G Nivedhitha - Academia.edu (original) (raw)
Papers by G Nivedhitha
International Journal of Scientific Research in Science and Technology, 2021
In today's world there are millions of diseases with various symptoms foreach, no human can p... more In today's world there are millions of diseases with various symptoms foreach, no human can possibly know about all of these diseases and the treatmentsassociated with them. So, the problem is that there isn’t any place where anyone can have the details of the diseases or the medicines/treatments. What if there is a placewhere you can find your health problem just by entering symptoms or the currentcondition of the person. It will help us to deduce the problem and to verify thesolution. The proposed idea is to create a system with artificial intelligence that canmeet these requirements. The AI can classify the diseases based on the symptomsand give the list of available treatments. The System is a text-to-text diagnosis chatbot that will engage patients in conversation with their medical issues and provides apersonalized diagnosis based on their symptoms and profile. Hence the people canhave an idea about their health and can take the right action.
2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2019
Credit card fraud events take place frequently and then result in huge financial losses. The numb... more Credit card fraud events take place frequently and then result in huge financial losses. The number of online transactions has grown in large quantities and online credit card transactions hold a huge share of these transactions. Online transactions have become an important and necessary part of our lives. As frequency of transactions is increasing, number of fraudulent transactions is also increasing rapidly. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This thesis presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.
International Journal of Scientific Research in Science and Technology, 2021
In today's world there are millions of diseases with various symptoms foreach, no human can p... more In today's world there are millions of diseases with various symptoms foreach, no human can possibly know about all of these diseases and the treatmentsassociated with them. So, the problem is that there isn’t any place where anyone can have the details of the diseases or the medicines/treatments. What if there is a placewhere you can find your health problem just by entering symptoms or the currentcondition of the person. It will help us to deduce the problem and to verify thesolution. The proposed idea is to create a system with artificial intelligence that canmeet these requirements. The AI can classify the diseases based on the symptomsand give the list of available treatments. The System is a text-to-text diagnosis chatbot that will engage patients in conversation with their medical issues and provides apersonalized diagnosis based on their symptoms and profile. Hence the people canhave an idea about their health and can take the right action.
2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2019
Credit card fraud events take place frequently and then result in huge financial losses. The numb... more Credit card fraud events take place frequently and then result in huge financial losses. The number of online transactions has grown in large quantities and online credit card transactions hold a huge share of these transactions. Online transactions have become an important and necessary part of our lives. As frequency of transactions is increasing, number of fraudulent transactions is also increasing rapidly. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This thesis presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.