Ahmed Reda^*, Sherif Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1381-1399, 2022, DOI:10.32604/cmc.2022.019809 - 07 September 2021

Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a More >

R. Manjula Devi¹, M. Premkumar², Pradeep Jangir³, B. Santhosh Kumar⁴, Dalal Alrowaili⁵, Kottakkaran Sooppy Nisar^6,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 557-579, 2022, DOI:10.32604/cmc.2022.019611 - 07 September 2021

Abstract In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V- and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset.… More >

Faiz Ali Shah¹, Muhammad Attique Khan², Muhammad Sharif¹, Usman Tariq³, Aimal Khan⁴, Seifedine Kadry⁵, Orawit Thinnukool^6,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1491-1507, 2022, DOI:10.32604/cmc.2022.019490 - 07 September 2021

Abstract Fruit diseases seriously affect the production of the agricultural sector, which builds financial pressure on the country's economy. The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert. Hence, it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images. According to the literature, many automated methods have been developed for the recognition of fruit diseases at the early stage. However, these techniques still face some challenges, such as the similar symptoms… More >

Lijo Jacob Varghese¹, K. Dhayalini², Suma Sira Jacob³, Ihsan Ali^4,*, Abdelzahir Abdelmaboud⁵, Taiseer Abdalla Elfadil Eisa⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1053-1067, 2022, DOI:10.32604/cmc.2022.019435 - 07 September 2021

Abstract Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper… More >

Awais Mahmood^1,*, Muhammad Imran², Aun Irtaza³, Qammar Abbas⁴, Habib Dhahri^1,5, Esam Mohammed Asem Othman¹, Arif Jamal Malik⁶, Aaqif Afzaal Abbasi⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 963-979, 2022, DOI:10.32604/cmc.2022.019291 - 07 September 2021

Abstract Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that… More >

Asif Mehmood¹, Muhammad Attique Khan², Usman Tariq³, Chang-Won Jeong⁴, Yunyoung Nam^5,*, Reham R. Mostafa⁶, Amira ElZeiny⁷

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 343-360, 2022, DOI:10.32604/cmc.2022.019250 - 07 September 2021

Abstract Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames,… More >

Mavra Mehmood¹, Talha Javed², Jamel Nebhen³, Sidra Abbas^2,*, Rabia Abid¹, Giridhar Reddy Bojja⁴, Muhammad Rizwan¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 91-107, 2022, DOI:10.32604/cmc.2022.019127 - 07 September 2021

Abstract Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the More >

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 >