Goutami Chenumalla

@bmsit.ac.in

Assistant Professor,Department of Computer Science and Engineering
BMS institute of technology and management

Goutami Chenumalla


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https://researchid.co/goutamich

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Networks and Communications, Computer Science
3

Scopus Publications

Scopus Publications

  • Advancement in Diabetic Retinopathy Prediction: Utilizing Voting Classifiers Techniques for Early Detection
    Choon Kit Chan, P. Kavitha, A. Kalaivani, Goutami Chenumalla, A. Vanathi, et al.
    Engineering Technology and Applied Science Research, 2026
    Diabetic Retinopathy (DR), also known as diabetic eye disease, damages the retina and is linked to diabetes mellitus. According to previous studies, DR influences up to 80% of individuals and children who have had type I and II diabetes for more than 20 years. However, with proper care and cautious eye monitoring, severe blind forms of retinopathy and maculopathy can be avoided. This study used a voting classifier, combining the predictions of base models, such as Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosting (GB) classifiers, to improve the performance and accuracy of predictions.
  • FPGA-Based Image Compression for Wireless Communication Networks Using - CRAN Architecture
    Lakshmisha S K, Madhusudhan M V, Goutami Chenumalla, Impa B H, Bhavana A, et al.
    Journal of Machine and Computing, 2025
    This work introduces a Field Programmable Gate Array (FPGA) based image compression method utilizing Huffman coding (FICH) to enhance the efficiency of wireless networks, particularly within the Cloud-based Radio-Access-Network (C-RAN) architecture. The FICH method addresses image compression challenges in C-RAN, offering faster compression and decompression times compared to existing FPGA approaches. The findings include significant improvements in Bit-Error-Rate (BER), Symbol-Error-Rate (SER), and Error-Vector Magnitude (EVM), with average BER, SER, and EVM improvements of 37.85%, 24.64%, and 24.56% for fewer RRHs, and 96.10%, 91.13%, and 48.72% for more RRHs, respectively. Additionally, the FICH method demonstrated reduced encoding and decoding times, averaging 0.0545 seconds versus 0.0853 seconds when compared with existing approach. The approach also ensures robust and scalable compression, optimizing resource utilization with FPGA-based hardware acceleration. These advancements support the growing data demands of modern wireless networks.
  • AI-Powered Cross-Platform Compliance Management System for CIS Benchmark Auditing
    Chamarthi Sai Himaswitha, Goutami Chenumalla, Dhanalakshmi B K, Atmajit Pattnaik, Anca Ann Shibu
    2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025

RECENT SCHOLAR PUBLICATIONS

  • SURVEY ON IMPLEMENTATION OF INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING AND CYBERSECURITY TECHNIQUES
    G Chenumalla

MOST CITED SCHOLAR PUBLICATIONS

  • SURVEY ON IMPLEMENTATION OF INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING AND CYBERSECURITY TECHNIQUES
    G Chenumalla