Dr. Johnson Kolluri
Assistant Professor
Computer Science & Artficial Intelligence
NIT-Mizoram
14
DAA
Computer Vision, Deep Learning
Assistant Professor
Computer Science & Artficial Intelligence
NIT-Mizoram
14
DAA
Computer Vision, Deep Learning
(Ph.D) in CSE
M.Tech in CSE
B.Tech in CSE
Assistant Professor at SR University, from 2024-07-19 to .
Assistant Professor at Kakatiya Institute of Technology and Science -Warangal, from 2017-05-01 to 2023-06-28.
Expert Faculty at Debre Tabor University, Ethiopia, from 2014-11-17 to 2017-02-28.
Assistant Professor at Malla Reddy Institute of Technology-Hyderabad, from 2014-07-01 to 2014-11-01.
Assistant Professor at SR Engineering College , Warangal, from 2011-08-28 to 2014-06-30.
Assistant Professor at Ayaan College of Engineering and Technology-Hyderabad, from 2010-04-01 to 2011-05-10.
Ph.D
PG
UG
R. Vempati and J. Kolluri, "Transformer-Based Intrusion Detection and Protection Systems: An Innovative Method for Reducing Cyberattacks," 2025 2nd International Conference on Intelligent Systems for Cybersecurity (ISCS), Gurugram, India, 2025, pp. 1-5, doi: 10.1109/ISCS69371.2025.11386393.
V. G. S, P. Prasant, R. Madamala and J. Kolluri, "Optimi1zing Soil Moisture Prediction and Crop Yield Enhancement for Medicinal Plants Using Convolutional Neural Networks in Precision Agriculture," 2025 International Conference on Computing, Intelligence, and Application (CIACON), Durgapur, India, 2025, pp. 1-6, doi: 10.1109/CIACON65473.2025.11189722.
S. L. Lakshmi, V. R. Kanth and J. Kolluri, "Enhancing Text Classification with an Attention-Integrated CNN-SVM Hybrid Model," 2025 International Conference on Computing, Intelligence, and Application (CIACON), Durgapur, India, 2025, pp. 1-6, doi: 10.1109/CIACON65473.2025.11189691.
G. Sreenivasan, P. Prathima, J. Kolluri, K. Ashok, R. Vempati and S. K. Medishetti, "Fuzzy-DRL: Cost and Energy Efficient Task Scheduling in Cloud-Fog Computing Environment," 2026 8th International Conference on Intelligent Sustainable Systems (ICISS), Tirunelveli, India, 2026, pp. 650-657, doi: 10.1109/ICISS67859.2026.11453829.
G. Sujatha, K. Manchikanti, J. Kolluri, R. Vempati, R. S. Velamakanni and S. Kumar Medishetti, "XDL-IRTS: an Intelligent and Resilient Task Scheduling in Large-Scale Cloud Environments," 2026 8th International Conference on Intelligent Sustainable Systems (ICISS), Tirunelveli, India, 2026, pp. 1-9, doi: 10.1109/ICISS67859.2026.11454061.
Kolluri, J., Dash, S.K., Das, R. et al. Surveillance video–based pedestrian detection for smart transportation using machine learning techniques. Discov Appl Sci 8, 398 (2026). https://doi.org/10.1007/s42452-026-08466-8
Johnson Kolluri, Dr.Shaik Razia, Soumya Ranjan Nayak “Text Classification using Machine Learning and Deep Learning Models” published at International Conference on Artificial Intelligence in Manufacturing & Renewable Energy(ICAIMRE-2019),25&26th Oct-2019.
Johnson Kolluri and Ranjita Das “Ship Detection from Satellite Images with an Advanced deep learning model (single Shot Detector (SSD))” International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2022) June18-19,2022, conference proceedings by Springer Smart Innovation, Systems and Technologies(SIST).
Johnson Kolluri, vinay kumar kotte,M.S.B Phridviraj and Dr.Shaik Razia ”Reducing Overfitting Problem in Machine Learning Using Novel L1/4 Regularization Method” published at Fourth International Conference on Trends in Electronics and Informatics (ICOEI-2020), DVD Part Number: CFP20J32-DVD; ISBN: 978-1-7281-5517-3, June-2020.
Johnson Kolluri, Ranjita Das "An efficient Synergic Model with Contrast Limited Adaptive Histogram Equalization model for Object Classification in Ship Detection," 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 2022, pp. 1-7, doi: 10.1109/MysuruCon55714.2022.9972532.
Ganesh, V., Johnson Kolluri, J., Maada, A.R., Ali, M.H., Thota, R., Nyalakonda, S. (2022). Real-Time Video Processing for Ship Detection Using Transfer Learning. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_54.
Johnson Kolluri, V Ganesh and vinay kumar kotte, “Diabetics Prediction using Logistic Regression and Feature Normalization” published at Fourth International Conference on Innovative Communication and smart Electrical systems (ICSES-2021), Organised by:St.Joseph’s Institute of Technology,Chennai,India; ISBN: 978-1-6654-3520-8, 24th &25th September,2021.
Johnson Kolluri, V. ChandraShekar Rao, Gouthami velakanti, Siripuri Kiran, sumukham Sravanthi , S.Venkatramulu “Text Classification Using Deep Neural Networks” published at 5th International Conference On Intelligent Computing and Communication (ICICC-2021), paper ID-108 held at Dayananda Sagar University, Bengaluru ,India, November-2021
Johnson Kolluri, K.Vinay Kumar, C.Srinivas, Siripuri Kiran, Swapna Saturi, Ravula Rajesh “COVID-19 Detection from X-rays using Deep Learning Model” published at 5th International Conference On Intelligent Computing and Communication (ICICC-2021), paper ID-108 held at Dayananda Sagar University, Bengaluru ,India, November-2021.
Johnson kolluri, N Anusha, Dr. C.V GuruRao “Refinement Trust Model Creation For Service Oriented Architecture Using Interface Theory” Published at International Journal of Computer Science Information and Engineering, Technologies, Issue-3-Vol-3-series-1, ISSN:2277-4408,01-september-2013.
Johnson Kolluri, Rajitha Jampala,” Observing the Protection of Confidential Location System for WSNs” published at International Journal Computer Technology and Applications , vol 3(5), ISSN:2229-6093,1807-1812,sep-oct 2012.
Johnson Kolluri, Sumera Mohammadi, Dr.C.V.Gururao” The Future of Wireless -IEEE STANDARD 802.16 for Global Broadband Wireless Access ” published at International Journal of Research Computational Technology, vol.2 Issue.3, ISSN:0975-5665, June-2012.
Johnson Kolluri, Dr.Shaik Rajia, Dr.Niranjan Polala “A Comparative Study of Deep Learning Models used for Object identification” published at Compliance Engineering Journal(UGC-Care Journal) Vol-10/Issue-09, ISSN 0898-3577, 2019.
Johnson Kolluri, Ch Anila, S.P Anand Raj “Secure Data Transfer In CLOUD By Using Aes” published at International journal of Engineering and Science Research Vol-3/Issue-10/4864-4871, ISSN 2277-2685, October-2013.
Johnson kolluri, N Anusha, Dr. C.V GuruRao “Refinement Trust Model Creation For Service Oriented Architecture Using Interface Theory” Published at International Journal of Computer Science Information and Engineering, Technologies, Issue-3-Vol-3-series-1, ISSN:2277-4408,01-september-2013.
Kolluri, J., & Das, R. (2025). Multimodal image analysis based pedestrian detection using optimization with classification by hybrid machine learning model. International Journal of Image, Graphics and Signal Processing, 17(1), 31–44. https://doi.org/10.5815/ijigsp.2025.01.03
Johnson Kolluri, Sandeep Kumar Dash and Ranjita Das “Plant Disease Identification Based on Multimodal Learning” International Journal of Intelligent Systems and Applications in Engineering(2024). ISSN:2147-6799, Volume 12, No 15S (2024).
Johnson Kolluri, Ranjita Das “An Evaluation of Deep Learning- Based Object Identification” IJRITCC(2022). https://doi.org/10.17762/ijritcc.v10i1s.5795, ISSN: 2321-8169 Volume: 10 Issue: 1, 9 November 2022.
Johnson Kolluri, Sandeep Kumar Dash, Ranjita Das “MM_Fast_RCNN_ResNet: Construction of Multomodel Faster RCNN Inception and ResNet V2 for Pedestrian Tracking and Detection” is accepted in the International Journal of Maritime Engineering (ISSN/E-ISSN : 1479-8751/1740- 0716).
Johnson Kolluri, Ranjita Das “Intelligent Multimodal Pedestrian Detection using Hybrid Metaheuristic Optimization with Deep Learning Model” Image and Vision Computing(2023). https://doi.org/10.1016/j.imavis.2023.104628, March-2023.
Johnson Kolluri, D. Suresh Babu, B. Raju, S. Swapna, D. Ramesh & Rajitha Bonagiri “Dengue symptoms classification analysis with improved conditional probability decision analysis” Appl Nanosci (2022). https://doi.org/10.1007/s13204-022-02387-9, 11 February 2022.
Title: Solar Park Monitoring and Fault Detection System Using IoT and Machine Learning Description: The Solar Park Monitoring and Fault Detection System using IoT and Machine Learning is an innovative solution designed to enhance the efficiency, reliability, and maintenance of large-scale solar power installations. This system integrates Internet of Things (IoT) technology with advanced Machine Learning (ML) algorithms to provide real-time data monitoring, predictive analytics, and automated fault detection. Key Feat
The proposed invention is a smart, non-invasive skin patch designed for continuous, real-time health monitoring. It tracks multiple vital biomarkers such as glucose, heart rate, hydration, and stress levels, providing personalized insights through AI-driven analysis. This innovation enables proactive healthcare management, offering timely predictions and alerts to users and healthcare providers. 1. Sensor Monitoring: The patch contains multiple biosensors that measure vital signs and health biomarkers
The invention presents a real-time emotion detection system that integrates ensemble natural language processing and machine learning techniques. The system includes an input processing module, an ensemble NLP module with transformer-based models and sentiment analysis algorithms, a feature aggregation unit, a machine learning classifier module, and an output/feedback module. By combining multiple NLP models and classifiers, the system improves accuracy, scalability, and adaptability in dynamic communication environments. It
A BCI headset that captures brain signals using EEG sensors and uses deep learning to detect user intent. It predicts actions and executes commands in a digital environment with reduced latency (100–150 ms), enabling faster and intuitive interaction
An advanced AI framework that combines Graph Neural Networks (GNN) and Transformer models to analyze multimodal data, including bioacoustic signals and environmental inputs, for early disaster prediction and adaptive response.
A multimodal AI-based system using Faster R-CNN with Inception and ResNet architectures to detect and track pedestrians from RGB and infrared data, ensuring high accuracy and reliable performance in real-time environments
A wearable device with biosensors that monitors physiological parameters and uses AI to analyze health data, predict risks, and provide personalized health insights for preventive care.
A smart bottle equipped with sensors to monitor liquid intake and medication usage, providing context-aware reminders and automated pill access to improve hydration and medication adherence, especially for elderly users.
