Pham Hoang Vuong¹, Trinh Tan Dat¹, Tieu Khoi Mai¹, Pham Hoang Uyen², Pham The Bao^1,*

Computer Systems Science and Engineering, Vol.40, No.1, pp. 237-246, 2022, DOI:10.32604/csse.2022.017685

Abstract Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and… More >

Mohamed Ezz^1,2,*, Murtada K. Elbashir^1,3, Hosameldeen Shabana^4,5

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2077-2092, 2021, DOI:10.32604/cmc.2021.018155

Abstract It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited. Delay in ICU admissions is associated with negative outcomes such as mortality and cost. Therefore, early identification of patients with a high risk of respiratory failure can prevent complications, enhance risk stratification, and improve the outcomes of severely-ill hospitalized patients. In this paper, we develop a model that uses the characteristics and information collected at the time of patients’ admissions and during their early period of hospitalization to accurately predict whether they will need… More >

Tian Liwei^1,2,*, Feng Li¹, Sun Yu³, Guo Yuankai⁴

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 855-868, 2021, DOI:10.32604/iasc.2021.016805

Abstract The prediction of the “ups and downs” of stock market prices is one of the important undertakings of the financial market. Since accurate prediction helps foster considerable economic benefits, stock market prediction has attracted significant interest by both investors and researchers. Efforts into building an accurate, stable and effective model to predict stock prices’ movements have been proliferating at a fast pace, to meet such a challenge. Firstly, this paper uses a correlation analysis to analyze the attributes of a stock dataset, processing missing values, determining the data attributes to be retained data, then divide… More >

Youseef Alotaibi^1,*, Muhammad Noman Malik², Huma Hayat Khan³, Anab Batool², Saif ul Islam⁴, Abdulmajeed Alsufyani⁵, Saleh Alghamdi⁶

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3323-3338, 2021, DOI:10.32604/cmc.2021.016727

Abstract Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used More >

Yuanmin Shi^1,2, Siran Yin^1,2, Ze Chen^1,2, Leiming Yan^1,2,*

Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 9-16, 2021, DOI:10.32604/jihpp.2021.012193

Abstract Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the More >

Ameer A. Badr^1,2,*, Alia K. Abdul-Hassan²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1713-1729, 2021, DOI:10.32604/cmc.2021.016732

Abstract Age estimation in short speech utterances finds many applications in daily life like human-robot interaction, custom call routing, targeted marketing, user-profiling, etc. Despite the comprehensive studies carried out to extract descriptive features, the estimation errors (i.e. years) are still high. In this study, an automatic system is proposed to estimate age in short speech utterances without depending on the text as well as the speaker. Firstly, four groups of features are extracted from each utterance frame using hybrid techniques and methods. After that, 10 statistical functionals are measured for each extracted feature dimension. Then, the… More >

Jingxin Yu^1,3, Wengang Zheng^1,*, Linlin Xu³, Lili Zhangzhong¹, Geng Zhang², Feifei Shan¹

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 989-1003, 2020, DOI:10.32604/iasc.2020.010130

Abstract Accurate estimation of reference evapotranspiration (ET0) is a critical prerequisite for the development of agricultural water management strategies. It is challenging to estimate the ET0 of a solar greenhouse because of its unique environmental variations. Based on the idea of ensemble learning, this paper proposed a novel ET0i estimation model named PSO-XGBoost, which took eXtreme Gradient Boosting (XGBoost) as the main regression model and used Particle Swarm Optimization (PSO) algorithm to optimize the parameters of XGBoost. Using the meteorological and soil moisture data during the two-crop planting process as the experimental data, and taking ET0i More >

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… More >

Xijun Zhang^*, Qirui Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 95-109, 2020, DOI:10.32604/cmes.2020.011013

Abstract According to the time series characteristics of the trajectory history data, we predicted and analyzed the traffic flow. This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity, stationary and abnormality of time series. It can improve the traffic flow prediction effect, achieve efficient traffic guidance and traffic control. The model combined the characteristics of LSTM (Long Short-Term Memory) network and XGBoost (Extreme Gradient Boosting) algorithms. First, we used the LSTM model that increases dropout layer to train the data set after… 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 More >