Zhongyi Hu

Wuhan University | WHU · College of Information Management

With epidemics plaguing the world, understanding the co-evolution of information and epidemic diffusion networks is important for epidemic prevention policies. On the basis of the microscopic Markov chain approach method, this study proposes an aware-susceptible-infected model (ASI) to explore the effect of information literacy in the coupled multi...

Obesity, associated with having excess body fat, is a critical public health problem that can cause serious diseases. Although a range of techniques for body fat estimation have been developed to assess obesity, these typically involve high-cost tests requiring special equipment. Thus, the accurate prediction of body fat percentage based on easily...

Financial news disclosures provide valuable information for traders and investors while making stock market investment decisions. Essential but challenging, the stock market prediction problem has attracted significant attention from both researchers and practitioners. Conventional machine learning models often fail to interpret the content of fina...

Online reviews are becoming increasingly important for decision-making. Consumers often refer to online reviews for opinions before making a purchase. Marketers also acknowledge the importance of online reviews and use them to improve product success. However, the massive amount of online review data, as well as its unstructured nature, is a challe...

Purpose The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately. Design/methodology/approach Hyperlink indicators along with URL-based features are used to build the identification mode...

Background and Objective: The term ‘obesity’ refers to excessive body fat, and it is a chronic disease associated with various complications. Although a range of techniques for body fat estimation have been developed to assess obesity, they are typically associated with high-cost tests requiring special equipment. Accurate prediction of the body fa...

Concrete is one of the most commonly used construction materials in civil engineering. Being able to accurately predict concrete components based on concrete strength, slump and flow is crucial for saving manpower and financial resources. The reverse prediction nature of this task, however, makes it a very difficult problem to solve. Relative error...

Fuzzy systems are widely used for solving complex and non-linear problems that cannot be addressed using precise mathematical models. Their performance, however, is critically affected by how they are constructed as well as their fuzzy rule base. Inspired by neural networks that apply a multi-layer structure to improve their performance, we propose...

Predicting adsorption energies of reaction intermediates is critical for determining catalytic reaction mechanisms. Here, we present three combined representations for predicting adsorption energies of carbon reforming species on transition metal surfaces. Among the three combined representations, the Elemental Properties and Spectral London Axilro...

This study uses the recently proposed dynamic model averaging (DMA) and dynamic model selection (DMS) framework to develop forecasting models of Chinese soybean futures price with eight predictors, which allows both coefficients and forecasting models to evolve over time. Specifically, covering an out-of-sample period from August 2, 2005 to May 26,...

Residuary resistance prediction is an important initial step in the process of designing a sailing yacht. Being able to predict the residuary resistance accurately is crucial for calculating the required propulsive power and ensuring good performance of the sailing yacht. This paper presents a two-layer Wang-Mendel (WM) fuzzy approach to improve th...

Body fat prediction is a step toward addressing obesity issues. In this paper, we propose a machine learningbased prediction model incorporating a novel fuzzy adaptive global learning binary colonization method for feature selection. Two fuzzy inference systems are used to select input features more purposefully. The proposed model is validated aga...

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more be...

Stock market prediction plays an important role in financial decision-making for investors. Many of them rely on news disclosures to make their decisions in buying or selling stocks. However, accurate modelling of stock market trends via news disclosures is a challenging task, considering the complexity and ambiguity of natural languages used. Unli...

Being able to detect anomalies for predicting machine breakdown is of critical importance in the mining industry. These anomalies are usually found in unlabelled sensor data, and therefore unsupervised models represent the preferred choice for the task. In this chapter, we propose the use of a bio-inspired clustering model based on the Self-Organis...

Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue tha...

Malicious web domains represent a big threat to web users' privacy and security. With so much freely available data on the Internet about web domains' popularity and performance, this study investigated the performance of well-known machine learning techniques used in conjunction with this type of online data to identify malicious web domains. Two...

Accurate forecasting of mid-term electricity load is an important issue for power system planning and operation. Instead of point load forecasting, this study aims to model and forecast mid-term interval loads up to one month in the form of interval-valued series consisting of both peak and valley points by using MSVR (Multi-output Support Vector R...

Selection of input features plays an important role in developing models for short-term load forecasting (STLF). Previous studies along this line of research have focused pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid selection scheme that includes both filter and wrapper methods in constructing an appropriate p...

Interval time series prediction is one of the most challenging research topics in the field of time series modeling and prediction. In view of the remarkable function approximation capability of fully complex-valued radial basis function neural networks (FCRBFNNs), we set out to investigate the possibility of forecasting interval time series by den...

Accurate interval forecasting of agricultural commodity futures prices over future horizons is challenging and of great interests to governments and investors, by providing a range of values rather than a point estimate. Following the well-established “linear and nonlinear” modeling framework, this study extends it to forecast interval-valued agric...

Background Short-term load forecasting is an important issue that has been widely explored and examined with respect to the operation of power systems and commercial transactions in electricity markets. Of the existing forecasting models, support vector regression (SVR) has attracted much attention. While model selection, including feature selectio...

Opposition-based Learning (OBL) has been reported with an increased performance in enhancing various optimization approaches. Instead of investigating the opposite point of a candidate in OBL, this study proposed a partial opposition-based learning (POBL) schema that focuses a set of partial opposite points (or partial opposite population) of an es...

In view of the significance of personal performance assessment in the working metrics of human resource management, this study proposes to set up a multi-class Support Vector Machine based model to elaborate how to evaluate the staffs' performance effectively and efficiently. Data samples are collected from a construction company in China, and used...

Highly accurate interval forecasting of electricity demand is fundamental to the success of reducing the risk when making power system planning and operational decisions by providing a range rather than point estimation. In this study, a novel modeling framework integrating bivariate empirical mode decomposition (BEMD) and support vector regression...

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic wit...

Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-ou...

Following the “decomposition-and-ensemble” principle, the empirical mode decomposition (EMD)-based modeling framework has been widely used as a promising alternative for nonlinear and nonstationary time series modeling and prediction. The end effect, which occurs during the sifting process of EMD and is apt to distort the decomposed sub-series and...

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on it...

Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with optimum solutions, while patt...

Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterated strategy or direct strategy...

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibit...

An accurate prediction of crude oil prices over long future horizons is challenging and of great interest to governments, enterprises, and investors. This paper proposes a revised hybrid model built upon empirical mode decomposition (EMD) based on the feed-forward neural network (FNN) modeling framework incorporating the slope-based method (SBM), w...

Reasons for contradictory findings regarding the gender moderate effect on computer self-efficacy in the adoption of e-learning/mobile learning are limited. Recognizing the multilevel nature of the computer self-efficacy (CSE), this study attempts to explore gender differences in the adoption of mobile learning, by extending the Technology Acceptan...

With regard to the nonlinearity and irregularity along with implicit seasonality and trend in the context of air passenger traffic forecasting, this study proposes an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) modeling framework incorporating a slope-based method to restrain the end effect issue occurring duri...

Although the Empirical Mode Decomposition (EMD)-based decomposition and ensemble framework for time series forecasting has been widely used, the end effect of EMD has not been addressed adequately. This study proposed to incorporate Mirror Method (MM), capable of dealing with the problem of end effect, into a hybrid modeling framework with Ensemble...

The hybrid linear and nonlinear modeling framework has been widely used as a promising method for time series forecasting. However, there have been very few, if any, large scale comparative studies for the hybrid linear and nonlinear framework for air passenger traffic forecasting. So, we hope this study would fill this gap. The linear models selec...

In this study, a hybrid decomposition and ensemble framework incorporating Ensemble empirical mode decomposition (EEMD) and selected modeling methodologies are proposed for stock price forecasting. Under the framework, the original stock price series was first decomposed into several subseries including a number of intrinsic mode functions (IMFs) a...

Non-Normal demand is the demand with infrequent demand occurrences or irregular demand sizes, which is very difficult to forecast. In this study, an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) learning approach is proposed to forecast demand in these two cases. This approach is under a "decomposition-and-ensemb...