Abdulaziz Alhumam^*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1463-1482, 2022, DOI:10.32604/cmc.2022.029473

Abstract The most resource-intensive and laborious part of debugging is finding the exact location of the fault from the more significant number of code snippets. Plenty of machine intelligence models has offered the effective localization of defects. Some models can precisely locate the faulty with more than 95% accuracy, resulting in demand for trustworthy models in fault localization. Confidence and trustworthiness within machine intelligence-based software models can only be achieved via explainable artificial intelligence in Fault Localization (XFL). The current study presents a model for generating counterfactual interpretations for the fault localization model's decisions. Neural system approximations and disseminated presentation of… More >

Ibtisam Aldawish¹, Hamid A. Jalab^2,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1307-1316, 2022, DOI:10.32604/cmc.2022.029445

Abstract The lungs CT scan is used to visualize the spread of the disease across the lungs to obtain better knowledge of the state of the COVID-19 infection. Accurately diagnosing of COVID-19 disease is a complex challenge that medical system face during the pandemic time. To address this problem, this paper proposes a COVID-19 image enhancement based on Mittag-Leffler-Chebyshev polynomial as pre-processing step for COVID-19 detection and segmentation. The proposed approach comprises the Mittag-Leffler sum convoluted with Chebyshev polynomial. The idea for using the proposed image enhancement model is that it improves images with low gray-level changes by estimating the probability… More >

Yan Sun^1,2,*, Cun Zhu¹, Qi Cui³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1817-1825, 2022, DOI:10.32604/cmc.2022.029408

Abstract Research on the feature of speech and image signals are carried out from two perspectives, the time domain and the frequency domain. The speech and image signals are a non-stationary signal, so FT is not used for the non-stationary characteristics of the signal. When short-term stable speech is obtained by windowing and framing the subsequent processing of the signal is completed by the Discrete Fourier Transform (DFT). The Fast Discrete Fourier Transform is a commonly used analysis method for speech and image signal processing in frequency domain. It has the problem of adjusting window size to a for desired resolution.… More >

Syed Ali Raza^1,2, Sagheer Abbas¹, Taher M. Ghazal^3,4, Muhammad Adnan Khan^5,6, Munir Ahmad¹, Hussam Al Hamadi^7,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1927-1942, 2022, DOI:10.32604/cmc.2022.029400

Abstract In the world of big data, it's quite a task to organize different files based on their similarities. Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user. Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities. Then, the browsing process can be performed quickly and easily. This research aims to design a system to automatically organize files based on their similarities in terms of content. The proposed… More >

Runze Li¹, Jiaohua Qin^1,*, Yun Tan¹, Neal N. Xiong²

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1571-1583, 2022, DOI:10.32604/cmc.2022.029378

Abstract Most existing coverless video steganography algorithms use a particular video frame for information hiding. These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness. We propose a coverless video steganography method based on frame sequence perceptual distance mapping. In this method, we introduce Learned Perceptual Image Patch Similarity (LPIPS) to quantify the similarity between consecutive video frames to obtain the sequential features of the video. Then we establish the relationship map between features and the hash sequence for information hiding. In addition, the MongoDB database is used to store… More >

Mohammad Adnan Aladaileh, Mohammed Anbar^*, Iznan H. Hasbullah, Abdullah Ahmed Bahashwan, Shadi Al-Sarawn

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1403-1416, 2022, DOI:10.32604/cmc.2022.029369

Abstract The emergence of a new network architecture, known as Software Defined Networking (SDN), in the last two decades has overcome some drawbacks of traditional networks in terms of performance, scalability, reliability, security, and network management. However, the SDN is vulnerable to security threats that target its controller, such as low-rate Distributed Denial of Service (DDoS) attacks, The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component. Therefore, there is an urgent need to propose a detection approach for this type of attack… More >

Samar Elbedwehy^1,*, T. Medhat², Taher Hamza³, Mohammed F. Alrahmawy³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1483-1500, 2022, DOI:10.32604/cmc.2022.029313

Abstract Image captioning is an emerging field in machine learning. It refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an image. Image captioning requires a complex machine learning process as it involves two sub models: a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful captions. Attention-based vision transformers models have a great impact in vision field recently. In this paper, we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different… 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 >

Tahani Alatawi^*, Ahamed Aljuhani

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1067-1086, 2022, DOI:10.32604/cmc.2022.029283

Abstract The rapid development of the Internet of Things (IoT) in the industrial domain has led to the new term the Industrial Internet of Things (IIoT). The IIoT includes several devices, applications, and services that connect the physical and virtual space in order to provide smart, cost-effective, and scalable systems. Although the IIoT has been deployed and integrated into a wide range of industrial control systems, preserving security and privacy of such a technology remains a big challenge. An anomaly-based Intrusion Detection System (IDS) can be an effective security solution for maintaining the confidentiality, integrity, and availability of data transmitted in… More >

Romany F. Mansour^1,*, Sara A. Althubiti², Fayadh Alenezi³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 849-864, 2022, DOI:10.32604/cmc.2022.029265

Abstract Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine learning (ML) models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools. This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification (CVOML-SLDC) model. The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images. Primarily, the CVOML-SLDC model derives a gaussian filtering (GF) approach to pre-process the input images and graph… More >