Dr JOHN BABU GUTTIKONDA

EDUCATION

B.Tech(CHE) from Osmanua University
M.Tech(Computer Science) from JNTUH
from JNTUH

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science, Computer Vision and Pattern Recognition, Computer Science Applications

Scopus Publications

HSICNet a novel deep learning architecture for hyperspectral image classification in remote sensing and environmental monitoring
K. Purnachand, Rafath Samrin, John Babu Guttikonda, Rajitha Kotoju, Phani Sridhar Addepalli, A. Mohan
Scientific Reports, 2026
Leveraging the rich spectral and spatial information, hyperspectral image classification (HSIC) plays a vital role in remote sensing, which is significant for land-cover mapping and environmental monitoring. However, hyperspectral images exhibit high dimensionality, significant spectral redundancy, and a limited number of annotated samples, making classification challenging. However, the original data may be complicated by more specific spectral-spatial interdependencies, which the so-called feature extraction in either standard film learning or early CNN-based models doesn’t capture. Simultaneously, recent approaches that embed attention or a transformer into the architecture suffer from high computational cost, overfitting, and scalability issues. These limitations highlight the need for a new, flexible, and computationally efficient deep-learning framework for various HSIC scenarios. We propose HSICNet, a unique dual-branch network architecture for deep learning that separately captures spectral and spatial features, followed by an attention-based feature fusion strategy to facilitate the interplay between low-, mid, and high-level feature representations. To alleviate the curse of dimensionality and redundancy, we also include a PCA-based dimensionality reduction module. The work involves optimising the proposed model for accuracy, computational efficiency, and generalisation across all classes. HSICNet is extensively evaluated by conducting experiments on three benchmark hyperspectral datasets, including Indian Pines, Pavia University, and Salinas, and its performance is superior to the current state-of-the-art algorithms. With overall accuracy up to 99.35%, it achieves statistically significant improvements in the Kappa coefficient, F1-score, and average accuracy by a large margin. Ablations verify our choice of a dual-branch design and attention fusion to improve classification scores. With a strong architecture and the advantage of HSICNet’s lightweight nature, it has potential for use in real-time, scalability-sensitive remote sensing applications such as precision agriculture, smart cities, and environmental monitoring. Its robust generalizability and interpretability also permit deployment in heterogeneous and adaptive operational settings.
KAN-Steganalysis: A Kolmogorov-Arnold Network Framework for Interpretable and Efficient Image Steganalysis
John Babu Guttikonda, V. Murali Mohan, Prasad Emmadi
Iccids 2026 9th International Conference on Computational Intelligence in Data Science, 2026
Hiding information in a carrier is called Steganography, whereas the process of detecting the presence of hidden information within any carrier such as digital images, audio, text is known as Steganalysis. In the recent decade the advent of Convolutional Neural Networks(CNN) has boosted the performance of Steganalysis techniques to higher level and eliminated the feature selection phase which requires considerable domain expertise. But in spite of better performance than traditional machine learning based Steganalysis, CNN based techniques have disadvantages like, huge number of parameters and moreover their performance cannot be interpreted. These are major problems in the case of limited data and subtle stego noise. In this paper we discuss and assess the applicability of Kolmogorov-Arnold Networks(KANs) for the task of Steganalysis instead of Convolutional Neural Networks(CNN). KAN networks use learnable spline functions instead of fixed point wise activation used in CNNs. KANs also offer higher interpretability The KolmogorovArnold Networks architecture consists of high-pass aware spline activations, adaptive channel gating mechanism and curvature regularization. As a result the number of parameters decreases compared to CNN. BOSSBase 1.01 dataset is used in our work and the detection accuracy of KAN based Steganalysis is found to be on par with the contemporary CNN based Steganalysis techniques such as SRNet and Yedroudj-Net with requirement of 40% less parameters. This spline based representation leads to interpretable activation patterns with which we can have the insight into the decision process of the Steganalysis model. These results establish the potential of functional neural networks as an efficient and transparent alternative for modern image Steganalysis.
CyberShieldDL: A Hybrid Deep Learning Architecture for Robust Intrusion Detection and Cyber Threat Classification
S. Venkatramulu, John Babu Guttikonda, Desidi Narsimha Reddy, Y. Madhavi Reddy, M. Sirisha
Indonesian Journal of Electrical Engineering and Informatics, 2025
In modern network environments, securing systems from newly emerging attacks is essential, and a constructive approach is the use of an IDS (Intrusion Detection System). When faced with attacks that are not in the list of predefined patterns, traditional IDS methods such as signature-based detection or standalone machine learning models may not function properly to detect such attacks because they are not adaptable and not designed to deal with this type of attack. The current IDS systems that employ deep-learning architectures have enhanced detection capabilities; however, most prior art systems are limited by partial feature learning, which only learns features of either spatial or temporal traffic structures. Meanwhile, the lack of contextaware mechanisms, such as attention, limits their ability to attend more to the most informative network components, leading to suboptimal detection performance and generalization. To counter this issue, in this work, we introduce CyberShieldDL, which is the first deep learning-based IDS framework with a novel hybrid architecture: IntruNet-Hybrid, combining Convolutional Neural Networks (CNN) for spatial pattern extraction, Bidirectional Long Short-Term Memory (Bi-LSTM) networks for sequential feature extraction, and an attention mechanism to learn the salient features for intrusion detection dynamically. To create the framework, an optimized preprocessing and feature selection pipeline is presented to effectively and costeffectively prepare the model input. Extensive experiments on the CICIDS2017 dataset demonstrate that CyberShieldDL consistently outperforms the state-of-the-art, achieving an overall accuracy of 98.35% and high precision, recall, and F1-score in various attack scenarios. Cross-dataset validations on NSL-KDD and UNSW-NB15 also verify the system's generalization. The design provides a scalable and flexible solution for realworld network security, offering the flexibility and adaptability necessary to enhance classification accuracy and robustness against evolving attack patterns. Its modular construction enables us to extend it for real-time deployment and future adversarial robustness easily.
Adaptive Channel Recalibration for Deep Learning-Driven Steganalysis
John Babu Guttikonda
2025 IEEE International Conference on Computer Electronics Electrical Engineering and their Applications Ic2e3 2025, 2025
Steganalysis is the detection of hidden messages within digital media. Steganalysis techniques have witnessed significant advancements with the integration of deep learning techniques. Traditional methods which rely on manual or handcrafted features and machine learning classifiers have demonstrated effectiveness but were limited in the capability of generalization and adaptability to diverse steganographic schemes. Recent deep learning-based steganalysis approaches leverage Convolutional Neural Networks (CNNs) to automate feature extraction, but their ability to detect subtle steganographic noise remains a challenge. This paper proposes an Adaptive Channel Recalibration (ACR) mechanism to enhance deep learning-driven steganalysis. Adaptive Channel Recalibration dynamically assigns importance scores to feature maps, allowing the network to selectively emphasize discriminative features while suppressing irrelevant activations. By integrating ACR within a deep residual network (ResNet) framework, the model’s ability to detect weak steganographic signals has been improved while maintaining computational efficiency. The proposed approach is evaluated on the BOSSbase 1.01 dataset with various steganographic methods, including WOW, S-UNIWARD, and HILL. Experimental results demonstrate that this method outperforms existing steganalysis techniques, including Green Steganalyzer and Graph Attention Steganalysis, achieving superior detection accuracy and robustness. This work highlights the potential of adaptive feature recalibration in improving steganalysis performance and sets a foundation for further research in deep learning-driven steganalysis.
Securing Data in Images Using Cryptography and Steganography Algorithms
International Journal of Intelligent Systems and Applications in Engineering, 2024
Machine Learning Based Precision Agriculture using Ensemble Classification with TPE Model
Latha M, Mandadi Vasavi, Chunduri Kiran Kumar, Balamanigandan R, John Babu Guttikonda, Rajesh Kumar T
Journal of Machine and Computing, 2024
Many tasks are part of smart farming, including predicting crop yields, analysing soil fertility, making crop recommendations, managing water, and many more. In order to execute smart agricultural tasks, researchers are constantly creating several Machine Learning (ML) models. In this work, we integrate ML with the Internet of Things. Either the UCI dataset or the Kaggle dataset was used to gather the data. Effective data pretreatment approaches, such as the Imputation and Outlier (IO) methods, are necessary to manage the intricacies and guarantee proper analysis when dealing with data that exhibits irregular patterns or contains little changes that can have a substantial influence on analysis and decision making. The goal of this research is to provide a more meaningful dataset by investigating data preparation approaches that are particular to processing data. Following the completion of preprocessing, the data is classified using an average approach based on the Ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Neural Network (PNN), and Clustering-Based Decision Tree (CBDT) techniques. The next step in optimising the hyperparameter tuning of the proposed ensemble classifier is to employ a new Tree-Structured Parzen Estimator (TPE). Applying the suggested TPE based Ensemble classification method resulted in a 99.4 percent boost in accuracy
Early Detection of Brain Stroke using Machine Learning Techniques
Vempati Krishna, J. Sasi Kiran, PVRD Prasada Rao, G. Charles Babu, G. John Babu
Proceedings 2nd International Conference on Smart Electronics and Communication Icosec 2021, 2021
The brain is the most complex organ in the human body. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke). Early brain stroke prediction yields a higher amount that is profitable for the initiating time. Brain stroke is caused primarily by people’s lifestyle decisions, particularly in the current scenario by evolving elements such as high blood sugar, heart disease, obesity, diabetes, and hypertension. This research study has used various machine learning (ML) algorithms like K nearest neighbour, logistic regression, random forest (RF) classifier and SVC. This research work designs a model using one among the following algorithms with high accuracy to predict the stroke for newly given inputs.
A new steganalysis approach with an efficient feature selection and classification algorithms for identifying the stego images
John Babu Guttikonda, Sridevi R.
Multimedia Tools and Applications, 2019
A meta classification model for stegoanalysis using generic NN
, John Babu Guttikonda, Sridevi Rangu, and
International Journal of Recent Technology and Engineering, 2019
The core idea behind deep learning is that comprehensive feature representations can be efficiently learned with the deep architectures which are collected of stacked layer of trainable non linear operation. However, because of the diversity of image content, it is hard to learn effective feature representations directly from images for steGAnalysis. SteGAnalysis may be generally figured as binary classification issue. This technique, which is called a universal/blind steGAnalysis, will become the principle stream around current steGAnalytic algorithms. In the preparation phase, effective features which are sensitive with message embedding are concentrated on highlight possibility control by steGAnographier. Then, a binary classifier will be discovered looking into pairs from claiming blanket pictures and their relating stegos pointing with Figure a limit on recognize steGAnography. On testing phase, those prepared classifier is used to anticipate labels from claiming new enter pictures. Past exploration indicated that it will be rather critical to power spread Characteristics Also stego offers to be paired, i. e. SteGAnalytic offers from claiming spread pictures And their stego pictures ought further bolstering be safeguarded in the preparing situated. Otherwise, breaking cover-stego pairs in distinctive sets might present biased error and prompt to a suboptimal execution. Proposed approaches have to fix the kernel of first layer as the HPF (high-pass filter). It is so-called pre-processing layer. We suggested another technic with characteristic decrease done which characteristic Choice and extraction And classifier preparation need aid performed at the same time utilizing a generic calculation. That generic calculation optimizes An characteristic weight vector used to scale the individual features in the unique example vectors. A masker vector may be likewise utilized to concurrent Choice of a characteristic subset. We utilize this technobabble clinched alongside mix with those RESNET, and look at the outcomes with established characteristic Choice and extraction systems.
Contemporary stegnalysis schemes for reliable detection of steganography
G. John Babu, R. Sridevi
Proceedings of the 2017 International Conference on Wireless Communications Signal Processing and Networking Wispnet 2017, 2017
Steganalysis is the process of detecting the hidden information in the carrier. Most used carriers for steganography are images due to the redundant information present in the images and frequency of their use on the Internet. Steganalysis methods are classified into two categories, Targeted steganalysis and universal steganalysis. Targeted steganalysis is based on analysis of individual and known steganographic scheme. Blind steganalysis methods detect steganographic schemes created by unknown random stego-systems. The objective of steganalysis algorithms is to distinguish stego images from pure images. A classifier is built based on stego and pure images. When the knowledge of steganographic scheme is not available, a general steganalyzer is built, which is trained with a set of pure images and a set of stego images generated by various steganographic algorithms. The performance of steganalysis algorithm depends on three important aspects, preprocessing technique, feature selection & extraction and classification. This paper presents the contemporary steganalysis schemes discussing the details and comparing various aspects of these methods.

RECENT SCHOLAR PUBLICATIONS

CyberShieldDL: A Hybrid Deep Learning Architecture for Robust Intrusion Detection and Cyber Threat Classification
S Venkatramulu, JB Guttikonda, DN Reddy, YM Reddy, M Sirisha
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) 13 (3 … , 2025
2025
Citations: 8
Adaptive Channel Recalibration for Deep Learning-Driven Steganalysis
JB Guttikonda
2025 IEEE International Conference on Computer, Electronics, Electrical … , 2025
2025
Enhancing Predictive Accuracy in Machine Learning: Techniques for Model Optimization and Feature Selection"s
JBG Ajay Kumar Boyat
Nanotechnology Perceptions 20 (6), 4566-4578 , 2024
2024
Enhancing Predictive Accuracy in Machine Learning: Techniques for Model Optimization and Feature Selection"s
PPD Dr. Ajay Kumar Boyat1, Dr. John Babu Guttikonda2, Ajay Tiwari3, Dr ...
Nanotechnology Perceptions 20 (6), 4566-4578 , 2024
2024
Securing data in images using cryptography and steganography algorithms
O Guttikonda, J. B.
International Journal of Intelligent Systems and Applications in Engineering … , 2024
2024
Citations: 10
Deep learning based effective steganalysis
S Guttikonda, J. B., & Rangu
International Journal of Innovative Technology and Exploring Engineering … , 2020
2020
A new steganalysis approach with an efficient feature selection and classification algorithms for identifying the stego images
JB Guttikonda, S R
Multimedia Tools and Applications 78 (15), 21113-21131 , 2019
2019
Citations: 14
A meta classification model for stegoanalysis using generic NN
JS Rangu
International Journal of Recent Technology and Engineering (IJRTE) 8 (2 … , 2019
2019
StegNet: An efficient CNN-based steganalyzer
SR John Babu
International Journal of Computer Sciences and Engineering (IJCSE) 7 (3 … , 2019
2019
A survey on different feature extraction and classification techniques used in image steganalysis
J Babu, S Rangu, P Manogna
Journal of Information security 8 (3), 186-202 , 2017
2017
Citations: 25
Contemporary stegnalysis schemes for reliable detection of steganography
GJ Babu, R Sridevi
2017 International Conference on Wireless Communications, Signal Processing … , 2017
2017
Citations: 2
A Novel Approach for Spectral Imagery Based on Edge Detector using Sparse Spatio-Spectral Masks
GJ BABU, B RAMANI, B VANI
2015
Multi-Pixel Steganography
R Sridevi, GJ Babu
International Journal of Computer Science and Information Security 10 (6), 61 , 2012
2012

MOST CITED SCHOLAR PUBLICATIONS

A survey on different feature extraction and classification techniques used in image steganalysis
J Babu, S Rangu, P Manogna
Journal of Information security 8 (3), 186-202 , 2017
2017
Citations: 25
A new steganalysis approach with an efficient feature selection and classification algorithms for identifying the stego images
JB Guttikonda, S R
Multimedia Tools and Applications 78 (15), 21113-21131 , 2019
2019
Citations: 14
Securing data in images using cryptography and steganography algorithms
O Guttikonda, J. B.
International Journal of Intelligent Systems and Applications in Engineering … , 2024
2024
Citations: 10
CyberShieldDL: A Hybrid Deep Learning Architecture for Robust Intrusion Detection and Cyber Threat Classification
S Venkatramulu, JB Guttikonda, DN Reddy, YM Reddy, M Sirisha
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) 13 (3 … , 2025
2025
Citations: 8
Contemporary stegnalysis schemes for reliable detection of steganography
GJ Babu, R Sridevi
2017 International Conference on Wireless Communications, Signal Processing … , 2017
2017
Citations: 2
Adaptive Channel Recalibration for Deep Learning-Driven Steganalysis
JB Guttikonda
2025 IEEE International Conference on Computer, Electronics, Electrical … , 2025
2025
Enhancing Predictive Accuracy in Machine Learning: Techniques for Model Optimization and Feature Selection"s
JBG Ajay Kumar Boyat
Nanotechnology Perceptions 20 (6), 4566-4578 , 2024
2024
Enhancing Predictive Accuracy in Machine Learning: Techniques for Model Optimization and Feature Selection"s
PPD Dr. Ajay Kumar Boyat1, Dr. John Babu Guttikonda2, Ajay Tiwari3, Dr ...
Nanotechnology Perceptions 20 (6), 4566-4578 , 2024
2024
Deep learning based effective steganalysis
S Guttikonda, J. B., & Rangu
International Journal of Innovative Technology and Exploring Engineering … , 2020
2020
A meta classification model for stegoanalysis using generic NN
JS Rangu
International Journal of Recent Technology and Engineering (IJRTE) 8 (2 … , 2019
2019
StegNet: An efficient CNN-based steganalyzer
SR John Babu
International Journal of Computer Sciences and Engineering (IJCSE) 7 (3 … , 2019
2019
A Novel Approach for Spectral Imagery Based on Edge Detector using Sparse Spatio-Spectral Masks
GJ BABU, B RAMANI, B VANI
2015
Multi-Pixel Steganography
R Sridevi, GJ Babu
International Journal of Computer Science and Information Security 10 (6), 61 , 2012
2012