Cited by Comparative study of sampling strategies for machine learning-based landslide susceptibility assessment

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

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

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

Species distribution modeling of Malva neglecta Wallr. weed using ten different machine learning algorithms: An approach to site-specific weed management (SSWM) DOI

Emran Dastres,

Hassan Esmaeili, Mohsen Edalat

et al.

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 167, P. 127579 - 127579

Published: March 5, 2025

Language: Английский

Citations

Four Optimization Meta-heuristic Approaches in Evaluating Groundwater Quality (Case study: Shiraz Plain) DOI

Hossein Moayedi, Marjan Salari,

The-Chuyen Nguyen

et al.

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 20, 2024

Language: Английский

Citations

Multi-hazard susceptibility mapping in the Salt Lake watershed DOI

Sima Pourhashemi,

Mohammad Ali Zangane Asadi,

Mahdi Boroughani

et al.

Environmental Challenges, Journal Year: 2024, Volume and Issue: unknown, P. 101079 - 101079

Published: Dec. 1, 2024

Language: Английский

Citations

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

Comparative study of sampling strategies for machine learning-based landslide susceptibility assessment DOI

Xiaodong Liu, Ting Xiao,

Shaohe Zhang

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: 38(12), P. 4935 - 4957

Published: Nov. 13, 2024

Language: Английский

Citations