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  • Open Access


    Meta-Heuristic Optimized Hybrid Wavelet Features for Arrhythmia Classification

    S. R. Deepa1, M. Subramoniam2,*, R. Swarnalatha3, S. Poornapushpakala2, S. Barani2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 745-761, 2023, DOI:10.32604/iasc.2023.034211

    Abstract The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and… More >

  • Open Access


    Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

    Ghada Atteia1, El-Sayed M. El-kenawy2,3, Nagwan Abdel Samee1,*, Mona M. Jamjoom4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, Ahmad Taher Azar8,9, Nima Khodadadi10,11, Reham A. Ghanem12, Mahmoud Y. Shams13

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1883-1900, 2023, DOI:10.32604/cmc.2023.031723

    Abstract The rapid population growth results in a crucial problem in the early detection of diseases in medical research. Among all the cancers unveiled, breast cancer is considered the second most severe cancer. Consequently, an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses. Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective, reliable, and rapid responses, which could help in decreasing the death risk. In this paper, we propose… More >

  • Open Access


    Optimization Techniques in University Timetabling Problem: Constraints, Methodologies, Benchmarks, and Open Issues

    Abeer Bashab1, Ashraf Osman Ibrahim2,*, Ibrahim Abakar Tarigo Hashem3, Karan Aggarwal4, Fadhil Mukhlif5, Fuad A. Ghaleb5, Abdelzahir Abdelmaboud6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6461-6484, 2023, DOI:10.32604/cmc.2023.034051

    Abstract University timetabling problems are a yearly challenging task and are faced repeatedly each semester. The problems are considered non-polynomial time (NP) and combinatorial optimization problems (COP), which means that they can be solved through optimization algorithms to produce the aspired optimal timetable. Several techniques have been used to solve university timetabling problems, and most of them use optimization techniques. This paper provides a comprehensive review of the most recent studies dealing with concepts, methodologies, optimization, benchmarks, and open issues of university timetabling problems. The comprehensive review starts by presenting the essence of university timetabling as… More >

  • Open Access


    Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

    Doaa Sami Khafaga1, Amel Ali Alhussan1,*, El-Sayed M. El-kenawy2,3, Ali E. Takieldeen3, Tarek M. Hassan4, Ehab A. Hegazy5, Elsayed Abdel Fattah Eid6, Abdelhameed Ibrahim7, Abdelaziz A. Abdelhamid8,9

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 749-765, 2022, DOI:10.32604/cmc.2022.029605

    Abstract One of the most common kinds of cancer is breast cancer. The early detection of it may help lower its overall rates of mortality. In this paper, we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images. The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest. In addition, to properly train the machine learning models, data augmentation is applied to increase the number of segmented regions using various scaling ratios. On the other hand, to extract the relevant features from the… More >

  • Open Access


    Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm

    Mashar Gencal1,*, Mustafa Oral2

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 727-737, 2022, DOI:10.32604/csse.2022.023018

    Abstract Some species of females, e.g., chicken, bird, fish etc., might mate with more than one males. In the mating of these polygamous creatures, there is competition between males as well as among their offspring. Thus, male reproductive success depends on both male competition and sperm rivalry. Inspired by this type of sexual life of roosters with chickens, a novel nature-inspired optimization algorithm called Roosters Algorithm (RA) is proposed. The algorithm was modelled and implemented based on the sexual behavior of roosters. 13 well-known benchmark optimization functions and 10 IEEE CEC 2018 test functions are utilized… More >

  • Open Access


    A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

    Youseef Alotaibi*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1879-1895, 2021, DOI:10.32604/cmc.2020.013739

    Abstract A new secured database management system architecture using intrusion detection systems (IDS) is proposed in this paper for organizations with no previous role mapping for users. A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm. A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’ profiles. Then, queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious. The IDS… More >

  • Open Access


    A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

    Eslam Mohammed Abdelkader1,2,*, Osama Moselhi3, Mohamed Marzouk4, Tarek Zayed5

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 643-661, 2020, DOI:10.32604/iasc.2020.010100

    Abstract Image segmentation is one of the fundamental stages in computer vision applications. Several meta-heuristics have been applied to solve the segmentation problems by extending the Otsu and entropy functions. However, no single-objective function can optimally handle the diversity of information in images besides the multimodality issues of gray-level images. This paper presents a self-adaptive multi-objective optimization-based method for the detection of crack images in reinforced concrete bridges. The proposed method combines the flexibility of information theory functions in addition to the invasive weed optimization algorithm for bi-level thresholding. The capabilities of the proposed method are More >

  • Open Access


    A Novel Binary Firey Algorithm for the Minimum Labeling Spanning Tree Problem

    Mugang Lin1,2,*, Fangju Liu3, Huihuang Zhao1,2, Jianzhen Chen1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 197-214, 2020, DOI:10.32604/cmes.2020.09502

    Abstract Given a connected undirected graph G whose edges are labeled, the minimum labeling spanning tree (MLST) problem is to find a spanning tree of G with the smallest number of different labels. The MLST is an NP-hard combinatorial optimization problem, which is widely applied in communication networks, multimodal transportation networks, and data compression. Some approximation algorithms and heuristics algorithms have been proposed for the problem. Firey algorithm is a new meta-heuristic algorithm. Because of its simplicity and easy implementation, it has been successfully applied in various fields. However, the basic firefly algorithm for the MLST problem More >

  • Open Access


    QoS-Aware Energy-Efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-Intelligence Meta-Heuristics

    Amit Chhabra1, *, Gurvinder Singh2, Karanjeet Singh Kahlon2

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 813-834, 2020, DOI:10.32604/cmc.2020.010934

    Abstract Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service (HPCaaS) to users for executing HPC applications. However, the broader use of the Cloud services, the rapid increase in the size, and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment. Besides this, users have become more demanding in terms of Quality-of-service (QoS) expectations in terms of execution time, budget cost, utilization,… More >

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