Tianshu Wei¹, Jinjie Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1599-1612, 2024, DOI:10.32604/cmc.2024.048098

Abstract Zero-shot learning enables the recognition of new class samples by migrating models learned from semantic features and existing sample features to things that have never been seen before. The problems of consistency of different types of features and domain shift problems are two of the critical issues in zero-shot learning. To address both of these issues, this paper proposes a new modeling structure. The traditional approach mapped semantic features and visual features into the same feature space; based on this, a dual discriminator approach is used in the proposed model. This dual discriminator approach can further enhance the consistency between… More >

Harsh Mankodiya¹, Priyal Palkhiwala¹, Rajesh Gupta^1,*, Nilesh Kumar Jadav¹, Sudeep Tanwar¹, Osama Alfarraj², Amr Tolba², Maria Simona Raboaca^3,4,*, Verdes Marina⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1123-1142, 2023, DOI:10.32604/cmc.2023.038556

Abstract The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework to develop a secure online… More >

Sixian Chan^1,2, Binghui Wu¹, Guodao Zhang³, Yuan Yao⁴, Hongqiang Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 411-428, 2024, DOI:10.32604/cmes.2023.029485

Abstract In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection methods have the potential… More > Graphic Abstract

Xiuye Liu, Aihua Wu^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 543-559, 2023, DOI:10.32604/cmc.2023.039004

Abstract The human motion generation model can extract structural features from existing human motion capture data, and the generated data makes animated characters move. The 3D human motion capture sequences contain complex spatial-temporal structures, and the deep learning model can fully describe the potential semantic structure of human motion. To improve the authenticity of the generated human motion sequences, we propose a multi-task motion generation model that consists of a discriminator and a generator. The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom… More >

Kyeongseok Jang¹, Seongsoo Cho², Kwang Chul Son^3,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3537-3551, 2023, DOI:10.32604/cmc.2023.030557

Abstract In the proposed paper, a parallel structure type Generative Adversarial Network (GAN) using edge and texture information is proposed. In the existing GAN-based model, many learning iterations had to be given to obtaining an output that was somewhat close to the original data, and noise and distortion occurred in the output image even when learning was performed. To solve this problem, the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure. In the network, each edge information and texture information were received as inputs, learning was performed, and each… More >

J. S. Sujin^1,*, S. Sophia²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 157-171, 2023, DOI:10.32604/csse.2023.023747

Abstract Digital picture forgery detection has recently become a popular and significant topic in image processing. Due to advancements in image processing and the availability of sophisticated software, picture fabrication may hide evidence and hinder the detection of such criminal cases. The practice of modifying original photographic images to generate a forged image is known as digital image forging. A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery. In order to make the forgeries real and inconspicuous, geometric or post-processing techniques are… More >

Anisha Rebinth^1,*, S. Mohan Kumar²

Computer Systems Science and Engineering, Vol.42, No.3, pp. 867-880, 2022, DOI:10.32604/csse.2022.022955

Abstract In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD) using soft computing techniques is presented. As the biomedical images such as fundus images are often acquired in high resolution, the Region of Interest (ROI) for glaucoma diagnosis must be selected at first to reduce the complexity of any system. The DMGD system uses a series of pre-processing; initial cropping by the green channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), blood vessel detection and removal by Gaussian Derivative Filters (GDF) and inpainting algorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from… More >

Seo-young Lim, Jeongho Cho^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6039-6051, 2022, DOI:10.32604/cmc.2022.024862

Abstract Recently, the technology of digital image forgery based on a generative adversarial network (GAN) has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person's face or a specific part with the original image. Thus, much attention has been paid to digital image forgery as a social issue. Further, document forgery through GANs can completely change the meaning and context in a document, and it is difficult to identify whether the document is forged or not, which is dangerous. Nonetheless, few studies have been… More >

Xiaojie Li¹, Yongpeng Ren¹, Hongping Ren¹, Canghong Shi², Xian Zhang¹, Lutao Wang¹, Imran Mumtaz³, Xi Wu¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5021-5037, 2022, DOI:10.32604/cmc.2022.023071

Abstract Recently, deep learning-based image outpainting has made greatly notable improvements in computer vision field. However, due to the lack of fully extracting image information, the existing methods often generate unnatural and blurry outpainting results in most cases. To solve this issue, we propose a perceptual image outpainting method, which effectively takes the advantage of low-level feature fusion and multi-patch discriminator. Specifically, we first fuse the texture information in the low-level feature map of encoder, and simultaneously incorporate these aggregated features reusability with semantic (or structural) information of deep feature map such that we could utilize more sophisticated texture information to… More >