Search

Datasets are the basis for research on deep learning methods in computer vision. The impact of the percentage of training sets in a dataset on the performance of neural network models needs to be further explored. In this paper, a twice equal difference enumeration (TEDE) algorithm is proposed to investigate the effect of different training set percentages in the dataset on the performance of the network model, and the optimal training set percentage is determined. By selecting the Pascal VOC dataset and dividing it into six different datasets from largest to smallest, and then dividing each dataset into the datasets to be analyzed according to five different training set percentages, the YOLOv5 convolutional neural network is used to train and test the 30 datasets to determine the optimal neural network model corresponding to the training set percentages. Finally, tests were conducted using the Udacity Self-Driving dataset with a self-made Tire Tread Defects (TTD) dataset. The results show that the network model performance is superior when the training set accounts for between 85% and 90% of the overall dataset. The results of dataset partitioning obtained by the TEDE algorithm can provide a reference for deep learning research. [ABSTRACT FROM AUTHOR]

This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms. [ABSTRACT FROM AUTHOR]

This study was aimed at developing an automatic exam system as an intelligent and high-quality higher math examination solution for Department of Computer Engineering at Pai Chai University (PCU) based on S/W Engineering. As a research and development (R&D) project, the study used the rational unified process (RUP) method for software development. RUP describes how to effectively use commercial and reliable methods to develop and deploy software system. It is a heavyweight process; therefore, it is particularly suitable for large teams to develop large projects. This paper describes the RUP process of this R&D project what we named it as Automatic Higher Mathematically Exam System (AHMES). AHMES provides a new way to automatically generate exam questions. In the study, our team and the requesting department collected the requirements and selected methods and tools. Then, our team designed and implemented the framework of the system, completed the development of some main functions, tested and summarized the system, and planned the future work. [ABSTRACT FROM AUTHOR]