Browsing by Author "Nuha, Musfika"

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A Case Study and Fraud Rate Prediction in e-Banking Systems Using Machine Learning and Data Mining
(Scopus, 2021) Nuha, Musfika; Mahmud, Sakib; Sattar, Abdus
Recently banking sector of Bangladesh is undergoing in a revolutionizing change. Over the last few years, Bangladesh’s banking industry has achieved remarkable momentum. Especially radical change has come in e-banking and mobile banking sectors. Because of convenience, easy to use, time saving and less complexity, both educated and uneducated people are using those facilities. At the same time, fraudulent activity is also rising rapidly. It is noticed that fraudsters use scary tactics and emotional manipulation to obtain sensitive or confidential customer information instead of coding-based hacking process. As a result, cyber security is the main challenge for the banking sector in Bangladesh. The purpose of the research is to determine the key factors behind increasing fraudulent activities. Concurrently, this study focuses on the relationship between lack of awareness and likeliness to be affected by fraud. In order to acquire the specified purpose of this study, several investigations were conducted on primary and secondary data. Results show that there is a strong correlation between lack of awareness and likeliness to be affected by fraud. 76% people have no idea about e-banking and mobile banking fraud. Furthermore, our findings show that 86.3% of victims of e-banking or mobile banking fraud had no prior knowledge of this type of fraud. Simultaneously, 13.7% of victims in those sectors had prior knowledge of fraud. It is obvious that, behind this type of fraud, lack of knowledge and awareness can be a major fact.
A Case Study and Fraud Rate Prediction in e-Banking Systems Using Machine Learning and Data Mining
(Springer, 2021) Nuha, Musfika; Mahmud, Sakib; Sattar, Abdus
Recently banking sector of Bangladesh is undergoing in a revolutionizing change. Over the last few years, Bangladesh’s banking industry has achieved remarkable momentum. Especially radical change has come in e-banking and mobile banking sectors. Because of convenience, easy to use, time saving and less complexity, both educated and uneducated people are using those facilities. At the same time, fraudulent activity is also rising rapidly. It is noticed that fraudsters use scary tactics and emotional manipulation to obtain sensitive or confidential customer information instead of coding-based hacking process. As a result, cyber security is the main challenge for the banking sector in Bangladesh. The purpose of the research is to determine the key factors behind increasing fraudulent activities. Concurrently, this study focuses on the relationship between lack of awareness and likeliness to be affected by fraud. In order to acquire the specified purpose of this study, several investigations were conducted on primary and secondary data. Results show that there is a strong correlation between lack of awareness and likeliness to be affected by fraud. 76% people have no idea about e-banking and mobile banking fraud. Furthermore, our findings show that 86.3% of victims of e-banking or mobile banking fraud had no prior knowledge of this type of fraud. Simultaneously, 13.7% of victims in those sectors had prior knowledge of fraud. It is obvious that, behind this type of fraud, lack of knowledge and awareness can be a major fact.
Crime Rate Prediction and Spot Detection System Using Machine Learning and Data Mining
(Daffodil International University, 2020-07-26) Mahmud, Sakib; Nuha, Musfika
Analysis of crime is a methodological approach to the identification and assessment of criminal patterns and trends. In a number of respects cost our community profoundly. We have to go to many places every day for our daily purposes and many times in our everyday lives we face numerous safety problems such as hijack, kidnapping, harassment, etc. In general, we see that when we need to go anywhere at first, we are searching for google maps, google maps show that one, two, or more ways to get to the destination, but we always choose the shortcut route, but we don't understand the path situation correctly. Is it really secure or not that's why we face many unpleasant circumstances, in this job we use different clustering approaches of data mining to analyzing the crime rate of Bangladesh and us also used K-Nearest Neighbor(KNN) algorithm to train our dataset, For our job, we are using main and secondary data, By, analyzing the data, we find out for many places the prediction rate of different crime and use the algorithm to determine the prediction rate of the path. Finally, to find out our save route, we use the forecast rate. This job will assist individuals become aware of the crime area and discover their secure way to the destination.
Crime Rate Prediction Using Machine Learning and Data Mining
(Soft Computing Techniques and Applications. Advances in Intelligent Systems and Computing, Springer, 2020-11-28) Mahmud, Sakib; Nuha, Musfika; Sattar, Abdus
Analysis of crime is a methodological approach to the identification and assessment of criminal patterns and trends. In a number of respects cost our community profoundly. We have to go many places regularly for our daily purposes, and many times in our everyday lives we face numerous safety problems such as hijack, kidnapping, and harassment. In general, we see that when we need to go anywhere at first, we are searching for Google Maps; Google Maps show one, two, or more ways to get to the destination, but we always choose the shortcut route, but we do not understand the path situation correctly. Is it really secure or not that’s why we face many unpleasant circumstances; in this job, we use different clustering approaches of data mining to analyze the crime rate of Bangladesh and we also use K-nearest neighbor (KNN) algorithm to train our dataset. For our job, we are using main and secondary data. By analyzing the data, we find out for many places the prediction rate of different crimes and use the algorithm to determine the prediction rate of the path. Finally, to find out our safe route, we use the forecast rate. This job will assist individuals to become aware of the crime area and discover their secure way to the destination.
Crime Rate Prediction Using Machine Learning and Data Mining
(Scopus, 2021) Mahmud, Sakib; Nuha, Musfika; Sattar, Abdus
Analysis of crime is a methodological approach to the identification and assessment of criminal patterns and trends. In a number of respects cost our community profoundly. We have to go many places regularly for our daily purposes, and many times in our everyday lives we face numerous safety problems such as hijack, kidnapping, and harassment. In general, we see that when we need to go anywhere at first, we are searching for Google Maps; Google Maps show one, two, or more ways to get to the destination, but we always choose the shortcut route, but we do not understand the path situation correctly. Is it really secure or not that’s why we face many unpleasant circumstances; in this job, we use different clustering approaches of data mining to analyze the crime rate of Bangladesh and we also use K-nearest neighbor (KNN) algorithm to train our dataset. For our job, we are using main and secondary data. By analyzing the data, we find out for many places the prediction rate of different crimes and use the algorithm to determine the prediction rate of the path. Finally, to find out our safe route, we use the forecast rate. This job will assist individuals to become aware of the crime area and discover their secure way to the destination.