Mohamed A.G. Hazber^1,*, Ebrahim Mohammed Senan^2,3, Hezam Saud Alrashidi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3229-3254, 2025, DOI:10.32604/cmes.2025.062302 - 03 March 2025

Abstract Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions, and it has many types, from normal to serious. Hepatitis is diagnosed through many blood tests and factors; Artificial Intelligence (AI) techniques have played an important role in early diagnosis and help physicians make decisions. This study evaluated the performance of Machine Learning (ML) algorithms on the hepatitis data set. The dataset contains missing values that have been processed and outliers removed. The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique (SMOTE). The features of the data set were processed… More >

Syed Tasnimul Karim Ayon^1,#, Farhan Md. Siraj^1,#, Jia Uddin^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361 - 03 January 2025

Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >

Saad Akbar^1,2, Humera Azam¹, Sulaiman Sulmi Almutairi^3,*, Omar Alqahtani⁴, Habib Shah⁴, Aliya Aleryani⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1075-1104, 2024, DOI:10.32604/cmc.2024.050913 - 18 July 2024

Abstract The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools. In this article, a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19, pneumonia, and normal conditions in chest X-ray images (CXIs) is proposed coupled with Explainable Artificial Intelligence (XAI). Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3, VGG16, and VGG19 that excel in the task of feature extraction. The methodology is further enhanced by the inclusion of the t-SNE (t-Distributed… More >

Zeyad Ghaleb Al-Mekhlafi¹, Ebrahim Mohammed Senan², Taha H. Rassem³, Badiea Abdulkarem Mohammed^4,5,*, Nasrin M. Makbol⁵, Adwan Alownie Alanazi¹, Tariq S. Almurayziq¹, Fuad A. Ghaleb⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 775-796, 2022, DOI:10.32604/cmc.2022.024492 - 24 February 2022

Abstract Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to… More >

D. Jerusha^*, T. Jaya

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1121-1132, 2022, DOI:10.32604/csse.2022.022997 - 08 February 2022

Abstract Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a… More >

Hung-Hsiang Wang¹, Chih-Ping Chen^2,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 237-254, 2022, DOI:10.32604/iasc.2022.020665 - 26 October 2021

Abstract Machine learning can classify the image clarity of low-cost nailfold capillaroscopy (NC) and can be applied to the design verification for other medical devices. The method can be beneficial for systems that require a large number of image datasets. This investigation covers the design, integration, image sharpness estimation, and deconvolution sharpening of the NC. The study applies this device to record two videos and extract 600 photos, including blurry and sharp images. It then uses the Laplace operator method for blur detection of the pictures. Statistics are recorded for each image’s Laplace score and the… More >

Shinjin Kang¹, Soo Kyun Kim^2,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3725-3736, 2021, DOI:10.32604/cmc.2021.016828 - 06 May 2021

Abstract In this study, we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment. We focus on the users’ app usage to analyze unusual behavior, especially in indoor spaces. This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased. Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied for effective singular… More >