The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficienc...
The adoption of Proton Exchange Membrane (PEM) fuel cells (FCs) is of great significance in diverse industries, as they provide high efficiency and environmental advantages, enabling the transition to sustainable and clean energy solutions. This stud...
Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy a...
In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The devel...
In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refining process. In this study, to sim...
Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS)...
The present study evaluates the application of different artificial intelligence methods associated with remote sensing data processing for assessing water quality parameters, with a focus on fish cage farming in the reservoirs. Three AI methods were...
This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the...
The optimum design of tall buildings, which have a proportionately huge quantity of structural elements and a variety of design code constraints, is a very computationally expensive process. In this paper, a novel strategy, with a combination of evol...
The key contribution of this paper is to determine the optimal operating parameters of the methane reforming process for hydrogen production. The proposed strategy contained two phases: ANFIS modelling and optimization. Four input controlling paramet...
The foundation of energy-efficient architectural design is modeling heating and cooling loads (HLs and CLs), which defines the heating and cooling apparatus constraints necessary to maintain a suitable interior air environment. It is possible that an...
The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and ou...
In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty an...
Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutio...
This study proposes a hybrid portfolio optimization framework integrating financial network analysis, Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and GA for asset allocation in BIST. The empirical analysis focuses on constituent firms within the...
Fuzzy systems are widely used in most fields of science and engineering, mainly because the models they produce are robust, accurate, easy to evaluate and capture real-world uncertainty better than do the classical alternatives. We propose a new meth...
This study presents a novel integrated framework for the structural analysis and optimization of multi-floor buildings by combining validated theoretical models with machine learning and evolutionary algorithms. The proposed Process–Action&ndas...