Qianyao Zhu¹, Kaizhou Gao^1,*, Wuze Huang¹, Zhenfang Ma¹, Adam Slowik²

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3573-3589, 2024, DOI:10.32604/cmc.2024.055244 - 12 September 2024

Abstract The flow shop scheduling problem is important for the manufacturing industry. Effective flow shop scheduling can bring great benefits to the industry. However, there are few types of research on Distributed Hybrid Flow Shop Problems (DHFSP) by learning assisted meta-heuristics. This work addresses a DHFSP with minimizing the maximum completion time (Makespan). First, a mathematical model is developed for the concerned DHFSP. Second, four Q-learning-assisted meta-heuristics, e.g., genetic algorithm (GA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), and differential evolution (DE), are proposed. According to the nature of DHFSP, six local search operations… More >

S. R. Deepa¹, M. Subramoniam^2,*, R. Swarnalatha³, S. Poornapushpakala², S. Barani²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 745-761, 2023, DOI:10.32604/iasc.2023.034211 - 29 April 2023

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 >

Ghada Atteia¹, El-Sayed M. El-kenawy^2,3, Nagwan Abdel Samee^1,*, Mona M. Jamjoom⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, Ahmad Taher Azar^8,9, Nima Khodadadi^10,11, Reham A. Ghanem¹², Mahmoud Y. Shams¹³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1883-1900, 2023, DOI:10.32604/cmc.2023.031723 - 06 February 2023

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 >

Abeer Bashab¹, Ashraf Osman Ibrahim^2,*, 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 - 28 December 2022

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 >

Doaa Sami Khafaga¹, Amel Ali Alhussan^1,*, El-Sayed M. El-kenawy^2,3, Ali E. Takieldeen³, Tarek M. Hassan⁴, Ehab A. Hegazy⁵, Elsayed Abdel Fattah Eid⁶, Abdelhameed Ibrahim⁷, Abdelaziz A. Abdelhamid^8,9

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 749-765, 2022, DOI:10.32604/cmc.2022.029605 - 18 May 2022

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 >

Mashar Gencal^1,*, Mustafa Oral²

Computer Systems Science and Engineering, Vol.42, No.2, pp. 727-737, 2022, DOI:10.32604/csse.2022.023018 - 04 January 2022

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 >

Youseef Alotaibi^*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1879-1895, 2021, DOI:10.32604/cmc.2020.013739 - 26 November 2020

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 >

Eslam Mohammed Abdelkader^1,2,*, 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 >

Mugang Lin^1,2,*, Fangju Liu³, Huihuang Zhao^1,2, Jianzhen Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 197-214, 2020, DOI:10.32604/cmes.2020.09502 - 18 September 2020

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 >

Amit Chhabra^{1, *}, Gurvinder Singh², Karanjeet Singh Kahlon²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 813-834, 2020, DOI:10.32604/cmc.2020.010934 - 10 June 2020

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 >