Dr JOHN BABU GUTTIKONDA

@anurag.ac.in

Associate Professor, Department of CSE(AI&ML)
ANURAG ENGINEERING COLLEGE

Dr JOHN BABU GUTTIKONDA


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https://researchid.co/johnbabug

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
10

Scopus Publications

59

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

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, et al.
    Scientific Reports, 2026
  • 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
  • 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
  • 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
  • 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, et al.
    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