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Maddileti, Kuruva
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Prevention of credit card fraud transaction using GA feature selection for web-based application Sreekanth, Kavuri; Mamidi, Ratnababu; Reddy, Thumu Srinivas; Maddileti, Kuruva; Deepthi, Darivemula
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1645-1652

Abstract

Credit card fraud (CCF) is a regular event that generates financial losses. A considerable share of the significantly increased volume of internet transactions is made with credit cards. CCF detection programmes are consequently highly prioritised by banks and other financial organisations. These fraudulent transactions can come in a wide variety of formats and categories. To maintain data integrity, financial institutions support digital transactions. One of the most popular ways to pay the products and services can be done by both online and offline by using a credit card. Thus, there is a higher possibility of fraud during these financial transactions. This informs programmers to the requirement for a reliable technique for identifying successful fraud. Credit card users and businesses that accept credit cards have recently had to contend with the serious issue of CCF. Application-level frauds and transaction level frauds are the two categories into which CCF controlled frauds are divided. Therefore, utilizing genetic algorithm (GA) feature selection for web-based applications, it is advised to use this strategy as a method for the prevention of CCF transaction. This method's performance is evaluated based on a number of factors, including accuracy, recall, and specificity.
Internet of things based smart agriculture using K-nearest neighbor for enhancing the crop yield Dasari, Kalyankumar; Kharde, Mukund Ramdas; Maddileti, Kuruva; Pasupuleti, Venkat Rao; Ram, Mylavarapu Kalyan; Sujana, Challapalli; Komali, Govindu; Fariddin, Shaik Baba
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp436-445

Abstract

Agriculture is one of the major occupations in India and is one of the significant contributors to the economy of India. The agriculture plays a vital role in country gross domestic product (GDP) and is also part of civilization. The production of crop influences the economies of countries. However, still the agriculture filed stands technologically backward. In addition, the lack of favourable weather conditions might result loss of crops yields. The farmers need awareness about their soils, timely weather updates and techniques to improve their soil for growing healthy crops. Hence it is essential to develop a system which can technologically support the farmers for suggesting the crop and improving crop yields. With the development of electronics, researchers have been developed many applications and micro controllerbased systems to do agricultural operations. The internet of things (IoT) has opened many opportunities to design and implements a smart agriculture system and machine learning (ML) algorithm can help to obtain accurate performance. Hence, in this analysis, IoT based smart agriculture using K-nearest neighbor (KNN) for enhancing the crop yields is presented. With the combination of IoT and ML algorithm this system is designed which integrates primary agriculture operations such as recommendation of crops, automated watering and fertilizers recommendation.
An efficient implementation of credit card fraud detection using CatBoost algorithm Suryanarayana, Vadhri; Maddileti, Kuruva; Satyanarayana, Dune; Jyothi, R Leela; Sreekanth, Kavuri; Mande, Praveen; Miriyala, Raghava Naidu; Sudhakar, Oggi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1914-1923

Abstract

Transaction fraud has grown to be an important issue in worldwide, banking and commerce security is easier access to trade information. Every day, there are more and more incidents of transaction fraud, which causes large financial losses for both consumers and financial professionals. The ability to identify transaction fraud is getting closer to reality due to improvements in computer science's machine learning (ML) and data mining areas. So, one of them that is becoming dangerous is credit card fraud (CCF). Millions of people are experiencing financial loss and identity theft as a result of these malicious operations. The CCF of many illegal activities that fraudsters are always using new methods to carry out. One major problem facing financial services sector is CCF. To overcome this, categorical boosting (CatBoost) algorithm is explained as a solution to these problems. Fraud or fraudulent transactions are identified using this effective CatBoost algorithm implementation for identification of CCF. Thus, in terms of accuracy, precision, and detection rate this method gives better performance.
Co-Authors Dasari, Kalyankumar Deepthi, Darivemula Fariddin, Shaik Baba Jyothi, R Leela Kharde, Mukund Ramdas Komali, Govindu Mamidi, Ratnababu Mande, Praveen Miriyala, Raghava Naidu Pasupuleti, Venkat Rao Ram, Mylavarapu Kalyan Reddy, Thumu Srinivas Satyanarayana, Dune Sreekanth, Kavuri Sudhakar, Oggi Sujana, Challapalli Suryanarayana, Vadhri