Hanumanthu Bukya

@kitsw.ac.in

Assistant Professor
Kakatiya Institute of Technology and Science Warangal

Hanumanthu Bukya


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https://researchid.co/bhcsekits
Academic Affiliation Current: Assistant Professor
Details From (Month, Year) To (Month, Year) Name of the Organization
Assistant Professor July 2010 Till date Kakatiya Institute of Technology & Science, Warangal.
Assistant Professor May 2008 June 2010 Balaji Institute of Technology & Science, Narsampet, Warangal.
Lecturer July 2004 May 2008 Aurora’s Research & Technological Institute(Ramappa Engineering College)
, Warangal.

EDUCATION

Education
Degree Details (specialization) Institute/University Month , Year of Registered Month , Year completed
Ph.D. Computer Science & Engineering Kakatiya University, Warangal April,2019 November, 2022
(Data Analytics) Kakatiya University, Warangal November,2022
M.Tech. Computer Science & Engineering (SE) Aurora’s Research & Technological Institute (Ramappa Engineering College ), Warangal/ Jawaharlal Nehru Technological University,
Hyderabad. September 2006 December 2008
B.Tech. Computer Science & Engineering Aurora’s Research & Technological Institute (Ramappa Engineering College), Warangal/ Jawaharlal Nehru Technological University, Hyderabad. August 2000 April 2004

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Artificial Intelligence, Computer Networks and Communications
18

Scopus Publications

153

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Hybrid acoustic-deep features with auto encoders for speech emotion recognition
    Kogila Raghu, Manchala Sadanandam, Bh Hanumanthu
    Multimedia Tools and Applications, 2026
  • Retraction note: Hybrid TDR-MI Based Wireless Sensor Network for Underground Water Pipeline Leakage Detection and Localization Using Pressure Residuals and Classifiers (Wireless Personal Communications, (2024), 139, 2, (803-823), 10.1007/s11277-024-11623-0)
    Ramdas Vankdothu, Hanumanthu Bhukya, Raghu Ram Bhukya
    Wireless Personal Communications, 2026
  • Crop Disease Detection by Deep Joint Segmentation and Hybrid Classification Model: A CAD-Based Agriculture Development System
    Raghuram Bhukya, Shankar Vuppu, A Harshvardhan, Hanumanthu Bukya, Suresh Salendra
    Journal of Phytopathology, 2025
    Precise detection of crop disease at the early stage is a crucial task, which will reduce the spreading of disease by taking preventive measures. The main goal of this research is to propose a hybrid classification system for detecting crop disease by utilising Modified Deep Joint (MDJ) segmentation. The detection of crop diseases involves five stages. They are data acquisition, pre‐processing, segmentation, feature extraction and disease detection. In the initial stage, image data of diverse crops is gathered in the data acquisition phase. According to the work, we are considering Apple and corn crops with benchmark datasets. The input image is subjected to pre‐processing by utilising the median filtering process. Subsequently, the pre‐processed image under goes a segmentation process, where Modified Deep Joint segmentation is proposed in this work. From the segmented image, features like shape, colour, texture‐based features and Improved Median Binary Pattern (IMBP)‐based features are extracted. Finally, the extracted features are given to the hybrid classification system for identifying the crop diseases. The hybrid classification model includes Bidirectional Long Short‐Term Memory (Bi‐LSTM) and Deep Belief Network (DBN) classifiers. The outcome of both the classifiers is the score, which is subjected to an improved score level fusion model, which determines the final detection results. Finally, the performance of the proposed hybrid model is evaluated over existing methods for various metrics. At a training data of 90%, the proposed scheme attained an accuracy of 0.965, while conventional methods achieved less accuracy rates.
  • Hybrid TDR-MI Based Wireless Sensor Network for Underground Water Pipeline Leakage Detection and Localization Using Pressure Residuals and Classifiers
    Ramdas Vankdothu, Hanumanthu Bhukya, Raghu Ram Bhukya
    Wireless Personal Communications, 2024
    The pipeline leakage detection and leak localization trouble is a highly demanding and dangerous issue. Underground pipelines are critical for transporting enormous fluid volumes (e.g., water) across extended distances. Not only would solving this issue save the nation a great deal of money and resources, but it will also save the environment. However, because of the harsh climatic conditions below earth, current leak detection systems are not enough for monitoring subterranean pipelines. To address these issues, this study suggests a hybrid wireless sensor network for monitoring subterranean pipelines that is based on magnetic induction and time domain reflectometry (TDR). TDR is installed in this instance below a wireless sensor network that is based on MI. TDR significantly reduces the time needed for inspection while accurately locating the leak. Based on MI technology, we provide a wireless sensor network for inexpensive, real-time leak detection in subterranean pipelines. Through the integration of data from several sensor types located within and around subterranean pipes, MISE-PIPE detects leaks. Ad-hoc WSNs are employed in pressure measurement. (WDNs) is a popular subject that has drawn attention from scholars lately. Since leak localisation has a significant influence on the human population and the economy, time and accuracy are essential components. A broad leak localisation technique is proposed using statistical classifiers operating in the residual space. Classifiers are trained using leak data from every node in the network, accounting for demand uncertainty, noise from sensor preservatives, and leak size. After localising and identifying leaks, all monitoring data is sent to the CH using the K-means clustering technique, which performs two vital tasks: optimum clustering, extending the Network Lifetime, and maintaining Quality of Service. The K-Means technique is used to optimise the clustering process. The K-means clustering technique is used to transfer all monitoring data to the CH for the purpose of pipeline leak identification and localisation. Unlike the current underground pipeline monitoring system, our proposed Hybrid TDR-MI-based wireless sensor network allows precise real-time leak identification and localisation.
  • Weighted ensemble CNN for lung nodule classification: an evolutionary approach
    Amrita Naik, Damodar Reddy Edla, Saidi Reddy Parne, Hanumanthu Bhukya
    Multimedia Tools and Applications, 2024
  • Handling uncertainty using optimal clustering with rough sets-based rule generation model for data classification
    Hanumanthu Bhukya, Manchala Sadanandam
    Expert Systems, 2024
    In recent times, MapReduce has become a popular tool for handling big data. At the same time, uncertainty is related to arbitrariness, fuzziness, ambiguity, irregularity and incomplete knowledge. In RS theory, the uncertainty behaviour of the data in the dataset of interest is managed by using upper and lower approximate sets and classification accuracy. The RS model is integrated with data clustering technique for optimal outcomes. With this motivation, this study designs an Optimal Clustering with RS‐Based Rule Generation Model (OC‐RSRGM) for data classification on MapReduce environment. The OC‐RSRGM technique aims to generate an optimal set of rules using RST for the data classification process and it involves a two‐stage process namely Optimal Fuzzy c‐Means Clustering (OFCM) and RSRGM‐based rule generation with classification. The OFCM technique is derived to eradicate the local optimal problem of the FCM (Fuzzy c‐Means) model using Barnacles Mating Optimizer (BMO). It provides the decision‐makers with all the information needed to design appropriate mechanisms to support their decision‐making activities. The Hadoop MapReduce tool is used to handle big data. The proposed method combines an FCM, BMO, RS theory to accomplish effective decision‐making. The OC‐RSRGM technique can be employed to continuous value dataset where data point does not offer any class details and it might be uncertain. To validate the performance of OC‐RSRGM technique, a detailed experimental analysis is carried out to highlights the betterment of OC‐RSRGM technique. The proposed OC‐RSRGM technique has obtained an effective outcome with the CT of 5.43 s.
  • Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications
    S. Deepa, A. Umamageswari, S. Neelakandan, Hanumanthu Bhukya, I. V. Sai Lakshmi Haritha, et al.
    International Journal of Cooperative Information Systems, 2024
    Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot.
  • Deep Learning Algorithms for Speech Emotion Recognition with Hybrid Spectral Features
    Raghu Kogila, Manchala Sadanandam, Hanumanthu Bhukya
    SN Computer Science, 2024
  • A Self-Operational Convolutional Neural Networks With Convergent Cross-Mapping and Its Application in Parkinson's Disease Classification
    Kaushik Sekaran, A. Harshavardhan, N. Sandhya, C. Sudha, Gujjeti Nagaraju, et al.
    IEEE Access, 2024
    Parkinson’s disease (PD) is a progressive neurodegenerative disease with multiple motor and non-motor characteristics. PD patients commonly face vocal impairments during the early stages of the disease. Therefore, diagnosis systems based on vocal disorders are at the forefront of recent PD detection studies. Our study proposes two frameworks based on Convolutional Neural Networks to classify Parkinson’s disease (PD). In recent years, Convolutional Neural Networks (CNNs) have proven highly effective in various medical applications, particularly disease classification. However, standard CNN designs have significant limitations because they require extensive manual calibration and supervision, which can result in biases and poor performance in practical applications. This paper proposes the Self-Operating Convolutional Neural Network (SOCNN) in conjunction with Convergent Cross-Mapping (CCM) to address these issues. The SOCNN architecture is intended to modify its internal parameters automatically, eliminating the need for manual intervention during training and increasing the model’s adaptability to unknown data. Adopting CCM principles, we construct a seamless connection between the input and output domains, allowing for rapid information transfer and preservation, which are crucial for accurate disease classification. To this end, we construct causal networks, extract network features, and perform deep learning analysis to distinguish Parkinson’s disease patients (PD) from age and gender-matched healthy controls (HC). Using a large dataset of Parkinson’s Disease (PD) patients and healthy controls, the effectiveness of the proposed SOCNN with CCM is evaluated. Specifically, we use the SOCNN-CCM to compute the centrality of the network nodes, which act as features for the classification models. Extensive experiments are conducted to compare the SOCNN to conventional CNN models and innovative techniques. The results demonstrate that the SOCNN-CMM outperforms state-of-the-art in terms of accuracy, sensitivity, and specificity when classifying Parkinson’s patients, confirming its diagnostic potential.
  • Brain tumor image identification and classification on the internet of medical things using deep learning
    B. Raghuram, Bhukya Hanumanthu
    Measurement Sensors, 2023
  • Assessing the Impact of Migration from SOA to Microservices Architecture
    Vinay Raj, Hanumanthu Bhukya
    SN Computer Science, 2023
  • RoughSet based Feature Selection for Prediction of Breast Cancer
    Hanumanthu Bhukya, M Sadanandam
    Wireless Personal Communications, 2023
  • Effective Contact Tracing of Covid Patients Using Machine Learning Technique
    Netha Sri Dattha Tumati, Vuppu Shankar, B. Hanumanthu
    2023 1st International Conference on Optimization Techniques for Learning Icotl 2023 Proceedings, 2023
  • Intelligent IDS: Venus Fly-Trap Optimization with Honeypot Approach for Intrusion Detection and Prevention
    Sai Chaithanya Movva, Suresh Nikudiya, Varsha S. Basanaik, Damodar Reddy Edla, Hanumanthu Bhukya
    Wireless Personal Communications, 2023
  • Voice Based Assistance About Surroundings of Blind and Visually Impaired People
    Nishikanth Annamaneni, Vuppu Shankar, B. Hanumanthu, V. Chandra Shekhar Rao, M. Sujatha, et al.
    2023 1st International Conference on Optimization Techniques for Learning Icotl 2023 Proceedings, 2023
  • Design of metaheuristic rough set-based feature selection and rule-based medical data classification model on MapReduce framework
    Hanumanthu Bhukya, Sadanandam Manchala
    Journal of Intelligent Systems, 2022
  • Rough sets base associative classification rules extraction from big data
    Hanumanthu Bhukya, M.Sadanandam
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • SQL injection attack prevention based on decision tree classification
    B. Hanmanthu, B. Raghu Ram, P. Niranjan
    Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control Isco 2015, 2015

RECENT SCHOLAR PUBLICATIONS

  • Retraction Note: Hybrid TDR-MI Based Wireless Sensor Network for Underground Water Pipeline Leakage Detection and Localization Using Pressure Residuals and Classifiers
    R Vankdothu, H Bhukya, RR Bhukya
    Wireless Personal Communications, 1-2 , 2026
    2026
  • Hybrid tdr-mi based wireless sensor network for underground water pipeline leakage detection and localization using pressure residuals and classifiers
    R Vankdothu, H Bhukya, RR Bhukya
    Wireless Personal Communications, 1-21 , 2024
    2024
    Citations: 6
  • Weighted ensemble CNN for lung nodule classification: an evolutionary approach
    A Naik, DR Edla, SR Parne, H Bhukya
    Multimedia Tools and Applications 83 (26), 68441-68466 , 2024
    2024
    Citations: 6
  • Deep belief network-based user and entity behavior analytics (ueba) for web applications
    S Deepa, A Umamageswari, S Neelakandan, H Bhukya, ...
    International Journal of Cooperative Information Systems 33 (02), 2350016 , 2024
    2024
    Citations: 4
  • Handling uncertainty using optimal clustering with rough sets‐based rule generation model for data classification
    H Bhukya, M Sadanandam
    Expert Systems 41 (6), e13026 , 2024
    2024
    Citations: 1
  • Brain tumor image identification and classification on the internet of medical things using deep learning
    B Raghuram, B Hanumanthu
    Measurement: Sensors 30, 100905 , 2023
    2023
    Citations: 18
  • Deep learning algorithms for speech emotion recognition with hybrid spectral features
    R Kogila, M Sadanandam, H Bhukya
    SN Computer Science 5 (1), 17 , 2023
    2023
    Citations: 5
  • Assessing the impact of migration from SOA to microservices architecture
    V Raj, H Bhukya
    SN Computer Science 4 (5), 577 , 2023
    2023
    Citations: 16
  • RoughSet based feature selection for prediction of breast cancer
    H Bhukya, M Sadanandam
    Wireless Personal Communications 130 (3), 2197-2214 , 2023
    2023
    Citations: 11
  • Intelligent IDS: Venus fly-trap optimization with honeypot approach for intrusion detection and prevention
    SC Movva, S Nikudiya, VS Basanaik, DR Edla, H Bhukya
    Wireless Personal Communications 128 (2), 1041-1063 , 2023
    2023
    Citations: 9
  • Design of metaheuristic rough set-based feature selection and rule-based medical data classification model on MapReduce framework
    H Bhukya, S Manchala
    Journal of Intelligent Systems 31 (1), 1002-1013 , 2022
    2022
    Citations: 9
  • Rough set driven feature selection and rule based medical data classification approaches based on MapReduce computing framework
    H BHUKYA, M Sadanandam
    2022
  • RoughSet based Feature Selection for Prediction of Breast Cancer
    H BHUKYA, S Manchala
    2022
    Citations: 1
  • Design of metaheuristic rough set-based feature selection and rule-based medical data classification model on MapReduce framework
    S Manchala, H Bhukya
    2022
  • Intelligent IDS: venus fly-trap optimization with honeypot approach for intrusion detection and prevention
    MS Chaithanya, S Nikudiya, VS Basanaik, D Reddy, H Bhukya
    2021
    Citations: 4
  • MapReduce-driven rough set fuzzy classification rule generation for big data processing
    H Bhukya, M Sadanandam
    Intelligent Systems, Technologies and Applications: Proceedings of Sixth … , 2021
    2021
    Citations: 3
  • Confidentiality Defense and Interruption Circumvention for Cloudlet-based Health Information Distribution
    DRBBH B.Raju
    Journal of Interdisciplinary Cycle Research , 2020
    2020
  • An Resourceful Repossession over Documents Encoded by Characteristics in Cloud Constructed Organizations
    DRBBH B.Raju
    Aut Aut Research Journal , 2020
    2020
  • Rough Sets base Incremental Associative Classification Rules Generation on MapReduce Framework
    HBM Sadanandam
    International Journal of Advanced Science and Technology , 2020
    2020
  • Face smile determination using face and smile detection for perceptual user interfaces (PUIs) for real-time interaction
    HB A. Harshavardhan,T. Archana ,M. Sridevi
    Materials Today: Proceedings , 2020
    2020
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • SQL Injection Attack prevention based on decision tree classification
    B Hanmanthu, BR Ram, P Niranjan
    2015 IEEE 9th International conference on Intelligent Systems and Control … , 2015
    2015
    Citations: 27
  • Brain tumor image identification and classification on the internet of medical things using deep learning
    B Raghuram, B Hanumanthu
    Measurement: Sensors 30, 100905 , 2023
    2023
    Citations: 18
  • Assessing the impact of migration from SOA to microservices architecture
    V Raj, H Bhukya
    SN Computer Science 4 (5), 577 , 2023
    2023
    Citations: 16
  • RoughSet based feature selection for prediction of breast cancer
    H Bhukya, M Sadanandam
    Wireless Personal Communications 130 (3), 2197-2214 , 2023
    2023
    Citations: 11
  • Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks
    AVKP anga Vijay kumar ,A. Harshavardhan ,Hanumanthu Bhukya
    Materials Today: Proceedings , 2020
    2020
    Citations: 10
  • Intelligent IDS: Venus fly-trap optimization with honeypot approach for intrusion detection and prevention
    SC Movva, S Nikudiya, VS Basanaik, DR Edla, H Bhukya
    Wireless Personal Communications 128 (2), 1041-1063 , 2023
    2023
    Citations: 9
  • Design of metaheuristic rough set-based feature selection and rule-based medical data classification model on MapReduce framework
    H Bhukya, S Manchala
    Journal of Intelligent Systems 31 (1), 1002-1013 , 2022
    2022
    Citations: 9
  • Fuzzy Associative Classifier For Distributed Mining
    BH B Raghuram, Jayadev Gyani
    International Conference And Workshop On Emerging Trends In Technology, 431-435 , 2012
    2012
    Citations: 7
  • Hybrid tdr-mi based wireless sensor network for underground water pipeline leakage detection and localization using pressure residuals and classifiers
    R Vankdothu, H Bhukya, RR Bhukya
    Wireless Personal Communications, 1-21 , 2024
    2024
    Citations: 6
  • Weighted ensemble CNN for lung nodule classification: an evolutionary approach
    A Naik, DR Edla, SR Parne, H Bhukya
    Multimedia Tools and Applications 83 (26), 68441-68466 , 2024
    2024
    Citations: 6
  • Parallel optimal grid-clustering algorithm exploration on mapreduce framework
    B Hanmanthu, R Rajesh, P Niranjan
    International Journal of Computer Applications 180 (05), 2018 , 2018
    2018
    Citations: 6
  • Deep learning algorithms for speech emotion recognition with hybrid spectral features
    R Kogila, M Sadanandam, H Bhukya
    SN Computer Science 5 (1), 17 , 2023
    2023
    Citations: 5
  • Deep belief network-based user and entity behavior analytics (ueba) for web applications
    S Deepa, A Umamageswari, S Neelakandan, H Bhukya, ...
    International Journal of Cooperative Information Systems 33 (02), 2350016 , 2024
    2024
    Citations: 4
  • Intelligent IDS: venus fly-trap optimization with honeypot approach for intrusion detection and prevention
    MS Chaithanya, S Nikudiya, VS Basanaik, D Reddy, H Bhukya
    2021
    Citations: 4
  • MapReduce-driven rough set fuzzy classification rule generation for big data processing
    H Bhukya, M Sadanandam
    Intelligent Systems, Technologies and Applications: Proceedings of Sixth … , 2021
    2021
    Citations: 3
  • Third Party Privacy Preserving Protocol For Perturbation Based Classification Of Vertically Fragmented Databases
    APN B.Hanmanthu, B.Raghuram
    International Conference On Emerging Trends In Electrical, Communication And … , 2012
    2012
    Citations: 3
  • Face smile determination using face and smile detection for perceptual user interfaces (PUIs) for real-time interaction
    HB A. Harshavardhan,T. Archana ,M. Sridevi
    Materials Today: Proceedings , 2020
    2020
    Citations: 2
  • Apriori Algorithm base Model of Opinion Mining for Drug Review
    M Alekhya, BR Ram, B Hanmanthu
    International Journal of Advanced Trends in Computer Science & Engineering 9 … , 2015
    2015
    Citations: 2
  • Handling uncertainty using optimal clustering with rough sets‐based rule generation model for data classification
    H Bhukya, M Sadanandam
    Expert Systems 41 (6), e13026 , 2024
    2024
    Citations: 1
  • RoughSet based Feature Selection for Prediction of Breast Cancer
    H BHUKYA, S Manchala
    2022
    Citations: 1

Publications

B.Hanmanthu, B.Raghuram, and P.Niranjan, “Optimal Grid-Clustering technique for Bigdata analytics,” In Proceedings of IEEE Sponsored Third International Conference on Innovations in information Embadded and Communication ( ICIIECS'16), 17th March 2016 to 18th March 2016 Organized by Karpagam College of Engineering,Coimbatore, Tamil Nadu, .
B.Hanmanthu, B.Raghuram, and P.Niranjan, “SQL Injection Attack Prevention Based on Decision Tree Classification,” In Proceedings of IEEE Sponsored 9th International Conference on Intelligent Systems and Control (ISCO) 2015, 9th -10th January, 2015 Organized by Karpagam College of Engineering,Coimbatore, Tamil Nadu, .
B.Hanmanthu, B.Raghuram, “Intelligent Agent Framework For Recommender System Based On Collaborative Filtering,” In Proceedings Of Elsevier, 2nd International Conference On Advanced Computing Methodologies (ICACM - 2013), 02-03 August 2013, (GRIET) Hyderabad, Andhra Pradesh, .
B.Hanmanthu, B.Raghuram, And P.Niranjan, “Third Party Privacy Preserving Protocol For Perturbation Based Classification Of Vertically Fragmented Databases,” In Proceedings Of Elsevier, International Conference On Emerging Trends In Electrical, Communication And Information Technologies (Icecit - 2012), 21-23 December 2012, (Srit) Anantapur - 515 701, Andhra Pradesh, .
B Raghuram, Jayadev Gyani, B.Hanmanthu “Fuzzy Associative Classifier For Distributed Mining,” In Proceedings Of Ijca, Intern

GRANT DETAILS

Grant received during the year: 2019-20
Sanction no.: 26636 DST/ICPS/SCST/ 2019/123
Date: 31.03.2019
Amount: 7,00,000.00
Name of the Scheme: National level Conferences/Workshops/Seminars/Brain Storming Sessions etc of three days duration under Scheduled Tribes (ST) category under ICPS programme.
Title of the Project : Emerging Trends in Artificial Intelligence
Grant received during the year: 2019-20
Sanction no.: 26344 DST/ICPS/SCST/ 2019/524
Date: 31.03.2019
Amount: 9,00,000.00
Name of the Scheme: National level Training programmes: In-house Short term training/FDP Programmes for Faculty/UG/PG/Doctoral students of two weeks duration under ICPS programme of DST.