Feature Selection and Hyperparameters Optimization Employing a Hybrid Model Based on Genetic Algorithm and Artificial Neural Network: Forecasting Dividend Payout Ratio

Konak, Fatih; Bülbül, Mehmet Akif; Türkoǧlu, Diler

doi:10.1007/s10614-023-10530-z

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…PSO was used in this study, due to its minimum parameter requirement, success in solving multidimensional problems, and ease of implementation. The optimization-based hybrid architecture was implemented on the MATLAB platform for coding flexibility and ease of working with multidimensional matrices [37]. The figures and graphs in the following sections of the study were plotted in the MATLAB software (MATLAB R2021a) environment.…”

Section: Proposed Modelmentioning

confidence: 99%

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

Bülbül,

Işık

2024

Biomimetics

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

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Section: Proposed Modelmentioning

confidence: 99%

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

Bülbül,

Işık

2024

Biomimetics

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A novel hybrid deep learning model for early stage diabetes risk prediction

Bülbül

2024

J Supercomput

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Diabetes is a prevalent global disease that significantly diminishes the quality of life and can even lead to fatalities due to its complications. Early detection and treatment of diabetes are crucial for mitigating and averting associated risks. This study aims to facilitate the prompt and straightforward diagnosis of individuals at risk of diabetes. To achieve this objective, a dataset for early stage diabetes risk prediction from the University of California Irvine (UCI) database, widely utilized in the literature, was employed. A hybrid deep learning model comprising genetic algorithm, stacked autoencoder, and Softmax classifier was developed for classification on this dataset. The performance of this model, wherein both the model architecture and all hyperparameters were specifically optimized for the given problem, was compared with commonly used methods in the literature. These methods include K-nearest neighbor, decision tree, support vector machine, and convolutional neural network, utilizing tenfold cross-validation. The results obtained with the proposed method surpassed those obtained with other methods, with higher accuracy rates than previous studies utilizing the same dataset. Furthermore, based on the study’s findings, a web-based application was developed for early diabetes diagnosis.

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Collection Efficiency of Cyclone Separators: Comparison between New Machine Learning-Based Models and Semi-Empirical Approaches

Bregolin,

Danieli,

Masi

2024

Waste

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Cyclones are employed in many waste treatment industries for the dust collection or abatement purposes. The prediction of the dust collection efficiency is crucial for the design and optimization of the cyclone. However, this is a difficult task because of the complex physical phenomena that influence the removal of particles. Aim of the paper is to present two new meta-models for the prediction of the collection efficiency curve of cyclone separators. A Backpropagation Neural Network (BPNN) and Support Vector Regression (SVR) models were developed using Python environment. These were trained with a set of experimental data taken from the literature. The prediction capabilities of the models were first assessed by comparing the estimated collection efficiency for several cyclones against the corresponding experimental data. Second, by comparing the collection efficiency curves predicted by the models and those obtained from classic models available in the literature for the cyclones included in the validation dataset. The BPNN demonstrated better predictive capability than the SVR, with an overall mean squared error of 0.007 compared to 0.015, respectively. Most important, a 40% to 90% accuracy improvement of the literature models predictions was achieved.

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Feature Selection and Hyperparameters Optimization Employing a Hybrid Model Based on Genetic Algorithm and Artificial Neural Network: Forecasting Dividend Payout Ratio

Cited by 4 publications

References 31 publications

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

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

A novel hybrid deep learning model for early stage diabetes risk prediction

Collection Efficiency of Cyclone Separators: Comparison between New Machine Learning-Based Models and Semi-Empirical Approaches

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