has been cited by the following article(s):
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[1]
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A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance
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Risks,
2024 |
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[2]
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A Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling.
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Journal of Applied …,
2023 |
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[3]
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Prediction of the Premium Production of Some Insurance Companies Operating in Turkey with Artificial Neural Networks
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Turkish Journal of Forecasting,
2022 |
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[4]
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Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by Multiple Linear Regression and Artificial Neural Networks
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Computational …,
2022 |
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[5]
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Forecasting premium adequacy to claim paid ratio in life insurance industry with COVID-19 effect using multilayer perceptron neural network
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AIP Conference …,
2022 |
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[6]
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Model basis of information and analytical security system of corporate business structures
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2020 |
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[7]
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Statistical Analysis and Impact Assesment of Techno logical Environment of Artificial Intelligence Environment in the Banking System of the RA.
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2020 |
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[8]
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MODELS OF FORECASTING IN THE MECHANISM OF EARLY INFORMING AND PREVENTION OF FINANCIAL CRISES IN CORPORATE SYSTEMS
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2018 |
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[9]
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State-of-the-art in artificial neural network applications: A survey
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2018 |
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[10]
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Models of estimation in the mechanism of early informing and prevention of financial crises in corporate systems
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2018 |
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[11]
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FORECASTING FERTILIZER SALES REVENUE USING FEED-FORWARD ARTIFICIAL NEURAL NETWORKS FOR A MEDIUM-SCALE FERTILIZER …
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2018 |
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[12]
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Modelling of the enterprise functioning stability using the automatic control theory apparatus
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2017 |
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[13]
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МОДЕЛИРОВАНИЕ УСТОЙЧИВОСТИ ФУНКЦИОНИРОВАНИЯ ПРЕДПРИЯТИЯ С ИСПОЛЬЗОВАНИЕМ АППАРАТА ТЕОРИИ АВТОМАТИЧЕСКОГО …
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2017 |
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[14]
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A Comparative Study of Zero-inflated, Hurdle and Artificial Neural Network Models in Claim Count Modeling
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International Journal of Statistics and Systems [IJSS],
2017 |
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[15]
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Modeling of the enterprise functioning stability using the automatic control theory apparatus
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2017 |
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[16]
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A Comparative Study of Zero-inflated, Hurdle Models with Artificial Neural Network in Claim Count Modeling
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International Journal of Statistics and Systems [IJSS],
2017 |
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[17]
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Моделирование устойчивости функционирования предприятия с использованием аппарата теории автоматического управления
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2017 |
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[18]
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Comparative performance for predictive modelling in motor insurance claims
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2016 |
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[19]
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Predictive Modelling for Motor Insurance Claims Using Artificial Neural Networks
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International Journal of Advances in Soft Computing and its Applications,
2016 |
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[20]
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Efficient Automatic Image Annotation using Weighted Feature Fusion and its Optimization using Genetic Algorithm
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Communications on Applied Electronics,
2015 |
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[21]
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Efficient Automatic Image Annotation Using Optimized Weighted Complementary Feature Fusion Using Genetic Algorithm
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Procedia Computer Science,
2015 |
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[22]
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Content Based Image Retrieval of User's Interest Using Feature Fusion and Optimization Using Genetic Algorithm: A Survey
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International Journal of Computer Science and Information Technologies,
2014 |
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[23]
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ІВ Ніколаєв
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[1]
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A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance
Risks,
2024
DOI:10.3390/risks12040062
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[2]
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A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance
Risks,
2024
DOI:10.3390/risks12040062
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[3]
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Forecasting premium adequacy to claim paid ratio in life insurance industry with COVID-19 effect using multilayer perceptron neural network
THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCES (THE 3RD ICMSc): A Brighter Future with Tropical Innovation in the Application of Industry 4.0,
2022
DOI:10.1063/5.0111946
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[4]
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Prediction of the Premium Production of Some Insurance Companies Operating in Turkey with Artificial Neural Networks
Turkish Journal of Forecasting,
2022
DOI:10.34110/forecasting.1223653
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[5]
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2019
DOI:10.12794/metadc1538717
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[6]
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State-of-the-art in artificial neural network applications: A survey
Heliyon,
2018
DOI:10.1016/j.heliyon.2018.e00938
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