Yu-Fu Ko^1,* , Jiann-Wen Woody Ju²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1305-1328, 2021, DOI:10.32604/cmes.2021.017589

Abstract Higher-order multiscale structures are proposed to predict the effective elastic properties of 3-phase particle reinforced composites by considering the probabilistic spherical particles spatial distribution, the particle interactions, and utilizing homogenization with ensemble volume average approach. The matrix material, spherical particles with radius a₁, and spherical particles with radius a₂, are denoted as the 0^th phase, the 1^st phase, and the 2^nd phase, respectively. Particularly, the two inhomogeneity phases are different particle sizes and the same elastic material properties. Improved higher-order (in ratio of spherical particle sizes to the distance between the centers of spherical particles) bounds on effective elastic properties… More >

Abdelhameed Ibrahim^1,*, Hattan F. Abutarboush², Ali Wagdy Mohamed^3,4, Mohamad Fouad¹, El-Sayed M. El-kenawy^5,6

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 199-213, 2022, DOI:10.32604/cmc.2022.021886

Abstract Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average… More >

Muhammad Shahid Bhatti^1,*, Saman Azhar¹, Abid Sohail¹, Mohammad Hijji², Hamna Ayemen¹, Areesha Ramzan¹

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 389-406, 2022, DOI:10.32604/cmc.2022.021384

Abstract In the age of the internet, social media are connecting us all at the tip of our fingers. People are linkedthrough different social media. The social network, Twitter, allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights. This paper serves the purpose of text processing of a multilingual dataset including Urdu, English, and Roman Urdu. Explore machine learning solutions for sentiment analysis and train models, collect the data on government from Twitter, apply sentiment analysis, and provide a python library that classifies text sentiment.… More >

R. D. Pubudu L. Indrasiri¹, Ernesto Lee², Vaibhav Rupapara³, Furqan Rustam⁴, Imran Ashraf^5,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 489-515, 2022, DOI:10.32604/cmc.2022.019636

Abstract Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.… More >

Abdulwahab Ali Almazroi¹, Raja Sher Afgun Usmani^2,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 389-400, 2022, DOI:10.32604/iasc.2022.020588

Abstract COVID-19 pandemic has affected more than 144 million people and spread to over 200 countries. The prediction of COVID-19 behaviour and trend is crucial to prevent its spreading. Kingdom of Saudi Arabia (KSA) is Asia’s fifth largest country, and it hosts the two holiest cities of the Islamic world. KSA hosts millions of pilgrims every year, and it is of great importance to predict the COVID-19 spread to organize these religious activities and bring life to normality in KSA. This study proposes four tree-based ensemble methods to predict the COVID-19 daily new cases in KSA. Tree-based ensemble methods are suggested… More >

Shobhit Verma¹ , Nonita Sharma¹ , Aman Singh² , Abdullah Alharbi³ , Wael Alosaimi³ , Hashem Alyami⁴, Deepali Gupta⁵, Nitin Goyal^{5 ,*}

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6041-6055, 2022, DOI:10.32604/cmc.2022.021747

Abstract This research work proposes a new stack-based generalization ensemble model to forecast the number of incidences of conjunctivitis disease. In addition to forecasting the occurrences of conjunctivitis incidences, the proposed model also improves performance by using the ensemble model. Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for the duration of the first week of January 2010 to the last week of December 2019. Pre-processing techniques such as imputation of missing values and logarithmic transformation are applied to pre-process the data sets. A stacked generalization ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural… More >

Olutomilayo Olayemi Petinrin¹, Faisal Saeed², Xiangtao Li¹, Fahad Ghabban², Ka-Chun Wong^1,3,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4745-4762, 2022, DOI:10.32604/cmc.2022.020523

Abstract Bioactive compounds in plants, which can be synthesized using N-arylation methods such as the Buchwald-Hartwig reaction, are essential in drug discovery for their pharmacological effects. Important descriptors are necessary for the estimation of yields in these reactions. This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation. The algorithms were evaluated based on computational time and the number of selected descriptors. Analyses show that robust performance is obtained with more descriptors, compared to cases where fewer descriptors are selected. The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted… More >

G. Madhu¹, B. Lalith Bharadwaj¹, Rohit Boddeda², Sai Vardhan¹, K. Sandeep Kautish³, Khalid Alnowibet⁴, Adel F. Alrasheedi⁴, Ali Wagdy Mohamed^5,6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5467-5469, 2022, DOI:10.32604/cmc.2022.020455

Abstract COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by our research. Its initial purpose… More >

Saadullah Farooq Abbasi^1,2, Harun Jamil³, Wei Chen^2,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4619-4633, 2022, DOI:10.32604/cmc.2022.020318

Abstract Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed… More >

Talha Saeed, Chu Kiong Loo^*, Muhammad Shahreeza Safiruz Kassim

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076

Abstract

Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More >