Hong-Liang Dai

Guangzhou University · School of Economics and Statistics

Fuzzy hybrid models are efficient mathematical tools for managing unclear and vague data in real-world scenarios. This research explores the q-rung orthopair fuzzy soft set (q-ROFSS), which presents incomplete and ambiguous details in decision-making problems. The main intention of this study is to describe and evaluate the characteristics of the c...

This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir–orifices. The results are compared with the standard ANFIS and two hybrid models...

The objective of this investigation is to produce maps identifying areas prone to landslides (LSMs) by utilizing multiple machine learning techniques, including the harmony search algorithm (HS), shuffled frog leaping algorithm (SFLA), evaporation rate water cycle algorithm (ERWCA), and whale optimization algorithm (WOA). To create a comprehensive...

In the manufacturing industry, compositional data (CoDa) is a vital quality characteristic to be monitored. The proposed study has introduced a Hotelling T2 control chart using principal component analysis to monitor CoDa explicitly. The proposed method overcomes the limitations of previous approaches by utilizing isometric log-ratio transformation...

This study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned by particle swarm optimization, whale optimization, marine predators algorithms, and single relevance vector machine methods. Variou...

This research offers a fast and accurate method for measuring the biogas production rate throughout biogas production. An agricultural biogas plant's measurement of eight process variables served as the source of experimental data used to create the models. Biomass type, reactor/feeding, volatile solids, pH, organic load rate, hydraulic retention t...

Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applic...

This study searches the feasibility of a new hybrid extreme leaning machine tuned with improved reptile search algorithm (ELM-IRSA), in river flow modeling. The outcomes of the new method were compared with single ELM and hybrid ELM-based methods including ELM with salp swarm algorithm (SSA), ELM with equilibrium optimizer (EO) and ELM with reptile...

Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verificat...

Compositional data (CoDa) has been monitored in statistical process monitoring, where multivariate control charts (CCs) such as Hotelling T2c, MEWMA-CoDa, and MCUSUM-CoDa are commonly used to determine if a process is in-control. However, these charts can encounter problems when there is an out-of-control (OOC) process due to various factors such a...

This study investigates the feasibility of a relevance vector machine tuned with improved Manta-Ray foraging optimization (RVM-IMRFO) in predicting monthly pan evaporation using limited climatic input data (e.g. temperature). The accuracy of the RVM-IMRFO was evaluated by comparing it with RVM tuned by gray wolf optimization, RVM tuned with a whale...

Drought modeling is vital for designing and managing water resource systems due to its significant effects on agriculture and other components of the environment. This study evaluates the prediction accuracy of two newly developed heuristic methods, optimally pruned extreme learning machine (OP-ELM) and dynamic evolving neural-fuzzy inference syste...

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimiz...

For better estimation of renewable environmental friendly and carbon-free energy resources, precise prediction of solar energy is very essential. However, accurate prediction of solar energy is a challenging task due to its fluctuations and due to climatic factors those make it very nonlinear in nature. Therefore, in this study, the novel robust so...

In reinforced concrete structures, the utilization of composite rebar has been increased by considering their high corrosion resistance, anti-magnetic properties, and significant tensile strength. According to the lower elasticity modulus of composite rebar in comparison with steel rebar, concrete beams reinforced including composite rebar possess...

Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm optimization (FFAPSO), is...

This study mainly addresses the problem of the instability of forecasting stock price investment and the directly in determining investment proportion, Trend Peak Price Tracing (TPPT) is proposed. First of all, considering the influence of stock price anomaly, TPPT strategy sets adjustable historical window width and uses slope value to judge predi...

How to carry out an investment portfolio efficiently and reasonably has become a hot issue. This study mainly addresses the problem of the instability of forecasting stock price investment and the difficulty in determining investment proportion, and trend peak price tracing (TPPT) is proposed. First of all, Because of the influence of stock price a...

As an important part of machine learning, classification learning has been applied in many practical fields. It is valuable that to discuss class imbalance learning in several fields. In this research, we provide a review of class imbalanced learning methods from the data driven methods and algorithm driven methods based on numerous published paper...

The classification of imbalanced data is a major challenge for machine learning. In this paper, we presented a fuzzy total margin based support vector machine (FTM-SVM) method to handle the class imbalance learning (CIL) problem in the presence of outliers and noise. The proposed method incorporates total margin algorithm, different cost functions...

Classification of protein sequences into functional and structural families based on machine learning methods is a hot research topic in machine learning and Bioinformatics. In fact, the underlying protein classification problem is a huge multiclass problem. Generally, the multiclass problem can be reduced to a set of binary classification problems...

We develop a novel classifier in a kernel feature space, which can be used to handle the class imbalanced problem in the presence of noise and outliers. In many applications, each input point may not be fully assigned to one of two classes or multiclasses. Based on the Laplacian classifier (LC), we applied a fuzzy membership to each input point and...

：提出了一种新型具有良好特性的支持向量机－－－全间隔自适应模糊支持向量机（ＴＡＦＳＶＭ）。运用实值 遗传算法（ＲＧＡ）对其进行参数优选‚得到一种新的智能模型－－－实值遗传算法优化的全间隔自适应模糊支持向量机 （ＲＧＡＴＡＦＳＶＭ）模型‚并且应用于四种不同的水质数据分类。实验结果表明‚提出的模型相对标准支持向量机、ＢＰ神 经网络和单因子分类方法具有较高的分类精度和较高的稳定性‚是一种有效的水质分类方法。