Guorong Qi¹, Jian Mao^1,*, Kai Huang¹, Zhengxian You², Jinliang Lin²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2159-2176, 2025, DOI:10.32604/cmc.2024.058396 - 17 February 2025

Abstract Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features; Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection… More >

Israa Ismail^1,*, Ghada Eltaweel¹, Mohamed Meselhy Eltoukhy^1,2

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3193-3209, 2024, DOI:10.32604/cmc.2023.043873 - 15 May 2024

Abstract Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs. Super-resolution is of paramount importance in the context of remote sensing, satellite, aerial, security and surveillance imaging. Super-resolution remote sensing imagery is essential for surveillance and security purposes, enabling authorities to monitor remote or sensitive areas with greater clarity. This study introduces a single-image super-resolution approach for remote sensing images, utilizing deep shearlet residual learning in the shearlet transform domain, and incorporating the Enhanced Deep Super-Resolution network (EDSR). Unlike conventional approaches that estimate residuals between high and… More >

Bo Wang^1,*, Yongxin Zhang¹, Shihui Ji², Binbin Zhang¹, Xiangyu Wang¹, Jiyong Zhang¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3625-3642, 2023, DOI:10.32604/cmc.2023.036754 - 31 March 2023

Abstract A model that can obtain rapid and accurate detection of coronavirus disease 2019 (COVID-19) plays a significant role in treating and preventing the spread of disease transmission. However, designing such a model that can balance the detection accuracy and weight parameters of memory well to deploy a mobile device is challenging. Taking this point into account, this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model, which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and accuracy. The convolutional neural network can extract… More >

Kazim Ali^1,*, Adnan N. Qureshi¹, Muhammad Shahid Bhatti², Abid Sohail², Muhammad Hijji³, Atif Saeed²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1167-1186, 2023, DOI:10.32604/csse.2023.032508 - 03 November 2022

Abstract Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is… More >

Kazim Ali^1,*, Adnan N. Qureshi¹, Muhammad Shahid Bhatti², Abid Sohail², Mohammad Hijji³

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2237-2256, 2023, DOI:10.32604/iasc.2023.028810 - 19 July 2022

Abstract Deep learning-based systems have succeeded in many computer vision tasks. However, it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks. These attacks can quickly spoil deep learning models, e.g., different convolutional neural networks (CNNs), used in various computer vision tasks from image classification to object detection. The adversarial examples are carefully designed by injecting a slight perturbation into the clean images. The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense, Generative Adversarial Network Defense, Deep Regret Analytic Generative… More >

Zhe Wang¹, Liguo Zhang^1,2,*, Tong Shuai³, Shuo Liang³, Sizhao Li^1,4

Journal of New Media, Vol.4, No.1, pp. 27-39, 2022, DOI:10.32604/jnm.2022.027826 - 21 April 2022

Abstract Research shows that deep learning algorithms can effectively improve a single image's super-resolution quality. However, if the algorithm is solely focused on increasing network depth and the desired result is not achieved, difficulties in the training process are more likely to arise. Simultaneously, the function space that can be transferred from a low-resolution image to a high-resolution image is enormous, making finding a satisfactory solution difficult. In this paper, we propose a deep learning method for single image super-resolution. The MDRN network framework uses multi-scale residual blocks and dual learning to fully acquire features in… More >

A. Devipriya¹, P. Prabu², K. Venkatachalam³, Ahmed Zohair Ibrahim^4,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 651-666, 2022, DOI:10.32604/cmc.2022.020820 - 03 November 2021

Abstract This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of… More >

Kang Yang¹, Jielin Jiang^1,2,*, Zhaoqing Pan^1,2

Journal of New Media, Vol.2, No.1, pp. 1-10, 2020, DOI:10.32604/jnm.2020.09356 - 14 August 2020

Abstract Due to the huge difference of noise distribution, the result of a mixture of multiple noises becomes very complicated. Under normal circumstances, the most common type of mixed noise is to add impulse noise (IN) and then white Gaussian noise (AWGN). From the reduction of cascaded IN and AWGN to the latest sparse representation, a great deal of methods has been proposed to reduce this form of mixed noise. However, when the mixed noise is very strong, most methods often produce a lot of artifacts. In order to solve the above problems, we propose More >