Titlebook: Artificial Intelligence in Data and Big Data Processing; Proceedings of ICABD Ngoc Hoang Thanh Dang,Yu-Dong Zhang,Bo-Hao Chen Conference pr

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Determinanten der Familienmodellwahl,cted to obtain more accurate results. The experimental results showed that using a Genetic algorithm can take short time to generate a course schedule with high accuracy while maintaining free human errors.

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Euphonious

Integrating Nôm Language Model Into Nôm Optical Character Recognitionation of the Nôm language model (whose training corpus is limited) helps boost top-1 performance by a small margin: +0.04% on an already high result of the training dataset, +2.72 and +0.14% on the two testing datasets, respectively.

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An Updated IoU Loss Function for Bounding Box Regressionmance. To improve convergence and performance in object detection, many researchers have modified and proposed Intersection over Union (IoU) loss functions. In existing researches, the loss functions have some main drawbacks. Firstly, the IoU-based loss functions are inefficient enough to perform th

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Automatic Generation of Course Schedules Using Genetic AlgorithmIt establishes a routine that informs the student of their responsibilities throughout the semester. Creating a well-constructed course schedule takes a long time and a lot of human effort when managers have to put up subjects, classes, lecturers into constrained duration. To solve these issues, we

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Integrating Nôm Language Model Into Nôm Optical Character Recognition model for Nôm woodblock-print images. Among top-N results of a character prediction, we pick out a single and more accurate result using Nôm language model (LM). An OCR-LM weighting mechanism is introduced to integrate the language model into the OCR baseline as a post-processing stage. Our OCR bas

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