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Reddy, Thumu Srinivas
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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.
Design and analysis for robotic arm position for automatic electric vehicle Kharde, Mukund Ramdas; Kalam, Sayyad Abdul; Teku, Kalyani; Reddy, Thumu Srinivas; Satya Srinivas, Gollapalli Veera; Kollamudi, Pavani; Fariddin, Shaik Baba; Kumar, Gopinati Pranay
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.pp1517-1526

Abstract

Nowadays electric vehicles (EV) utilization is increasing. Because of charging issues, EVs are troubling people at the time of the journey because of the lack of charging stations. Therefore, to overcome these issues, robotic arm position for automatic electric vehicle is introduced in this analysis. This vehicle is operated through solar, so charging issues are overcome. The robotic arm position for automatic electric vehicle is fully automated by 4 infrared radiation (IR) sensors, which are placed in variations, back and other sides with particular speed limit variations, so that accidents can be avoided. The Flux in hand gloves can operate without manual operation while driver is sleeping. This analysis uses Raspberry Pi, python software with machine learning (ML) algorithm (support vector machine). Hence, this robotic arm position for automatic electric vehicle shows better results in terms of charging issues, accident ratio and driver presence.
Co-Authors Deepthi, Darivemula Fariddin, Shaik Baba Kalam, Sayyad Abdul Kharde, Mukund Ramdas Kollamudi, Pavani Kumar, Gopinati Pranay Maddileti, Kuruva Mamidi, Ratnababu Satya Srinivas, Gollapalli Veera Sreekanth, Kavuri Teku, Kalyani