A deep learning-based image recognition algorithm for fecal shape of domestic rabbits

Demolombe, Jingjing; Yuan, Tali; Zhang, Xiao; Shao, Longfei; Gong, Liheng; Xu, Ting

doi:10.3166/ria.32.s1.67-78

Cited by 3 publications

(1 citation statement)

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“…Artificial intelligence technology is a technology that studies how to let the computer complete the intelligent work that originally only human can complete. As one of the most advanced information technologies in the world [3], artificial intelligence technology has made many breakthroughs in a number of fields like speech recognition, automation control, corporate management and teaching system. Under this background, this paper focuses on the application of artificial intelligence in college students' autonomous learning [4].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Autonomous English Learning among College Students

Han¹

2019

Int. J. Emerg. Technol. Learn.

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To improve the English proficiency of college students, this paper attempts to promote autonomous English learning among these students using artificial intel-ligence technology. Specifically, the principles, features and application fields of artificial intelligence were introduced in details, and the requirements, structure, database and processes of autonomous English learning systems were discussed at length. On this basis, the backpropagation (BP) neural network, a typical artifi-cial intelligence technique, was selected to design the modules of the autonomous English learning system for college students. The proposed system was then im-plemented in integrated open environment of Visual Studio, the architecture of the browser/server system and the SQL Server database on the Windows platform. The results show that the proposed system provides college students with intelli-gent and open self-evaluation tests and effective learning feedbacks. The research findings shed new light on improving the English learning of college students and reliving the pressure on college English teachers.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Autonomous English Learning among College Students

Han¹

2019

Int. J. Emerg. Technol. Learn.

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Simulation of Evacuating Crowd Based on Deep Learning and Social Force Model

Liang

Zhao

et al. 2019

IEEE Access

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It is always difficult to evacuate crowds in public places like subway stations. The traditional crowd behavior simulation models often ignore two important issues in crowd evacuation: pedestrian tracking and individual differences. To solve the problem, this paper combines social force model (SFM) with deep learning into a novel pedestrian detection method. Firstly, several deep learning algorithms for pedestrian detection were compared, and the best ones for sparse and dense crowds were determined. Next, the pedestrian positions in a real video were acquired by the selected algorithms, and converted into actual coordinates in the scene. Then, the evacuation process was simulated with our method and the SFM based on these coordinates. The results show that our model output closer-to-reality results than the SFM. The research findings shed important new light on evacuation in crowded areas. INDEX TERMS Deep learning, social force model (SFM), crowd simulation.

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College English Teaching Status and Individualized Teaching Design in The Context of Mobile Learning

Zhang

Zuo

2019

Int. J. Emerg. Technol. Learn.

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This paper aims to optimize the design of individualized classroom teaching for mobile English learning in colleges, and find a scientific and effective English teaching mode and strategy for mobile learning. For this purpose, the application and evolution of mobile learning in English teaching was investigated, and the concept and theoretical bases of mobile learning were introduced in details. Through literature review, questionnaire survey and face-to-face interview, the status of mobile English learning among college students was analysed qualitatively and quantitatively from three dimensions, namely, the mobile device, learning resource and learning attitude. Then, several strategies for individualized teaching reform were presented through status analysis. Finally, the application of individualized mobile English learning was demonstrated through case studies on public platforms like Weibo and WeChat. The research findings shed new light on the individualized English learning among college students and lay a scientific basis for the application of mobile learning in college English education.

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A deep learning-based image recognition algorithm for fecal shape of domestic rabbits

Cited by 3 publications

References 0 publications

Application of Artificial Intelligence in Autonomous English Learning among College Students

Application of Artificial Intelligence in Autonomous English Learning among College Students

Simulation of Evacuating Crowd Based on Deep Learning and Social Force Model

College English Teaching Status and Individualized Teaching Design in The Context of Mobile Learning

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