Sidra Abbas¹, Gabriel Avelino Sampedro^2,3, Shtwai Alsubai⁴, Ahmad Almadhor⁵, Tai-hoon Kim^6,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 665-680, 2023, DOI:10.32604/cmc.2023.041031

Abstract Cardiac disease is a chronic condition that impairs the heart’s functionality. It includes conditions such as coronary artery disease, heart failure, arrhythmias, and valvular heart disease. These conditions can lead to serious complications and even be life-threatening if not detected and managed in time. Researchers have utilized Machine Learning (ML) and Deep Learning (DL) to identify heart abnormalities swiftly and consistently. Various approaches have been applied to predict and treat heart disease utilizing ML and DL. This paper proposes a Machine and Deep Learning-based Stacked Model (MDLSM) to predict heart disease accurately. ML approaches such as eXtreme Gradient Boosting (XGB),… More >

Wen Yee Wong¹, Khairunnisa Hasikin^1,*, Anis Salwa Mohd Khairuddin², Sarah Abdul Razak³, Hanee Farzana Hizaddin⁴, Mohd Istajib Mokhtar⁵, Muhammad Mokhzaini Azizan⁶

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1361-1384, 2023, DOI:10.32604/cmc.2023.038045

Abstract A common difficulty in building prediction models with realworld environmental datasets is the skewed distribution of classes. There are significantly more samples for day-to-day classes, while rare events such as polluted classes are uncommon. Consequently, the limited availability of minority outcomes lowers the classifier’s overall reliability. This study assesses the capability of machine learning (ML) algorithms in tackling imbalanced water quality data based on the metrics of precision, recall, and F1 score. It intends to balance the misled accuracy towards the majority of data. Hence, 10 ML algorithms of its performance are compared. The classifiers included are AdaBoost, Support Vector… More >

Ali G. Alkhathami^*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 235-247, 2022, DOI:10.32604/iasc.2022.024952

Abstract Breast cancer is one of the main causes of death in women. When body tissues start behaves abnormally and the ratio of tissues growth becomes asymmetrical then this stage is called cancer. Invasive ductal carcinoma (IDC) is the early stage of breast cancer. The early detection and diagnosis of invasive ductal carcinoma is a significant step for the cure of IDC breast cancer. This paper presents a convolutional neural network (CNN) approach to detect and visualize the IDC tissues in breast on histological images dataset. The dataset consists of 90 thousand histopathological images containing two categories: Invasive Ductal Carcinoma positive… More >

R. D. Pubudu L. Indrasiri¹, Ernesto Lee², Vaibhav Rupapara³, Furqan Rustam⁴, Imran Ashraf^5,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 489-515, 2022, DOI:10.32604/cmc.2022.019636

Abstract Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.… More >

G. Madhu¹, B. Lalith Bharadwaj¹, Rohit Boddeda², Sai Vardhan¹, K. Sandeep Kautish³, Khalid Alnowibet⁴, Adel F. Alrasheedi⁴, Ali Wagdy Mohamed^5,6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5467-5469, 2022, DOI:10.32604/cmc.2022.020455

Abstract COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by our research. Its initial purpose… More >

Nany Katamesh, Osama Abu-Elnasr^*, Samir Elmougy

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 589-606, 2021, DOI:10.32604/cmc.2021.015761

Abstract Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information, the document classification task becomes an interesting area for controlling data behavior. This paper presents a document classification multimodal for categorizing textual semi-structured and unstructured documents. The multimodal implements several individual deep learning models such as Deep Neural Networks (DNN), Recurrent Convolutional Neural Networks (RCNN) and Bidirectional-LSTM (Bi-LSTM). The Stacked Ensemble based meta-model technique is used to combine the results of the individual classifiers to produce better results, compared to those reached by any of the above mentioned… More >