Usman Ahmad¹, Muhammad Junaid Ali², Faizan Ahmed Khan³, Arfat Ahmad Khan⁴, Arif Ur Rehman¹, Malik Muhammad Ali Shahid⁵, Mohd Anul Haq^6,*, Ilyas Khan⁷, Zamil S. Alzamil⁶, Ahmed Alhussen⁸

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2125-2140, 2023, DOI:10.32604/csse.2023.031008

Abstract Building an automatic fish recognition and detection system for large-scale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species. However, it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes. To solve these issues, we propose a transfer learning-based technique in which we use Efficient-Net, which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database, which is a large scale dataset. Furthermore, prior to the activation layer, we use Global Average Pooling (GAP) instead of dense layer with… More >

Jun Liu¹, Geng Yuan², Changdi Yang², Houbing Song³, Liang Luo^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1571-1587, 2023, DOI:10.32604/cmes.2022.023195

Abstract The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates many unreliable parameters and makes… More >

Shui-Hua Wang¹, Muhammad Attique Khan², Vishnuvarthanan Govindaraj³, Steven L. Fernandes⁴, Ziquan Zhu⁵, Yu-Dong Zhang^6,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2797-2813, 2022, DOI:10.32604/cmc.2022.020140

Abstract (Aim) To make a more accurate and precise COVID-19 diagnosis system, this study proposed a novel deep rank-based average pooling network (DRAPNet) model, i.e., deep rank-based average pooling network, for COVID-19 recognition. (Methods) 521 subjects yield 1164 slice images via the slice level selection method. All the 1164 slice images comprise four categories: COVID-19 positive; community-acquired pneumonia; second pulmonary tuberculosis; and healthy control. Our method firstly introduced an improved multiple-way data augmentation. Secondly, an n-conv rank-based average pooling module (NRAPM) was proposed in which rank-based pooling—particularly, rank-based average pooling (RAP)—was employed to avoid overfitting. Third, a novel DRAPNet was proposed… More >

Jianwei Chen, Jianbo Li^*, Ying Li

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 847-864, 2020, DOI:10.32604/cmes.2020.010240

Abstract Predicting human mobility has great significance in Location based Social Network applications, while it is challenging due to the impact of historical mobility patterns and current trajectories. Among these challenges, historical patterns tend to be crucial in the prediction task. However, it is difficult to capture complex patterns from long historical trajectories. Motivated by recent success of Convolutional Neural Network (CNN)-based methods, we propose a Union ConvGRU (UCG) Net, which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories. Specifically, we first incorporate historical trajectories into hidden states by a shared-weight layer, and then utilize… More >