Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier DOI Creative Commons
Mehmet Akif Bülbül, Mehmet Fatih Işık

Biomimetics, Journal Year: 2024, Volume and Issue: 9(5), P. 304 - 304

Published: May 19, 2024

The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients' clinical characteristics and findings to determine plans, making accurate predictions essential efficient resource utilization optimal support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, Softmax Classifier was developed predicting survival. evaluated using Haberman's Survival dataset Echocardiogram from UCI. results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, Bagging applied same datasets. indicate that proposed outperforms other methods both datasets surpasses reported literature dataset. light obtained, models obtained can be used as decision system determining care methods.

Language: Английский

Effects of the February 6, 2023, Kahramanmaraş earthquake on structures in Kahramanmaraş city DOI
Fatih Avcil, Ercan Işık, Rabia İzol

et al.

Natural Hazards, Journal Year: 2023, Volume and Issue: 120(3), P. 2953 - 2991

Published: Nov. 28, 2023

Language: Английский

Citations

50
Machine learning based computational approach for crack width detection of self-healing concrete DOI
Fadi Althoey, Muhammad Nasir Amin,

Kaffayatullah Khan

et al.

Case Studies in Construction Materials, Journal Year: 2022, Volume and Issue: 17, P. e01610 - e01610

Published: Oct. 25, 2022

Language: Английский

Citations

56
Detection and classification of pneumonia using novel Superior Exponential (SupEx) activation function in convolutional neural networks DOI
Serhat Kılıçarslan, Cemil Közkurt, Selçuk Baş

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 217, P. 119503 - 119503

Published: Jan. 10, 2023

Language: Английский

Citations

39
A Comprehensive Review on RSM-Coupled Optimization Techniques and Its Applications DOI
Susaimanickam Anto,

M. Premalatha,

A. J. Amalanathan

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(8), P. 4831 - 4853

Published: June 23, 2023

Language: Английский

Citations

24
Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures DOI Creative Commons
Ehsan Harirchian, Seyed Ehsan Aghakouchaki Hosseini, Viviana Novelli

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101750 - 101750

Published: Jan. 5, 2024

Seismic fragility assessment provides a substantial tool for assessing the seismic resilience of these buildings. However, using traditional numerical methods to derive curves poses significant challenges. These often overlook diverse range buildings found in different regions, as they rely on standardized assumptions and parameters. Consequently, may not accurately capture response various building types. Alternatively, extensive data collection becomes essential address this knowledge gap by understanding local construction techniques identifying relevant This is crucial developing reliable analytical approaches that can curves. To overcome challenges, research employs four Machine Learning (ML) techniques, namely Support Vector Regression (SVR), Stochastic Gradient Descent (SGD), Random Forest (RF), Linear (LR), probability collapse terms Peak Ground Acceleration (PGA). achieve objective, comprehensive input/output dataset consisting on-site collected from 646 masonry walls Malawi used. Adopted ML models are trained tested entire then again only most highly correlated features. The study includes comparative analysis efficiency accuracy each approach influence used analyses. (RF) technique emerges efficient deriving surveyed achieved lowest values evaluation metrics methods. scored Mean Absolute Percentage Error (MAPE) 16.8 %, Root Square (RMSE) 0.0547. results highlight potential particularly RF, derivation with proper levels accuracy.

Language: Английский

Citations

14
Prediction of permeability coefficient of soil using hybrid artificial neural network models DOI Creative Commons
Majid M. Kharnoob, Tarak Vora,

A K Dasarathy

et al.

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 14, 2025

Language: Английский

Citations

1
Optimization of artificial neural network structure and hyperparameters in hybrid model by genetic algorithm: iOS–android application for breast cancer diagnosis/prediction DOI
Mehmet Akif Bülbül

The Journal of Supercomputing, Journal Year: 2023, Volume and Issue: 80(4), P. 4533 - 4553

Published: Sept. 14, 2023

Language: Английский

Citations

23
Optimization of Ultrasonic-Assisted Extraction Conditions for Bioactive Components and Antioxidant Activity of Poria cocos (Schw.) Wolf by an RSM-ANN-GA Hybrid Approach DOI Creative Commons
Shiqi Chen, Huixia Zhang,

Liu Yang

et al.

Foods, Journal Year: 2023, Volume and Issue: 12(3), P. 619 - 619

Published: Feb. 1, 2023

In this study, a response surface methodology and an artificial neural network coupled with genetic algorithm (RSM-ANN-GA) was used to predict estimate the optimized ultrasonic-assisted extraction conditions of Poria cocos. The ingredient yield antioxidant potential were determined different independent variables ethanol concentration (X1; 25–75%), time (X2; 30–50 min), solution volume (mL) (X3; 20–60 mL). optimal predicted by RSM-ANN-GA model be 55.53% for 48.64 min in 60.00 mL solvent four triterpenoid acids, 40.49% 30.25 20.00 activity total polysaccharide phenolic contents. evaluation two modeling strategies showed that provided better predictability greater accuracy than P. These findings guidance on efficient cocos feasible analysis/modeling optimization process natural products.

Language: Английский

Citations

21
Determination of Natural Fundamental Period of Minarets by Using Artificial Neural Network and Assess the Impact of Different Materials on Their Seismic Vulnerability DOI Creative Commons
Ercan Işık, Naida Ademović, Ehsan Harirchian

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(2), P. 809 - 809

Published: Jan. 6, 2023

Minarets are slender and tall structures that built from different types of materials. Modern materials also starting to be used in such with the recent developments material technology. The seismic vulnerability dynamic behavior minarets can vary, depending on characteristics. Within this study’s scope, thirteen Türkiye were chosen as variables. A sample minaret model was an example nine heights reveal how characteristic change affects behavior. Information mechanical characteristics given for all types. Natural fundamental periods, displacements, base shear forces attained structural analyses each selected material. empirical period formula is proposed using obtained taken into consideration. At same time, natural periods first ten modes 13 study estimated established Artificial Neural Network (ANN) model. real experimental compared values by ANN fewer parameters, 99% results successful. In addition, time history evaluate performance (three considered). specific case, acceleration record 2011 Van (Eastern Turkiye) earthquake (Mw = 7.2) Performance levels determined according It has been concluded significantly affect minarets.

Language: Английский

Citations

19
A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings DOI Open Access
Mehmet Fatih Işık, Fatih Avcil, Ehsan Harirchian

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(12), P. 9715 - 9715

Published: June 18, 2023

The realistic determination of damage estimation and building performance depends on target displacements in performance-based earthquake engineering. In this study, were obtained by performing pushover analysis for a sample reinforced-concrete model, taking into account 60 different peak ground accelerations each the five stories. Three estimation, such as limitation (DL), significant (SD), near collapse (NC), acceleration numbers stories, respectively. It aims to develop an artificial neural network (ANN)-based sustainable model predict under seismic risks mid-rise regular buildings, which make up large part existing stock, using all data obtained. For purpose, hybrid structure was established with particle swarm optimization algorithm (PSO), structure’s hyper parameters optimized. models created order most successfully. found that ANN particles best position revealed produced successful results calculation score. 99% DL SD NC determining buildings. also should be used estimating risks.

Language: Английский

Citations

18