Jaber S. Alzahrani¹, Reem M. Alshehri², Mohammad Alamgeer³, Anwer Mustafa Hilal^4,*, Abdelwahed Motwakel⁴, Ishfaq Yaseen⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4267-4281, 2022, DOI:10.32604/cmc.2022.027369 - 21 April 2022

Abstract Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful… More >

R. Madhumathi^1,*, A. Meena Kowshalya², R. Shruthi¹

Computer Systems Science and Engineering, Vol.43, No.2, pp. 849-860, 2022, DOI:10.32604/csse.2022.023568 - 20 April 2022

Abstract Sentiment analysis is the process of determining the intention or emotion behind an article. The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion. The data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual polarity. In social behavior, sentiment can be thought of as a latent variable. Measuring and comprehending this behavior could help us to better understand the social issues. Because sentiments are domain specific, sentimental analysis in a specific context is critical in any real-world scenario. Textual sentiment analysis is done in… More >

Amer Mirkhan¹, Numan Çelebi^2,*

Computer Systems Science and Engineering, Vol.43, No.2, pp. 767-783, 2022, DOI:10.32604/csse.2022.023249 - 20 April 2022

Abstract In most of the scientific research feature selection is a challenge for researcher. Selecting all available features is not an option as it usually complicates the research and leads to performance drop when dealing with large datasets. On the other hand, ignoring some features can compromise the data accuracy. Here the rough set theory presents a good technique to identify the redundant features which can be dismissed without losing any valuable information, however, exploring all possible combinations of features will end with NP-hard problem. In this research we propose adopting a heuristic algorithm to solve More >

S. Prabu^1,*, B. Thiyaneswaran², M. Sujatha³, C. Nalini⁴, Sujatha Rajkumar⁵

Computer Systems Science and Engineering, Vol.43, No.2, pp. 737-749, 2022, DOI:10.32604/csse.2022.022739 - 20 April 2022

Abstract Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the… More >

R. Anand Babu^1,*, S. Kannan²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 307-323, 2022, DOI:10.32604/iasc.2022.024098 - 15 April 2022

Abstract An Intrusion detection system (IDS) is extensively used to identify cyber-attacks preferably in real-time and to achieve integrity, confidentiality, and availability of sensitive information. In this work, we develop a novel IDS using machine learning techniques to increase the performance of the attack detection process. In order to cope with high dimensional feature-rich traffic in large networks, we introduce a Bat-Inspired Optimization and Correlation-based Feature Selection (BIOCFS) algorithm and an ensemble classification approach. The BIOCFS is introduced to estimate the correlation of the identified features and to choose the ideal subset for training and testing… More >

Abdullah S. AL-Malaise AL-Ghamdi¹, Mahmoud Ragab^2,3,4,*, F. J. Alsolami⁵, Hani Choudhry^3,6, Ibrahim Rizqallah Alzahrani⁷

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2275-2289, 2022, DOI:10.32604/cmc.2022.026226 - 29 March 2022

Abstract Industrial Control Systems (ICS) can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate effectively. Internet of Things (IoT) integrates numerous sets of sensors and devices via a data network enabling independent processes. The incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things (IIoT), which find use in water distribution system, power plants, etc. Since the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection, an… More >

Thavavel Vaiyapuri¹, K. Priyadarshini², A. Hemlathadhevi³, M. Dhamodaran⁴, Ashit Kumar Dutta⁵, Irina V. Pustokhina^6,*, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2429-2444, 2022, DOI:10.32604/cmc.2022.026204 - 29 March 2022

Abstract Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based More >

Junaid Rashid¹, Samina Kanwal², Jungeun Kim^1,*, Muhammad Wasif Nisar², Usman Naseem³, Amir Hussain⁴

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3195-3211, 2022, DOI:10.32604/cmc.2022.026064 - 29 March 2022

Abstract Heart disease is one of the leading causes of death in the world today. Prediction of heart disease is a prominent topic in the clinical data processing. To increase patient survival rates, early diagnosis of heart disease is an important field of research in the medical field. There are many studies on the prediction of heart disease, but limited work is done on the selection of features. The selection of features is one of the best techniques for the diagnosis of heart diseases. In this research paper, we find optimal features using the brute-force algorithm,… More >

Manar Ahmed Hamza^1,*, Siwar Ben Haj Hassine², Ibrahim Abunadi³, Fahd N. Al-Wesabi^2,4, Hadeel Alsolai⁵, Anwer Mustafa Hilal¹, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2581-2596, 2022, DOI:10.32604/cmc.2022.024764 - 29 March 2022

Abstract Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique More >

G. Prema Arokia Mary^1,*, N. Suganthi²

Computer Systems Science and Engineering, Vol.43, No.1, pp. 333-345, 2022, DOI:10.32604/csse.2022.021164 - 23 March 2022

Abstract Parkinson’s disease (PD) is a neurodegenerative disease in the central nervous system. Recently, more researches have been conducted in the determination of PD prediction which is really a challenging task. Due to the disorders in the central nervous system, the syndromes like off sleep, speech disorders, olfactory and autonomic dysfunction, sensory disorder symptoms will occur. The earliest diagnosing of PD is very challenging among the doctors community. There are techniques that are available in order to predict PD using symptoms and disorder measurement. It helps to save a million lives of future by early prediction.… More >