Department of Electrical and Photonics Engineering, Technical University of Denmark, Copenhagen, Denmark Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven

College of Information Technology, Jilin Agricultural University, Changchun, China Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a

1 School of Computer Science and Artificial Intelligence, Hefei Normal University, Hefei, China 2 School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China 3 Electricnic Information Engineering, Xi’ an Technological University, Xi’ an, China Utilizing deep features from electroencephalography (EEG) data for emotional music composition provides a novel approach for creating personalized and emotionally rich music. Compared to textual data, converting continuous EEG and

1 School of Information Engineering, Chuzhou Polytechnic, Chuzhou, China 2 School of Computer, Qinghai Normal University, Xining, China Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, and easy loss of image feature information, a super-resolution reconstruction algorithm of multi-scale dilated convolution network based on dilated convolution is proposed

1 Suzhou Industrial Park Institute of Service Outsourcing, Suzhou, China 2 School of Computer Engineering, Suzhou Vocational University, Suzhou, China Combining item feature information helps extract comprehensive sequential patterns, thereby improving the accuracy of sequential recommendations. However, existing methods usually combine features of each item using a vanilla attention mechanism. We argue that such a combination ignores the interactions between features and does not model integrated

1 Faculty of Computing and AI, Air University, Islamabad, Pakistan 2 Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia 3 Department of Information and Communication Technology, Centre for Artificial Intelligence Research, University of Agder, Grimstad, Norway 4 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia 5 Department of Computer

Faculty of Education, University of Macau, Macau, Macau SAR, China In swimming, the posture and technique of athletes are crucial for improving performance. However, traditional swimming coaches often struggle to capture and analyze athletes’ movements in real-time, which limits the effectiveness of coaching. Therefore, this paper proposes RL-CWtrans Net: a robot vision-driven multimodal swimming training system that provides precise and real-time guidance and feedback to swimmers. The system utilizes

1 School of Finance, Changchun Finance College, Changchun, China 2 VanJee Technology Co., Ltd., Beijing, China 3 Department of Mechani cal and Electrical Engineering, Changchun University of Technology, Changchun, China 4 Department of Control Engineering, Changchun University of Technology, Changchun, China To reduce transportation time, a discrete zeroing neural network (DZNN) method is proposed to solve the shortest path planning problem with a single starting point and a single target point. The

1 College of Computer, Qinghai Normal University, Xining, China 2 Qinghai Provincial Key Laboratory of IoT, Xining, China 3 The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining, China Aiming at the problem that the existing methods are insufficient in dealing with the background noise anti-interference of underwater fish images, a contrastive learning method of ignoring background called CLIB for underwater fish image classification is proposed to improve the

1 Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang, China 2 College of Science, China Jiliang University, Zhejiang, China 3 MeritData Technology Co., Ltd., Shanxi, China Background: Combining machine learning (ML) with gait analysis is widely applicable for diagnosing abnormal gait patterns. Objective: To analyze gait adaptability characteristics in stroke patients, develop ML models to identify individuals with GAD, and select optimal