M. Premkumar¹, R. Sowmya², Pradeep Jangir³, Kottakkaran Sooppy Nisar^4,*, Mujahed Aldhaifallah⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2227-2242, 2021, DOI:10.32604/cmc.2021.015565

Abstract The Equilibrium Optimizer (EO), Grey Wolf Optimizer (GWO), and Whale Optimizer (WO) algorithms are being recently developed for engineering optimization problems. In this paper, the EO, GWO, and WO algorithms are applied individually for a brushless direct current (BLDC) design optimization problem. The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state. The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale, respectively. The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total mass.… More >

Abdul Rehman Javed^1,*, Rabia Abid², Bakhtawar Aslam², Hafiza Ammara Khalid², Mohammad Zubair Khan³, Omar H. Alhazmi³, Muhammad Rizwan²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1933-1950, 2021, DOI:10.32604/cmc.2021.015272

Abstract With the popularity of green computing and the huge usage of networks, there is an acute need for expansion of the 5G network. 5G is used where energy efficiency is the highest priority, and it can play a pinnacle role in helping every industry to hit sustainability. While in the 5G network, conventional performance guides, such as network capacity and coverage are still major issues and need improvements. Device to Device communication (D2D) communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques. The issue of energy utilization in the IoT based… More >

Yasir Shahzad^1,*, Huma Javed¹, Haleem Farman², Jamil Ahmad², Bilal Jan³, Abdelmohsen A. Nassani⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2463-2480, 2021, DOI:10.32604/cmc.2021.014904

Abstract Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling technique. The proposed model is… More >

Monagi H. Alkinani^1,*, E. A. Zanaty², Sherif M. Ibrahim³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1577-1593, 2021, DOI:10.32604/cmc.2021.014803

Abstract This paper presents a novel method utilizing wavelets with particle swarm optimization (PSO) for medical image compression. Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding. It transfers images into subband details and approximations using a modified Haar wavelet (MHW), and then applies a threshold. PSO is applied for selecting a particle assigned to the threshold values for the subbands. Nine positions assigned to particles values are used to represent population. Every particle updates its position depending on the global best position (gbest) (for all details subband) and local best position (pbest) (for… More >

Sally M. Elghamrawy¹, Aboul Ella Hassnien^2,*, Vaclav Snasel³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767

Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization… More >

Hegazy Rezk^1,2,*, Ahmed Fathy^3,4, Mokhtar Aly^5,6, Mohamed N. Ibrahim^7,8,9

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2271-2281, 2021, DOI:10.32604/cmc.2021.014590

Abstract Hydrocarbons, carbon monoxide and other pollutants from the transportation sector harm human health in many ways. Fuel cell (FC) has been evolving rapidly over the past two decades due to its efficient mechanism to transform the chemical energy in hydrogen-rich compounds into electrical energy. The main drawback of the standalone FC is its slow dynamic response and its inability to supply rapid variations in the load demand. Therefore, adding energy storage systems is necessary. However, to manage and distribute the power-sharing among the hybrid proton exchange membrane (PEM) fuel cell (FC), battery storage (BS), and supercapacitor (SC), an energy management… More >

A. Muthumari¹, J. Banumathi², S. Rajasekaran³, P. Vijayakarthik⁴, K. Shankar⁵, Irina V. Pustokhina⁶, Denis A. Pustokhin^7,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1863-1879, 2021, DOI:10.32604/cmc.2021.013614

Abstract Cloud computing offers internet location-based affordable, scalable, and independent services. Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events, for instance, pandemic situations. To handle massive information, clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data, with elevated velocity and modified configurations. Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server. Data deduplication also saves network bandwidth. In this paper, a new… More >

Rajesh Kumar Sahoo¹, Morched Derbali^2,*, Houssem Jerbi³, Doan Van Thang⁴, P. Pavan Kumar⁵, Sipra Sahoo⁶

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2321-2336, 2021, DOI:10.32604/cmc.2021.013014

Abstract Software testing has been attracting a lot of attention for effective software development. In model driven approach, Unified Modelling Language (UML) is a conceptual modelling approach for obligations and other features of the system in a model-driven methodology. Specialized tools interpret these models into other software artifacts such as code, test data and documentation. The generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the requirements. This paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm (BGA). For generating the… More >

Abdul Hannan Khan^1,2, Muhammad Adnan Khan^3,*, Sagheer Abbas², Shahan Yamin Siddiqui^1,2, Muhammad Aanwar Saeed⁴, Majed Alfayad⁵, Nouh Sabri Elmitwally^6,7

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1399-1412, 2021, DOI:10.32604/cmc.2021.012737

Abstract Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization… More >

Shichao Wang, Yu Xue^*, Weiwei Jia

Journal on Big Data, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jbd.2021.010364

Abstract In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. In such cases, Feature Selection (FS) is often involved. Meanwhile, owing to their excellent global search ability, evolutionary computation techniques have been widely employed to the FS. So, as a powerful global search method and calculation fast than other EC algorithms, PSO can solve features selection problems well. However, when facing a large number of feature selection, the efficiency of PSO drops significantly. Therefore, plenty of works have been done to improve this situation.… More >