cloud infrastructure, PulseGuard integrates wearable devices to continuously collect and analyze patient data, enabling immediate detection of heart irregularities. The system's machine learning models provide responsive anomaly detection, alerting healthcare providers promptly during critical events. Its cloud-based architecture ensures scalability, supporting many patients without sacrificing real-time processing. The experimental evaluations show a 97% accuracy in detecting cardiac anomalies with a detection latency of under one second, outperforming traditional methods in precision and speed. This quick response is crucial in emergencies, reducing time to intervention and potentially enhancing patient outcomes. © 2025 ieee.

关键词： Anomaly detection

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Mitigating data Sparsity in Point-of-Interest Recommendation systems Using Multi-Modal User Profiles 10

Mitigating Data Sparsity in Point-of-Interest Recommendation...

引用

10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Arthan, Sudarat Tamee, Kreangsak Naresuan University Department of Computer Science and Information Technology Phitsanulok Thailand Naresuan University Research Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Department of Computer Science and Information Technology Phitsanulok Thailand

ISBN: (纸本)9798331543273

This study presents a solution to the data sparsity issue in POI recommendation systems by introducing a multi-modal user profile approach called Visual-Textual Collaborative Filtering (VTCF). VTCF integrates textual POI descriptions from Wikipedia with user-generated photos to improve the representation of user preferences. Using publicly available geo-tagged photo datasets, we compared VTCF with various baseline methods, including traditional user-based collaborative filtering and approaches based on visual and contextual preferences. The experimental results demonstrate that our approach successfully addresses the challenges of sparse check-in data, resulting in more accurate and relevant recommendations. The findings suggest that VTCF is a promising method for improving recommendation accuracy. Future work could explore additional data sources and techniques to further enhance POI recommendation systems. Moreover, computational trade-offs and scalability should be considered, as integrating textual and visual data adds system complexity. Strategies such as batch processing and parallel computation could help mitigate these issues for large-scale applications. © 2025 ieee.

关键词： Recommender systems

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TruChain: A Blockchain-based Access Control to Improve the Security of Smart Water Grid systems 17

TruChain: A Blockchain-based Access Control to Improve the S...

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Mondal, Susmita Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several parameters associated with water quality and distribution, leveraging the possibility of performing remote operations. For robust architecture, remote operations rely on application-specific sensors, communication networks, edge control, and cloud computation. Needless to say, the centralized control of edge and cloud infrastructure is prone to attacks and failures, such as unauthorized access or single-point-of-failure. We propose TruChain, a Blockchain-based access control mechanism to eliminate the probability of access takeover and aggregate quorum-based validation on sensory data for transparent provenance reliability. We also provide access revocation to strengthen security by restricting access to sensitive data stored in a privately distributed cloud. We believe our solution has empirical decentralization and a verifiable authorization method by maintaining a tamper-evident, auditable trail of interactive events, building trust without third-party intervention. © 2025 ieee.

关键词： Cloud security

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A Review on Soybean Leaf Pathogens Classification Methodologies

A Review on Soybean Leaf Pathogens Classification Methodolog...

引用

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

作者： Kavathekar, Pranav Deepak Bhushanwar, Kush Agal, Sanjay Parul University Piet Computer Science & Engineering Department Gujarat Vadodara India

ISBN: (纸本)9798331509828

Soybean leaf diseases are one of the major challenges to agriculture in many countries. However, it is advisable to detect and classify these diseases properly and on time. This review focuses on the recent studies concerning various approaches for soybean leaf diseases detection such as Machine Learning, Deep Learning, Transfer Learning. The goal of this paper is to propose a concise and algorithmic overview of the methods, the involved datasets, the means of preparing the data, and the metrics for evaluating the performance of these methods. This review discusses the achievements of the past, challenges of the present, and prospects of future detection of soybean leaf diseases. Its purpose is to assist researchers and practitioners to disseminate existing information and identify directions for improving disease detection systems to produce better innovative results more quickly. © 2025 ieee.

关键词： Adversarial machine learning

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

引用

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

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 ieee.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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A Comparative Study of Traditional and Deep Learning Models Using Emotional Customer data in Automotive Sales Forecasting

A Comparative Study of Traditional and Deep Learning Models ...

引用

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

作者： Godata, Gempar Bambang Irmawati Universitas Multimedia Nusantara Information Systems Study Program Banten Indonesia

ISBN: (纸本)9798331513320

This study investigates the effectiveness of traditional and deep learning models in predicting automotive sales outcomes using customer acoustic and emotional parameters extracted from sales call recordings. A primary dataset of sales conversations was processed using an internal tool, which converts audio into numerical data based on 11 acoustic features such as pitch, volume, and speech rate. Traditional machine learning models, including AdaBoost, Support Vector Machine (SVM), and Random Forest, were compared with deep learning models like Dense Neural Networks (DNN) and TabNet. The optimized DNN model achieved the highest performance, with an F1-Score of 0.7298, balancing precision and recall effectively. TabNet provided competitive results with an F1-Score of 0.7237, offering additional benefits of feature interpretability through its attention mechanism. These findings suggest that while traditional models, particularly AdaBoost, perform adequately, deep learning models, especially DNN and TabNet, are better equipped to capture the complex, non-linear relationships in acoustic and emotional data. The results provide actionable insights for optimizing sales strategies through data-driven decision-making. It offers a foundation for future advancements in predictive analytics within automotive industries using voicebased datasets. © 2025 ieee.

关键词： Contrastive Learning

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Applying Communication Privacy Management Theory to Youth Privacy Management in AI Contexts 4

Applying Communication Privacy Management Theory to Youth Pr...

引用

4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Campbell, Molly Joshi, Sandhya Barthwal, Ankur Shouli, Austin Shrestha, Ajay Kumar Vancouver Island University Computer Science Department Nanaimo Canada Vancouver Island University VIU Affiliate Nanaimo Canada

ISBN: (纸本)9798331518882

The rapid integration of Artificial Intelligence (AI) technologies into the lives of young digital citizens has escalated privacy concerns and the need for critical examination. This study uses Communication Privacy Management (CPM) Theory to understand how youth and critical stakeholders navigate these concerns. A total of 306 participants were surveyed, comprising 146 AI professionals, 127 parents and educators and 33 youths (aged 16-19). Employing a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from open-ended responses. Descriptive statistics reveal distinct perspectives among different demographics regarding data ownership, education, transparency and trust, parental role and perceived risks and benefits associated with AI systems. Structural equation modelling identified key influences on youth privacy management, highlighting the significance of transparency and trust, education and awareness, and parental data sharing among AI professionals, parents, educators, and young digital citizens. The qualitative analysis further underscored unique concerns, emphasizing a lack of understanding and data misuse contributed to the feeling of helplessness shared by all stakeholders. This study underscores the importance of integrating diverse stakeholders' perspectives in the development of AI systems to address the complex challenges faced by youth. Recommendations include collaborative policymaking, implementing user-centric design practices, and enhancing privacy education to empower young digital citizens. © 2025 ieee.

关键词： Differential privacy

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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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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

引用

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

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 ieee.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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Predictive Modeling of Social Media data Using Machine Learning Techniques 2

Predictive Modeling of Social Media Data Using Machine Learn...

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

作者： Anitha, T. Srihitha, G. Sai Aiswarya, G.R.P Lakshmi Raghi, K.R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331505745

Social media platforms, such as YouTube, generate an extensive amount of unstructured data, offering valuable insights into user behavior, engagement patterns, and preferences. This project focuses on predictive analysis of YouTube data using a hybrid machine learning framework. Various video metadata, including descriptions, titles, tags, views, likes, and comments, are analyzed to forecast audience engagement and trends. The pipeline begins with data preprocessing, including handling missing values, removing duplicates, and preparing the data for analysis. Exploratory data Analysis (EDA) is performed to uncover hidden patterns and relationships, followed by feature selection techniques to identify the most influential variables. The dataset is then split into training and testing subsets, and advanced machine learning models are employed to predict video performance and user engagement. The results are evaluated using key performance metrics, delivering actionable insights to improve content strategies and audience targeting. This study highlights the effectiveness of machine learning techniques in optimizing YouTube content and enhancing decision-making in digital marketing. © 2025 ieee.

关键词： Tweets

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