Anwar Ullah¹, Xinguo Yu^1,*, Muhammad Numan²

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2359-2383, 2023, DOI:10.32604/cmc.2023.041219

Abstract Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved, including digit deformation, noise interference between frames, blurred output, and the need for temporal coherence across frames. In this paper, we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network (DD-GAN). The DD-GAN comprises a Deep Deconvolutional Neural Network (DDNN) as a Generator (G) and a modified Deep Convolutional Neural Network (DCNN) as a Discriminator (D) to ensure temporal coherence between adjacent frames. The proposed research involves several steps.… More >

Xi Zhao¹, Haizheng Yu^1,*, Hong Bian²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 181-198, 2023, DOI:10.32604/cmc.2023.041479

Abstract In recent years, Pix2Pix, a model within the domain of GANs, has found widespread application in the field of image-to-image translation. However, traditional Pix2Pix models suffer from significant drawbacks in image generation, such as the loss of important information features during the encoding and decoding processes, as well as a lack of constraints during the training process. To address these issues and improve the quality of Pix2Pix-generated images, this paper introduces two key enhancements. Firstly, to reduce information loss during encoding and decoding, we utilize the U-Net++ network as the generator for the Pix2Pix model, incorporating denser skip-connection to minimize… More >

Yalong Xie¹, Aiping Li^1,*, Biyin Hu², Liqun Gao¹, Hongkui Tu¹

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2707-2726, 2023, DOI:10.32604/cmc.2023.037039

Abstract Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space, and a balance module… More >

Jianfeng Lu^1,*, Xinyi Liu¹, Mengtao Shi¹, Chen Cui^1,2, Mahmoud Emam^1,3

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2865-2882, 2023, DOI:10.32604/iasc.2023.039742

Abstract Ceramic tiles are one of the most indispensable materials for interior decoration. The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures. In this paper, we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network (GAN). The generated tile images can be tailored to meet the specific needs of the user for the tile textures. The proposed method consists of four steps. Firstly, a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.… More >

Wenlu Ji¹, Yingqi Liao^1,*, Liudong Zhang²

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2825-2848, 2023, DOI:10.32604/iasc.2023.039618

Abstract Existing power anomaly detection is mainly based on a pattern matching algorithm. However, this method requires a lot of manual work, is time-consuming, and cannot detect unknown anomalies. Moreover, a large amount of labeled anomaly data is required in machine learning-based anomaly detection. Therefore, this paper proposes the application of a generative adversarial network (GAN) to massive data stream anomaly identification, diagnosis, and prediction in power dispatching automation systems. Firstly, to address the problem of the small amount of anomaly data, a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled… More >

Runlian Zhang^1,*, Rui Shu¹, Yongzhuang Wei¹, Hailong Zhang², Xiaonian Wu¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1911-1927, 2023, DOI:10.32604/cmc.2023.041187

Abstract S-boxes can be the core component of block ciphers, and how to efficiently generate S-boxes with strong cryptographic properties appears to be an important task in the design of block ciphers. In this work, an optimized model based on the generative adversarial network (GAN) is proposed to generate 8-bit S-boxes. The central idea of this optimized model is to use loss function constraints for GAN. More specially, the Advanced Encryption Standard (AES) S-box is used to construct the sample dataset via the affine equivalence property. Then, three models are respectively built and cross-trained to generate 8-bit S-boxes based on three… More >

Bingcai Wei, Di Wang, Zhuang Wang, Liye Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1975-1988, 2023, DOI:10.32604/cmes.2023.027727

Abstract With the increasing popularity of artificial intelligence applications, machine learning is also playing an increasingly important role in the Internet of Things (IoT) and the Internet of Vehicles (IoV). As an essential part of the IoV, smart transportation relies heavily on information obtained from images. However, inclement weather, such as snowy weather, negatively impacts the process and can hinder the regular operation of imaging equipment and the acquisition of conventional image information. Not only that, but the snow also makes intelligent transportation systems make the wrong judgment of road conditions and the entire system of the Internet of Vehicles adverse.… More > Graphic Abstract

Yixin Tang^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1195-1212, 2023, DOI:10.32604/cmc.2023.039182

Abstract Visual illustration transformation from real-world to cartoon images is one of the famous and challenging tasks in computer vision. Image-to-image translation from real-world to cartoon domains poses issues such as a lack of paired training samples, lack of good image translation, low feature extraction from the previous domain images, and lack of high-quality image translation from the traditional generator algorithms. To solve the above-mentioned issues, paired independent model, high-quality dataset, Bayesian-based feature extractor, and an improved generator must be proposed. In this study, we propose a high-quality dataset to reduce the effect of paired training samples on the model’s performance.… More >

Jason Elroy Martis¹, Sannidhan Manjaya Shetty^2,*, Manas Ranjan Pradhan³, Usha Desai⁴, Biswaranjan Acharya^5,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 915-938, 2023, DOI:10.32604/cmc.2023.038847

Abstract In the past, sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes. However, relying on eyewitness observations can lead to discrepancies in the depictions of the sketch, depending on the experience and skills of the sketch artist. With the emergence of modern technologies such as Generative Adversarial Networks (GANs), generating images using verbal and textual cues is now possible, resulting in more accurate sketch depictions. In this study, we propose an adversarial network that generates human facial sketches using such cues provided by an observer. Additionally, we… More >

M. Muzaffar Hameed^1,2, Rodina Ahmad^1,*, Laiha Mat Kiah¹, Ghulam Murtaza³, Noman Mazhar¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1267-1289, 2023, DOI:10.32604/cmc.2023.035063

Abstract Offline signature verification (OfSV) is essential in preventing the falsification of documents. Deep learning (DL) based OfSVs require a high number of signature images to attain acceptable performance. However, a limited number of signature samples are available to train these models in a real-world scenario. Several researchers have proposed models to augment new signature images by applying various transformations. Others, on the other hand, have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples. Hence, augmenting a sufficient number of signatures with variations is still a challenging task. This study proposed OffSig-SinGAN: a deep… More >