AlAli Moneera, AlQahtani Maram, AlJuried Azizah, Taghareed AlOnizan, Dalia Alboqaytah, Nida Aslam^*, Irfan Ullah Khan

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1681-1696, 2021, DOI:10.32604/cmc.2020.013466

Abstract Online advertisements have a significant influence over the success or failure of your business. Therefore, it is important to somehow measure the impact of your advertisement before uploading it online, and this is can be done by calculating the Click Through Rate (CTR). Unfortunately, this method is not eco-friendly, since you have to gather the clicks from users then compute the CTR. This is where CTR prediction come in handy. Advertisement CTR prediction relies on the users’ log regarding click information data. Accurate prediction of CTR is a challenging and critical process for e-advertising platforms these days. CTR prediction uses… More >

Abdu Gumaei^1,2,*, Mabrook Al-Rakhami¹, Mohamad Mahmoud Al Rahhal³, Fahad Raddah H. Albogamy³, Eslam Al Maghayreh³, Hussain AlSalman¹

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 315-329, 2021, DOI:10.32604/cmc.2020.012045

Abstract The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function of the training process and… More >

Xianghui Lu¹, Junliang Fan², Lifeng Wu^1,*, Jianhua Dong³

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 699-723, 2020, DOI:10.32604/cmes.2020.011004

Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET₀) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET₀ using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme gradient boosting (XGBoost) model… More >

Shuo He^1,∗, Jianbin Zheng^1,†, Jiale Lin^2,‡, Tao Tang^1,§, Jintao Zhao^1,¶, Hongbao Liu^1,ll

Computer Systems Science and Engineering, Vol.35, No.3, pp. 141-149, 2020, DOI:10.32604/csse.2020.35.141

Abstract Nowadays, many payment service providers use the discounts and other marketing strategies to promote their products. This also raises the issue of people who deliberately take advantage of such promotions to reap financial benefits. These people are known as ‘scalper parties’ or ‘econnoisseurs’ which can constitute an underground industry. In this paper, we show how to use machine learning to assist in identifying abnormal scalper transactions. Moreover, we introduce the basic methods of Decision Tree and Boosting Tree, and show how these classification methods can be applied in the detection of abnormal transactions. In addition, we introduce a graph computing… More >

Rizwan Ali Naqvi¹, Muhammad Adnan Khan^{2, *}, Nauman Malik², Shazia Saqib², Tahir Alyas², Dildar Hussain³

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1221-1236, 2020, DOI:10.32604/cmc.2020.011686

Abstract Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent. Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for writing. The communication using the Roman characters, which are used in the script of Urdu language on social media, is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information supply. English Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the… More >

Yuwen Chen^{1, 2, 3, *}, Xiaolin Qin^{1, 3}, Lige Zhang^{1, 3}, Bin Yi⁴

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 495-510, 2020, DOI:10.32604/cmc.2020.011278

Abstract The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text… More >

Yongmei Zhang^{1, *}, Jianzhe Ma², Lei Hu³, Keming Yu⁴, Lihua Song^{1, 5}, Huini Chen¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1929-1944, 2020, DOI:10.32604/cmc.2020.010556

Abstract The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract… 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 >

Jing Li¹, Yong Yang^{2, *}, Hongmei Ge¹, Li Zhao³, Ruxue Guo^{3, 4}

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 151-162, 2020, DOI:10.32604/cmc.2020.05649

Abstract Coal rock mass instability fracture may result in serious hazards to underground coal mining. Acoustic emissions (AE) stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass. AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock. This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method (CWT). It is further adopt an efficient Light Gradient Boosting Machine (LightGBM) by several cascaded sub weak classifier models to merge AE features at different views of frequency for… More >

Aziguli Wulam^1,2, Yingshuai Wang^1,2, Dezheng Zhang^1,2,*, Jingyue Sang³, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1003-1013, 2019, DOI:10.32604/cmc.2019.07704

Abstract In recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of the tree model, which can… More >