Sharif Digital Repository

Nowadays, electrical energy is one of the crucial requirements in human beings’ lives. Since the majority of the world’s energy is met by fossil fuel, the problems such as global warming, the reduction of fossil fuel resources, and unpredictable oscillation of the prices of such fuels have led to a serious crisis for the people of the world. Furthermore, due to the increasing energy demand, the economic development of most countries has a strong correlation with fossil fuel prices. The abovementioned problems have made a lot of countries take alternative policies in terms of generating energy, one of which is using renewable energy resources. This approach is reported to be clean and... 

In this study, a brief definition of data mining and its variants were mentioned. Then the methods and algorithms for clustering and their application in the field of healthcare is studied. Concidering the available data for anemia disease, including numeric and categorical attributes, the k-medoids clustering algorithm was selected. This algorithm is one of the simple, powerful and most widely used methods for clustering. The drawbacks of this algorithm are as follow: requires a user input on the number of clusters, depends on the initial data and traps in the local optima. In this thesis, an improved method of clustering-based on Random Forest and k-medoids algorithms has been developed.... 

Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for capacity estimation. First, new features are extracted from cyclic charge/discharge cycles and used as health indicators. Three algorithms are used to characterize the relationship between extracted features and battery capacity. Decision tree, random forest and boosting algorithms are trained using a... 

Credit risk is the possibility of a loss resulting from a borrower's failure to repay a loan or meet contractual obligations. All banks and financial institutions need to manage credit risk of their lending portfolios to maximize risk-adjusted rate of return and obey regulatory rules. The most commonly used method for determining credit risk is to calculate the maximum loss within the “Value at Risk” framework.Previous studies proposed different models for Credit VaR calculation like Vasicek Model and Credit Risk Plus Model. Furthermore, due to the high growth of computing power and easy access to information, the application of data-driven models such as Machine Learning has been increasing... 

Wildfires are complex phenomena with many indeterminate and highly unpredictable driving factors that have remained unresolved. During the last decade, machine learning methods have successfully excelled in wildfire prediction as an alternative to traditional field research methods by elucidating the relationship between historical wildfire events and various important variables. The main purpose of this research is to evaluate the random forest machine learning approach and the logistic regression statistical approach to prepare a wildfire susceptibility map using data related to historical wildfires and effective variables in the Okanogan region in Washington province of the United States... 

The field of Genome-Wide Asocciation Studies (GWAS) plays a vital role in understanding the genetic basis of complex traits and diseases. In this thesis, the focus is on investigating the effectiveness of two approaches combining Differential Evolution (DE) with Random Forest (RF) and support vector machine (SVM) for feature selection in the context of GWAS. Arabidopsois Thaliana dataset is used as experimental dataset for comparative analysis. The main goal is to achieve more efficient feature selection while maintaining competitive accuracy compared to RF and SVM without using DE. This research includes conducting experiments using DE with RF and DE with SVM followed by a comprehensive... 

Prediction is an important issue in many areas. Proper planning for the future requires careful forecasting, so providing accurate methods, especially in the financial field, is invaluable. In this study, the main problem is predicting the price of global gold. Factors for gold prices include oil, gas, silver, soybeans, copper, the s & p500 index, the Dow Jones index, the British and Japanese stock market indices, the dollar index, the multi-currency exchange rate (pound-euro-yuan-yen) with the dollar to The title of the influential factors in this research is considered. The time frame of this research is daily, in other words, the data is collected on a daily basis and the goal is to...