Mingyong Li, Qiqi Li, Zheng Jiang, Yan Ma^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1401-1414, 2023, DOI:10.32604/csse.2023.034757

Abstract In recent years, the development of deep learning has further improved hash retrieval technology. Most of the existing hashing methods currently use Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to process image and text information, respectively. This makes images or texts subject to local constraints, and inherent label matching cannot capture fine-grained information, often leading to suboptimal results. Driven by the development of the transformer model, we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs. Specifically, we use a BERT network to extract text… More >

Zhiyun Yang^1,#, Qi Liu^1,#,*, Hao Wu¹, Xiaodong Liu², Yonghong Zhang³

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 45-64, 2023, DOI:10.32604/cmes.2022.022045

Abstract Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation, where radar echo maps were used to predict their consequent moment, so as to recognize potential severe convective weather events. However, these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation, due to the size limitation of convolution filter, lack of global feature, and less attention to… More > Graphic Abstract

Yang Zhang¹, Liru Qiu², Yongkai Zhu¹, Long Wen^1,*, Xiaoping Luo^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 873-894, 2022, DOI:10.32604/cmes.2022.022322

Abstract Pneumonia is part of the main diseases causing the death of children. It is generally diagnosed through chest X-ray images. With the development of Deep Learning (DL), the diagnosis of pneumonia based on DL has received extensive attention. However, due to the small difference between pneumonia and normal images, the performance of DL methods could be improved. This research proposes a new fine-grained Convolutional Neural Network (CNN) for children’s pneumonia diagnosis (FG-CPD). Firstly, the fine-grained CNN classification which can handle the slight difference in images is investigated. To obtain the raw images from the real-world chest X-ray data, the YOLOv4… More >

Sathit Prasomphan^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1295-1307, 2023, DOI:10.32604/csse.2023.025293

Abstract Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales. A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases. Using the classification technique, distinguishing cultural heritage architecture may be difficult. This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. The key contribution of this research was the creation of a retrieval model that could handle incremental streams of… More >

Zhilin Zhang¹, Ting Zhang¹, Zhaoying Liu^1,*, Peijie Zhang¹, Shanshan Tu¹, Yujian Li², Muhammad Waqas³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1527-1539, 2022, DOI:10.32604/cmc.2022.029297

Abstract The fine-grained ship image recognition task aims to identify various classes of ships. However, small inter-class, large intra-class differences between ships, and lacking of training samples are the reasons that make the task difficult. Therefore, to enhance the accuracy of the fine-grained ship image recognition, we design a fine-grained ship image recognition network based on bilinear convolutional neural network (BCNN) with Inception and additive margin Softmax (AM-Softmax). This network improves the BCNN in two aspects. Firstly, by introducing Inception branches to the BCNN network, it is helpful to enhance the ability of extracting comprehensive features from ships. Secondly, by adding… More >

Hadee Madadum^*, Yasar Becerikli

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 681-695, 2022, DOI:10.32604/iasc.2022.023831

Abstract Convolutional Neural Network (ConNN) implementations on Field Programmable Gate Array (FPGA) are being studied since the computational capabilities of FPGA have been improved recently. Model compression is required to enable ConNN deployment on resource-constrained FPGA devices. Logarithmic quantization is one of the efficient compression methods that can compress a model to very low bit-width without significant deterioration in performance. It is also hardware-friendly by using bitwise operations for multiplication. However, the logarithmic suffers from low resolution at high inputs due to exponential properties. Therefore, we propose a modified logarithmic quantization method with a fine resolution to compress a neural network… More >

Fan Yang¹, Jie Xu^1,*, Haoliang Wei¹, Meng Ye², Mingzhu Xu¹, Qiuru Fu¹, Lingfei Ren³, Zhengwen Huang⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2963-2980, 2022, DOI:10.32604/cmc.2022.022810

Abstract Zanthoxylum bungeanum Maxim, generally called prickly ash, is widely grown in China. Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum. Traditional method for recognizing the degree of infection of Zanthoxylum rust mainly rely on manual experience. Due to the complex colors and shapes of rust areas, the accuracy of manual recognition is low and difficult to be quantified. In recent years, the application of artificial intelligence technology in the agricultural field has gradually increased. In this paper, based on the DeepLabV2 model, we proposed a Zanthoxylum rust image segmentation model based on the FASPP module… More >

Yue Li, Jin Liu^*, Shengjie Shang

Journal on Big Data, Vol.3, No.3, pp. 111-118, 2021, DOI:10.32604/jbd.2021.017169

Abstract Visual Question Answering (VQA) has attracted extensive research focus and has become a hot topic in deep learning recently. The development of computer vision and natural language processing technology has contributed to the advancement of this research area. Key solutions to improve the performance of VQA system exist in feature extraction, multimodal fusion, and answer prediction modules. There exists an unsolved issue in the popular VQA image feature extraction module that extracts the fine-grained features from objects of different scale difficultly. In this paper, a novel feature extraction network that combines multi-scale convolution and self-attention branches to solve the above… More >

Jingtang Luo¹, Jingru Liao², Chenlin Zhang³, Ziqi Wang⁴, Yuhang Zhang², Jie Xu^2,*, Zhengwen Huang⁵

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1225-1239, 2022, DOI:10.32604/iasc.2022.022812

Abstract Accurate estimation of communication bandwidth is critical for the sensing and controlling applications of smart grid. Different from public network, the bandwidth requirements of smart grid communication network must be accurately estimated in prior to the deployment of applications or even the building of communication network. However, existing methods for smart grid usually model communication nodes in coarse-grained ways, so their estimations become inaccurate in scenarios where the same type of nodes have very different bandwidth requirements. To solve this issue, we propose a fine-grained estimation method based on multivariate nonlinear fitting. Firstly, we use linear fitting to calculate the… More >

Jiaye Pan¹, Yi Zhuang^{1, *}, Xinwen Hu^{1, 2}, Wenbing Zhao³

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 607-622, 2020, DOI:10.32604/cmc.2020.09853

Abstract Nowadays cloud architecture is widely applied on the internet. New malware aiming at the privacy data stealing or crypto currency mining is threatening the security of cloud platforms. In view of the problems with existing application behavior monitoring methods such as coarse-grained analysis, high performance overhead and lack of applicability, this paper proposes a new fine-grained binary program monitoring and analysis method based on multiple system level components, which is used to detect the possible privacy leakage of applications installed on cloud platforms. It can be used online in cloud platform environments for fine-grained automated analysis of target programs, ensuring… More >