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1. Improvement of AHMES Using AI Algorithms.

Author

Chen, Le and Song, JeongYoung

Subjects
ARTIFICIAL intelligence, COMPUTER engineering, COMPUTER engineers, MATHEMATICS, ALGORITHMS
Abstract

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]

Published
2022
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2. Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation

Author

Kwang Nam Choi, Jeong Heon Kim, Toufique Ahmed Soomro, Farhan Akram, Shafiullah Soomro, Robòtica i Visió Intel.ligents, and Enginyeria Informàtica i Matemàtiques

Subjects
Diagnostic Imaging, Article Subject, experimental model, Ingeniería informática, Normal Distribution, 02 engineering and technology, lcsh:Computer applications to medicine. Medical informatics, General Biochemistry, Genetics and Molecular Biology, Pattern Recognition, Automated, Level set, Computer engineering, Image Interpretation, Computer-Assisted, 1748-670X, Image Processing, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, quantitative study, Cluster Analysis, Humans, Computer Simulation, Computer vision, image segmentation, Mathematics, Energy functional, Active contour model, Models, Statistical, General Immunology and Microbiology, business.industry, Imatge -- Segmentació, Applied Mathematics, Brain, Reproducibility of Results, 020206 networking & telecommunications, General Medicine, Image segmentation, Function (mathematics), Image Enhancement, Magnetic Resonance Imaging, Term (time), Enginyeria informàtica, Modeling and Simulation, lcsh:R858-859.7, 020201 artificial intelligence & image processing, Artificial intelligence, business, Algorithm, Algorithms, Intensity (heat transfer), Energy (signal processing), Research Article
Abstract

This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods.

Published
2016
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