Maryam Zaffar^1,2,*, Manzoor Ahmed Hashmani¹, Raja Habib², KS Quraishi³, Muhammad Irfan⁴, Samar Alqhtani⁵, Mohammed Hamdi⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1893-1920, 2022, DOI:10.32604/cmc.2022.018295 - 07 September 2021

Abstract Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions, for the improvement of quality of education and to meet the dynamic needs of society. The selection of features for student's performance prediction not only plays significant role in increasing prediction accuracy, but also helps in building the strategic plans for the improvement of students’ academic performance. There are different feature selection algorithms for predicting the performance of students, however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection… More >

Sunil Kr. Jha^1,*, Zulfiqar Ahmad²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 567-594, 2021, DOI:10.32604/cmes.2021.016817 - 08 October 2021

Abstract Health care data mining is noteworthy in disease diagnosis and recognition procedures. There exist several potentials to further improve the performance of machine learning based-classification methods in healthcare data analysis. The selection of a substantial subset of features is one of the feasible approaches to achieve improved recognition results of classification methods in disease diagnosis prediction. In the present study, a novel combined approach of feature generation using latent semantic analysis (LSA) and selection using ranker search (RAS) has been proposed to improve the performance of classification methods in lymph disease diagnosis prediction. The performance… More >

Diaa Salam Abd Elminaam^1,2,*, Nabil Neggaz³, Ibrahim Abdulatief Ahmed^4,5, Ahmed El Sawy Abouelyazed⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4129-4149, 2021, DOI:10.32604/cmc.2021.019047 - 24 August 2021

Abstract At present, the immense development of social networks allows generating a significant amount of textual data, which has facilitated researchers to explore the field of opinion mining. In addition, the processing of textual opinions based on the term frequency-inverse document frequency method gives rise to a dimensionality problem. This study aims to detect the nature of opinions in the Arabic language employing a swarm intelligence (SI)-based algorithm, Harris hawks algorithm, to select the most relevant terms. The experimental study has been tested on two datasets: Arabic Jordanian General Tweets and Opinion Corpus for Arabic. In More >

Mohamed Abdel-Basset¹, Reda Mohamed¹, Ripon K. Chakrabortty², Michael J. Ryan², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2883-2901, 2021, DOI:10.32604/cmc.2021.017854 - 24 August 2021

Abstract This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance, redundancy, or less information; this pre-processing process is often known as feature selection. This technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization (GNDO) supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary values. Further, a novel… More >

Jiyoung Yun, Mainak Basak, Myung-Mook Han^*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2827-2843, 2021, DOI:10.32604/cmc.2021.017266 - 24 August 2021

Abstract Coronavirus disease 2019 (COVID-19) has been termed a “Pandemic Disease” that has infected many people and caused many deaths on a nearly unprecedented level. As more people are infected each day, it continues to pose a serious threat to humanity worldwide. As a result, healthcare systems around the world are facing a shortage of medical space such as wards and sickbeds. In most cases, healthy people experience tolerable symptoms if they are infected. However, in other cases, patients may suffer severe symptoms and require treatment in an intensive care unit. Thus, hospitals should select patients… More >

Heming Jia^1,2,*, Yao Li², Kangjian Sun², Ning Cao¹, Helen Min Zhou³

Computer Systems Science and Engineering, Vol.39, No.3, pp. 321-335, 2021, DOI:10.32604/csse.2021.017536 - 12 August 2021

Abstract In this paper, a hybrid model based on sooty tern optimization algorithm (STOA) is proposed to optimize the parameters of the support vector machine (SVM) and identify the best feature sets simultaneously. Feature selection is an essential process of data preprocessing, and it aims to find the most relevant subset of features. In recent years, it has been applied in many practical domains of intelligent systems. The application of SVM in many fields has proved its effectiveness in classification tasks of various types. Its performance is mainly determined by the kernel type and its parameters.… More >

Revathi Jothiramalingam¹, Anitha Jude², Duraisamy Jude Hemanth^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 875-906, 2021, DOI:10.32604/cmes.2021.016485 - 11 August 2021

Abstract The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications. It does, though, have certain drawbacks. For other electrocardiographic anomalies such as Left Bundle Branch Block and Left Ventricular Hypertrophy syndrome, the ECG signal with Myocardial Infarction is difficult to interpret. These diseases cause variations in the ST portion of the ECG signal. It reduces the clarity of ECG signals, making it more difficult to diagnose these diseases. As a result, the specialist is misled into making an erroneous diagnosis by using the incorrect therapeutic More >

Yu Xue^1,*, Asma Aouari¹, Romany F. Mansour², Shoubao Su³

Journal of Cyber Security, Vol.3, No.2, pp. 117-124, 2021, DOI:10.32604/jcs.2021.017018 - 02 August 2021

Abstract One of the main problems of machine learning and data mining is to develop a basic model with a few features, to reduce the algorithms involved in classification’s computational complexity. In this paper, the collection of features has an essential importance in the classification process to be able minimize computational time, which decreases data size and increases the precision and effectiveness of specific machine learning activities. Due to its superiority to conventional optimization methods, several metaheuristics have been used to resolve FS issues. This is why hybrid metaheuristics help increase the search and convergence rate More >

Adel Hamdan Mohammad^*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 65-80, 2021, DOI:10.32604/iasc.2021.016140 - 26 July 2021

Abstract Intrusion detection is an important topic that aims at protecting computer systems. Besides, feature selection is crucial for increasing the performance of intrusion detection. This paper employs a new hybrid feature selection model for intrusion detection. The implemented model uses Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms in a new manner. In addition, this study introduces two new models called (PSO-GWO-NB) and (PSO-GWO-ANN) for feature selection and intrusion detection. PSO and GWO show emergent results in feature selection for several purposes and applications. This paper uses PSO and GWO to select features… More >

Abdullah Muhammad, Salwani Abdullah, Nor Samsiah Sani^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1783-1799, 2021, DOI:10.32604/cmc.2021.018593 - 21 July 2021

Abstract Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning… More >