Yujun Zhang^*, Dezhi Han, Peng Chen

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2657-2675, 2023, DOI:10.32604/cmc.2023.042311

Abstract Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information in SAR target detection, we… More >

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 >

PRIYADHARSHINI THANJAVUR SRIRAMAMOORTHI^1,2, GAYATHRI GOPAL^1,2, SHIBI MURALIDAR^1,2, SAI RAMANAN ESWARAN^1,2, DANUSH NARAYAN PANNEERSELVAM^1,2, BHUVANESWARAN MEIYANATHAN^1,2, SRICHANDRASEKAR THUTHIKKADU INDHUPRAKASH^1,2, SENTHIL VISAGA AMBI^1,2,*

BIOCELL, Vol.47, No.11, pp. 2335-2350, 2023, DOI:10.32604/biocell.2023.029379

Abstract The novel coronavirus pneumonia (COVID-19) is spreading worldwide and threatening people greatly. The routes by which SARS-CoV-2 causes lung injury have grown to be a major concern in the scientific community since patients with new Coronavirus, severe acute respiratory syndrome coronavirus (SARS-CoV-2) have a high likelihood of developing acute respiratory distress syndrome (ARDS) in severe cases. The mortality rate of COVID-19 has increased over the period due to rapid spread, and it becomes crucial to understand the disease epidemiology, pathogenic mechanisms, and suitable treatment strategies. ARDS is a respiratory disorder and is one of the clinical manifestations observed in patients… More > Graphic Abstract

Jebran Khan¹, Kashif Ahmad², Kyung-Ah Sohn^1,3,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159

Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language processing (NLP) applications. However, these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication. These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form. The intuition of the proposed scheme is to generate adversarial examples… More >

VIBHAV VARSHNEY^1,*, PRASHANT SINGH KUSHWAH², NEETU AGRAWAL¹, AHSAS GOYAL^1,*, GOVIND SINGH²

BIOCELL, Vol.47, No.10, pp. 2141-2149, 2023, DOI:10.32604/biocell.2023.030853

Abstract The persistent global pandemic, COVID-19, stems from the pathogenic influence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), yielding an unprecedented worldwide crisis. With reference to a WHO report, the count of COVID-19 cases had exceeded 754 million by February 03, 2023. Intriguingly, emerging research has spotlighted the intricate interplay of gut microbiota and mitochondrial entities, acting as potent immunomodulatory factors at the cellular and molecular levels. This interconnection operates through a series of dynamic mechanisms. SARS-CoV-2 infection perturbs the delicate equilibrium of gut microbiota, leading to dysbiosis—a signature biomarker. This imbalance is intrinsically linked to exacerbated COVID-19 progression. Mechanistically,… More >

AMGAD M. RABIE^*

BIOCELL, Vol.47, No.10, pp. 2133-2139, 2023, DOI:10.32604/biocell.2023.030057

Abstract Despite the global decline in the severity of the coronavirus disease 2019 (COVID-19) cases, the disease still represents a major concern to the relevant scientific and medical communities. The primary concern of drug scientists, virologists, and other concerned specialists in this respect is to find ready-to-use suitable and potent anticoronaviral therapies that are broadly effective against the different species/strains of the coronaviruses in general, not only against the current and previous coronaviruses (e.g., the recently-appeared severe acute respiratory syndrome coronavirus 2 “SARS-CoV-2”), i.e., effective antiviral agents for treatment and/or prophylaxis of any coronaviral infections, including those of the coming ones… More > Graphic Abstract

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 >

Weizheng Wang^1,3, Xiangqi Wang^2,*, Xianmin Pan¹, Xingxing Gong³, Jian Liang³, Pradip Kumar Sharma⁴, Osama Alfarraj⁵, Wael Said⁶

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3859-3876, 2023, DOI:10.32604/cmc.2023.041346

Abstract Image-denoising techniques are widely used to defend against Adversarial Examples (AEs). However, denoising alone cannot completely eliminate adversarial perturbations. The remaining perturbations tend to amplify as they propagate through deeper layers of the network, leading to misclassifications. Moreover, image denoising compromises the classification accuracy of original examples. To address these challenges in AE defense through image denoising, this paper proposes a novel AE detection technique. The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network (CNN) network structures. The used detector model integrates the classification results of different models as the input to the detector and calculates the… More >

Saima Yasmeen¹, Muhammad Usman Yaseen^1,*, Syed Sohaib Ali², Moustafa M. Nasralla³, Sohaib Bin Altaf Khattak³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3671-3689, 2023, DOI:10.32604/cmc.2023.041195

Abstract SAR images commonly suffer from speckle noise, posing a significant challenge in their analysis and interpretation. Existing convolutional neural network (CNN) based despeckling methods have shown great performance in removing speckle noise. However, these CNN-based methods have a few limitations. They do not decouple complex background information in a multi-resolution manner. Moreover, they have deep network structures that may result in many parameters, limiting their applicability to mobile devices. Furthermore, extracting key speckle information in the presence of complex background is also a major problem with SAR. The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based… 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 >