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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

Neural Computing and Applications 2025年 1-19页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience

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Alexandria Engineering Journal 2025年 127卷 380-391页

作者： Alrashdi, Ibrahim Abd El-Aziz, Rasha M. Taloba, Ahmed I. Farsi, Mohammed Department of Computer Science College of Computer and Information Sciences Jouf University Al-Jouf Sakaka73211 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu966144 Saudi Arabia

Major concerns occur in maintaining a sustainable food supply due to population expansion, supply chain interruptions, and climate-related changes. Traditional forecasting models, such as ARIMA, LSTM, and GRU, fail to deal with the dynamic fluctuations and long-range correlations. These methods often have reduced accuracy, high computational cost, and poor generalization when applied to shifting patterns in the food supply system. Based on the abovementioned limitations, this study proposes a Hybrid TCN-Transformer Model by combining the strength of Temporal Convolutional Networks (TCN) with Transformer Attention to provide accurate and efficient food supply prediction. Thus, the project's key objective is to develop a deep learning system that effectively handles massive quantities of temporal data while preserving dependence in short and long durations. The proposed method is novel since it integrates TCN's causal convolutions for extracting sequential patterns. It uses a Transformer Model's self-attention mechanism to capture the complex interactions across time steps. Hybrid design enables faster training, increased interpretability, and better prediction accuracy than current methods. Results from experiments have revealed that the suggested model surpasses the performance of the stand-alone TCN, ARIMA, LSTM, and GRU models in terms of accuracy of predictions, efficiency of computations, and adaptability. Findings thus far suggest deep learning-based predictive analytics has helped improve the food supply chain management by mitigating food wastage, making it environmentally resilient. © 2025 The Authors

关键词： Deep learning

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AI-powered dining: text information extraction and machine learning for personalized menu recommendations and food allergy management

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International Journal of information Technology (Singapore) 2025年第4期17卷 2107-2115页

作者： Brahimi, Samiha Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia

Individuals with food allergies face limitations in social events and restaurant dining. Artificial intelligence solutions should be offered to this category. In this paper, a recommender system is proposed for the benefit of people with food allergies. The system aims to identify convenient options for the user in a restaurant/hotel menu. The system collects user’s allergy information and the restaurant menu, it extracts dishes names using a machine learning model. Then it conducts search about the recipes of these dishes and identify allergen-free ones. The system has been implemented as a mobile application involving a Naïve Bayes classification model and a web search API. The performance of the classifier was significant (accuracy 87%). Yet, an enhancement approach was introduced to increase the accuracy to 90%. In addition, an expert-driven test has been conducted and 98.5% of the system allergen identification was accurate in comparison with the original recipes used by restaurants’ chefs. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： information retrieval Machine learning Menu recommendation Recommender systems

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Domain generalization with semi-supervised learning for people-centric activity recognition

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Science China(information Sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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Research in computer information systems at Georgia State University: A balanced approach 24

Research in Computer Information Systems at Georgia State Un...

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24th Annual Hawaii International Conference on System Sciences, HICSS 1991

作者： Vaishnavi, Vijay K. Yoon, Seok-Joong Buchanan, Gary C. Department of Computer Information Systems Georgia State University P.O. Box 4015 AtlantaGA30302-4015 United States

An overview is provided of the research conducted in computer information systems at Georgia State in the areas of systems development, software engineering, management of information systems, expert/decisionsupport systems and AI, data communications, data structures and algorithms, and computer graphics. The balanced approach underlying this research is discussed as it relates to the doctoral program along with how this approach has influenced the program in terms of research, dissertations, and courses taught. © 1991 IEEE.

关键词： computer graphics

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Exploring Artificial Intelligence’s Potential in Developing Advanced Distributed Denial of Service Defense Strategies

Exploring Artificial Intelligence’s Potential in Developing...

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International Conference on Computing and Machine Learning, CML 2024

作者： Alhindi, Ahmad Computer Science and Artificial Intelligence Department College of Computer and Information Systems Makkah Saudi Arabia

ISBN: (纸本)9789819778386

Facing the growing menace of distributed denial of service (DDoS) attacks, there’s a critical demand for enhanced detection mechanisms. This necessitates a shift from traditional methodologies to more advanced, dynamic detection techniques. The significance of advanced computational methods in pinpointing DDoS threats is increasingly recognized. This paper delves into the crucial role these techniques play in detecting DDoS activities, with a focus on the adaptability of network-based algorithms and the proficiency of advanced models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and autoencoders in identifying complex attack patterns that traditional methods may overlook. Our findings reveal that while advanced computational strategies offer considerable benefits over conventional methods in terms of detection accuracy and adaptability to evolving threats, they also present unique challenges, including higher computational demands and the need for continuous model training to keep pace with new attack vectors. The comparative analysis underscores the operational advantages and potential limitations of these approaches, providing a nuanced understanding of their application in real-world scenarios. This investigation aims to present a current view of advancements in digital security techniques and to serve as a basis for future research and the development of innovative, practical solutions. It suggests that enhancing the resilience of digital infrastructures against DDoS attacks will require not only technological innovation but also a comprehensive strategy that includes ongoing research, collaboration among stakeholders, and the adoption of adaptive, scalable detection systems capable of responding to the dynamic nature of cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Multi-radio fast routing recovery protocol based on spectrum situation awareness

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Chinese Journal of Aeronautics 2025年第3期38卷 435-446页

作者： Chuanxin ZHOU Chao DONG Xiaojun ZHU Fuhui ZHOU Qihui WU Zhengrui QIN Ministry of Industry and Information Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China School of Computer Science and Information Systems Northwest Missouri State UniversityMaryville 64468USA

UAV networks often encounter jamming attacks, under which multi-radio protocols have to switch radios to accelerate communication recovery. However, the existing protocols rely on exchange of hello messages to detect jamming, leading to long sensing time and thus slow routing recovery. To address the issues raised by jamming attacks, we propose a new routing protocol, Electromagnetic Spectrum situation awareness Optimized Link State Routing (ESOLSR) protocol, to improve the existing OLSRv2 protocol. ESOLSR utilizes the spectrum situation awareness capability from the physical layer, and adopts joint-updating of link status, updating of interface functions, and adaptive adjustment of parameters. Our simulation results show that the improved protocol, ESOLSR, can recover routing and resume normal communication 26.6% faster compared to the existing protocols.

关键词： UAV ad hoc network OLSR protocol Routing recovery Electromagnetic spectrum situation awareness Multi-radio

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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Bayesian Deep Learning Meets Self-Attention: A Risk-Aware Approach to Advertisement Optimization

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IEEE Access 2025年 13卷 86413-86429页

作者： Jha, Arti Bhatia, Ashutosh Tiwari, Kamlesh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani Campus India

In the highly competitive landscape of e-commerce advertising, maximizing Return on Advertising Spend (ROAS) is critical, yet remains inherently uncertain due to auction-based bidding dynamics and fluctuating market conditions. Traditional deterministic models fail to capture this uncertainty, necessitating a probabilistic approach that balances predictive accuracy with interpretability. To address this challenge, the paper proposes a novel Hierarchical Bayesian Deep Learning framework that integrates a Bayesian Belief Network (BBN) for structured probabilistic reasoning and a Mixture Density Network (MDN) for full distributional modeling of ROAS. The BBN models dependencies among campaign variables, offering interpretable insights, while the hierarchical deep learning architecture overcomes scalability limitations in high-dimensional settings through self-attention mechanisms. Experiments demonstrate up to 22.8% lower RMSE and 27.4% better Negative Log Likelihood (NLL) and up to 31.2% lower Kullback-Leibler divergence (KLD) than state-of-the-art methods (DeepAR, Prophet, NGBoost), achieving an R² of 98% with an inference speed of 5.2 ms per campaign, making real-time bidding feasible. Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. By providing precise, uncertainty-aware, and explainable predictions, this approach enables adaptive bidding strategies, optimized budget allocation, and risk management, setting a new benchmark for intelligent decision-making in digital advertising. © 2013 IEEE.

关键词： Deep Learning Online Advertising Optimization Probabilistic Forecasting Return on Ad Spend (ROAS) Uncertainty Quantification

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Responding to platform owner moves: A 14-year qualitative study of four enterprise software complementors

Responding to platform owner moves: A 14-year qualitative st...

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作者： Kude, Thomas Huber, Thomas L. Faculty of Information Systems and Applied Computer Sciences University of Bamberg Germany Department of Information Systems Decision Sciences and Statistics ESSEC Business School Cergy France

Integrating the competition and the cooperation perspectives on value co-creation in platform ecosystems, this study explores complementor responses to platform owner moves that adversely affect the complementor's positioning. Our primary focus is to discern dynamic patterns of complementor responses and to understand their role in the process of re-stabilising the complementor's positioning. To this end, we conducted a 14-year longitudinal study, analysing 21 move-response instances across four platform partnerships. Our findings reveal three distinct complementor response archetypes: insist, pivot, and detach. Over time, complementors combine these archetypes into three unique response patterns. Through progressive diverging, complementors sidestep platform owner moves by diversifying their offer beyond the focal ecosystem. Although this can entail substantial multi-homing costs, it reduces dependence on the platform owner and bolsters resilience against future moves. Through adaptive oscillating, complementors use platform owner moves as opportunities to gradually diversify their offer within the focal ecosystem. This stepwise market expansion makes them resilient against future moves, while mitigating costs and efforts related to multi-homing. Through persistent insisting, complementors can re-establish their previous positioning, but at the cost of increased dependence on the platform owner, leaving the complementor vulnerable to future moves. We synthesise these findings in our process model of complementor positioning. Emphasising the importance of complementor responses in fostering resilient software ecosystems, this model bears important implications for research on platform governance and platform-dependent entrepreneurship. © 2024 The Authors. information systems Journal published by John Wiley & Sons Ltd.

关键词： Ecosystems

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