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Sudhakar, Oggi
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Region based lossless compression for digital images using entropy coding Vamsikrishna, Mangalapalli; Sudhakar, Oggi; Bugge, Bhagya Prasad; Kumar, Asileti Suneel; Thankachan, Blessy; Subrahmanyam, K.B.V.S.R.; Deepthi, Natha; Mande, Praveen
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.pp1870-1879

Abstract

Image compression is a method for reducing video and image storage space. Moreover, enhancing the performance of the transmission and storage processes is important. The region based coding technique is important for compressing and sending medical images. In the medical field, lossless compression can help telemedicine applications achieve high efficiency. It affects image quality and takes a long time to encode. As a result, this study proposes region-based lossless compression for digital images using entropy coding. The best performance is achieved by segmenting these areas. In this case, an integer wavelet transform (IWT) is utilized after the ROI of the image was manually generated. The IWT compression method is helpful for reversibly reconstructing the original image to the required quality. For enhancing the quality of compression, entropy coding is utilized. By passing images of varying sizes and formats, various quantitative metrics can be determined. The simulation results demonstrate that the region based lossless compression technique utilizing range blocks and iterations resulted in reduced encoding time and improved quality.
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 Bugge, Bhagya Prasad Deepthi, Natha Jyothi, R Leela Kumar, Asileti Suneel Maddileti, Kuruva Mande, Praveen Miriyala, Raghava Naidu Satyanarayana, Dune Sreekanth, Kavuri Subrahmanyam, K.B.V.S.R. Suryanarayana, Vadhri Thankachan, Blessy Vamsikrishna, Mangalapalli