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Remote Patient Healthcare Monitoring and Pose Recognition over Video Sensors

Traitement du Signal 2025年第2期42卷 995-1005页

作者： Alabdullah, Bayan Tayyab, Muhammad Alhasson, Haifa F. Alshammari, Naif S. Jalal, Ahmad Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Computer Science Air University Islamabad44000 Pakistan Department of Information Technology College of Computer Qassim University Buraydah52571 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Majmaah University Majmaah11952 Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of

Considering the fact of increasing population and as a result, the number of patients is constantly increasing the delivery of medical services must be prompt and of good quality. There is no question that any perfect healthcare system should track the given activities, behaviours, schedules, and even general health of the patients. The focus of this study is in utilizing ML and DL in tracing patients and diagnosing ailments. Mounted cameras from different orientations and placements are intended for health monitoring and event surveillance for possible medical issues in the future. Classical ML and more advanced DL methods are applied for analyzing physiological and behavioral patterns to identify health conditions and stress the importance of high accuracy and real-time performance for practical purposes. Initial outcomes show that patient outcomes are enhanced by getting timely medical advice while decreasing hospital-based burdens on staff. Two challenging datasets, ETRI-Activity3D and NTU-RGB-D, are pre-processed to remove unimportant data and reduce computational load. A strong multi-feature description system can extract gradient values, and the process is followed by feature matching and feature optimization. Then, in the last Maximum Entropy Markov Model (MEMM) classifier is used, achieving an accuracy of 97.2% for ETRI-Activity3D, and 98.3% for the NTU-RGB-D dataset. ©2025 The authors.

关键词： Remote patient monitoring

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Understanding User Attention and Engagement in Digital Marketing: An Eye-Tracking Analysis of Native Advertisements 24

Understanding User Attention and Engagement in Digital Marke...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Theodora Kremantala Stefanos Balaskas Maria Rigou Computer Engineering and Informatics Department University of Patras Patras Greece Department of Management Science and Technology University of Patras Patras Greece

ISBN: (纸本)9798400713170

This study investigates the effectiveness of various types of native ads in terms of capturing users' attention and engagement using eye-tracking data to analyze user interactions. To this end, 47 participants compared four types of native ads with two traditional banner ads embedded within a website designed specifically for the needs of the study. Results confirm that native ads, and particularly content integrated ones like recommendation widgets and native ads in-feed, attract higher levels of attention compared to traditional banner ads. These ads maintain high deception rates, with users mostly failing to recognize them, a fact that raises questions about users' trust and annoyance. The variation that creates the highest click-through rate is the promoted listing, while typical banner advertisements attracted limited engagement suffering from banner blindness. This study highlights the delicate balance between effective ad placement and ethical transparency by advertisers, emphasizing the need for clear labelling to maintain user trust. These findings present useful insights for marketers and interface designers, with guidance on creating effective yet usable ad designs in digital spaces.

关键词： dark design patterns

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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Computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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TTT-SEG: A Multi-structure Segmentation on Cardiac MRI using Vision Testing-time Training Network

TTT-SEG: A Multi-structure Segmentation on Cardiac MRI using...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Su, Vanessa Yuan, Xiaohan Pan, Shaoyan Hu, Mingzhe Chen, Xiaoqian Tian, Sibo Yang, Xiaofeng Department of Computer Science Emory University AtlantaGA United States Department of Radiation Oncology School of Medicine Emory University AtlantaGA United States Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9781510685987

We propose TTT-SEG, a Test-Time Training Segmentation model, for automatic cardiac organ segmentation on MRIs. Manual contouring in MR imaging is time-consuming and labor-intensive, making automatic segmentation crucial for efficient therapy workflows. Despite advancements in Convolutional Neural Networks (CNNs) and Vision Transformer-based Networks achieving state-of-the-art accuracy, their performance is hindered by the lack of large-scale MRI datasets, leading to poor generalization on new patients. TTT-SEG addresses this issue by integrating a CNN with a novel learning-to-learn strategy. Our model features a dual-loop mechanism: a Reconstruction Learning Loop (RLL) for learning self-supervised features and an Adaptive Learning Loop (ALL) for segmentation features. Unlike the traditional networks, the RLL learns even during testing, improving segmentation accuracy for specific samples. Evaluating TTT-SEG on the Automated Cardiac Diagnosis Challenge (ACDC) dataset shows superior performance in segmenting the left and right ventricles and myocardium, outperforming methods like Vnet, nnUnet, SwinUNETR, and nnFormer. These results demonstrate TTT-SEG's potential to enhance cardiac organ segmentation and improve cardiac-related diagnosis and therapy workflows. © 2025 SPIE.

关键词： Electrocardiography

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Optimizing Logistics in E-Commerce Using Deep Random Forest for Enhanced User Satisfaction 3

Optimizing Logistics in E-Commerce Using Deep Random Forest ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Nisha, G.Anne Sreetha, P. Seetha, A. Manickam, K. Jagannathan, Sharath Kumar Sekar, S.D. Kyoto Computer Gakuin Department of Informatics Kyoto Japan Kpr Institute of Engineering and Technology Department of Biomedical Engineering Tamil Nadu India S. A. Engineering College Department of Information Technology Thiruverkadu Chennai India Sona College of Technology Department of Mathematics Tamil Nadu India Data Science Institute Saint Peter's University NJ United States R.M.K.Engineering College Department of Mechanical Engineering TamilNadu Chennai India

ISBN: (纸本)9798331542375

Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep Random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages. © 2025 IEEE.

关键词： Marketplaces

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Editorial

Learning and Analytics in Intelligent Systems

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Learning and Analytics in Intelligent Systems 2025年 47卷 vii-x页

作者： Tsihrintzis, George A. Favorskaya, Margarita N. Kountcheva, Roumiana Patnaik, Srikanta Department of Informatics University of Piraeus Piraeus Greece Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia TK Engineering Sofia Bulgaria Interscience Institute of Management and Technology Odisha Bhubaneswar India

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A Mamdani Fuzzy Inference System for Comprehensive Diabetes Risk Evaluation

A Mamdani Fuzzy Inference System for Comprehensive Diabetes ...

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International Conference on Electrical, Computer and Communication engineering (ECCE)

作者： Tanjina Ahmed Tuly Tanjid Ahammed Shafin Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shamsul Arefin Mohammad Shorif Uddin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetes mellitus is a chronic condition characterized by insufficient insulin production and the inability to effectively utilize the insulin hormone, leading to elevated blood glucose levels. As diabetes is incurable and persists throughout a patient's lifetime, effective management requires early identification of risk factors to ensure continuous and feasible treatment. This study proposes a unique decision support system based on the Mamdani Fuzzy Inference System (MFIS) to predict diabetes risk. This study utilizes a publicly available diabetes dataset containing 442 data that includes nine key health metrics such as BMI, blood glucose, cholesterol levels, and blood pressure to predict the risk of diabetes. The dataset aims to support early diagnosis and better diabetes management by providing insights into individual risk factors. This resource contributes to the development of more accurate decision-support systems in healthcare. We identify key variables and establish a membership function to assess individual risk. The Fuzzy Inference System (FIS) is well-suited for handling complex and uncertain conditions, making it an effective tool for this application. The advantages of the MFIS include its ability to incorporate expert knowledge through linguistic variables and fuzzy rules. This system demonstrates the practical benefits of MFIS in developing intelligent decision-making tools, particularly in the domain of diabetes risk prediction.

关键词： Fuzzy logic Decision support systems Accuracy Uncertainty Predictive models Blood pressure Diabetes Glucose Insulin Blood

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Predicting Heat Stroke Risk: A Clinical Decision Support System using Fuzzy Association Mining Approach

Predicting Heat Stroke Risk: A Clinical Decision Support Sys...

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International Conference on Electrical, Computer and Communication engineering (ECCE)

作者： Abdullah Al Noman Rinvi Jaman Riti Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shorif Uddin Mohammad Shamsul Arefin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Heat stroke is a serious medical condition that requires immediate treatment and is exacerbated by intense heat and climate change. This paper introduces a Clinical Decision Support System (CDSS) for heat stroke risk assessment based on Fuzzy Association Rule Mining. The system evaluates key attributes extracted from the Stanford Bioengineering Senior Capstone Project dataset, including daily water intake, cardiovascular history, heat index, environmental and rectal temperatures, blood pressure, pulse rate, humidity, age, and sex. These attributes help identify detailed patterns associated with heat stroke risk. Using fuzzy logic, the CDSS addresses the inherent vagueness of medical information through a set of "if- then" rules designed for healthcare practitioners. The system is tested with historical data, demonstrating its effectiveness in recognizing critical parameters to provide personalized, timely attention to potential heat stroke cases. It aims to reduce heat- related illnesses and fatalities by enabling rapid data-driven decision-making in healthcare. Despite certain limitations, this study highlights the necessity of intelligent systems for proactive health management.

关键词： Decision support systems Climate change Fuzzy systems Risk management Predictive models Medical services Medical conditions

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Educational data mining: a 10-year review

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Discover Computing 2025年第1期28卷 1-25页

作者： Kalita, Emi Oyelere, Solomon Sunday Gaftandzhieva, Silvia Rajesh, Kandala N. V. P. S. Jagatheesaperumal, Senthil Kumar Mohamed, Asmaa Elbarawy, Yomna M. Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif Theodorou, Paraskevi Hilčenko, Slavoljub Hazarika, Jiten Ali, Tazid Centre for Computer Science and Applications Dibrugarh University Dibrugarh 786004 India Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå 931 87 Sweden Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter United Kingdom Research Group On Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School (JBS) University of Johannesburg Johannesburg South Africa Faculty of Mathematics and Informatics University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria School of Electronics Engineering VIT-AP University Vijayawada India Department of Electronics & Communication Engineering Mepco Schlenk Engineering College Sivakasi 626005 India Department of Mathematics Faculty of Science Al-Azhar University (Girls Branch) Cairo 11754 Egypt Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir 10200 Turkey Engineering and Informatics Department Klaipėdos Valstybinė Kolegija/Higher Education Institution Klaipeda 92294 Lithuania Department of Digital Systems University of Piraeus Piraeus Greece College for Vocational Education of Preschool Teachers and Coaches Subotica Serbia Department of Statistics Dibrugarh University Dibrugarh 786004 India

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations. © The Author(s) 2025.

关键词： Artificial intelligence in education Education data mining Explainability in education Multimodal learning analytics

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Classification of the Heartbeats in Electrocardiograms with K-Nearest Neighbors Algorithm, Random Forests, and Support Vector Machines - A Pilot Study 21st

Classification of the Heartbeats in Electrocardiograms with ...

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21st Scientific Conference on Medical and Sport Technologies, 2024

作者： Vural, Muzaffer Samed Heryan, Katarzyna Sieciński, Szymon Bilko, Pawel Grzegorzek, Marcin Faculty of Automatic Control Electrical Engineering and Biomedical Engineering Department of Measurement and Electronics AGH University of Science and Technology Aleja Adama Mickiewicza 30 Kraków30-059 Poland Institute of Medical Informatics University of Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Faculty of Biomedical Engineering Department of Medical Informatics and Artificial Intelligence Silesian University of Technology Franklina Delano Roosevelta 40 Zabrze41-800 Poland Department of Clinical Engineering Academy of Silesia Rolna 43 Katowice40-555 Poland German Research Center for Artificial Intelligence Ratzeburger Allee 160 Lübeck23562 Germany

ISBN: (纸本)9783031821424

Electrocardiography (ECG) is vital for diagnosing and monitoring cardiovascular diseases. Automatic interpretation of ECG signals, particularly classifying heartbeats, is essential in diagnosing these conditions. We compared the performance of three classifiers (K-Nearest Neighbors (KNN), Random Forests (RF), and Support Vector Machine (SVM)) using heartbeat and ECG signal features. We trained the classifiers on 173 heartbeats from 11 recordings in the MIT-BIH Arrhythmia (MITDB) Database. To address imbalance, we used SMOTE (Synthetic Minority Over-sampling Technique) to increase the number of abnormal heartbeats. We divided the data into 70% for training set and 30% for testing set, evaluating performance through 10-fold cross-validation. Results demonstrated state-of-the-art performance for KNN, RF, and SVM. KNN achieved an overall accuracy, positive predictive value, and sensitivity of 0.977, 0.978, and 0.978, respectively. RF achieved an overall accuracy, precision, and sensitivity of 0.971, 0.972, and 0.971, respectively. SVM achieved a recall of 0.988, 0.989, and 0.988. SVM exhibited the highest overall performance in heartbeat classification (0.99), followed by KNN (0.98) and RF (0.97). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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