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Zirkus Empathico 2.0: a multiplayer serious mobile game for children with autism spectrum disorder (ASD), with a focus on enhancing social and emotional development

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Multimedia Tools and Applications 2025年 1-24页

作者： Hassan, Ahmed Pinkwart, Niels Shafi, Muhammad Department of Computer Science and Informatics Berlin School of Business and Innovation Karl Marx Strasse 97 - 99 Berlin12043 Germany Institute for Informatic Humboldt University Berlin Rudower Chaussee 25 Berlin Berlin12489 Germany School of Computing Ulster University Belfast Campus United Kingdom Faculty of Computing & Information Technology Sohar University Sohar Oman

This study explores the effectiveness of the multiplayer serious mobile game "Zirkus Empathico (ZE) 2.0" in enhancing social-emotional behaviors in children diagnosed with autism spectrum disorder (ASD). It aims to assess how such games can foster emotional recognition, empathy, and prosocial behavior, thereby supporting the holistic development of children with ASD. An eight-week randomized controlled trial (RCT) was conducted between January and April 2021 at autism centers in Islamabad, Pakistan, and Berlin, Germany. The study involved 107 children aged 5–11 years, with 60 diagnosed with ASD and 47 typically developing children serving as an Active Control Group (ACG). Participants were engaged in sessions using the bilingual"ZE 2.0"game, incorporating multiplayer and emotional learning modules. The intervention required 120 min of weekly training supplemented by daily sessions. Post-intervention assessments demonstrated significant improvements in the ASD group compared to the control group in areas of empathy and emotional awareness, as evidenced by higher scores on the Griffith Empathy Measure (GEM) and multiplayer module evaluations. Notable was the ability of participants to apply learned social skills in dynamic, real-world contexts, which suggests the game’s effectiveness transcends the controlled settings typically associated with ASD interventions. ZE 2.0 adds to the social and teamwork competencies of children with ASD. Besides encouraging peers to be proactive in communications and cooperation, the multiplayer mode provides an immersion learning atmosphere for facilitating social and emotional development. Such an outcome underlines the perspectives of serious games for use as a framework for the treatment and education of children who have ASD. In this manner, serious games can be utilized as a supplementary or alternative means of treating ASD. Future research may focus on distinguishing the effects of intervention from those of other therapy modalit

关键词： Serious games

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Automatic computation of bending sequences for double-head wire bending machines

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International Journal of Computer Integrated Manufacturing 2025年第4期38卷 451-462页

作者： Baraldo, Andrea Bascetta, Luca Caprotti, Fabrizio Chourasiya, Sumit Ferretti, Gianni Ponti, Angelo Sakcak, Basak BLM SPA - BLM GROUP via Selvaregina 30 CO Cantù Italy Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy Center of Ubiquitous Computing Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland

Determining a feasible bending sequence for a double-head bending machine is a challenging and time-consuming task, even for an expert operator. An automatic bending sequence computation algorithm for a single-head bending machine was proposed in a previous work, exploiting A (Formula presented.) graph search algorithm and the representation of the workpiece as an equivalent manipulator. This paper extends that approach to the case of a double-head bending machine, whose complexity, in terms of number of possible solutions to the problem of computing the feasible bending sequences, and of determination of the better sequences, that should exploit parallelism thanks to the execution of simultaneous bends on the two heads, is definitely higher. In this case, the wire has been modelled using two serial-link manipulators with a common base, and the A (Formula presented.) cost function has been modified to promote simultaneous bends. An axes reachability-based path generator and the possibility of partial sequence search have been incorporated as well, to manage the run-time challenges due to an increased number of bends. In this way, an efficient bending sequence computation algorithm for a double-head wire bending machine has been devised, whose performance has been assessed on two industrial test cases, demonstrating its effectiveness and usefulness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Wire

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Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4140-4155页

作者： Vo, Van Nhan Long, Nguyen Quoc Dang, Viet-Hung Ho, Tu Dac Tran, Hung Chatzinotas, Symeon Tran, Dinh-Hieu Sanguanpong, Surasak So-In, Chakchai Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Department of Information Security and Communication Technology Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology - NTNU Norway DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology National Economics University Hanoi12116 Viet Nam University of Luxembourg Luxembourg Department of Computer Engineering Faculty of Engineering Kasetsart University Bangkok10900 Thailand College of Computing Khon Kaen University Khon Kaen40002 Thailand

This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. A secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions. © 2020 IEEE.

关键词： Drones

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Combination of machine learning and data envelopment analysis to measure the efficiency of the Tax Service Office

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PeerJ Computer Science 2025年 11卷 e2672-e2672页

作者： Soffan, Shofinurdin Bramantoro, Arif Alzahrani, Ahmad A. Faculty of Information Technology Universitas Budi Luhur Jakarta Indonesia School of Computing and Informatics Universiti Teknologi Brunei Bandar Seri Begawan Brunei Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The Tax Service Office, a division of the Directorate General of Taxes, is responsible for providing taxation services to the public and collecting taxes. Achieving tax targets efficiently while utilizing available resources is crucial. To assess the performance efficiency of decision-making units (DMUs), data envelopment analysis (DEA) is commonly employed. However, ensuring homogeneity among the DMUs is often necessary and requires the application of machine learning clustering techniques. In this study, we propose a three-stage approach: Clustering, DEA, and Regression, to measure the efficiency of all tax service office units. Real datasets from Indonesian tax service offices were used while maintaining strict confidentiality. Unlike previous studies that considered both input and output variables, we focus solely on clustering input variables, as it leads to more objective efficiency values when combining the results from each cluster. The results revealed three clusters with a silhouette score of 0.304 and Davies Bouldin Index of 1.119, demonstrating the effectiveness of fuzzy c-means clustering. Out of 352 DMUs, 225 or approximately 64% were identified as efficient using DEA calculations. We propose a regression algorithm to measure the efficiency of DMUs in new office planning, by determining the values of input and output variables. The optimization of multilayer perceptrons using genetic algorithms reduced the mean squared error by about 75.75%, from 0.0144 to 0.0035. Based on our findings, the overall performance of tax service offices in Indonesia has reached an efficiency level of 64%. These results show a significant improvement over the previous study, in which only about 18% of offices were considered efficient. The main contribution of this research is the development of a comprehensive framework for evaluating and predicting tax office efficiency, providing valuable insights for improving performance. © (2025), (PeerJ Inc.). All rights reserved.

关键词： Mean square error

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Technological acceptance of non-native speakers on language learning mobile application using augmented reality

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Multimedia Tools and Applications 2025年 1-32页

作者： Suwadi, Nur Asylah Lam, Meng Chun Majid, Nazatul Aini Abd Jalaluddin, Nor Hashimah Aisyah, Aznur Kasdan, Junaini Hussain, Afifuddin Husairi Ahmad, Azlan Ma’arof, Daing Zairi Mixed Reality and Pervasive Computing Lab Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia Bangi43600 Malaysia Center for Research in Language and Linguistics Faculty of Social Sciences and Humanities Universiti Kebangsaan Malaysia Bangi43600 Malaysia Universiti Kebangsaan Malaysia Bangi43600 Malaysia Pusat Citra Universiti Universiti Kebangsaan Malaysia Bangi43600 Malaysia

Although mobile applications have been widely used, there is limited research on the acceptance of mobile-based applications for language learning among foreign learners. Hence, we propose an extended version of the technology Acceptance Model (TAM) that analyzes the key aspects affecting the acceptance of the mobile application by foreign learners. The extended TAM includes external constructs such as augmented reality (AR), content quality, design quality, and interactivity to consider both system and individual levels. Eighty international students from various countries studying Malay were given a TAM questionnaire online. Results revealed positive effects, and notably, the external constructs of AR, content quality, and interactivity are prerequisite factors in extending the TAM. The findings revealed that interactivity is the most significant external construct of Perceived Ease of Use (PEOU), followed by Perceived Usefulness (PU), which exhibits a significant contribution to the user’s attitude. Therefore, these results could guide how educator and higher education centers should approach the incorporation of these technologies in classrooms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Augmented reality

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Forecasting New Mobile Site Location Suitability Using Machine Learning 8

Forecasting New Mobile Site Location Suitability Using Machi...

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8th International Conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Rabbani, Zeeshan Ebrahim Hosseini, Seyed Pervez Chattha, Shahbaz Alandjani, Gasim Abosaq, Nasser Abdul Majid, Mazlina School of Information Technology Whitecliffe College Auckland New Zealand School of Information Technology Whitecliffe College Christchurch New Zealand School of Information Technology Whitecliffe College Wellington New Zealand Yanbu Industrial College Computer Science and Engineering Yanbu Saudi Arabia Universi Malaysia Pahang Al-Sultan Faculty of Computing Abdullah Pekan Malaysia

ISBN: (纸本)9798331539696

The increasing density of mobile networks has made the identification of optimal new coverage sites a critical challenge for mobile operators. Traditional methods relying on manual inspections and council plans are labour-intensive and insufficient. This study addresses this issue by leveraging data science and machine learning techniques to predict the suitability of new mobile cell sites. Utilizing data from Radio Spectrum Management New Zealand and Stats New Zealand, the study employs Random Forest as Machine learning model. Key factors considered include existing operator information and population density. The methodology integrates data cleaning, feature engineering, and model training, with validation performed using Power BI. Results demonstrate an accuracy of 84.62% for the Random Forest model, highlighting the importance of geographical location and population density in site suitability predictions. This approach will streamline the site selection process and enhance service quality, positioning operators as leaders in underserved areas. © 2025 IEEE.

关键词： Random forests

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A Federated Learning Approach to Strengthen IoT-Based Intrusion Detection Against Low-Rate DDoS Attacks 2

A Federated Learning Approach to Strengthen IoT-Based Intrus...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ahmad, Iftikhar King Abdulaziz University Faculty of Computing and Information Technology Department of Information Technology Jeddah21589 Saudi Arabia

ISBN: (纸本)9798331506995

The rising cyber threats have made it necessary to enforce cybersecurity initiatives by organizations. Traditional approaches to intrusion detection systems (IDSs) are mostly centralized and therefore face substantial challenges like data privacy violations, high resource consumption and communication overhead. One promising solution to this issue is federated learning (FL), decentralized machine learning that can enable training models on edge devices to preserve data privacy. Further, FL, a global model is trained with multiple entities without sharing raw data so it can keep their privacy and communication costs are reduced. This is of great value above all in environments as IoT networks where importance of data sensitivity and privacy are crucial. FL achieves this without compromising much on scalability and resource efficiency compared to centralized approaches, which are two of their biggest limitations. Therefore, this paper discusses recent developments in FL for IDS and provides comprehensive analysis along with some initial results of FL in IoT-based intrusion detection. The proposed model achieves 99% accuracy in detecting low-rate DDoS attacks, which outperforms existing approaches. © 2025 IEEE.

关键词： Federated learning

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Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1233-1288页

作者： Mohamad Khairulamirin Md Razali MasriAyob Abdul Hadi Abd Rahman Razman Jarmin Chian Yong Liu Muhammad Maaya Azarinah Izaham Graham Kendall Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Center for Artificial Intelligence Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Medicine Universiti Kebangsaan Malaysia Medical CentreCheras56000Kuala LumpurMalaysia School of Engineering and Computing MILA UniversityNilai71800Negeri SembilanMalaysia School of Computer Science University of Nottingham-Malaysia CampusSemenyih43500SelangorMalaysia

The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem *** hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search *** selection hyper-heuristics have different imple-mentation ***,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different *** question is how to effectively employ them to produce an efficient search ***,the algorithms that competed in the inaugural CHeSC have not been collectively *** work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is *** analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter *** review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these *** effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic *** are also compared with recent trends in *** work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

关键词： Hyper-heuristics search algorithms optimization heuristic selection move acceptance learning diversification parameter control

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural computing and Applications 2025年 1-23页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images

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IEEE Access 2025年 13卷 84024-84037页

作者： Munir, Kashif Usama Tanveer, Muhammad Alyamani, Hasan J. Bermak, Amine Ur Rehman, Atiq Khwaja Fareed University of Engineering and Information Technology Institute of Information Technology Rahim Yar Khan 64200 Pakistan King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah 21589 Saudi Arabia Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

Pneumonia is a critical respiratory condition characterized by inflammation in the alveoli, leading to impaired gas exchange and severe respiratory distress. It poses a significant threat to high-risk populations, including neonates, geriatric patients, and immunocompromised individuals. Early and precise detection is crucial to optimizing treatment strategies and reducing morbidity and mortality rates. To address this problem, we propose PneuX-Net, an ensemble-based feature extraction framework that integrates multiple machine learning (ML) models Random Forest (RF), Gaussian Naïve Bayes (GNB) and K-Nearest Centroid (KNC). Random Forest (RF) builds multiple decision trees to identify complex, non-linear patterns within the feature space. Gaussian Naïve Bayes (GNB) utilizes a probabilistic framework based on Bayes’ theorem to manage uncertainty in feature distributions. K-Nearest Centroid (KNC) improves class distinction by clustering feature vectors according to their proximity to centroids. The ensemble methodology harnesses the complementary strengths of these models, improving feature representation and mitigating overfitting, a prevalent issue in white-box models. To validate the effectiveness of PneuX-Net, we conduct extensive experimentation using a 10-fold cross-validation approach. Our results demonstrate that the PneuX-Net+KNC model achieves a remarkable 99.91% accuracy, highlighting its robustness and reliability in pneumonia classification tasks. This ensemble-driven methodology underscores the potential of machine learning in augmenting clinical decision-making by providing an accurate and automated pneumonia detection framework. © 2013 IEEE.

关键词： healthcare AI machine learning medical diagnosis pneumonia detection PneuX-Net

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