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Karanam Sunil Kumar
R.V. College of Engineering
Electrical & Electronics Engineering

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Hybrid machine learning model towards neuroimaging analysis for detection and grading brain tumors Lakshminarayana Munirathnam; Rashmi Shivaswamy; Suraksha Prakash; Reshma Shafi; Karanam Sunil Kumar
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10988

Abstract

Neuroimaging analysis enables detailed observation of brain tumors, with growing adoption of advanced imaging techniques in clinical practice. Limitations of conventional approaches in supporting proactive decisionmaking and reliable grading are increasingly addressed through machine learning. However, earlier models often faced challenges of limited generalization and computational burden. To overcome these issues, this study introduces a hybrid convolution neural network–support vector machine (CNN–SVM) framework that combines ResNet-50 feature extraction with a feature weighting (FW) strategy and SVM-based classification for improved diagnostic precision. The system is further enhanced with a clinically guided grading scheme, mapping classification outputs into malignant, benign, and healthy categories for greater interpretability. The proposed model was evaluated on three benchmark neuroimaging datasets (Figshare, Kaggle magnetic resonance imaging (MRI), and BraTS-2019) and achieved up to 98.6% accuracy with high sensitivity and specificity, while retaining low computational cost and rapid inference, outperforming conventional CNN-only methods.
Resource optimization of approximate convolutional neural network-based accelerator on FPGA platform Pooja Alana Puttaswamy; Kasaragod Poornima Kamath; Priyadarshini Jainapur; Karanam Sunil Kumar; Kumar Puttaswamy Gowd
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11142

Abstract

A significant number of machine learning techniques use convolutional neural networks (CNN). The hardware acceleration is essential due to the tremendous computation demands of CNNs, as well as the need for improved energy performance and lower usage response time. This manuscript presents the resource-optimized CNN-based hardware accelerator on a field-programmable gate array (FPGA) platform. The design uses LeNet-5 architecture for handwritten digits classification using the MNIST dataset. The CNN accelerator uses three different optimization approaches in this work, including fixed-point (FP) data optimization with shortened bits, approximate multiply-accumulate (MAC) operations, and loop unrolling features with a pipelining mechanism to optimize the hardware resources. These approaches improve the latency and resources, and overall performance of the CNN architecture. The CNN-based accelerator is designed and implemented on the Xilinx Zynq platform with the high-level synthesis (HLS) tool. The proposed MAC unit obtains a latency of 3.58 ms, with a frequency of 120.69 MHz on chip. The design uses only 96 block random access memory (BRAMs), digital signal processing (DSP) units of 106 and obtains the accuracy of 99.01%. The proposed accelerator improves the performance over state-of-the-art accelerators in concerning the area, power and accuracy.
Hybrid AI-driven anomaly detection and sequential attack classification for securing IoT networks Gauri Sameer Rapate; Ambuja Krishnappa; Sarala Duggonahalli Veeresh; Karanam Sunil Kumar; Bellary Kursheed
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11048

Abstract

Internet of things (IoT) systems are often inherently heterogeneous and the constantly evolving cyber threat presents a variety of attack vectors that can expose sensitive data across multiple mission-critical applications. The existing intrusion detection methods are often prone to zero-day attacks and specific to limited known intrusions. This paper designs a hybrid and multi level cyber-threat detection framework based on the robust data preprocessing scheme, correlation-based optimal feature selection and integrated anomaly and intrusion detection using a supervised learning approach. In the first stage, a random forest (RF)-based binary anomaly detector is designed as a fast primary threat filter against zero-day threats by detecting traffic anomalies without any prior attack signal. In the second stage, an adaptive, time-aware long short-term memory (LSTM) model performs multi-class intrusion classification using time-lag analysis in traffic flows to accurately identify and classify known attack types with high precision. The proposed framework is evaluated on the network flow telemetry of network–internet of things–version 2 (NF-ToN-IoT-V2) dataset and achieved 99% accuracy in both binary and multiclass settings, with a lower response time of 7.8 ms.
An intelligence framework for threat detection and response in cloud–IoT-assisted enterprise environments Amith Shekhar Chandrashekhar; Sarala D V; Ambuja K; Rajani Kallhalli Channarayappa; Karanam Sunil Kumar
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11046

Abstract

Cloud-internet of things (IoT)-enabled enterprise environments have become an integral part of modern infrastructures, but their increasing interconnectedness makes them vulnerable to sophisticated and rapidly evolving cyber threats. Existing methods for intrusion detection and threat intelligence often suffer from limitations such as high false alarms, low adaptability to new attacks, and computational overhead. To address these challenges, this paper presents an intelligent hybrid framework for threat detection and response in cloud-IoT-enabled enterprises. The proposed system adopts a two-stage architecture: an autoencoder (AE)-based anomaly detector serves as the first security layer to identify deviations from normal traffic behavior, while a convolutional neural network-long short-term memory (CNN-LSTM) model with an attention mechanism serves as the second layer to classify known attack categories with high accuracy. A response mechanism is further integrated to log events, assign severity scores, apply automated protections, and generate real-time alerts, transforming detection into proactive prevention. The system has been evaluated on the benchmark CSE-CIC-IDS2018 dataset, where the anomaly detector achieved an accuracy of 98.4% with a false positive rate of 2%, while the CNN-LSTM-Attention intrusion classifier achieved an accuracy of 99.42%.
Co-Authors Ambuja K Ambuja Krishnappa Amith Shekhar Chandrashekhar Bellary Kursheed Gauri Sameer Rapate Kasaragod Poornima Kamath Kumar Puttaswamy Gowd Lakshminarayana Munirathnam Pooja Alana Puttaswamy Priyadarshini Jainapur Rajani Kallhalli Channarayappa Rashmi Shivaswamy Reshma Shafi Sarala D V Sarala Duggonahalli Veeresh Suraksha Prakash