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Engineering Intelligence for The Real World

At the Center for AI Research (CAIR), we are cultivating a vibrant and forward-looking research ecosystem focused on solving real-world challenges through intelligent, scalable technologies. Our work spans key frontiers of innovation—Edge AI, High-Performance Computing (HPC), Data Science, and Computational Medicine—driving solutions that are not only fast and reliable, but also future-ready.

CAIR bridges the gap between foundational research and applied innovation. By fostering strong collaborations with industry leaders, academic institutions, and technology partners, we ensure that our breakthroughs go beyond the lab to create measurable impact. From accelerating healthcare insights to enabling intelligent infrastructure, CAIR is where engineering meets intelligence, and ideas evolve into real-world solutions.

Engineering Intelligence
  • PUBLICATIONS AND PATENTS

Research That Resonates

  • Uday Kulkarni, SM Meena, Sunil V Gurlahosur,
  • HOQF-M: Hybrid Optimization of Quantization Friendly MobileNet Architecture for Vision Based Applications on Edge Devices, Journal of SN Computer Science
  • Uday Kulkarni., Apoorv Bagal., Sunil Gurlahosur V., Sumedh Kulkarni., Siddhnt Saptasagar., Anant Alias Sudeep Suhas Pingulkar., Meena Maralappanavar S,
  • Future Sales Prediction Using Regression and Deep Learning Techniques, Journal of Advances in Communication and Applications
  • Uday Kulkarni., Shashank Hegde., Abhishek Hosamani., Vivek P Marakumbi., Ganesh Vernekar., Akshay R Shanbhag., Meena Maralappanavar S., Sunil Gurlahosur V
  • Optimal Solution for Distance Detection Using Deep Learning Techniques on Embedded Devices, This chapter is published in the book Advances in Communication and Applications
  • Kartik Kalal., Dhiraj Bhandare., Vaishnavi Patil., Akshay Joshi., Uday Kulkarni., Meena Maralappanavar S,
  • Image Captioning Using CNN-LSTM, This work is published in the edited volume Advances in Communication and Applications
  • Pratham Madnur., Prathamkumar Shetty., Gouri Parashetti., Sneha Varur., Meena Maralappanavar S,
  • Advancing in Cricket Analytics: Novel Approaches for Pitch and Ball Detection Employing OpenCV and YOLOv8, IEEE 9th International Conference for Convergence in Technology
  • C Muttal, S C, M Pandekamat, V Hiremath, R Pujari,
  • Optimizing Vehicle Emissions: Identifying High-Impact Vehicles and Exploring Sustainable Fuel Options, International Conference on Advanced Network Technologies and Intelligent Computing
  • Appu Chalawadi, Chinmay C Kamadolli, Shrilakshmi A Shetty, Vaishnavi Mahantesh Karale, Aadesh Bafna, Channabasappa Muttal, Shashank Hegde,
  • Image Denoising using Traditional and Attention Based Techniques: A Survey, IEEE Conference on Engineering Informatics (ICEI)
  • Uday Kulkarni., Meena Maralappanavar S., Raghavendra Hallyal., Prasanna Sulibhavi., Sunil Gurlahosur V., Shankru Guggari., Akshay R Shanbhag,
  • Optimisation of deep neural network model using Reptile meta learning approach, Journal of Cognitive Computation and Systems journal
  • Uday Kulkarni., Meena Maralappanavar S., Raghavendra Hallyal., Prasanna Sulibhavi., Sunil Gurlahosur V., Shankru Guggari., Akshay R Shanbhag,
  • Optimisation of deep neural network model using Reptile meta learning approach, Journal of Cognitive Computation and Systems
  • Uday Kulkarni., Kushagra Tomar., Mayuri Kalmat., Rakshita Bandi., Pranav Jadhav., Meena Maralappanavar S,
  • Attention based Image Caption Generation (ABICG) using Encoder-Decoder Architecture, 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)
  • Meena Maralappanavar S., Pratham V Madnur., Shreyas H Dabade., Ashlesha Khanapure., Sheldon Rodrigues., Shashank Hegde., Uday Kulkarni,
  • Enhancing Deep Neural Networks through Pruning followed by Quantization Pipeline: A Comprehensive Review, 2nd International Conference on Futuristic Technologies
  • Vinayak Sudhakar Kone., Atrey Anagal., Swaroop Anegundi., Pranali Jadhav., Uday Kulkarni., Meena Maralappanavar ,
  • Voice-based Gender and Age Recognition System, International Conference on Advancement in Computation & Computer Technologies (InCACCT)
  • Sitanshu S Hallad., Uday Kulkarni., Abhishek Patil., Tanvi Bhujannavar., Satwik Kulkarni., Meena Maralappanavar S,
  • A Survey on Filter Pruning Techniques for Optimization of Deep Neural Networks, Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
  • Winston Dcosta., Meena Maralappanavar S., Sunil Gurlahosur V., Uday Kulkarni,
  • Optimization of multi-objectives for Adaptive Reverse Recommendation method on edge devices, 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)
  • Sujay Tadahal., Gopal Bhogar., Meena Maralappanavar S., Uday Kulkarni., Sunil Gurlahosur V., Shashidhara B Vyakaranal,
  • Post-training 4-bit Quantization of Deep Neural Networks, 3rd International Conference for Emerging Technology (INCET)
  • Prasanna H Sulibhavi., Raghavendra A Hallyal., Rohini K Katti., Amruta E Kabade., Sujay S Thadal., Meena Maralappanavar S., Uday Kulkarni., Sunil Gurlahosur V,
  • A Study On Meta Learning Optimization Techniques IEEE 7th International conference for Convergence in Technology (I2CT)
  • Narayan Kulkarni., Nidhi Singh., Yamini Joshi., Nikhil Hasabi., Meena Maralappanavar S., Uday Kulkarni., Sunil Gurlahosur V,
  • Hybrid Optimization for DNN Model Compression and Inference Acceleration 2nd International Conference on Intelligent Technologies (CONIT)
  • Bhavana Kumbar., Ankita Mane., Varsha Chalageri., Shashidhara B Vyakaranal., Meena Maralappanavar S., Sunil Gurlahosur V., Uday Kulkarni,
  • A Comparative Study on Adversarial Attacks and Defense Mechanisms 2nd International Conference on Intelligent Technologies (CONIT)
  • Sunil Gurlahosur V., Meena Maralappanavar S., Uday Kulkarni., Winston Dcosta., Vineet Lokur., Rohan V Sirigeri., Sajal Porwal., S P Sammed., Uma Mudengudi,
  • Comparative Analysis of Neural Architecture Search Methods for Classification of Cultural Heritage Sites, this paper was presented at the Lecture Notes in Electrical Engineering (LNEE) Conference.
  • Shankru Guggari, B Chandan Srivastava, Vivek Kumar, Halli Harshita, Vishal Farande, Uday Kulkarni, SM Meena,
  • Ru-net: A novel approach for gastro-intestinal tract image segmentation using convolutional neural network, International Conference on Applied Machine Learning and Data Analytics
  • Bidari, Indira, and Satyadhyan Chickerur.
  • “Deep recurrent residual U-Net with semi-supervised learning for deforestation change detection”, SN Computer Science, 5(7), 2024: 893.
  • Konnurmath, Guruprasad, and Satyadhyan Chickerur.
  • “Real-time frame rate controlled dynamic voltage and frequency scaling for GPU power consumption optimization.” AIP Conference Proceedings, 3131(1), AIP Publishing, 2024.
  • Jadhav, Varsha S., Rajesh Yakkundimath, and Guruprasad Konnurmath.
  • “Cervical cancer severity characterization using machine learning techniques”. Indian Journal of Gynecologic Oncology, 22(4), 2024: 1–10.
  • Badiger, Ramesh M., Rajesh Yakkundimath, Guruprasad Konnurmath, and Praveen M. Dhulavvagol.
  • “Deep learning approaches for age-based gesture classification in South Indian Sign Language”, Engineering, Technology & Applied Science Research, 14(2), 2024: 13255–13260.
  • Kampli, Gurunath, Satyadhyan Chickerur, and Manoj kumar Chitawadagi.
  • “IoT system implementation for real-time concrete strength prediction: Experimental design, variance evaluation, cost analysis, and implementation ease”, Innovative Infrastructure Solutions, 9(7), 2024: 260.
  • Koluragi, P., Deshpande, Y., Dhumale, G., Chikkamath, S., Nirmala, S.R., & Budihal, S.V.
  • Multi-model approach for Alzheimer disease detection and classification. 2024 3rd International Conference for Innovation in Technology (INOCON), Bangalore, India, 2024, pp. 1–5. doi: 10.1109/INOCON60754.2024.10511750
  • Shidramshettar, R., Bonageri, A., Kumbargeri, V., Chikkamath, S., Nirmala, S.R., & Budihal, S.
  • Enhanced retinal vessel segmentation using U-Net framework. 2024 IEEE International Conference on Contemporary Computing and Communications (InC4), Bangalore, India, 2024, pp. 1–5. doi: 10.1109/InC460750.2024.10649089
  • Medhi, J.P., Nirmala, S.R., Choudhury, S., & Dandapat, S.
  • Improved detection and analysis of macular edema using modified guided image filtering with modified level set spatial fuzzy clustering on optical coherence tomography images. Biomedical Signal Processing and Control, 79(2), 2023: 1041–1049. (Q1)
  • Kaushik, P., & Nirmala, S.R.
  • Automated system for detection and classification of cystoid macular oedema using OCT images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023: 1–15. (Q2) https://doi.org/10.1080/21681163.2023.2237606
  • Saikia, S.J., & Nirmala, S.R.
  • Identification of disease genes and assessment of eye-related diseases caused by disease genes using JMFC and GDLNN.Computer Methods in Biomechanics and Biomedical Engineering, 25(4), 2022: 359–370.
  • Mazumdar, J.B., & Nirmala, S.R.
  • Deep learning framework for biometric authentication using retinal images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(3), 2022: 740–749. (Q1)
  • Sandilya, M., & Nirmala, S.R.
  • “Compressed sensing MRI reconstruction using convolutional dictionary learning and Laplacian prior.” In Senjyu, T., et al. (Eds.), IOT with Smart Systems, Smart Innovation, Systems and Technologies, vol. 251. Springer, Singapore, 2022. https://doi.org/10.1007/978-981-16-3945-6_65
  • Kanakaraddi, S.G., Chikaraddi, A.K., Aivalli, N., Maniyar, J., & Singh, N.
  • Sentiment analysis of COVID-19 tweets using machine learning and natural language processing. Advances in Intelligent Systems and Computing, vol. 1415. Springer, Singapore, 2022. https://doi.org/10.1007/978-981-16-7330-6_28
  • Gull, K., Kanakaraddi, S., & Chikaraddi, A.
  • COVID-19 outbreak prediction using additive time series forecasting model. Trends in Sciences, 19(22), 2022: 1919.
  • Desai, S.D.
  • Desai, S., et al.
  • Low-dose imaging: Prediction of projections in sinogram space. In Smys, S., et al. (Eds.), Computational Vision and Bio-Inspired Computing, Advances in Intelligent Systems and Computing, vol. 1318. Springer, Singapore, 2021. https://doi.org/10.1007/978-981-33-6862-0_43
  • Desai, S.D., et al.Interpolation-based low dose CT image reconstruction. Procedia Computer Science, 171, 2020: 2760–2769. https://doi.org/10.1016/j.procs.2020.04.300
  • Desai, S.D., et al. Breast cancer detection using GAN for limited labeled dataset. 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), Bhimtal, India, 2020, pp. 34–39. doi: 10.1109/CICN49253.2020.9242551
  • Ballari, G., Giraddi, S., Chickerur, S., & Kanakaraddi, S. An explainable AI-based skin disease detection. In ICT Infrastructure and Computing: Proceedings of ICT4SD 2022, pp. 287–295. Springer Nature Singapore.
  • Patil, S.S., Patil, M.S., Chickerur, S., Giraddi, S., Shahapur, S.N., & Hadalageri. Distributed deep learning with data parallelism for diabetic retinopathy classification. In Smart Trends in Computing and Communications, 2023, pp. 49–60. Springer Nature Singapore.
  • Giraddi, V., Giraddi, S., Kanakaraddi, S., & Patil, M. Assessment of lung cancer histology using EfficientNet. In Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2023, pp. 395–403.
  • Sulibhavi, P.H., Kanakaraddi, S.G., & Giraddi, S. A study on kidney segmentation techniques using DNN models. In Smart Computing and Communication, 2023, pp. 839–849. Springer Nature Singapore.
  • Giraddi, S., Mugdha, S., Kanakaraddi, S., & Chickerur, S.
  • Glaucoma diagnosis: Handcrafted features versus deep learning. In Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2022, pp. 869–881. Springer Nature Singapore.
  • Patil, S.A., Patil, M.S., Giraddi, S., Chickerur, S., Boormane, V.M., & Gamanagatti, G.
  • Pipeline parallelism in distributed deep learning for diabetic retinopathy classification. Procedia Computer Science, 215, 2022: 393–402.
  • Jalannavar, A., Kanakaraddi, S.G., & Handur, V.S.
  • Migraine prediction using deep learning model. 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India, 2022, pp. 1–8. doi: 10.1109/ICERECT56837.2022.10059843
  • Korti, S., & Kanakaraddi, S.G. Depression detection from Twitter posts using NLP and machine learning techniques. 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India, 2022, pp. 1–6. doi: 10.1109/ICERECT56837.2022.10059773
  • Sulibhavi, P.H., & Kanakaraddi, S.G. A study on kidney segmentation techniques using DNN models. Lecture Notes in Networks and Systems, 650, 2023: 839–849.
  • RESEARCH FOCUS AREAS

Domains of Impact

01

Edge AI

02

High Performance Computing

03

Data Science

04

Cloud Computing