Theses and Dissertations

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    Credit Card Fraud Detection Using MachineLearning Algorithms
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2021) Tank, Ekta; Das, Manik Lal
    Credit Card payment facilitating people to pay for goods and service quickly. The credit payment is gaining popularity day by day because of its benefits. With the popularity of credit card payment, crime related to credit card fraud is also increasing. Credit card fraud leads to a colossal amount of loss of financial institutions like banks and the customer. Detecting fraud is costly and time-confusing, Though it is too important to detect fraud and prevent fraud in the future. In this thesis, the challenges of credit card fraud are discussed. Credit Card fraud is treated as the classification problem, and experiments are carried out with Decision Tree, Random Forest and SVM. Credit Card data will always be highly imbalanced in nature, having fewer number of frauds than normal transactions. To deal with this problem, resampling techniques are performed on the dataset. The credit card fraud problem is also considered an anomaly detection problem having fraud as an anomaly. The main objective of the research is to find an effective approach to detect fraud. This thesis compares the classification approach with the anomaly detection approach. Also, classification results are tried to improve using data level resampling techniques. Comparison results are discussed in Result Section.
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    ItemOpen Access
    Modelling and short term forecasting of flash floods in an urban environment
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Ogale, Suraj; Srivastava, Sanjay
    Rapid urbanization, climate change and extreme rainfall has resulted in a growingnumber of cases of urban flash floods. It is important to predict the occurrenceof flood so that the aftermath of the flood can be minimized. Flood forecasting isa major exercise performed to determine the chances of a flood when suitableconditions are present. Short term forecasting or nowcasting is a dominant techniqueused in urban cities for prediction of the very near future incident up to sixhours. In orthodox methods of flood forecasting, current weather conditions areexamined using conventional methods such as use of radar, satellite imaging andcomplex calculation involving complicated mathematical equations.Recent developments in Information and Communication Technology(ICT) andMachine Learning(ML) has helped us to study this hydrological problem alongwith many real world situation in different perspective. The main aim of thisthesis is to design a theoretical model that accounts parameters causing an urbanflash flood and develop a prediction tool for the forecasting of near future event.To test the soundness of a model, data syntheses is performed and the results areseen using the artificial neural network.