Karim Gasmi^1,*, Lassaad Ben Ammar^2,, Hmoud Elshammari⁴, Fadwa Yahya²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2557-2573, 2023, DOI:10.32604/cmc.2023.033270

Abstract Convolution neural networks (CNNs) have proven to be effective clinical imaging methods. This study highlighted some of the key issues within these systems. It is difficult to train these systems in a limited clinical image databases, and many publications present strategies including such learning algorithm. Furthermore, these patterns are known for making a highly reliable prognosis. In addition, normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training. Furthermore, these systems are improperly regulated, resulting in more confident ratings for correct and incorrect classification, which are inaccurate and difficult to understand. This study… More >

Akbar Khan¹, Kushsairy Abdul Kadir^1,*, Jawad Ali Shah², Waleed Albattah³, Muhammad Saeed⁴, Haidawati Nasir⁵, Megat Norulazmi Megat Mohamed Noor⁵, Muhammad Haris Kaka Khel¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5825-5844, 2022, DOI:10.32604/cmc.2022.027077

Abstract With the rapid progress of deep convolutional neural networks, several applications of crowd counting have been proposed and explored in the literature. In congested scene monitoring, a variety of crowd density estimating approaches has been developed. The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task, as a large number of individuals stand nearby and, it is hard for detection techniques to recognize them, as the crowd can vary from low density to high density. To deal with such highly congested scenes, we have proposed the Congested Scene Crowd Counting… More >

Duo Ma^{1, 2, 3}, Hongyuan Fang^{1, 2, 3, *}, Binghan Xue^{1, 2, 3}, Fuming Wang^{1, 2, 3}, Mohammed A. Msekh⁴, Chiu Ling Chan⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1267-1291, 2020, DOI:10.32604/cmes.2020.09122

Abstract The crack is a common pavement failure problem. A lack of periodic maintenance will result in extending the cracks and damage the pavement, which will affect the normal use of the road. Therefore, it is significant to establish an efficient intelligent identification model for pavement cracks. The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix. It has been widely used in geotechnical engineering, computer vision, medicine, and other fields. However, there are three major problems in the application of neural networks to crack identification. There are too… More >