Bommareddy Lokesh

@vitap.ac.in

Assistant Professor Sr. Grade 1
Vellore Institute of Technology - Andhra Pradesh

Bommareddy Lokesh


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https://researchid.co/lokesh

RESEARCH INTERESTS

Software-defined networking
12

Scopus Publications

94

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • AI-driven protein pocket detection through integrating deep Q-networks for structural analysis
    Prashanth Choppara, Lokesh Bommareddy
    Journal of Computer Aided Molecular Design, 2025
  • Q-BAFNet: A Hybrid Quantum Classical Approach for Drug-Target Binding Affinity Prediction
    Prashanth Choppara, Bommareddy Lokesh
    IEEE Transactions on Computational Biology and Bioinformatics, 2025
  • A Machine Learning Approach to Genomic Task Scheduling in Fog Computing
    Prashanth Choppara, Bommareddy Lokesh
    Conference Proceedings 4th IEEE International Conference on Technology Engineering Management for Societal Impact Using Marketing Enterpreneurship and Talent Temsmet 2025, 2025
  • Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations
    Prashanth Choppara, Bommareddy Lokesh
    IEEE Access, 2025
    The rapid mutations of viruses such as COVID-19 pose significant challenges for vaccine development and effective disease management. In response to these challenges, this study introduces a novel quantum-enhanced LSTM (QLSTM) model designed to predict genetic mutations, specifically focusing on viral protein sequences. The QLSTM model leverages quantum computing techniques, including superposition and entanglement, to improve the model’s ability to handle the high-dimensional, nonlinear structure inherent in viral genomic datasets. These quantum enhancements allow the model to capture complex relationships in the data, improving its accuracy and performance in mutation detection compared to traditional methods. To improve model predictions, we use two key preprocessing techniques TF-IDF for efficient feature extraction and PCA for dimensionality reduction of genomic sequences. TF-IDF helps the model focus on the most informative nucleotide features, while PCA reduces the size of the data, making the model computationally efficient without sacrificing important information. The one-hot encoding technique is a standard technique in machine learning for encoding protein sequences into data that can be used in neural networks.The proposed QLSTM outperformed existing deep learning architectures such as the Attention-Augmented Convolutional Neural Network (AACNN), Stacked Recurrent Neural Network (Stacked RNN), Retention Network (RetNet), and Bidirectional Long Short Term Memory (BiLSTM). These results indicate that QLSTM not only provides high accuracy in mutation predictions in SARS-CoV-2 protein sequences but also provides deep insights into the functional implications of such mutations. The model identifies mutation hotspots that affect virus spread, immune evasion, and protein structure, providing key biological insights for future vaccine development and therapeutic strategies.
  • Efficient Task Scheduling and Load Balancing in Fog Computing for Crucial Healthcare Through Deep Reinforcement Learning
    Prashanth Choppara, Bommareddy Lokesh
    IEEE Access, 2025
    In healthcare, real-time decision making is crucial for ensuring timely and accurate patient care. However, traditional computing infrastructures, with their wide ranging capabilities, suffer from inherent latency, which compromises the efficiency of time-sensitive medical applications. This paper explores the potential of fog computing to better address this challenge, proposing a new framework that uses deep reinforcement learning (DRL) to advance task scheduling in crucial healthcare. The paper addresses the limitations of cloud computing systems. It proposes and replaces a fog computing architecture in supporting low latency for healthcare applications. This architecture reduces transmission latency by placing processing nodes close to the source of data generation, namely IoT-enabled healthcare devices. The foundation of this approach is the DRL model, which is designed to dynamically optimize the partition of computational tasks across fog nodes to improve both data throughput and operational response times. The effectiveness of the proposed DRL based fog computing model is validated with a series of simulations performed with the SimPy simulation environment. In such simulations, diverse healthcare scenarios, ranging from continuous patient monitoring systems to crucial emergency response applications, are recreated, providing a rich framework for testing the real-time processing capabilities of the model. This algorithm, DRL, has been fine-tuned and extensively implemented in these scenarios to show how the algorithm controls and optimizes tasks and their urgency in accordance with resource demand. By dynamically learning from real-time system states and optimizing task allocation to minimize delays, the DRL model reduces the makespan by up to 30% compared to traditional scheduling approaches. Comparative performance analysis indicated a 30% reduction in task completion times, a 40% reduction in operational latency, and a 25% improvement in fault tolerance relative to traditional scheduling approaches. The flexibility of the DRL model is further considered through its application to diverse real-time data processing contexts in industrial automation and smart traffic systems.
  • Leveraging Network Slicing in SDNs for Handling Application Failures
    Iaeng International Journal of Computer Science, 2024
  • Quantum Machine Learning for Prediction of Compound-Protein Interactions in Drug Discovery
    PRASHANTH CHOPPARA, BOMMAREDDY LOKESH
    2024 12th International Conference on Intelligent Systems and Embedded Design Ised 2024, 2024
    This paper focuses on quantum machine learning (quantum SVM) modeling for the identification of compound-protein interactions (CPIs) crucial in drug discovery and pharmacology. Estimation of various CPIs is one of the prerequisites for selecting potential drugs, improving the activity of lead compounds and studying the toxic effects of drugs. The quantum SVM model introduced above provides higher accuracy and better processing time compared to the quantum computing principles of q-features, q-kernels and classical models. In testing the model, two different datasets were used, a human dataset with 1,052 compounds and 852 proteins from twenty-one different categories and a C. elegans dataset with 1,434 compounds and 2,504 proteins from twenty-one different categories. Quantum SVM is characterized against three traditional machine learning algorithms, among the algorithms of supervised learning, K-Nearest Neighbors (KNN), Random Forest (RF) and Classical Support Vector Machine (SVM) are the most used algorithms. In tuning the decision model, evaluation criteria such as area under the curve (AUC), precision and recall were applied. It can be observed from the results reported in this study that quantum SVM gives better performance when compared to traditional models in terms of performance indices on both datasets. In particular, quantum SVM yielded the highest AUC values (0. 923 for the human dataset and 0. 927 for the C. elegans dataset), meaning a higher ability to rank true interacted compound-protein pairs compared to false positives. Furthermore, quantum SVM has better precision and recall, which can reduce the number of false positives and increase the number of correct interactions required for effective drug candidate identification and cost reduction in experimental validation.
  • Pneumonia Detection Using Deep CNN Algorithms
    Lakshman Rohith Sanagapalli, Harshitha Koppolu, Bommareddy Lokesh
    2024 12th International Conference on Intelligent Systems and Embedded Design Ised 2024, 2024
    The detection of pneumonia using medical imaging is critical for prompt and effective treatment. This study looks at how deep convolutional neural networks (CNNs) can be used to detect pneumonia using a CT scan dataset from Kaggle. The data set is divided into three categories pneumonia COVID-19 and normal. For this study the Pneumonia and COVID-19 classes were combined into a single ‘Disease’ category. The proposed model designed to address challenges such as high variances in infection characteristics and the scarcity of annotated data which is a combination of ResNet152V2 and CNN networks were then used for classification leveraging their powerful feature extraction capabilities achieves significant results. For the datasets mentioned above the suggested model yields validation accuracy of 90.56% and accuracy of 99.09%.
  • An IoT-Based Asthma Intensity Prediction Using Classification Models
    J. N.V.R. Swarup Kumar, B. Neeraj Nishant, S. Venkata Suraj, I.S. Siva Rao, Sasibhushana Rao Pappu, et al.
    2nd International Conference on Sustainable Computing and Smart Systems Icscss 2024 Proceedings, 2024
    Asthma is a chronic disease of breathing that increases a patient's risk of an attack. So, taking care of the patients and supervising their health condition is essential to decrease the risk. This article centers on IoT, the ThingSpeak cloud platform, and machine learning methods employed for the prediction of asthma intensity. These sensors include the KY-038 audio sensor, SEN-11574 pulse sensor, and ESP-32 microcontroller, which collect live health data. Data transfer to ThingSpeak is secure. With data protocols, the devices transmit end-to-end data for storage and sharing. The combined use of these IoT sensors enables continuous health tracking for asthma. This data is visualized through a user-friendly mobile app built on platforms like MIT App Inventor. The app facilitates data exchange and even QR code generation, promoting high user engagement. The obtained data is then processed using machine learning algorithms, including Decision Tree Classifiers, Random Forest Classifiers, K Nearest Neighbors, and Logistic Regression. These commonly used algorithms help identify patterns within the data. By analyzing this data, the ML models can predict when an asthma attack is likely to occur and its potential intensity. This enables optimal clinical decision-making, allowing for proactive measures and efficacious asthma management for early intervention, such as using an inhaler or seeking medical attention.
  • Flow-aware Segment Routing in SDN-enabled Data Center Networks
    Bommareddy Lokesh, Narendran Rajagopalan
    International Journal of Computer Network and Information Security, 2023
    The underlying objective of segment routing is to avoid maintenance of the per-flow state at forwarding devices. Segment routing (SR) enables the network devices to minimize their forwarding table size by generalizing the forwarding rules and making them applicable to multiple flows. In existing works, optimizing the trade-off between segment length and the number of co-flows sharing the segment is considered the key to determining optimal segment endpoints. However, the flow characteristics like the lifetime of flows, and dynamically altering routing paths are critical and impact the performance of SR. Ideally, network flows considered for SR are expected to persist for a longer duration and adhere to static routing paths. But our analysis of flow characteristics at a typical data center reveals that the majority of flows are short-lived. Also, network flows are subject to alter their routing paths frequently for several reasons. Considering short-lived flows and flows that dynamically alter their routing paths may lead to choosing unstable segment endpoints. Hence, it is necessary to study the flow characteristics for determining more stable segment endpoints. In this paper, the authors implemented the SR technique considering the flow characteristics at an SDN-enabled data center and the results show a significant improvement with respect to the stability of segment endpoints.
  • Orchestrator for Synchronizing Network Events in SDNs
    Bommareddy Lokesh, Narendran Rajagopalan
    IEEE Transactions on Network and Service Management, 2021
  • A Blockchain-based security model for SDNs
    Bommareddy Lokesh, Narendran Rajagopalan
    Proceedings of Conecct 2020 6th IEEE International Conference on Electronics Computing and Communication Technologies, 2020

RECENT SCHOLAR PUBLICATIONS

  • AI-driven protein pocket detection through integrating deep Q-networks for structural analysis
    P Choppara, L Bommareddy
    Journal of Computer-Aided Molecular Design 39 (1), 90 , 2025
    2025
    Citations: 1
  • A Machine Learning Approach to Genomic Task Scheduling in Fog Computing
    P Choppara, B Lokesh
    2025 IEEE 4th International Conference on Technology, Engineering … , 2025
    2025
  • Q-BAFNet: A Hybrid Quantum Classical Approach for Drug-Target Binding Affinity Prediction
    P Choppara, B Lokesh
    IEEE Transactions on Computational Biology and Bioinformatics , 2025
    2025
    Citations: 2
  • Efficient task scheduling and load balancing in fog computing for crucial healthcare through deep reinforcement learning
    P Choppara, B Lokesh
    IEEE Access 13, 26542-26563 , 2025
    2025
    Citations: 47
  • Leveraging quantum lstm for high-accuracy prediction of viral mutations
    P Choppara, B Lokesh
    IEEE Access 13, 25282-25300 , 2025
    2025
    Citations: 9
  • Pneumonia Detection Using Deep CNN Algorithms
    LR Sanagapalli, H Koppolu, B Lokesh
    2024 12th International Conference on Intelligent Systems and Embedded … , 2024
    2024
  • Quantum Machine Learning for Prediction of Compound-Protein Interactions in Drug Discovery
    P Choppara, B Lokesh
    2024 12th International Conference on Intelligent Systems and Embedded … , 2024
    2024
    Citations: 2
  • An IoT-based asthma intensity prediction using classification models
    JS Kumar, BN Nishant, SV Suraj, ISS Rao, SR Pappu, B Lokesh
    2024 2nd International Conference on Sustainable Computing and Smart Systems … , 2024
    2024
    Citations: 4
  • Leveraging Network Slicing in SDNs for Handling Application Failures.
    B Lokesh, N Rajagopalan
    IAENG International Journal of Computer Science 51 (4) , 2024
    2024
  • Flow-aware Segment Routing in SDN-enabled Data Center Networks
    B Lokesh, N Rajagopalan
    International Journal of Computer Network and Information Security (IJCNIS … , 2023
    2023
    Citations: 2
  • Orchestrator for synchronizing network events in SDNs
    B Lokesh, N Rajagopalan
    IEEE Transactions on Network and Service Management 18 (4), 4365-4375 , 2021
    2021
    Citations: 5
  • A Blockchain-based security model for SDNs
    B Lokesh, N Rajagopalan
    2020 IEEE International Conference on Electronics, Computing and … , 2020
    2020
    Citations: 22

MOST CITED SCHOLAR PUBLICATIONS

  • Efficient task scheduling and load balancing in fog computing for crucial healthcare through deep reinforcement learning
    P Choppara, B Lokesh
    IEEE Access 13, 26542-26563 , 2025
    2025
    Citations: 47
  • A Blockchain-based security model for SDNs
    B Lokesh, N Rajagopalan
    2020 IEEE International Conference on Electronics, Computing and … , 2020
    2020
    Citations: 22
  • Leveraging quantum lstm for high-accuracy prediction of viral mutations
    P Choppara, B Lokesh
    IEEE Access 13, 25282-25300 , 2025
    2025
    Citations: 9
  • Orchestrator for synchronizing network events in SDNs
    B Lokesh, N Rajagopalan
    IEEE Transactions on Network and Service Management 18 (4), 4365-4375 , 2021
    2021
    Citations: 5
  • An IoT-based asthma intensity prediction using classification models
    JS Kumar, BN Nishant, SV Suraj, ISS Rao, SR Pappu, B Lokesh
    2024 2nd International Conference on Sustainable Computing and Smart Systems … , 2024
    2024
    Citations: 4
  • Q-BAFNet: A Hybrid Quantum Classical Approach for Drug-Target Binding Affinity Prediction
    P Choppara, B Lokesh
    IEEE Transactions on Computational Biology and Bioinformatics , 2025
    2025
    Citations: 2
  • Quantum Machine Learning for Prediction of Compound-Protein Interactions in Drug Discovery
    P Choppara, B Lokesh
    2024 12th International Conference on Intelligent Systems and Embedded … , 2024
    2024
    Citations: 2
  • Flow-aware Segment Routing in SDN-enabled Data Center Networks
    B Lokesh, N Rajagopalan
    International Journal of Computer Network and Information Security (IJCNIS … , 2023
    2023
    Citations: 2
  • AI-driven protein pocket detection through integrating deep Q-networks for structural analysis
    P Choppara, L Bommareddy
    Journal of Computer-Aided Molecular Design 39 (1), 90 , 2025
    2025
    Citations: 1
  • A Machine Learning Approach to Genomic Task Scheduling in Fog Computing
    P Choppara, B Lokesh
    2025 IEEE 4th International Conference on Technology, Engineering … , 2025
    2025
  • Pneumonia Detection Using Deep CNN Algorithms
    LR Sanagapalli, H Koppolu, B Lokesh
    2024 12th International Conference on Intelligent Systems and Embedded … , 2024
    2024
  • Leveraging Network Slicing in SDNs for Handling Application Failures.
    B Lokesh, N Rajagopalan
    IAENG International Journal of Computer Science 51 (4) , 2024
    2024