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Articulation disorder, as a medical difficulty, currently mainstream speech recognition technologies are not well adapted to the needs of this field. At the same time, speech recognition technology for dysarthria utilizes a combination of pre training and personalized training to further improve algorithm performance and reduce recognition word error rate through data-driven methods. However, currently, speech recognition technology for dysarthria still has a certain distance from practical commercial use, and its development is limited by data scale and technology. So far, there have been no comprehensive articles on speech recognition for dysarthria. It is urgent to compare and analyze the construction methods and advanced technologies of various datasets in this field, in order to facilitate researchers entering the field to quickly acquire knowledge in this field. This paper conducts a survey on existing datasets, mainstream algorithms, and evaluation methods, and summarizes the scale, form, and characteristics of mainstream speech impairment datasets at home and abroad. It analyzes the mainstream algorithms for speech recognition with dysarthria, and provides the performance and characteristics of different algorithms. Finally, the performance evaluation indicators of the algorithm model based on the severity level of patients with dysarthria are studied, and future research directions are discussed, in order to provide help for the researchers engaged in speech recognition with dysarthria and assist in the rapid development of this field. [ABSTRACT FROM AUTHOR]

The intelligent classroom is a popular application scenario in the field of artificial intelligence. This paper proposes a face detection algorithm based on improved YOLOv5, named YOLOv5-SASA, to address the issues of missed or false detection caused by small or occluded faces in images captured by cameras located far away or at an angle. The algorithm consists of three parts. Firstly, the CSPDarknet53 network is utilized in the backbone layer, and the BasicRFB module is used in the final spatial pooling layer to enhance the network's feature extraction ability. Secondly, the NWD loss function is employed to improve the model's robustness in detecting small targets. Thirdly, the independent self- attention mechanism module SASA is introduced in the head layer to address the issue of face occlusion and reduce the model's parameter count. Finally, the improved YOLOv5 network is optimized by reducing the number of neurons in the middle layer channels and adjusting the learning rate to avoid overfitting. Experimental results demonstrate that the proposed method outperforms the original network in the easy, medium, and hard levels of the WiderFace validation set, achieving accuracies of 97.5%, 96.3%, and 86.5%, respectively, which effectively improves the accuracy of face detection in classroom scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Message propagation algorithms from statistical physics are widely used in various fields of artificial intelligence, especially in solving combinatorial optimization problems. According to the related literatures of message propagation algorithm, this paper summarized the history of message propagation algorithm and its related application. According to the development of message propagation algorithm, it introduced the concepts of information propagation algorithm for solving the satisfiability problem, which mainly involved the warning propagation algorithm, the belief propagation algorithm and the survey propagation algorithm. This paper described the convergence and effectiveness research of the three kind of algorithms, summarized the application of each algorithm in related fields, and summarized the research path and application direction of message propagation algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Model counting is the number of models for a given proposition formula, which is a basic problem in the field of artificial intelligence. There are many exactly-one constraints in many practical problems such as Bayesian networks, bounded model detection, and accurate set coverage. A common processing method is to encode exactly-one constraints as CNF formulas, and then call the model counter to solve them. This method expands the scale of the proposition formula and easily leads to too long solution time. This paper respectively proposes the ECR algorithm that restores exactly-one constraints from the CNF formula and the ECP algorithm that handles exactlyone constraints. The ECR algorithm can significantly improve the solution efficiency of the C2D compiler. Based on the latest model counter ExactMC, this paper improves the model counter ECMC that can recognize and handle exactly-one constraints separately. The experimental results show that the time efficiency of ECMC is significantly improved compared to ExactMC. [ABSTRACT FROM AUTHOR]

Objectives: In recent years, the brain‑like navigation algorithm is a new research hotspot, which is expected to achieve autonomous navigation by imitating the ability of biological navigation. The core is‑ sue is how to improve generalization ability. Methods: This paper introduces the research background and theoretical basis of the brain‑like navigation algorithm. After investigation, we propose a computational framework of brain ‑ like navigation algorithm. The outstanding works in this field are discussed and analyzed under this framework, and we carried out experimental verification of some basic methods. Results： The main contributions of this paper are: (1) Comprehensively introduces and summarizes the theoretical basis and outstanding works in this field. (2) Proposes the computational framework of the brain‑like navigation algorithm, which scientifically defines the functions of different modules of the algorithm. (3) Through theoretical analysis and experimental verification, we summarized valuable conclusions and expectations. Conclusions: In terms of model design, mature methods of deep learning can also be applied to this problem, but need more modifications to further improve navigation capabilities; in terms of model training, combining the advantages of multiple learning algorithms is hopeful to further improve the generalization ability. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI), machine learning (ML) algorithms has been widely used in stroke diagnosis, treatment and prognosis, and has shown great application potential. This paper summarizes the application status of ML algorithms in stroke research from the aspects of ML algorithms research, ML algorithms in stroke diagnosis and classification diagnosis, outcome prediction, risk factor identification and so on, and looks into the future development direction. [ABSTRACT FROM AUTHOR]

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)