YouNova

23CS06002_23CS06009_23CS06018

GoogleChallengeResponse

improving_dataset_for_fraud_detection_algorithms

The question of class imbalance has become more pronounced with the application of learning algorithms in real applications. It has received significant attention in the machine learning and data mining community. This problem is present in fraud detection, medical diagnostics, and a number of other areas where training data contains significantly more representatives of one class (called the majority class) than the other class (called the minority class). Machine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class, which is the most interesting from a learning point of view and also involves a high cost when it is not well classified. However, the imbalance ratio is not the only cause of poor performance when learning from imbalanced data. Another critical factor that accompanies imbalanced data in the real world is the presence of a number of instances of the two classes being overlapped in feature space. This problem is commonly referred to as class overlap and we have called it "behavioral noise". In this paper, we propose One Side Behavioral Noise Reduction (OSBNR) approach to deal with the problem of class imbalance in the presence of a behavioral noise level. OSBNR is based on two stages. Firstly, a clustering is applied to groups similar instances of the minority class in multiple behavior clusters. Secondly, we select and eliminate instances of the majority class, considered as behavioral noise, which overlap with the behavior clusters of the minority class. The results of experiments conducted on a representative public dataset confirm that the proposed approach is effective for class imbalance problem in the presence of behavioral noise.

LY_EVs_Project

MyEncryptionCode

NotifyMe

PrEazy

A digital prescription system to digitalize the process of prescription generation to avoid fake prescriptions and to keep records of previous prescriptions for all users

Secured_Entry_Identification_System

A python project to recognize and register an employee by his face as mean of identification and storing his number and designation

speech-to-image

This android project helps blind people communicate with normal people who don't know how to talk using sign language. Images used are of American Sign Language.

Travelicious

Travelicious, is a website that is mostly focused on day-trips and pre-planned tours that you can book through their service. The website provides the users to go through a detailed itinerary explaining the activities for each day.This webpage was created using HTML,CSS,ASP and VBScript.

YouNova/23CS06002_23CS06009_23CS06018

YouNova/LY_EVs_Project

YouNova/GoogleChallengeResponse

YouNova/improving_dataset_for_fraud_detection_algorithms

The question of class imbalance has become more pronounced with the application of learning algorithms in real applications. It has received significant attention in the machine learning and data mining community. This problem is present in fraud detection, medical diagnostics, and a number of other areas where training data contains significantly more representatives of one class (called the majority class) than the other class (called the minority class). Machine learning techniques struggle to deal with imbalanced data by focusing on minimizing the error rate for the majority class while ignoring the minority class, which is the most interesting from a learning point of view and also involves a high cost when it is not well classified. However, the imbalance ratio is not the only cause of poor performance when learning from imbalanced data. Another critical factor that accompanies imbalanced data in the real world is the presence of a number of instances of the two classes being overlapped in feature space. This problem is commonly referred to as class overlap and we have called it "behavioral noise". In this paper, we propose One Side Behavioral Noise Reduction (OSBNR) approach to deal with the problem of class imbalance in the presence of a behavioral noise level. OSBNR is based on two stages. Firstly, a clustering is applied to groups similar instances of the minority class in multiple behavior clusters. Secondly, we select and eliminate instances of the majority class, considered as behavioral noise, which overlap with the behavior clusters of the minority class. The results of experiments conducted on a representative public dataset confirm that the proposed approach is effective for class imbalance problem in the presence of behavioral noise.

YouNova/MyEncryptionCode

YouNova/NotifyMe

YouNova/PrEazy

A digital prescription system to digitalize the process of prescription generation to avoid fake prescriptions and to keep records of previous prescriptions for all users

YouNova/Secured_Entry_Identification_System

A python project to recognize and register an employee by his face as mean of identification and storing his number and designation

YouNova/speech-to-image

This android project helps blind people communicate with normal people who don't know how to talk using sign language. Images used are of American Sign Language.

YouNova/Travelicious

Travelicious, is a website that is mostly focused on day-trips and pre-planned tours that you can book through their service. The website provides the users to go through a detailed itinerary explaining the activities for each day.This webpage was created using HTML,CSS,ASP and VBScript.

YouNova/YouNova

Config files for my GitHub profile.