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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology SydneyNSW2007 Australia Institute of Software Chinese Academy of Sciences Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 1982-2012 IEEE.

关键词： Qubits

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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STGCN-LSTM FOR OLYMPIC MEDAL PREDICTION: DYNAMIC POWER MODELING AND CAUSAL POLICY OPTIMIZATION

arXiv

引用

arXiv 2025年

作者： Wang, Yiquan Wang, Jiaying Huang, Tin-Yeh Yang, Jingyi Wang, Guanjie Xu, Zihao College of Mathematics and System Science Xinjiang University Xinjiang Urumqi830046 China Intelligent Software Research Center Institute of Software Chinese Academy of Sciences Beijing100190 China Shenzhen X-Institute Shenzhen518055 China College of Architecture and Environment Sichuan University Chengdu610065 China Department of Industrial and System Engineering Faculty of Engineering The Hong Kong Polytechnic University 999077 Hong Kong School of Design and Art Hunan Institute of Engineering Xiangtan411104 China School of Computer and Artificial Intelligence Huaihua University Huaihua418099 China Faculty of Science and Engeerning The University of Nottingham-Ningbo Ningbo315100 China

This paper proposes a novel hybrid model, STGCN-LSTM, to forecast Olympic medal distributions by integrating the spatio-temporal relationships among countries and the long-term dependencies of national performance. The Spatial-Temporal Graph Convolution Network (STGCN) captures geographic and interactive factors-such as coaching exchange and socio-economic links-while the Long Short-Term Memory (LSTM) module models historical trends in medal counts, economic data, and demographics. To address zero-inflated outputs (i.e., the disparity between countries that consistently yield wins and those never having won medals), a Zero-Inflated Compound Poisson (ZICP) framework is incorporated to separate random zeros from structural zeros, providing a clearer view of potential breakthrough performances. Validation includes historical backtracking, policy shock simulations, and causal inference checks, confirming the robustness of the proposed method. Results shed light on the influence of coaching mobility, event specialization, and strategic investment on medal forecasts, offering a data-driven foundation for optimizing sports policies and resource allocation in diverse Olympic contexts. © 2025, CC BY-NC-SA.

关键词： Resource allocation

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VGGBM-Net: A Novel Pixel-Based Transfer Features engineering for Automated Coffee Bean Diseases Classification

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IEEE Access 2025年 13卷 83580-83590页

作者： Muhammad Shadab Alam Hashmi Azam Mehmood Qadri Ali Raza Saleem Ullah Aseel Smerat Changgyun Kim Muhammad Syafrudin Norma Latif Fitriyani Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Software Engineering The University of Lahore Lahore Pakistan Department of Precision Medicine School of Medicine Sungkyunkwan University Suwon Republic of Korea Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact and Outcome Chitkara Institute of Engineering and Technology Chitkara University Rajpura Punjab India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Department of Electronic and AI System Engineering Kangwon National University Samcheok Republic of Korea Department of Artificial Intelligence and Data Science Sejong University Seoul Republic of Korea

Although coffee is a crucial commodity for developing nations’ economies, processing defects can significantly impact the safety, commercial value, and quality of coffee beans. Traditional coffee bean grading methods are labor-intensive and prone to error, necessitating automated and accurate classification of bean diseases. This study proposes a novel transfer learning-based deep neural network, VGGBM-Net, for classifying coffee bean diseases. It focuses on the timely and precise identification of diseases to improve export quality without compromising safety during production. In this research, the proposed approach is trained using a dataset USK-Coffee of coffee beans to classify healthy and unhealthy beans. The performance of multiple machine learning classifiers, including Random Forest (RF), Logistic Regression (LoR), LightGBM (LGBM), and K-Nearest Neighbor Classifier (KNC), is evaluated alongside neural network techniques such as Convolutional Neural Networks (CNN) and VGG-19. A novel transformation of the VGG-19 model for feature engineering based on transfer learning is introduced, where spatial features extracted from coffee bean images are transformed into class prediction probabilities using LGBM. These enhanced features are then used as inputs for advanced machine-learning algorithms. Unlike traditional models, this feature extraction enhances classification accuracy and robustness. This transformation improves feature extraction by capturing more discriminative patterns, leading to superior performance compared to conventional methods. Experimental results highlight the superior performance of the LGBM classifier, achieving an impressive 99% accuracy, recall, f1, and precision score of 98% with a computational runtime of just 0.084 seconds. K-fold cross-validation ensured the robustness of the models, and optimization techniques were applied to fine-tune parameters for maximum accuracy. This research establishes a highly effective framework for automat

关键词： Accuracy Feature extraction Diseases Deep learning Convolutional neural networks Transfer learning Production Nearest neighbor methods Training Classification algorithms

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Policy-based approach to detect and resolve policy conflict for static and dynamic architecture

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Journal of Theoretical and Applied Information Technology 2012年第2期37卷 268-278页

作者： Mansor, Abdelhamid Abdelhadi Wan Kadir, M.N.W. Anwar, Toni Elias, Hidayah Department of Computer Sciences Faculty of Mathematical Sciences University of Khartoum Sudan Software Engineering Department Faculty of Computer Science and Information System UniversitiTeknologi Malaysia Malaysia

Current research efforts are being directed to commit with the long-term view of self-management properties for wireless telecommunications. One of the key approaches that have been recognized as an enabler of such a view is policy-based management. Policy-based management has been mostly acknowledged as a methodology that provides flexibility, scalability, adaptability and support to automatically assign network resources, control Quality of Service and security, by considering administratively specified rules. The hype of policy-based management was to commit with these features in run-time as a result of changeable network conditions resulting from the interactions of users, applications and existing resources. To detect and resolve potential static and dynamic conflicts between the rules and configurations from different administrative domains, a policy-based manager coordination PobMC framework which is based on Event- Condition-Action (ECA) is proposed in this paper. The framework is responsible of potential conflicts between applications included in the system. Furthermore, the paper provides a guideline to avoid policy conflicts between different domains configurations, and resolve the conflicts during runtime. PobMC depends on, static and dynamic analysis to reduce potential conflicts. The paper briefly outlines some of the most prominent research issues that have been discussed for large-scale adoption of policy-based systems. Then we present a description of the conflict detection and resolution algorithms that achieves the automated enforcement of obligations for resource-handling based on management policies. © 2005 - 2012 JATIT & LLS. All rights reserved.

关键词： Quality of service

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Application of Artificial Intelligence Methods for Generating Mathematical Problems in Primary Education: Analysis of Effectiveness and Prospects for Automated Assessment 7

Application of Artificial Intelligence Methods for Generatin...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Salavatovich, Nazipov Rustam Yurievna, Avksentieva Elena ITMO University Faculty of Software Engineering and Computer System Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer System Saint-Petersburg Russia

ISBN: (纸本)9798350354973

The present study aims to evaluate the effectiveness of using modern neural network models (ChatGPT 4o, Claude 3.5 Sonnet, GigaChat 3.0, YandexGPT 3) for generating mathematical problems for elementary school students. The research conducts a comparative analysis of the quality of generated problems, assesses the possibility of their direct use without expert review, and develops recommendations for automating the quality control process. The methodology includes the generation of various types of mathematical problems, their expert evaluation according to developed criteria, and statistical analysis of the results. The study identifies the strengths and weaknesses of each model, determines the types of problems in which the models demonstrate the best results, and suggests directions for further improvement of AI systems in the educational context. © 2024 IEEE.

关键词： Neural network models

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A knowledge-based digital dashboard for higher learning institutions

引用

European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008

作者： Wan Mohd, Wan Maseri Binti Embong, Abdullah Zain, Jasni Mohd Faculty of Computer System and Software Engineering University Malaysia Pahang

ISBN: (纸本)3540874801

We propose a novel approach of knowledge discovery method by adopting dashboard concept and incorporating elements of data clustering, visualization and knowledge codification. The dashboard was designed to help the higher institution to explore the insight of student performance by analyzing significant patterns and tacit knowledge of the experts in order to improve decision making process. The system has been developed using system development life cycle (SDLC) methodology and coded in web-based and open source environment. The dashboard architecture and software are presented in this paper. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Data mining

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