A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting DOI Creative Commons
Jiandong Huang, Mohammadreza Koopialipoor, Danial Jahed Armaghani

et al.

Scientific Reports, Journal Year: 2020, Volume and Issue: 10(1)

Published: Nov. 10, 2020

Abstract This study presents a new input parameter selection and modeling procedure in order to control predict peak particle velocity (PPV) values induced by mine blasting. The first part of this was performed through the use fuzzy Delphi method (FDM) identify key variables with deepest influence on PPV based experts’ opinions. Then, second part, most effective parameters were selected be applied hybrid artificial neural network (ANN)-based models i.e., genetic algorithm (GA)-ANN, swarm optimization (PSO)-ANN, imperialism competitive (ICA)-ANN, bee colony (ABC)-ANN firefly (FA)-ANN for prediction PPV. Many ANN-based constructed according influential GA, PSO, ICA, ABC FA techniques 5 proposed PPVs Through simple ranking technique, best model selected. obtained results revealed that FA-ANN is able offer higher accuracy level compared other implemented models. Coefficient determination (R 2 ) (0.8831, 0.8995, 0.9043, 0.9095 0.9133) (0.8657, 0.8749, 0.8850, 0.9094 0.9097) train test stages GA-ANN, PSO-ANN, ICA-ANN, ABC-ANN FA-ANN, respectively. showed all can used solve problem, however, when highest performance needed, would choice.

Language: Английский

Effect of aggregate size, aggregate to cement ratio and compaction energy on ultrasonic pulse velocity of pervious concrete: prediction by an analytical model and machine learning techniques DOI
Navaratnarajah Sathiparan, Pratheeba Jeyananthan, Daniel Niruban Subramaniam

et al.

Asian Journal of Civil Engineering, Journal Year: 2023, Volume and Issue: 25(1), P. 495 - 509

Published: July 3, 2023

Language: Английский

Citations

24
Genetic justification of COVID‐19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm DOI Creative Commons
Panagiotis G. Asteris, Amir H. Gandomi, Danial Jahed Armaghani

et al.

Journal of Cellular and Molecular Medicine, Journal Year: 2024, Volume and Issue: 28(4)

Published: Feb. 1, 2024

Abstract Complement inhibition has shown promise in various disorders, including COVID‐19. A prediction tool complement genetic variants is vital. This study aims to identify crucial complement‐related and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID‐19 patients hospitalized between April 2020 2021 at three referral centres were analysed using artificial intelligence‐based algorithm predict (ICU vs. non‐ICU admission). recently introduced alpha‐index identified the 30 most predictive variants. DERGA algorithm, which employs multiple classification algorithms, determined of these key variants, resulting 97% accuracy predicting outcome. Individual variations ranged 40 161 per patient, with 977 total detected. demonstrates utility ranking a substantial number approach enables implementation well‐established algorithms that effectively relevance outcomes high accuracy.

Language: Английский

Citations

13
Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity DOI
Navaratnarajah Sathiparan, Pratheeba Jeyananthan

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: 39(8), P. 2486 - 2509

Published: Jan. 11, 2024

The current study aimed to investigate the possibility of predicting compressive strength geopolymer mortar by mix design parameters, ultrasonic pulse velocity (UPV) and machine learning techniques. Here is produced from eggshell ash rice husk as precursors, NaOH solution activator quarry waste fine aggregate. Twenty-seven combinations a total 189 cubes were cast tested for UPV strength. Seven different techniques used predict assessment tools: linear regression, artificial neural networks, boosted tree random forest K-Nearest Neighbor, support vector regression XGboost. Among diverse models evaluated in this study, XGboost exhibited remarkable efficacy forecasting mortar. investigation conducted using SHAP indicates that concentration shows most substantial influence on prediction

Language: Английский

Citations

11
Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm DOI
Panagiotis G. Asteris, Amir H. Gandomi, Danial Jahed Armaghani

et al.

European Journal of Internal Medicine, Journal Year: 2024, Volume and Issue: 125, P. 67 - 73

Published: March 8, 2024

Language: Английский

Citations

10
A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting DOI Creative Commons
Jiandong Huang, Mohammadreza Koopialipoor, Danial Jahed Armaghani

et al.

Scientific Reports, Journal Year: 2020, Volume and Issue: 10(1)

Published: Nov. 10, 2020

Abstract This study presents a new input parameter selection and modeling procedure in order to control predict peak particle velocity (PPV) values induced by mine blasting. The first part of this was performed through the use fuzzy Delphi method (FDM) identify key variables with deepest influence on PPV based experts’ opinions. Then, second part, most effective parameters were selected be applied hybrid artificial neural network (ANN)-based models i.e., genetic algorithm (GA)-ANN, swarm optimization (PSO)-ANN, imperialism competitive (ICA)-ANN, bee colony (ABC)-ANN firefly (FA)-ANN for prediction PPV. Many ANN-based constructed according influential GA, PSO, ICA, ABC FA techniques 5 proposed PPVs Through simple ranking technique, best model selected. obtained results revealed that FA-ANN is able offer higher accuracy level compared other implemented models. Coefficient determination (R 2 ) (0.8831, 0.8995, 0.9043, 0.9095 0.9133) (0.8657, 0.8749, 0.8850, 0.9094 0.9097) train test stages GA-ANN, PSO-ANN, ICA-ANN, ABC-ANN FA-ANN, respectively. showed all can used solve problem, however, when highest performance needed, would choice.

Language: Английский

Citations

58