Ramazan Ünlü¹, Ersin Namlı^{2, *}

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1383-1399, 2020, DOI:10.32604/cmc.2020.011335

Abstract From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory… More >

G. N. Baltas^{1, *}, F. A. Prieto¹, M. Frantzi², C. R. Garcia-Alonso¹, P. Rodriguez^{1, 3}

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1343-1357, 2020, DOI:10.32604/cmc.2020.011243

Abstract During the late months of last year, a novel coronavirus was detected in Hubei, China. The virus, since then, has spread all across the globe forcing Word Health Organization (WHO) to declare COVID-19 outbreak a pandemic. In Spain, the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid, Barcelona and other major cities. The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity, implement strict measures by putting the entire country in quarantine. The duration of these measures, depends on the… More >

Suhas S. Aralikatti¹, K. N. Ravikumar¹, Hemantha Kumar^1,*, H. Shivananda Nayaka¹, V. Sugumaran²

Structural Durability & Health Monitoring, Vol.14, No.2, pp. 127-145, 2020, DOI:10.32604/sdhm.2020.07595

Abstract The state of cutting tool determines the quality of surface produced on the machined parts. A faulty tool produces poor surface, inaccurate geometry and non-economic production. Thus, it is necessary to monitor tool condition for a machining process to have superior quality and economic production. In the present study, fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique. Cutting force and vibration signals were acquired to monitor tool condition during machining. A set of four tooling conditions namely healthy, worn flank, broken insert and extended tool overhang have been considered… More >

Tong Li¹, Shibin Zhang^{1, *}, Jinyue Xia²

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 401-438, 2020, DOI:10.32604/cmc.2020.010551

Abstract Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren… More >

Seokhoon Kim¹, Dae-Young Kim^{2, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 1-15, 2020, DOI:10.32604/cmc.2020.09545

Abstract The Internet of Things (IoT) has enabled various intelligent services, and IoT service range has been steadily extended through long range wide area communication technologies, which enable very long distance wireless data transmission. End-nodes are connected to a gateway with a single hop. They consume very low-power, using very low data rate to deliver data. Since long transmission time is consequently needed for each data packet transmission in long range wide area networks, data transmission should be efficiently performed. Therefore, this paper proposes a multicast uplink data transmission mechanism particularly for bad network conditions. Transmission delay will be increased if… More >

Amin Bemani¹, Alireza Baghban², Shahaboddin Shamshirband^{3, 4, *}, Amir Mosavi^{5, 6, 7}, Peter Csiba⁷, Annamaria R. Varkonyi-Koczy^{5, 7}

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1175-1204, 2020, DOI:10.32604/cmc.2020.07723

Abstract In the present work, a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide. Four different machine learning algorithms of radial basis function, multi-layer perceptron (MLP), artificial neural networks (ANN), least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and the dissociation constant of acid. To evaluate the proposed models, different graphical and statistical analyses, along with novel sensitivity analysis, are carried out.… More >

Muhammad Zubair Asghar¹, Fazli Subhan², Muhammad Imran¹, Fazal Masud Kundi¹, Adil Khan³, Shahboddin Shamshirband^{4, 5, *}, Amir Mosavi^{6, 7, 8}, Peter Csiba⁸, Annamaria R. Varkonyi Koczy⁸

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1093-1118, 2020, DOI:10.32604/cmc.2020.07709

Abstract Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine… More >

Caining Zhang¹, Xiaopeng Guo², Xiaoya Guo³, David Molony⁴, Huaguang Li², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Dalin Tang^1*,7, Rencan Nie^2,*, Jinde Cao⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 631-646, 2020, DOI:10.32604/cmes.2020.09718

Abstract Optical coherence tomography (OCT) is a new intravascular imaging technique with high resolution and could provide accurate morphological infor￾mation for plaques in coronary arteries. However, its segmentation is still com￾monly performed manually by experts which is time-consuming. The aim of this study was to develop automatic techniques to characterize plaque components and quantify plaque cap thickness using 3 machine learning methods including convolutional neural network (CNN) with U-Net architecture, CNN with Fully convolutional DenseNet (FC-DenseNet) architecture and support vector machine (SVM). In vivo OCT and intravascular ultrasound (IVUS) images were acquired from two patients at Emory University with informed consent… More >

Jianhua Dong¹, Lifeng Wu², Xiaogang Liu^{1, *}, Cheng Fan¹, Menghui Leng³, Qiliang Yang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 49-73, 2020, DOI: 10.32604/cmes.2020.09014

Abstract Solar radiation is an important parameter in the fields of computer modeling, engineering technology and energy development. This paper evaluated the ability of three machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Multivariate Adaptive Regression Splines (MARS), to estimate the daily diffuse solar radiation (R_d). The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters (including mean average temperature (T_a), theoretical sunshine duration (N), actual sunshine duration (n), daily average air relative humidity (RH), and extra-terrestrial solar radiation (R_a)). And their estimation accuracies were subjected to comparative analysis.… More >

Jian Liu¹, Wantao Wang¹, Jie Chen^{2, *}, Guozhong Sun³, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1187-1200, 2020, DOI:10.32604/cmc.2020.05883

Abstract Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of lesion images by various models… More >