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  • Open Access

    ARTICLE

    BHGSO: Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

    R. Manjula Devi1, M. Premkumar2, Pradeep Jangir3, B. Santhosh Kumar4, Dalal Alrowaili5, Kottakkaran Sooppy Nisar6,*

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

    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. The proposed technique transforms the… More >

  • Open Access

    ARTICLE

    A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

    Faiz Ali Shah1, Muhammad Attique Khan2, Muhammad Sharif1, Usman Tariq3, Aimal Khan4, Seifedine Kadry5, Orawit Thinnukool6,*

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

    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 of different fruit diseases and… More >

  • Open Access

    ARTICLE

    Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

    Lijo Jacob Varghese1, K. Dhayalini2, Suma Sira Jacob3, Ihsan Ali4,*, Abdelzahir Abdelmaboud5, Taiseer Abdalla Elfadil Eisa6

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

    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 presents a new Multi-Objective Grasshopper… More >

  • Open Access

    ARTICLE

    Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

    Awais Mahmood1,*, Muhammad Imran2, Aun Irtaza3, Qammar Abbas4, Habib Dhahri1,5, 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

    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 SVM based RF approach generating… More >

  • Open Access

    ARTICLE

    Human Gait Recognition: A Deep Learning and Best Feature Selection Framework

    Asif Mehmood1, Muhammad Attique Khan2, Usman Tariq3, Chang-Won Jeong4, Yunyoung Nam5,*, Reham R. Mostafa6, Amira ElZeiny7

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

    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, manipulation of the pre-trained… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for Network Intrusion Detection

    Mavra Mehmood1, Talha Javed2, Jamel Nebhen3, Sidra Abbas2,*, 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

    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 random forest recursive feature elimination… More >

  • Open Access

    ARTICLE

    A Hybrid Feature Selection Framework for Predicting Students Performance

    Maryam Zaffar1,2,*, 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

    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 of optimal features. In this… More >

  • Open Access

    ARTICLE

    Swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining

    Diaa Salam Abd Elminaam1,2,*, Nabil Neggaz3, Ibrahim Abdulatief Ahmed4,5, Ahmed El Sawy Abouelyazed4

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

    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 terms of accuracy and number… More >

  • Open Access

    ARTICLE

    Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm

    Mohamed Abdel-Basset1, Reda Mohamed1, Ripon K. Chakrabortty2, Michael J. Ryan2, Yunyoung Nam3,*, Mohamed Abouhawwash4,5

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

    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 restarting strategy (RS) is proposed… More >

  • Open Access

    ARTICLE

    Bayesian Rule Modeling for Interpretable Mortality Classification of COVID-19 Patients

    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

    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 who have a high risk… More >

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