Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models DOI
Hossein Moayedi, Marjan Salari,

Sana Abdul-Jabbar Ali

et al.

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(13)

Published: June 12, 2024

Language: Английский

A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes DOI Creative Commons
Hossein Moayedi, Atefeh Ahmadi Dehrashid,

Binh Nguyen Le

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Jan. 7, 2024

This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for training dataset, 80% dataset from sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used create an susceptibility model establishes unique original approach. After it confirmed be successful, algorithms were applied map this area, demonstrating integrity results. The AUC values computed every optimisation in study. optimal estimated accuracy indices populations 450 determined 0.9846 BSA-MLP databases. maximum value HBO-MLP databases different swarm sizes 0.9736. A size 350–300 is considered forecasting mapping models. With same constraints, TLBO-MLP scenario 0.996. 150 conditions train PO-MLP model, 0.9845. According these findings, worked best 50 150, respectively.

Language: Английский

Citations

10
A new procedure for optimizing neural network using stochastic algorithms in predicting and assessing landslide risk in East Azerbaijan DOI
Atefeh Ahmadi Dehrashid, Hailong Dong,

Marieh Fatahizadeh

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: March 21, 2024

Language: Английский

Citations

7
Validation of four optimization evolutionary algorithms combined with artificial neural network (ANN) for landslide susceptibility mapping: A case study of Gilan, Iran DOI
Hossein Moayedi, Maochao Xu, Pooria Naderian

et al.

Ecological Engineering, Journal Year: 2024, Volume and Issue: 201, P. 107214 - 107214

Published: Feb. 29, 2024

Language: Английский

Citations

5
Assessment of sodium adsorption ratio (SAR) in groundwater: Integrating experimental data with cutting-edge swarm intelligence approaches DOI

Zongwang Wu,

Hossein Moayedi, Marjan Salari

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: April 29, 2024

Language: Английский

Citations

5
Evaluation of student failure in higher education by an innovative strategy of fuzzy system combined optimization algorithms and AI DOI Creative Commons

Junting Nie,

Hossein Ahmadi Dehrashid

Heliyon, Journal Year: 2024, Volume and Issue: 10(7), P. e29182 - e29182

Published: April 1, 2024

This research suggests two novel metaheuristic algorithms to enhance student performance: Harris Hawk's Optimizer (HHO) and the Earthworm Optimization Algorithm (EWA). In this sense, a series of adaptive neuro-fuzzy inference system (ANFIS) proposed models were trained using these methods. The selection best-fit model depends on finding an excellent connection between inputs output(s) layers in training testing datasets (e.g., combination expert knowledge, experimentation, validation techniques). study's primary result is division participants into performance-based groups (failed non-failed). experimental data used build measured fourteen process variables: relocation, gender, age at enrollment, debtor, nationality, educational special needs, current tuition fees, scholarship holder, unemployment, inflation, GDP, application order, day/evening attendance, admission grade. During evaluation, scoring was created addition mean absolute error (MAE), square (MSE), area under curve (AUC) assess efficacy utilized approaches. Further revealed that HHO-ANFIS superior EWA-ANFIS. With AUC = 0.8004 0.7886, MSE 0.62689 0.65598, MAE 0.64105 0.65746, failure pupils assessed with most significant degree accuracy. MSE, MAE, precision indicators showed EWA-ANFIS less accurate, having amounts 0.71543 0.71776, 0.70819 0.71518, 0.7565 0.758. It found optimization have high ability increase accuracy performance conventional ANFIS predicting students' performance, which can cause changes management improve quality academic programs.

Language: Английский

Citations

4
Artificial intelligence and machine learning-powered GIS for proactive disaster resilience in a changing climate DOI Creative Commons

Justin Diehr,

Ayorinde Ogunyiola, Oluwabunmi Dada

et al.

Annals of GIS, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: March 7, 2025

Language: Английский

Citations

0
Novel embedding model predicting the credit card's default using neural network optimized by harmony search algorithm and vortex search algorithm DOI Creative Commons

Tianpei Xu,

Min Qu

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e30134 - e30134

Published: April 23, 2024

In today's banking and financial system, using a credit card has become indispensable. The industry existed due to shift in consumer preferences rise national economic growth. number of issuing banks, issuers, transaction volumes increased significantly. Nevertheless, owing the growth transactions made with cards, both total amount rate defaults on loans have issues that cannot be neglected. This issue must resolved ensure continued prosperous years come. Currently, few optimization algorithms—Whale algorithm (WOA), Harmony Search (HS), Multi-verse (MVO), Vortex (VS)—have been used achieve this purpose. However, because default data is volatile unequal, it challenging for typical algorithms offer steady approaches optimal performance. Studies indicated optimizing suitable properties can significantly improve To performance, some tuning was applied ANN. study will assess twenty-three parameters, efficacy all four compared ROC AUC evaluations. suggested model's performance contrasted scenario where classifiers were trained original data. contrast, values VS-MLP 0.7407 0.7271, while those HS-MLP 0.7074 0.6997. training testing phases, 0.7469 0.7329 from MVO-MLP 0.72 0.7185 WOA-MLP, respectively. results show accuracy HS, VSA, MVO, WOA are similar; MVO highest accuracy. benefit methodology, which may help resolve probabilities.

Language: Английский

Citations

2
Investigating the spatial foundations of rural entrepreneurship development using a hybrid method of MCDM, ANN and DTree algorithm DOI
Dandan Ye, Hossein Ahmadi Dehrashid, Hossein Moayedi

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: April 9, 2024

Language: Английский

Citations

2
Three intelligent computational models to predict the high-performance concrete mixture DOI
Hossein Moayedi, Loke Kok Foong,

Binh Nguyen Le

et al.

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 36(7), P. 3479 - 3498

Published: Dec. 4, 2023

Language: Английский

Citations

5
Modeling the total hardness (TH) of groundwater in aquifers using novel hybrid soft computing optimizer models DOI
Hossein Moayedi, Marjan Salari,

Sana Abdul-Jabbar Ali

et al.

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(13)

Published: June 12, 2024

Language: Английский

Citations

1