Zeyad Ghaleb Al-Mekhlafi¹, Ebrahim Mohammed Senan², Taha H. Rassem³, Badiea Abdulkarem Mohammed^4,5,*, Nasrin M. Makbol⁵, Adwan Alownie Alanazi¹, Tariq S. Almurayziq¹, Fuad A. Ghaleb⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 775-796, 2022, DOI:10.32604/cmc.2022.024492

Abstract Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in… More >

Amani Abdulrahman Albraikan¹, Siwar Ben Haj Hassine², Suliman Mohamed Fati³, Fahd N. Al-Wesabi^2,4, Anwer Mustafa Hilal^5,*, Abdelwahed Motwakel⁵, Manar Ahmed Hamza⁵, Mesfer Al Duhayyim⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 907-923, 2022, DOI:10.32604/cmc.2022.024488

Abstract Cyberbullying (CB) is a distressing online behavior that disturbs mental health significantly. Earlier studies have employed statistical and Machine Learning (ML) techniques for CB detection. With this motivation, the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification (ODL-CDC) technique for CB detection in social networks. The proposed ODL-CDC technique involves different processes such as pre-processing, prediction, and hyperparameter optimization. In addition, GloVe approach is employed in the generation of word embedding. Besides, the pre-processed data is fed into Bidirectional Gated Recurrent Neural Network (BiGRNN) model for prediction. Moreover, hyperparameter tuning of BiGRNN model is carried out with… More >

Kazem Nouri^1,*, Milad Fahimi¹, Leila Torkzadeh¹, Dumitru Baleanu^2,3

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1495-1514, 2022, DOI:10.32604/cmc.2022.024406

Abstract The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network.… More >

Tamer AbuKhalil¹, Bassam A. Y. Alqaralleh^2,*, Ahmad H. Al-Omari³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 57-71, 2022, DOI:10.32604/cmc.2022.024367

Abstract Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature… More >

Murad A. A. Almekhlafi¹, Taiseer Abdalla Elfadil Eisa², Fahd N. Al-Wesabi^3,4, Abdelzahir Abdelmaboud⁵, Manar Ahmed Hamza^6,*, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶, Mohammed Rizwanullah⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 227-242, 2022, DOI:10.32604/cmc.2022.024356

Abstract Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning, allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be received. Despite the increasing literature on the line of sight with different scenarios, no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their accuracy. Therefore, this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances… More >

Saleh Albahli^1,*, Farman Hassan², Ali Javed^2,3, Aun Irtaza^2,4

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 833-849, 2022, DOI:10.32604/cmc.2022.024267

Abstract COVID-19 has become a pandemic, with cases all over the world, with widespread disruption in some countries, such as Italy, US, India, South Korea, and Japan. Early and reliable detection of COVID-19 is mandatory to control the spread of infection. Moreover, prediction of COVID-19 spread in near future is also crucial to better plan for the disease control. For this purpose, we proposed a robust framework for the analysis, prediction, and detection of COVID-19. We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world.… More >

Abdelzahir Abdelmaboud¹, Fahd N. Al-Wesabi^1,2,3, Mesfer Al Duhayyim⁴, Taiseer Abdalla Elfadil Eisa⁵, Manar Ahmed Hamza^6,*, Mohammed Rizwanullah⁶, Abu Serwar Zamani⁶, Radwa Marzouk⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 679-693, 2022, DOI:10.32604/cmc.2022.024240

Abstract Internet of Things (IoT) with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications. At the same time, machine learning (ML) and data mining approaches are presented for accomplishing prediction and classification processes. With this motivation, this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection (IML-ELNVBD) in IoT environment. The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors, cameras, etc. which are then connected to the cloud server for further processing. In addition, the modelling and extraction of behaviour… More >

Qamar Abbas¹, Khalid Mahmood Malik², Abdul Khader Jilani Saudagar^3,*, Muhammad Badruddin Khan³, Mozaherul Hoque Abul Hasanat³, Abdullah AlTameem³, Mohammed AlKhathami³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1229-1250, 2022, DOI:10.32604/cmc.2022.024211

Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive… More >

Mesfer Al Duhayyim¹, Majdy M. Eltahir², Imène Issaoui³, Fahd N. Al-Wesabi^2,4, Anwer Mustafa Hilal⁵, Fuad Ali Mohammed Al-Yarimi², Manar Ahmed Hamza^5,*, Abu Sarwar Zamani⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 181-195, 2022, DOI:10.32604/cmc.2022.024206

Abstract The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such… More >

Shuguang Wei, Jiaqi Li^*, Zixu Zhao, Dong Yuan

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1035-1052, 2022, DOI:10.32604/cmc.2022.024201

Abstract This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then, the modal analyses of the… More >