BOMMARAJU SRINIVASA RAO

@gcet.edu.in

Professor Department of Computer Science and Engineering
Geethanjali College of Engineering and Technology

BOMMARAJU SRINIVASA RAO


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https://researchid.co/drbsrinivasarao.cse

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Cognitive Neuroscience, Artificial Intelligence
7

Scopus Publications

118

Scholar Citations

5

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • RETRACTED: Dynamic load balancing in IoT environments using type-2 fuzzy logic systems
    Bommaraju Srinivasa Rao, Kakoli Banerjee, C. Anand Deva Durai, S. Balu, Ashok Kumar Sahoo, A. Priyadharshini, Paladugu Rama Krishna, Revannath Babanrao Kakade
    Journal of Intelligent and Fuzzy Systems, 2025
    In recent years, the Internet of Things (IoT) has rapidly emerged as an essential technology, enabling seamless communication between billions of interconnected devices. These devices generate a massive amount of data that requires efficient management to ensure optimum performance in IoT environme nts. Dynamic load balancing (DLB) is a crucial technique employed to distribute workloads evenly across multiple computing resources, thereby reducing latency and increasing the overall efficiency of IoT networks. This paper presents a novel DLB approach based on type-2 fuzzy logic systems (T2FLS) to enhance the performance and reliability of IoT environments. The proposed T2FLS-based DLB technique addresses the inherent uncertainties and imprecisions in IoT networks by considering various parameters, such as workload, processing capability, and communication latency. A comprehensive performance evaluation is carried out to compare the proposed method with traditional DLB approaches. Simulation results demonstrate that the T2FLS-based DLB technique significantly improves the network’s response time, throughput, and energy efficiency, while also providing better adaptability and robustness to dynamic changes in IoT environments. This study contributes to the advancement of DLB techniques in IoT networks and lays the groundwork for further research in this field.
  • Machine learning-based predictive maintenance: enhancing industrial reliability through data-driven approaches
    S.J. Subhashini, Syed Asif Basha, B. Srinivasa Rao, S. Gayathri, Amol Mangrulkar
    International Journal of Basic and Applied Sciences, 2025
    This study investigated the application of machine learning for predictive maintenance (PM) using synthetic data simulating industrial ma-‎chinery failures. Different algorithms including random forest, support vector machine (SVM), artificial neural network (ANN), decision ‎tree (DT), and logistic regression (LR) were evaluated in two test scenarios. Decision tree (DT) and logistic regression (LR) showed the ‎best promise, despite challenges with data imbalance and data segmentation. However, these models are not yet suitable for industrial de-‎ployment due to the significant impact of misclassified faults. The results highlight the potential of machine learning to improve predictive ‎maintenance (PM), while further improvements are needed before it can replace human supervision‎.
  • Intelligent Fault Detection in HIL Simulations: Leveraging AI and ML for Enhanced Operational Reliability
    Nimit Rastogi, Nikhil Jain, B. Srinivasa Rao
    2025 4th International Conference on Range Technology Icort 2025, 2025
    With the escalating complexity of engineering systems, the demand for reliable fault detection mechanisms in real-time simulations has become imperative. Traditional methods often fall short of capturing intricate patterns and trends, necessitating the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This research presents a comprehensive framework for intelligent fault detection in Hardware-in-the-Loop (HIL) simulations, leveraging advanced ML algorithms to analyze data, predict trends, and detect anomalies with precision. The methodology encompasses fault inspection, ML model training, temporal interpolation, event timing alignment, comparative analysis, anomaly tracing, and classification. Extensive experimentation with real-world data validates the efficacy of the proposed framework, demonstrating high accuracy in anomaly detection across various fault types. The framework's adaptability to dynamic environments and its potential for enhancing operational reliability make it an asset in defence, automotive, industrial automation, healthcare, energy, and IoT domains.
  • An optimised deep learning approach for alzheimer’s disease classification
    Perla Pawan Phanieswar, Konda Sarvari Harshitha, Venkatrajam Marka, Battula Srinivasa Rao, Mudiyala Aparna
    Iaes International Journal of Artificial Intelligence, 2024
    <p>Alzheimer’s disease (AD) is a progressive and incurable brain disorder. It starts out subtly and gets worse with time. 60 to 70 percent of dementia cases are brought on by this illness. An Alzheimer’s patient is diagnosed every two seconds, according to research. The complexity of the brain makes it often very challenging to identify in elderly people. In the area of medical imaging, deep learning is growing. Several deep learning techniques that attempted to identify and categorise the magnetic resonance imaging (MRI) brain images into four stages of AD will be compared in this work. 6400 MRI brain images were extracted from a dataset and divided into training, validation, and testing datasets. In our research on twelve deep learning architectures, inceptionV3 has given the best results with 99.56% and 97.75% accuracy on train and validation, respectively, and on test data, the model has achieved an accuracy of 95.81%. We trained the models using optimised ImageNet weights, which resulted in higher accuracy across all twelve models.</p>
  • Exploring potential predictors of psychological distress among employees: A Generalized ARC-GRU Based Model
    G. Vasanti, R. Karthi, Amisha Bisht, B. Srinivasa Rao, Thulasimani T, S. Vasuki
    International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2024, 2024
    It seems to have an increase in employee reports of psychological distress across a wide range of industries. Research from the past indicates that a significant portion of the global workforce has dealt with emotional and mental health challenges. The worker's physical, social, and occupational functioning could be negatively impacted if these concerns are not addressed sooner. The three steps that make up this suggested approach are preprocessing, feature extraction, and model training. Factorization is a part of data preprocessing that involves encoding input values into categorical variables, which are also known as enumerated types. Data visualization, training speed, accuracy, and overfitting prevention are all enhanced by feature extraction. In order to train the model, a Generalized ARC-GRU was utilized. Traditional methods, such as GRU and GARC, are surpassed by the suggested methodology. After applying the method, the accuracy increased by 91.48 percent.
  • Privacy-Aware Artificial Intelligence with Homomorphic Encryption using Machine Learning
    B. Srinivasa Rao, Saumitra Chattopadhyay, Prashant Singh, Bramah Hazela, G. Sabarinathan, Kalva Yamini
    International Conference on Sustainable Computing and Smart Systems Icscss 2023 Proceedings, 2023
    Along with the expansion of machine learning (ML) applications, the amount of data required to create predictions increases. Big-data ML has always been limited by off-chip memory capacity and computational speed. Considerably, privacy is one of the limitations of big data, which can be solved by homomorphic encryption (HE). Due to the combination of HE and ML, the multi-party privacy-protected ML suggested in this research may assist numerous users in doing artificial intelligence (AI) without disclosing private data. The technique may train common models in situations of data abuse, particularly in private data protection. The model trained using the ML technique named Artificial Neural Network (ANN) has a similar impact to the model developed using all data on a single computer, according to experiments using the algorithm. The gradient data is simply transmitted by all parties, and homomorphic procedures in the main computing system combine the gradient data. Besides, the optimal key is selected using the significance of the Lion Algorithm (LA). After homomorphic procedures, the learning model is modified depending on the new gradient data.
  • An Artificial Intelligence Network based-Host Intrusion Detection System for Internet of Things Devices
    Mr Ashish Jain, B. Srinivasa Rao, Saumitra Chattopadhyay, Aniruddh Kumar, M. S. Muthuraman, A. Manjula
    2023 4th International Conference on Electronics and Sustainable Communication Systems Icesc 2023 Proceedings, 2023
    Internet of Things (IoT) is currently employed in almost all the areas, including applications in smart cities, smart homes, e-Wellbeing, and others. Due to its wider utilization, IoT security has become a serious concern. A secure Intrusion Detection System (IDS) for the Internet of Things is often built using artificial intelligence (AI) and its subsets, deep learning (DL), and machine learning (ML). Industrial IoT devices, which are readily available, are regularly used by researchers and industry experts. This research study investigates the possibility of deploying a DL-Based Host-IDS (DL-HIDS) on specific commercial IoT devices. In this study, an optimized Convolutional Neural Network (O-CNN) based on DL is used. The proposed model’s efficiency is evaluated by utilizing performance metrics like recall, precision, accuracy, and f1score. The proposed model’s effectiveness is verified by analyzing the promising results obtained from the implementation of the proposed DL-HIDS on various existing models.

RECENT SCHOLAR PUBLICATIONS

  • An efficient cryptanalysis scheme for secure data storage system in cloud using optimal dual encryption algorithm
    BS Rao, P Premchand
    International Journal of Services Operations and Informatics 10 (3), 222-241 , 2020
    2020
    Citations: 1
  • Green Fabrication And Characterization Of In2O3-SnO2 Nanocomposite From Acacia Gum
    BSR al
    Materials Today: Proceedings 18, 5351-5355 , 2019
    2019
    Citations: 9
  • A Secure and Computational-Efficient Multicast Key Distribution for Wireless Networks
    B Srinivasa Rao, P Premchand
    Cognitive Informatics and Soft Computing: Proceeding of CISC 2017, 91-101 , 2018
    2018
  • A Secure and Computational-Efficient Multicast Key Distribution for Wireless
    BS Rao, P Premchand
    Cognitive Informatics and Soft Computing: Proceeding of CISC 2017, 91 , 2018
    2018
  • Design and Implementation of a Hacker Detection Scheme: A Network Security Measure in Heterogeneous WSN
    B Srinivasa Rao, P Premchand
    Innovations in Computer Science and Engineering: Proceedings of the Fifth … , 2018
    2018
    Citations: 1
  • Simulation of FFDNN using FPGA
    BSRPP B.M.S.S.S. Aditya
    NTEST2018 1 (1), 29 , 2018
    2018
  • Evaluation of differential–linear cryptanalysis combined attack on cryptographic security system
    BS Rao, P Premchand
    Int. J. Appl. Eng. Res 13 (23), 16552-16563 , 2018
    2018
    Citations: 5
  • A review on combined attacks on security systems
    BS Rao
    2018
    Citations: 6
  • Anomalous Attacker Evidence and Detection System in WSN
    BS Rao, P Premchand
    International Journal of Applied Engineering Research 13 (19), 14313-14322 , 2018
    2018
  • Related Key and Rectangle-Boomerang Combined Attack On MDA
    BSRAOP PREMCHAND
    International Journal of Creative Research Thought 6 (1), 70-75 , 2018
    2018
  • Cryptographic Tamper Evidence and Detection System
    BS Rao, P Premchand
    International Journal of Pure and Applied Mathematics 118 (16), 199-211 , 2018
    2018
  • Attacker Evidence System in WSN
    B Srinivasa Rao, P Premchand
    International Conference on Computing, Analytics and Networks, 166-178 , 2017
    2017
    Citations: 2
  • Modeling and simulation of ground state divalent impurity cat – ion vacancy defect complex
    BSR M Rekha Rani
    J Material Sci Eng, 6 (7(Suppl)), 99 , 2017
    2017
  • HOME AUTOMATION USING MOBILE COMMUNICATION
    BS Rao
    IOSR Journal of Computer Engineering (p-ISSN2772-8727) 17 (6), 15 , 2015
    2015
  • Modeling and Simulation of Ground State Divalent Impurity(Ba ++) - Cation Vacancy Defect Complex
    BS RAO
    SVRM SCIENCE JOURNAL 2 (1), 16-26 , 2014
    2014
  • Computer simulation of point defects in MnO and FeO
    BS Rao, MR Rani, SDV Prasad
    AIP Conference Proceedings 1536 (1), 439-440 , 2013
    2013
  • Design of a Cryptographic Tamper Detection Scheme for Network Security
    BS Rao, SDV Prasad
    International Conference on Network Security and Applications, 214-224 , 2011
    2011
    Citations: 1
  • Computer Simulation of Point Defects in CoO and NiO
    SDV Prasad, BS Rao, SJ Babu, N Radhika, SP Sanyal
    Solid State Physics 1349 (1), 873-874 , 2011
    2011
  • A Proto-type for Home Automation using GSM technology
    BS Rao, SDV Prasad, RM Mohan
    2010 International Conference on Power, Control and Embedded Systems, 1-4 , 2010
    2010
    Citations: 20
  • High pressure structural phase transition in BaSe and BaTe
    B Srinivasa Rao, SP Sanyal
    Physica Status Solidi B (Basic Research);(Germany) 165 (2) , 1991
    1991
    Citations: 14

MOST CITED SCHOLAR PUBLICATIONS

  • Structural and elastic properties of sodium halides at high pressure
    BS Rao, SP Sanyal
    Physical Review B 42 (3), 1810 , 1990
    1990
    Citations: 53
  • A Proto-type for Home Automation using GSM technology
    BS Rao, SDV Prasad, RM Mohan
    2010 International Conference on Power, Control and Embedded Systems, 1-4 , 2010
    2010
    Citations: 20
  • High pressure structural phase transition in BaSe and BaTe
    B Srinivasa Rao, SP Sanyal
    Physica Status Solidi B (Basic Research);(Germany) 165 (2) , 1991
    1991
    Citations: 14
  • Green Fabrication And Characterization Of In2O3-SnO2 Nanocomposite From Acacia Gum
    BSR al
    Materials Today: Proceedings 18, 5351-5355 , 2019
    2019
    Citations: 9
  • A review on combined attacks on security systems
    BS Rao
    2018
    Citations: 6
  • Evaluation of differential–linear cryptanalysis combined attack on cryptographic security system
    BS Rao, P Premchand
    Int. J. Appl. Eng. Res 13 (23), 16552-16563 , 2018
    2018
    Citations: 5
  • Charge transfer effect on formation and binding energies of vancancy pairs in NaCl and KCl
    BS Rao, SP Sanyal
    physica status solidi (b) 164 (2), 351-356 , 1991
    1991
    Citations: 4
  • Attacker Evidence System in WSN
    B Srinivasa Rao, P Premchand
    International Conference on Computing, Analytics and Networks, 166-178 , 2017
    2017
    Citations: 2
  • High Pressure Elastic Constants of NaCl
    BSRSP Sanyal
    Phys. Stat. Sol. (b) 156, K 27- K32 , 1989
    1989
    Citations: 2
  • An efficient cryptanalysis scheme for secure data storage system in cloud using optimal dual encryption algorithm
    BS Rao, P Premchand
    International Journal of Services Operations and Informatics 10 (3), 222-241 , 2020
    2020
    Citations: 1
  • Design and Implementation of a Hacker Detection Scheme: A Network Security Measure in Heterogeneous WSN
    B Srinivasa Rao, P Premchand
    Innovations in Computer Science and Engineering: Proceedings of the Fifth … , 2018
    2018
    Citations: 1
  • Design of a Cryptographic Tamper Detection Scheme for Network Security
    BS Rao, SDV Prasad
    International Conference on Network Security and Applications, 214-224 , 2011
    2011
    Citations: 1
  • A Secure and Computational-Efficient Multicast Key Distribution for Wireless Networks
    B Srinivasa Rao, P Premchand
    Cognitive Informatics and Soft Computing: Proceeding of CISC 2017, 91-101 , 2018
    2018
  • A Secure and Computational-Efficient Multicast Key Distribution for Wireless
    BS Rao, P Premchand
    Cognitive Informatics and Soft Computing: Proceeding of CISC 2017, 91 , 2018
    2018
  • Simulation of FFDNN using FPGA
    BSRPP B.M.S.S.S. Aditya
    NTEST2018 1 (1), 29 , 2018
    2018
  • Anomalous Attacker Evidence and Detection System in WSN
    BS Rao, P Premchand
    International Journal of Applied Engineering Research 13 (19), 14313-14322 , 2018
    2018
  • Related Key and Rectangle-Boomerang Combined Attack On MDA
    BSRAOP PREMCHAND
    International Journal of Creative Research Thought 6 (1), 70-75 , 2018
    2018
  • Cryptographic Tamper Evidence and Detection System
    BS Rao, P Premchand
    International Journal of Pure and Applied Mathematics 118 (16), 199-211 , 2018
    2018
  • Modeling and simulation of ground state divalent impurity cat – ion vacancy defect complex
    BSR M Rekha Rani
    J Material Sci Eng, 6 (7(Suppl)), 99 , 2017
    2017
  • HOME AUTOMATION USING MOBILE COMMUNICATION
    BS Rao
    IOSR Journal of Computer Engineering (p-ISSN2772-8727) 17 (6), 15 , 2015
    2015