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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Leong, Hoilam Wu, Jiang Lai, Yoke Chin Faulon, Loïc Sun, Xu Lee, Boon Giin University of Nottingham Faculty of Science and Engineering Department of Mechanical Materials and Manufacturing Engineering Ningbo China School of Computer Science China

ISBN: (纸本)9798331514846

As the global population ages, sarcopenia - age-related muscle decline - demands innovative solutions. This paper introduces GRIPPY, a VR grip controller that transforms basic handgrip exercises into immersive, gamified tasks. By integrating sensor-based interaction and VR gameplay, older adults can strengthen grip while maintaining higher motivation. A preliminary test with three participants (ages 68-80) revealed key usability insights, underscoring GRIPPY's potential for enhanced rehabilitation. Future work will refine its design and validate long-term efficacy. © 2025 IEEE.

关键词： Elderly Rehabilitation Grip Strength Immersive Exercise Interactive Training Sarcopenia Virtual Reality

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Dataset of Large Gathering Images for Person Identification and Tracking

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computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor Majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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An Adaptive Metaheuristic Framework for Changing Environments 13

An Adaptive Metaheuristic Framework for Changing Environment...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ahmed, Bestoun S. Karlstad University Department of Mathematics and Computer Science Karlstad65188 Sweden Czech Technical University in Prague Faculty of Electrical Engineering Department of Computer Science Prague16627 Czech Republic

ISBN: (纸本)9798350308365

The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a dynamic representation of problems, a real-time sensing system, and adaptive techniques to navigate continuously changing optimization environments. Through a simulated dynamic optimization problem, the AMF's capability is demonstrated to detect environmental changes and proactively adjust its search strategy. This framework utilizes a differential evolution algorithm that is improved with an adaptation module that adjusts solutions in response to detected changes. The capability of the AMF to adjust is tested through a series of iterations, demonstrating its resilience and robustness in sustaining solution quality despite the problem's development. The effectiveness of AMF is demonstrated through a series of simulations on a dynamic optimization problem. Robustness and agility characterize the algorithm's performance, as evidenced by the presented fitness evolution and solution path visualizations. The findings show that AMF is a practical solution to dynamic optimization and a major step forward in the creation of algorithms that can handle the unpredictability of real-world problems. © 2024 IEEE.

关键词： Adaptive Metaheuristics Dynamic Optimization Evolutionary Computation Optimization in Changing Environments Real-Time Algorithm Adaptation

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Complexity of Local Search for Euclidean Clustering Problems 35

Complexity of Local Search for Euclidean Clustering Problems

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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Manthey, Bodo Morawietz, Nils van Rhijn, Jesse Sommer, Frank Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Institute of Computer Science Friedrich Schiller University Jena Germany Institute of Logic and Computation TU Wien Austria

ISBN: (纸本)9783959773546

We show that the simplest local search heuristics for two natural Euclidean clustering problems are PLS-hard. First, we show that the Hartigan-Wong method, which is essentially the Flip heuristic, for k-Means clustering is PLS-hard, even when k = 2. Second, we show the same result for the Flip heuristic for Max Cut, even when the edge weights are given by the (squared) Euclidean distances between the points in some set X ⊆ Rd;a problem which is equivalent to Min Sum 2-Clustering. © Bodo Manthey, Nils Morawietz, Jesse van Rhijn, and Frank Sommer.

关键词： Local search (optimization)

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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Metaheuristics for Financial Investment Strategies: Applications Survey

Metaheuristics for Financial Investment Strategies: Applicat...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Bousbaa, Zineb Bencharef, Omar Cady Ayyad University Computer and Systems Engineering Laboratory Faculty of Sciences and Technology Computer Science Department Marrakesh Morocco

ISBN: (纸本)9798350322972

Machine learning has been widely used as part of financial markets investment strategies, whether for forecasting the financial assets exchange rate, managing market volatility, or solving different classification problems that help with decision-making. Building an investment strategy using a scientific approach requires a massive amount of data, good computational power, and some expertise in the finance industry. Machine learning applications to the financial field, such as price exchange rate prediction, market pattern recognition, or other trading strategy tasks, are considered optimization problems. As they require an efficient algorithm dedicated to finding a global optimum, they can be solved using metaheuristics. In this survey, we study how metaheuristic optimization techniques contribute to building a robust learning model dedicated to financial investment strategy applications. © 2023 IEEE.

关键词： Financial markets

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Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer's Patients 2

Integrating Decision Trees with Cloud Computing for Predicti...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Meenakshi, K. Ravinder, Bathini Pasha, Mohammed Rehaman Thinakaran, K. Jayanayudu, Dabbara Maniraj, S.P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Siddartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer's patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand. © 2024 IEEE.

关键词： Alzheimer's disease Cloud computing Decision trees Early detection Healthcare

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A Review of Fairness Conceptualizations in Electrical Distribution Grid Congestion Management

A Review of Fairness Conceptualizations in Electrical Distri...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： De Winkel, Eva Lukszo, Zofia Neerincx, Mark Dobbe, Roel Tu Delft Faculty of Technology Policy and Management Netherlands Tu Delft Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

ISBN: (纸本)9789531842976

Fairness has recently gained significant attention in the scientific literature on algorithmic control systems for congestion management. However, many diverse conceptualizations of fairness have been presented. This paper aims to categorize these varying conceptualizations by reviewing existing literature on congestion management. It examines how researchers approach decisions concerning the scoping of fairness problems, the selection of fairness principles, and the choice of evaluation metrics. Findings highlight a need for more justification of fairness conceptualizations in literature as well as a need for standardized evaluation metrics and more empirical grounding and validation. The insights provided can help researchers and practitioners consider fairness comprehensively in the design of algorithmic control systems for congestion management. © 2024 IEEE.

关键词： algorithmic control systems congestion management electrical distribution grids fairness

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Improved Alzheimer's Disease Early Prediction using Machine Learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier 4

Improved Alzheimer's Disease Early Prediction using Machine ...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Munawar, Madiha Monika Singh, T. Kishor Kumar Reddy, C. Hanafiah, Marlia Mohd Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Universiti Kebangsaan Malaysia Faculty of Science and Technology Malaysia

ISBN: (纸本)9798331515720

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector Machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. The obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). The Random Forest classifier also achieves higher Time Taken that is 483.654 sec. © 2024 IEEE.

关键词： Alzheimer's Disease Decision Trees Deep Learning Dementia Learning Approaches Machine Learning Random Forest

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