Vinothkumar Kolluru

@stevens institute of technology

Data science, Mathematical sciences
Stevens institute of technology

Vinothkumar Kolluru


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https://researchid.co/vinothkumarkolluru

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science, Computer Networks and Communications, Mathematics
31

Scopus Publications

458

Scholar Citations

13

Scholar h-index

18

Scholar i10-index

Scopus Publications

  • Novel federated learning-based approach for network attack detection
    Vinothkumar Kolluru, Sudeep Mungara, Ali Raza, Ghazia Aslam, Nagwan Abdel Samee, Imran Ashraf
    Journal of Cloud Computing, 2026
    Network attack detection and prevention are critical aspects of ensuring cybersecurity in today’s interconnected world. Traditional centralized models for detecting malicious activity often face challenges like data privacy concerns and inefficiencies in handling distributed data sources. To address these challenges, this research leverages federated learning. Being a decentralized approach that offers advantageous benefits of collaborative model training across different devices while keeping sensitive data localized. Using the NF-UQ-NIDS-v2 benchmark dataset, we developed a robust machine-learning pipeline. This included thorough data preparation and applying advanced feature engineering techniques to reduce noise and enhance the dataset’s quality. We build several advanced machine learning approaches and hybrid models. Building on this, we proposed an innovative federated learning model designed to improve attack detection accuracy while preserving data privacy. With the help of the proposed federated learning architecture, the client model logistic regression (LR) achieved high-performance accuracy scores of 99% for multi-class network attack detection. Also, we quantify FL communication efficiency by measuring per-round latency, bandwidth usage, and the impact of compression techniques such as sparsification, 8-bit quantization, and top-$$k$$ selection, reducing uplink payloads by up to 30$$\times$$. This research uniquely harmonizes the NF-UQ-NIDS-v2 dataset by consolidating diverse attack classes into a unified schema, analyzes communication overhead in FL by quantifying latency, bandwidth usage, and synchronization costs under compression, and incorporates a heterogeneity-aware aggregation strategy that re-weights client contributions based on data skewness, thereby improving stability in non-IID settings. The proposed approach demonstrates significant potential for real-world applications, particularly in environments where data distribution and security are paramount.
  • AI enabled geospatial intelligence for the energy transition: A comparative CNN study on CCUS, geothermal siting, and NetZero strategies
    Sudeep Mungara, Vinothkumar Kolluru, Thirunaavukkarasu Murugesan, Saptarshi Maiti, Shreyas Rajendra Hole
    Unconventional Resources, 2026
    Practical guidance about tradeoff choices between accuracy, efficiency and deployment ability in deep convolutional neural network architectures for land use and land cover classification has been largely unavailable because each study evaluates architectures differently. This paper provides a controlled comparative assessment of four popular convolutional neural network architectures for land use and land cover classification visual geometry group19, ResNet50, Inception_Version3 and MobileNet_V2 using the Euro_SAT benchmark which includes 27,000 Sentinel2 red green blue images that have been cut into 10 land uses classes. The convolutional neural network architectures were all trained and evaluated through the same preprocessing, augmentation, data splitting, training procedure and metric as follows: Overall accuracy/F1 macro averaging class by class confusion matrix convergence dynamics efficiency metrics (number of parameters and inference-oriented considerations). The results indicate that modern architectures provide significantly better than older sequential baselines: ResNet50 provided the highest total accuracy (>97%), along with consistent convergence behavior; InceptionV3 improved discrimination for classes with both ambiguous visual appearances and linear structures (river, highway); MobileNetV2 was able to achieve high accuracy (>94%), but had an order of magnitude less number of parameters than the other architectures and is well suited to lower source or real time application scenarios. Finally, this paper maps convolutional neural network outputs into an energy transition decision workflow (Renewable Siting → Corridor Constraints → Monitoring), and demonstrates how land use and land cover layers derived from convolutional neural networks can support net zero resource planning. • Benchmarks 4 CNNs on EuroSAT using identical training and evaluation setup. • ResNet50 achieved highest accuracy (>97%) for LULC classification. • MobileNetV2 reduced parameters by ∼6× while maintaining >94% accuracy. • InceptionV3 improved corridor feature detection (roads, rivers). • Enables decision-grade LULC for CCUS, geothermal, and net-zero planning.
  • RIS-aided 5G MISO network optimization with deep reinforcement learning
    Shreyas Rajendra Hole, Nikhil Mangrulkar, Gerald Adli, Vinothkumar Kolluru, Nitin Rakesh
    Discover Applied Sciences, 2026
    Emerging 6G wireless systems demand ever higher spectral efficiency. Reconfigurable Intelligent Surfaces (RIS) large arrays of passive reflecting elements can reconfigure the radio environment via programmable phase shifts. This work presents a deep reinforcement learning (DRL) approach to jointly optimize the multi-antenna base station beamforming and the RIS phase shifts in a down- link 5G MISO network. Using a DDPG-based agent, we maximize the sum rate (bps/Hz) by treating it as the DRL reward. Our simulations (based on Saglam’s RIS-MISO code demonstrate sum rates exceeding 28 bps/Hz in large configura- tions for example, over 30 bps/Hz at 30 dB transmit power with 32 BS antennas and 32 RIS elements. We analyze how key factors (BS/RIS size, power, learn- ing hyperparameters) affect performance through visualization. The DRL agent learns effective continuous beamforming and phase control without explicit chan- nel models. Results confirm that more antennas or RIS elements significantly boost throughput, and that careful tuning of DRL parameters (learning rate, exploration decay) is crucial. We include the Saglam repository as a reference to aid reproducibility.
  • HVLGAN: hybrid hierarchical scaled attention-enabled latent model for structure-based drug discovery
    Shreyas Rajendra Hole, Lakshmanan M., Jeevaraj R., Manumula Srinubabu, Shreekant Salotagi, Vinothkumar Kolluru
    Journal of Biomolecular Structure and Dynamics, 2026
    Structure-based drug design involves utilizing the three-dimensional structure of a biological target to guide the design and development of new therapeutic compounds. Traditionally, a huge number of structure-based drug discovery methods have been adopted, but their time-consuming, erroneous molecule formation, and highly complex characteristics prevent their extensive application in drug discovery. Therefore, to mitigate such intricacies, an effective Hybrid Hierarchical Scaled attention-enabled Variational Autoencoder-based Latent Generative Adversarial Network (HVLGAN) is proposed. The inclusion of the Graph-based pocket encoding (GPE) aided in the effective generation of the Simplified Molecular Input Line Entry System (SMILES) strings to stipulate the drug discovery process with reduced computational complexity. Further, the Hybrid Hierarchical Scaled (H2S) attention strategy generates additional significant details for the effective generation of new drug molecules. In addition, the incorporation of the latent encoder and decoder enhanced the drug discovery performance by effectively processing the high-dimensional features. Nevertheless, the Variational Autoencoder (VAE) alleviated the long-term dependency problems, thereby resulting in a faster drug discovery process. Moreover, the performance validation performed in terms of performance metrics showed efficacy by attaining 0.96 validity, 0.96 novelty, and 0.96 unique scores for 90 training percentages using the MOSES package.
  • Agentic AI for Healthcare Cybersecurity: Autonomous Threat Detection and Ethical Implementation Challenges
    Vinothkumar Kolluru, Saptarshi Maiti, Thirunaavukkarasu Murugesane, Nagarajuu Dasari, Sudeep Mungara, Sreedharbabu Seshagani
    2026 Innovations in Machine Engineering and Digital Conference Imed 2026, 2026
    Healthcare systems that integrate electronic health records (EHRs), clinical systems, and Internet-of-Medical-Things (IoMT) devices, face increasing cyber threats which directly endanger patient safety and disrupt continuity of care. We propose an implementation-focused cyber threat detection model and assess five models: Random Forest (RF), Gradient Boosted Trees (GBT), and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel, Logistic Regression (LR), as well as a contextual event summary-aware BERT model. [1]–[4]. We construct a single event schema pertaining to telemetry of relevance to healthcare (network/host/identity/context), implement time-forward splits to mitigate temporal leakage, and set decision thresholds for an asymmetric cost of healthcare (large penalty for false negatives). Outside of historical metrics, we propose MEDSEC-AGENT, a governed agentic orchestration layer that converts model scores into safe, auditable SOAR actions aligned with incident-handling guidance [5], zero trust controls [6], and health-sector practices [7]. We provide reproducible pseudocode, configuration patterns, and operational blueprints to accelerate responsible adoption in real hospital environments.
  • AI—Prediction of Neisseria gonorrhoeae Resistance at the Point of Care from Genomic and Epidemiologic Data
    Vinothkumar Kolluru, Shreyas Rajendra Hole, Ajeeb Sagar, Advaitha Naidu Chintakunta, R Jeevaraj, et al.
    Healthcare Switzerland, 2025
    Background: Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is an escalating global health challenge, affecting over 82 million individuals each year. The increasing resistance to commonly used antibiotics such as azithromycin, ciprofloxacin, and cefixime hinders timely and effective treatment, primarily due to the delayed detection of resistant strains. Methods: To overcome these limitations, a hybrid machine learning (ML) and deep learning (DL) framework was developed using a dataset comprising 3786 N. gonorrhoeae isolates. The dataset included clinical metadata and phenotypic resistance profiles. The preprocessing steps involved handling 23% data sparsity, imputing 31 skewed columns, and applying resampling and harmonisation techniques sensitive to data skewness. A predictive pipeline was constructed using both clinical variables and genomic unitigs, and a suite of 33 classifiers was evaluated. Results: The CatBoost model emerged as the top-performing ML algorithm, particularly due to its proficiency in handling categorical data, while a three-layered neural network served as the DL baseline. The ML models outperformed genome-wide association study (GWAS) benchmarks, achieving AUC scores of 0.97 (ciprofloxacin), 0.95 (cefixime), and 0.94 (azithromycin), representing a 4–7% improvement. SHAP analysis identified biologically relevant resistance markers, such as penA mosaic alleles and mtrR promoter mutations, validating the interpretability of the model. Conclusions: The study highlights the potential of ML-driven approaches to enhance the real-time prediction of antimicrobial resistance in N. gonorrhoeae. These methods can significantly contribute to antibiotic stewardship programs, although further validation is required in low-resource settings to confirm their generalisability and robustness across diverse populations.
  • Optimizing Solar Radiation Forecasting for Renewable Energy Systems: A Comparative Analysis of Machine Learning and Feature Engineering Techniques
    Ajeeb Sagar, Shreyas Hole, Vinoth Kolluru
    Solar Energy and Sustainable Development, 2025
    Accurate solar radiation prediction is pivotal for optimizing solar energy systems, as it allows for better energy storage, grid integration, and renewable energy planning. This study compares the predictive accuracy of three machine learning models—Random Forest, XGBoost, and Multilayer Perceptron (MLP)- in forecasting solar radiation based on a meteorological and temporal features dataset. The dataset, encompassing Temperature, humidity, wind speed, and sunrise/sunset times, was preprocessed through transformations (Box-Cox, logarithmic scaling) and feature selection methods (SelectKBest, Extra Trees Classifier) to enhance model performance. XGBoost demonstrated superior performance, achieving an R² of 0.93 and RMSE of 81.87, effectively capturing complex nonlinear relationships within the data. MLP, while slightly lower in R², yielded the lowest mean absolute error (MAE = 41.74), underscoring its precision in individual predictions. SelectKBest identified set Hour (sunset hour), Month, and Wind Direction as critical features, while Extra Trees prioritized Wind Direction, Minute, and Humidity, reflecting model-specific feature importance. Collectively, these models illustrate the benefits of integrating feature engineering with advanced machine learning for renewable energy optimization, with XGBoost and MLP demonstrating particular efficacy for accurate solar radiation forecasting. This study underscores the potential of machine learning in enhancing solar energy management, facilitating a more efficient transition to sustainable energy sources.
  • Smart plant disease diagnosis using multiple deep learning and web application integration
    Ahmed M.S. Kheir, Anis Koubaa, Vinothkumar Kolluru, Sudeep Mungara, Til Feike
    Journal of Agriculture and Food Research, 2025
    Accurate and efficient plant disease diagnosis is crucial for sustainable agriculture and global food security, as diseases significantly impact crop productivity. Despite advancements in deep learning, the performance and scalability of many models remain limited. This study addresses this gap by evaluating MobileViTv2, EfficientNet-B7, and a hybrid MobileViTv2-EfficientNet-B7 approach for classifying plant leaf images into four categories: healthy, rust, scab, and multiple diseases. Using a publicly available dataset of annotated leaf images, the models were trained and tested under optimized conditions. MobileViTv2 emerged as the superior model, achieving the highest classification accuracy (94 %) and F1 score (0.94). It demonstrated exceptional generalization capabilities, with Receiver Operating Characteristic (ROC) Area Under Curve (AUC) values of 0.95 for healthy, 0.97 for rust, and 0.99 for scab. In contrast, EfficientNet-B7 and the hybrid model performed moderately, highlighting MobileViTv2's efficiency in handling diverse image features. To demonstrate real-world applicability, the MobileViTv2 model was deployed in a web-based application. This platform enables real-time plant disease diagnosis with high confidence, identifying conditions such as rust (85.3 % confidence) and healthy leaves (90.2 % confidence). The user-friendly interface facilitates its integration into precision agriculture. This study highlights the strengths of MobileViTv2 for disease diagnosis, its scalability, and its potential to support decision-making in agriculture. Future work will focus on expanding the model to other crops and incorporating environmental variables for enhanced disease prediction. This research bridges the gap between advanced AI models and practical agricultural applications, offering a robust solution for early disease detection. • Introduced MobileViTv2 for robust and efficient plant disease diagnosis. • Developed a MobileViTv2-EfficientNet-B7 hybrid model to address dataset imbalances. • Deployed a web-based application for real-time plant disease diagnostics. • Tackled dataset imbalance by advanced augmentation and hyperparameter optimization. • Demonstrated the lightweight and efficient design of MobileViTv2 for scalable use.
  • Deep Learning-Based Vulnerability Detection for Web Application Security: A Dual-Stage Hamiltonian Neural Network Approach for SQLi and XSS Attacks
    Sonkarlay J. Y. Weamie, Kahsay Birhanu Tsadik, Vinothkumar Kolluru
    Lecture Notes in Computer Science, 2025
  • Explainable Machine Learning Models for Phishing Website Detection: Enhancing Transparency and Accuracy in Cybersecurity
    Sonkarlay J. Y. Weamie, Vinothkumar Kolluru, Kahsay Birhanu Tsadik, Charan Sundar Telaganeni
    Lecture Notes in Computer Science, 2025
  • UltraRes: ESRGAN-Powered AI Image Upscaling for Enhanced Clarity, Texture, and Detail Restoration in Digital Media
    Mohammed Uvez Khan, Aman Ramzan Sheikh, Shreyas Rajendra Hole, Vinothkumar Kolluru
    3rd International Conference on Data Science and Information System Icdsis 2025, 2025
  • Hybrid Deep Learning Architectures for Brain Tumor Classification Using Magnetic Resonance Imaging: ViT-GRU and GNet-SVM Models
    Gerald Adli, Swati Shukla, Yagnesh Challagundla, Vinothkumar Kolluru, Tharun Reddy Uggumudi
    Artificial Intelligence in Oncology Cancer Diagnosis and Treatment Medical Imaging and Personalized Medicine, 2025
  • SMARTMED: A Blockchain Solution for Privacy-Centric Healthcare and Insurance Integration
    E. Sujatha., S. Palpandi, Vinothkumar Kolluru, M. Jayanthi, G. Porkodi
    2025 IEEE 1st International Conference on Innovations in Engineering and Next Generation Technologies for Sustainability Icinvents 2025, 2025
  • EEG-Based Brain-Computer Interface for Robotic System Control Using Right-Left Hand Movements with Ensemble Model
    Yogini Dilip Borole, Yagnesh Challagundla, Vinothkumar Kolluru, Sachi Nandan Mohanty, Ming Yang, Advaitha Naidu Chintakunta
    Proceedings of the International Conference on Multimedia Information Processing and Retrieval Mipr, 2025
  • Deep Learning-Based Classification of Nanoscale SEM Features Using Inception
    Shreyas Rajendra Hole, Jeevaraj R, Ujjwal Jaiswal, Vinothkumar Kolluru, Shreekant Salotagi, Justindhas Y
    2025 International Conference on Biomedical Engineering and Sustainable Healthcare Icbmesh 2025 Proceedings, 2025
  • Machine Learning and Artificial Intelligence for Predictive Modeling in Antimicrobial Resistance Data Sets, Challenges, and Future Directions
    Ajeeb Sagar, Vinothkumar Kolluru, Ujjwal Jaiswal, Gaurav Kumavat, Shreyas Rajendra Hole, Ankit Kumar
    International Conference on Smart Systems for Applications in Electrical Sciences Icsses 2025, 2025
  • QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Complex Signal Processing with Machine Learning
    Vinothkumar Kolluru, E. Helen Parimala, Kokilavani T, Sunil Raj Y, Bastin Jesuraj, Afrig Aminuddin
    Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025
  • Hybrid PCA-Based Machine Learning Models for Predictive Analytics in Urban Health Monitoring Systems
    Shreyas Rajendra Hole, Girish S Bhavekar, Arvind Kumar Prajapati, Vinothkumar Kolluru, Suraj Rajesh Karpe, Jayavrinda Vrindavanam
    2025 IEEE 1st International Conference on Smart and Sustainable Developments in Electrical Engineering Ssdee 2025, 2025
  • A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction
    Shreyas Rajendra Hole, Vinothkumar Kolluru, Shreekant Salotagi, Yagnesh Challagundla, Sudeep Mungara, Sriramkumar R
    2025 IEEE 1st International Conference on Smart and Sustainable Developments in Electrical Engineering Ssdee 2025, 2025
  • Flu Prediction Using Deep Learning Models A Case Study on Influenza-Like Illness Data
    Shreyas Rajendra Hole, Shreekant Salotagi, Vinothkumar Kolluru, Gaurav Kumavat, Anup A Pachghare, Justindhas Y
    2025 International Conference on Biomedical Engineering and Sustainable Healthcare Icbmesh 2025 Proceedings, 2025
  • AI-Driven Mobile Healthcare: Societal Impact, Workforce Adaptation, and Enhanced Predictive Modeling for Diabetes Risk Assessment
    Shreyas Rajendra Hole, Vinothkumar Kolluru, Ranita Ganguly, Sagar Kolekar, Advaitha Naidu Chintakunta, Sudeep Mungara
    2025 International Conference on Pervasive Computational Technologies Icpct 2025, 2025
  • Hybrid RegNet-Swin Transformer for Precision Gastrointestinal Disease Classification A Deep Learning Approach Using the Kvasir Dataset
    Ajeeb Sagar, Ayden Xavier Alvito Joanes, Shreyas Rajendra Hole, Akshat Agarwal, Vinothkumar Kolluru, Gaurav Londhe
    3rd International Conference on Emerging Computation and Information Technologies Icecit 2025 Book of Abstracts, 2025
  • Predictive Analytics in AI Marketing: Transforming Consumer Engagement
    Ruby Dahhiya, Virender Kumar Dahiya, Nidhi Agarwal, Vinoth Kumar Kolluru, Yagnesh Challagundla, Advaitha Naidu Chintakunta
    2025 2nd International Conference on Computational Intelligence Communication Technology and Networking Cictn 2025, 2025
  • Chest X-ray Pneumonia Detection using Deep Learning
    Shreyas Rajendra Hole, Shreekant Salotagi, Vinothkumar Kolluru, Ajeeb Sagar, Gaurav J Sawale, Justindhas. Y
    2025 International Conference on Biomedical Engineering and Sustainable Healthcare Icbmesh 2025 Proceedings, 2025
  • Hybrid Approach of TabNet and Transformer-XGBoost for Predicting Traffic Flow in Smart Cities
    Shreyas Rajendra Hole, Vinothkumar Kolluru, Shreekant Salotagi, Yagnesh Challagundla, Sudeep Mungara, Mude Nagarjuna Naik
    2025 IEEE 1st International Conference on Smart and Sustainable Developments in Electrical Engineering Ssdee 2025, 2025
  • Data Driven Password Strength Estimation Using Linear Regression and K-Nearest Neighbors with Interactive Gradio Deployment
    Shreyas Rajendra Hole, Nikhil Mangrulkar, Pushpa Baban Ghogare, Vinothkumar Kolluru, Gaurav Kumavat, Shreekant Salotagi
    3rd International Conference on Emerging Computation and Information Technologies Icecit 2025 Book of Abstracts, 2025
  • AI-Driven Energy Optimization: Household Power Consumption Prediction With LSTM Networks and PyTorch-Ray Tune in Smart IoT Systems
    Vinoth Kumar Kolluru, Yagnesh Challagundla, Advaitha Naidu Chintakunta, Bappadittya Roy, Amine Bermak, Renuka Devi S M
    Proceedings of the International Conference on Microelectronics Icm, 2024
  • Real-Time Adaptive Intrusion Detection System [RTPIDS] for Internet of Things Using Federated Learning and Blockchain
    Sunil Raj Y, E. Helen Parimala, V. S Jayakumar Paul Bosco, Adarsh Samuel Raj J, P. J. Rosario Vasantha Kumar, Vinothkumar Kolluru
    Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics Icdici 2024, 2024
  • Transformative Effects of Artificial Intelligence on Workforce Dynamics in Industry 4.0
    Shreyas Rajendra Hole, Jayavrinda Vrindavanam, Girish S Bhavekar, Pratiksha V Chafle, Sumit Arun Hirve, Vinothkumar Kolluru, Akanksha Akshay Dhok
    2024 International Conference on Augmented Reality Intelligent Systems and Industrial Automation Ariia 2024, 2024
  • Impact of artificial intelligence on the development of employment and the labor market
    Shreyas Rajendra Hole, Jayavrinda Vrindavanam, Girish S Bhavekar, Nagarjuna Naik Mude, R. Sriramkumar, Vinothkumar Kolluru, Komal R Hole
    2024 International Conference on Augmented Reality Intelligent Systems and Industrial Automation Ariia 2024, 2024

RECENT SCHOLAR PUBLICATIONS

  • Reinforcement learning guided active crop localization with CNN detectors and an interactive decision dashboard for precision agriculture
    AMS Kheir, V Kolluru, G Adli, MGM Ali, Z Ding, A Koubaa, T Feike
    Information Processing in Agriculture , 2026
    2026
  • AI Enabled Geospatial Intelligence for the Energy Transition: A Comparative CNN Study on CCUS, Geothermal Siting, and NetZero Strategies
    S Mungara, V Kolluru, T Murugesan, S Maiti, SR Hole
    Unconventional Resources , 2026
    2026
  • Novel federated learning-based approach for network attack detection
    V Kolluru, S Mungara, A Raza, G Aslam, NA Samee, I Ashraf
    Journal of Cloud Computing , 2026
    2026
  • Agentic AI for Healthcare Cybersecurity: Autonomous Threat Detection and Ethical Implementation Challenges
    V Kolluru, S Maiti, T Murugesane, N Dasari, S Mungara, S Seshagani
    2026 Innovations in Machine, Engineering, and Digital Conference (IMED), 1-7 , 2026
    2026
  • SMARTMED: A Blockchain Solution for Privacy-Centric Healthcare and Insurance Integration
    E Sujatha, S Palpandi, V Kolluru, M Jayanthi, G Porkodi
    2025 IEEE First International Conference on Innovations in Engineering and … , 2026
    2026
  • RIS-aided 5G MISO network optimization with deep reinforcement learning
    SR Hole, N Mangrulkar, G Adli, V Kolluru, N Rakesh
    Discover Applied Sciences , 2026
    2026
  • Machine Learning Driven Prediction of Antenna Reflection Coefficients (S11) from Geometric Parameters
    SR Hole, DS Asudani, G Kumavat, V Kolluru, N Jain, S Salotagi
    IEEE 2025 International Conference On Emerging Computation and Information … , 2026
    2026
  • Hybrid RegNet-Swin Transformer for Precision Gastrointestinal Disease Classification A Deep Learning Approach Using the Kvasir Dataset
    A Sagar, AXA Joanes, SR Hole, A Agarwal, V Kolluru, G Londhe
    IEEE 2025 International Conference On Emerging Computation and Information … , 2026
    2026
  • Data Driven Password Strength Estimation Using Linear Regression and K-Nearest Neighbors with Interactive Gradio Deployment
    SR Hole, N Mangrulkar, PB Ghogare, V Kolluru, G Kumavat, S Salotagi
    IEEE 2025 International Conference On Emerging Computation and Information … , 2026
    2026
  • HVLGAN: hybrid hierarchical scaled attention-enabled latent model for structure-based drug discovery
    SR Hole, M Lakshmanan, R Jeevaraj, M Srinubabu, S Salotagi, V Kolluru
    Journal of Biomolecular Structure and Dynamics , 2026
    2026
    Citations: 1
  • Deep Learning for Diabetic Retinopathy Detection: An Elaborative Study Using Fundus Imaging and Generative Approaches
    V Kolluru, SS Reshmi, S Salotagi, SR Hole, T Murugesan, A Sagar
    2025 IEEE 1st International Conference on Recent Trends in Computing and … , 2025
    2025
  • Deep Learning for Knee Osteoarthritis Detection and Severity Grading: A Comprehensive Study Using Radiographic Imaging and Grad-CAM Interpretability
    V Kolluru, L Naveen, BS Akshatha, SR Hole, T Murugesan, A Sagar
    2025 IEEE 1st International Conference on Recent Trends in Computing and … , 2025
    2025
  • Leveraging Software-defined Wireless Networks for Intelligent Resource Management in 5G and Beyond Wireless Networks
    T Kaur, M Reddy, V Kolluru, S Nuthakki, M Soni
    Samba Siva and Kolluru, Vinothkumar and Nuthakki, Siddhartha and Nuthakki … , 2025
    2025
  • EEG-Based Brain-Computer Interface for Robotic System Control Using Right-Left Hand Movements with Ensemble Model
    YD Borole, Y Challagundla, V Kolluru, SN Mohanty, M Yang, ...
    2025 IEEE 8th International Conference on Multimedia Information Processing … , 2025
    2025
  • Hybrid deep learning architectures for brain tumor classification using magnetic resonance imaging: ViT-GRU and GNet-SVM models
    G Adli, S Shukla, Y Challagundla, V Kolluru, TR Uggumudi
    Artificial Intelligence in Oncology: Cancer Diagnosis and Treatment, Medical … , 2025
    2025
    Citations: 1
  • Coupling Process-Based Models with Machine Learning for Robust Predictions of Soil, Water, and Crop Dynamics
    AMS Kheir, MMA Shabana, A Attia, MGM Ali, MA Abd El-Aziz, V Kolluru, ...
    Resilient Agroecosystems: Innovations in Cropping Systems and Climate Change … , 2025
    2025
    Citations: 2
  • A novel deep learning technique for protecting web applications against XSS and SQLI attacks
    JYW Sonkarlay, V Kolluru, Y Challagundla, TTJ Tarpeh
    Journal of Information and Optimization Sciences 46 (6), Pages 1911–1921 , 2025
    2025
  • Hybrid deep learning-based IoT intrusion detection : A comparative study of CNN, GRU, LSTM, and hybrid architectures
    JY Sonkarlay, V Kolluru, ABJ Balyemah, Y Challagundla
    Journal of Information and Optimization Sciences 46 (Issue 6), Pages 1983–1994 , 2025
    2025
  • OpIDS-DL: Optimizing Intrusion Detection in IoT Networks: A Deep Learning Approach with Regularization and Dropout for Enhanced Cybersecurity
    A Pappurajan, V Kolluru, YS Raj, S Mungara, AN Chintakunta, ...
    Demystifying AI and ML for Cyber–Threat Intelligence, 89-102 , 2025
    2025
    Citations: 1
  • Machine Learning for Fake Profile Users Detection in Social Network Systems: A Review and Implementation Phase
    A Waghmare, V Kolluru, Y Challagundla, VS Aditya Bhrugumalla, ...
    Demystifying AI and ML for Cyber–Threat Intelligence, 345-357 , 2025
    2025

MOST CITED SCHOLAR PUBLICATIONS

  • Adaptive Learning Systems: Harnessing AI for Customized Educational Experiences
    V Kolluru, S Mungara, AN Chintakunta
    2018
    Citations: 66
  • AI-Driven Energy Optimization: Household Power Consumption Prediction With LSTM Networks and PyTorch-Ray Tune in Smart IoT Systems
    VK Kolluru, Y Challagundla, AN Chintakunta, B Roy, A Bermak, RD SM
    IEEE International Conference on Microelectronics (ICM), 1-6 , 2024
    2024
    Citations: 32
  • Smart plant disease diagnosis using multiple deep learning and web application integration
    AMS Kheir, A Koubaa, V Kolluru, S Mungara, T Feike
    Journal of Agriculture and Food Research 21, 101948 , 2025
    2025
    Citations: 27
  • Securing the IoT Ecosystem: Challenges and Innovations in Smart Device Cybersecurity
    V Kolluru, S Mungara, AN Chintakunta
    2019
    Citations: 24
  • Response surface modeling of sodium hypochlorite-based manganese oxidation in drinking water
    RD Alsaeed, A Aldarwish, V Kolluru
    DYSONA–Applied Science 6 (2), 334-342 , 2025
    2025
    Citations: 23
  • A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction
    SR Hole, V Kolluru, S Salotagi, Y Challagundla, S Mungara, S R
    2025 IEEE 1st International Conference on Smart and Sustainable Developments … , 2025
    2025
    Citations: 22
  • Combating Misinformation with Machine Learning: Tools for Trustworthy News Consumption
    V Kolluru, S Mungara, AN Chintakunta
    2020
    Citations: 17
  • Use of Predictive Analytics in Antimicrobial Resistance: A Review
    ANC Vinoth Kumar Kolluru, Yudhisthir Nuthakki, Sonika Koganti
    Cognizance Journal of Multidisciplinary Studies 4 (1), 404 - 414 , 2024
    2024
    Citations: 16
  • Hybrid PCA-Based Machine Learning Models for Predictive Analytics in Urban Health Monitoring Systems
    SR Hole, GS Bhavekar, AK Prajapati, V Kolluru, SR Karpe, ...
    2025 IEEE 1st International Conference on Smart and Sustainable Developments … , 2025
    2025
    Citations: 15
  • Predictive Analytics in AI Marketing: Transforming Consumer Engagement
    R Dahhiya, VK Dahiya, N Agarwal, VK Kolluru, Y Challagundla, ...
    IEEE 2025 2nd International Conference on Computational Intelligence … , 2025
    2025
    Citations: 15
  • Real-Time Adaptive Intrusion Detection System [RTPIDS] for Internet of Things Using Federated Learning and Blockchain
    EH Parimala, VSJP Bosco, PJRV Kumar, V Kolluru
    IEEE 5th International Conference on Data Intelligence and Cognitive … , 2024
    2024
    Citations: 15
  • Optimizing Solar Radiation Forecasting for Renewable Energy Systems:: A Comparative Analysis of Machine Learning and Feature Engineering Techniques
    A Sagar, S Hole, V Kolluru
    Solar Energy and Sustainable Development Journal 14 (1), 295-315 , 2025
    2025
    Citations: 13
  • Exploring Consumer Behaviors in E-Commerce Using Machine Learning
    S Koganti, AN Chintakunta, VK Kolluru, Y Nuthakki, S Mungara
    2023
    Citations: 13
  • Healthcare through AI: integrating deep learning, federated learning, and XAI for disease management
    V Kolluru, Y Nuthakki, S Mungara, S Koganti, AN Chintakunta, ...
    International Journal of Soft Computing and Engineering (IJSCE) 13, 21-30 , 2023
    2023
    Citations: 12
  • Integrating predictive analytics and computational statistics for cardiovascular health decision-making
    V Kolluru
    International Journal of Innovative Research and Creative Technology 9 (3), 1-13 , 2023
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    2022
    Citations: 12
  • Chest X-ray Pneumonia Detection using Deep Learning
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    2025 International Conference on Biomedical Engineering and Sustainable … , 2025
    2025
    Citations: 10
  • Machine Learning and Artificial Intelligence for Predictive Modeling in Antimicrobial Resistance Data Sets, Challenges, and Future Directions
    A Sagar, V Kolluru, U Jaiswal, G Kumavat, SR Hole, A Kumar
    IEEE 2025 3rd International Conference on Smart Systems for applications in … , 2025
    2025
    Citations: 10
  • Flu Prediction Using Deep Learning Models A Case Study on Influenza-Like Illness Data
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    2025 International Conference on Biomedical Engineering and Sustainable … , 2025
    2025
    Citations: 9
  • Deep Learning-Based Classification of Nanoscale SEM Features Using Inception
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    2025 International Conference on Biomedical Engineering and Sustainable … , 2025
    2025
    Citations: 9