Reza Rasinojehdehi; Seyyed Esmaeil Najafi
Abstract
An indispensable aspect of human life is energy. The escalating global population and the subsequent rise in the human need for energy, coupled with the constraints of fossil fuels, have compelled researchers to explore innovative techniques for energy production and the adoption of renewable energy ...
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An indispensable aspect of human life is energy. The escalating global population and the subsequent rise in the human need for energy, coupled with the constraints of fossil fuels, have compelled researchers to explore innovative techniques for energy production and the adoption of renewable energy sources. The construction of renewable power plants emerges as a paramount solution for achieving clean energy, a strategy successfully implemented in various countries globally, including India, China, the USA, Central Asian nations, and Africa. Strategically located and blessed with significant solar potential, Iran is a promising candidate for establishing solar power plants. Despite its high potential for constructing solar power plants, Iran faces limitations that require careful consideration. Investing in renewable power plant projects in Iran necessitates addressing various risks and uncertainties. This paper introduces an innovative approach to assessing the risks associated with solar power plants, utilizing an integrated method that combines Data Envelopment Analysis (DEA) and Support Vector Machine (SVM). In the initial phase, DEA cross-efficiency measures risk factors derived from Failure Modes and Effects Analysis (FMEA). This approach not only overcomes certain drawbacks of FMEA but also eliminates several limitations of DEA, enhancing the discrimination capability for decision units. Subsequently, a SVM is developed to monitor the process, concluding with tailored risk treatment and monitoring processes specifically designed for the unique context of Iran's solar energy landscape.
Reza Rasinojehdehi; Soheil Azizi
Abstract
The escalating annual insurance costs nationwide have sparked a growing interest among insurance industry managers and policymakers in analyzing insurance data to forecast future costs. Accurately predicting the number of claims and implementing appropriate policies can help mitigate potential losses ...
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The escalating annual insurance costs nationwide have sparked a growing interest among insurance industry managers and policymakers in analyzing insurance data to forecast future costs. Accurately predicting the number of claims and implementing appropriate policies can help mitigate potential losses for insurance companies and customers. This study focuses on predicting the amount of customer claims and utilizes data from 128 individuals insured by Iran insurance company. The dataset includes various attributes such as the age of the vehicle owner, type of car, age of the car itself, number of claims, and the corresponding claim amounts (measured in 10,000 Tomans) recorded in the year 1400. All features, except the claim amount (the target variable), were discretized into ordinal variables to ensure accurate analysis and address any outliers or data inconsistencies. Multiple linear regression was employed to predict the target variable, enabling an investigation into the influence of each feature on estimating the claim amount. The data analysis was conducted using IBM SPSS MODELER software, allowing for a comprehensive examination of the assumptions associated with the regression model. By leveraging this approach, insurance industry stakeholders can gain valuable insights into predicting claim amounts and make informed decisions to optimize their operations and minimize potential financial risks.
