Prof. C. Krishna Mohan's research is centered on advanced Representation Learning techniques, with a strong emphasis on Image and Video Content Analysis, Intelligent Transportation Systems, and Edge-deployable Artificial Intelligence. His work seamlessly integrates core machine learning methodologies with real-world problem-solving, enabling the development of scalable, data-efficient solutions for complex spatio-temporal data analysis. A central theme of his research is the design of robust representation learning techniques tailored for video understanding and image-based scene interpretation. In the domain of intelligent transportation systems (ITS), Prof. Mohan has led the development of cutting-edge AI solutions for traffic monitoring, vehicle re-identification, pedestrian behavior analysis, and anomaly detection. These systems are engineered to operate under real-time constraints, enabling efficient and low-latency deployment through Edge AI in scenarios such as autonomous navigation and on-device video analytics. In parallel, his ITS research also plays a pivotal role in enabling smart city infrastructure by providing scalable AI-driven tools for urban traffic optimization, public safety monitoring, and incident detection. By harnessing data from live surveillance feeds and embedded sensors, his work facilitates critical applications in geospatial intelligence, environmental monitoring, and urban planning. Prof. Mohan's ability to translate deep representation learning into practical, deployable, and impactful AI systems positions him at the forefront of applied artificial intelligence. His contributions continue to shape the evolution of edge-intelligent systems and domain-aware learning across both academia and industries.

I. Representation Learning for Urban Mobility and Implementation

Research in representation learning has played a pivotal role in advancing urban mobility solutions, especially in the context of complex and unstructured traffic environments prevalent in Indian cities. These efforts have led to the development of AI-driven models capable of accurately detecting critical traffic violations, such as helmetless riding, abrupt lane changes, and potential collision trajectories, while also predicting risky behaviors in dynamic traffic conditions. These systems enable real-time decision-making in resource-constrained and cost-sensitive urban settings. As part of the Indo-Japanese M2Smart [Link] (Multimodal Mobility Smart City) Project, intelligent mobility solutions have been conceptualized and deployed through a collaborative framework involving academic and industrial stakeholders. The project covers the full AI pipeline from multimodal data acquisition via traffic cameras and environmental sensors to scalable system-level implementations for intersection monitoring, smart parking, and dynamic traffic management. Field deployments, including testbeds on the IIT Hyderabad campus and a 30 km segment of National Highway NH-65, serve as real-world validation environments for building sustainable and data-driven smart city infrastructure. Additionally, the advancements in representation learning for intelligent transportation have been deployed in real-world settings, notably in Ahmedabad and Hyderabad cities in India, where AI-powered traffic monitoring systems, including helmet detection, chain snatching detection and accident prediction modules, are operational. This work on detecting street-level crimes such as snatch theft using real-world surveillance footage marks a significant advancement in urban safety analytics and has garnered widespread attention in the media.

• Societal Impact

The project has contributed to safer and more efficient urban transportation by enabling real-time detection of traffic violations, accident prediction, and intelligent monitoring. These AI-driven systems support improved traffic management and enforcement, helping authorities respond more effectively to road incidents. With deployments in cities like Ahmedabad and Hyderabad, the solutions are actively enhancing public safety and urban mobility.

• Open-Source Datasets and Codes

1. SkyEye Dataset: This is the first aerial dataset for monitoring intersections with mixed traffic and lane-less behavior. Around 1 hour of video each from 4 intersections, namely, Paldi (P), Nehru bridge - Ashram road (N), Swami Vivekananda bridge - Ashram road (V), and APMC market (A) in the city of Ahmedabad, India. [Link]
2. EyeonTraffic (EoT) Dataset: The EyeonTraffic (EoT) dataset is a large-scale aerial traffic dataset specifically curated to study traffic states in mixed, lane-less urban environments. It comprises three hours of high-resolution aerial videos recorded at three busy intersections in Ahmedabad, India—Paldi, Nehru Bridge, and APMC Market captured using a DJI Phantom 4 Pro drone at 50 FPS in 4K resolution. [Link]
3. IITH Helmet 1 & IITH Helmet 2 Datasets: This dataset is collected from the surveillance network at the Indian Institute of Technology Hyderabad, India (IITH) campuS. It is a 2-hour surveillance video dataset which is collected at 30 frames per second. This second dataset is acquired from the CCTV surveillance network of Hyderabad city in India. It is a 1.5 hour video which is collected at 25 frames per second. [Link]
4. Snatch 1.0 Dataset: This is a real-world surveillance video dataset curated to study the rare and complex activity of snatch thefts. It consists of 816 video clips, each 10 seconds long, extracted from over 4.5 hours of CCTV footage recorded by the Hyderabad Traffic Police [Link]
5. IITH Accident Dataset: This is a surveillance video dataset curated for real-world road accident detection. Collected from the Hyderabad City CCTV surveillance network, the dataset comprises videos recorded at 30 FPS, capturing a variety of challenging conditions including daylight, high sunlight, and nighttime scenarios [Link]
6. IIT Hyderabad Open-Air Parking Dataset: The dataset comprises 24 hours of continuous video data captured from an open-air off-street parking area at IIT Hyderabad, using a Hikvision Exir mini bullet camera installed at a height of 25 meters. [Link]
7. AIWD6 (Adverse Intermediate Weather Driving) Dataset: This is A large-scale dataset encompassing six specific transitional weather states, sunny to rainy, rainy to sunny, sunny to foggy, foggy to sunny, cloudy to rainy, and rainy to cloudy, generated across three time intervals using a Variational Autoencoder (VAE) based data interpolation technique. [Link]

• Patents

1. C. Krishna Mohan, Dinesh Singh, C. Vishnu, and Debaditya Roy. "Method and system for real time detection of traffic violation by two-wheeled riders", Indian Patent Office, Official journal No. 18/2019, Application no. 201741038813, May 03, 2019
2. Debaditya Roy, Dinesh Singh, C. Vishnu, and C. Krishna Mohan. "Method and system for detection of crime events in surveillance videos", Indian Patent Office, Official journal No. 21/2019, Application no. 201741041239, May 24, 2019
3. C. Krishna Mohan, Dinesh Singh, C. Vishnu, and Debaditya Roy. "Method and system for detection of accident in traffic surveillance video", Indian Patent Office, Official journal No. 31/2019, Application no. 201841003604, Aug. 02, 2019
4. Kondapally Madhavi, K Naveen Kumar, C Krishna Mohan, Sobhan Babu,“System And Method For Performing Adaptive Object Detection In An Autonomous Vehicle System”, Indian Patent Office, Application no. 202541001505, Jan, 07, 2025
5. Kondapally Madhavi, K Naveen Kumar, C Krishna Mohan, Sobhan Babu,“System and method for generating weather transition data for autonomous vehicle training”, Indian Patent Office, Application no. 202541000718, Jan, 03, 2025

• Handbook

• Handbook of Multimodal Transport for Smart City [Link]
• Summary of Handbook [Link]

• Media Links

1. Satreps (M2Smart) Project - V6 NEWS
   Open on YouTube [Video Link] [Timestamp : 6:45 To 12:30]
2. Business Today
   Article (July 24, 2018)
   Don't take off your helmet — Business Today [Link]
3. The Economic Times
   Article on AI to catch bikers without helmets
   IIT-Hyderabad develops AI to catch bikers without helmets — Economic Times [Link]
4. The Times of India
   Coverage on AI-enabled software for helmet detection
   IIT-Hyderabad develops AI-enabled software to catch bike riders without helmet — Times of India [Link]
5. The Hindu
   Article discussing electronic surveillance and future changes
   Electronic surveillance may see a marked change in future — The Hindu [Link]
6. The Better India
   Feature on IIT Hyderabad's AI module
   IIT-Hyderabad AI module for helmet detection — The Better India [Link]
7. Analytics India Magazine
   Industry coverage highlighting the AI system
   IIT-Hyderabad develops new AI-based system to catch bikers without helmets — Analytics India Magazine [Link]

II. Edge-AI for Mobile Platforms

A key focus of this research has been translating advances in representation learning into real-world, on-device AI solutions for mobile platforms. In collaboration with OPPO R&D India, two commercially deployed systems were developed, showcasing the potential of Edge-AI [Link] in enhancing user experience through efficient and intelligent mobile applications. The first solution is a real-time AI-based video bokeh system that delivers high-quality background defocus effects in mobile video streams. It leverages optimized deep learning models for human segmentation and blur rendering, specifically tailored for the computational constraints of mobile hardware. The second solution addresses image management by enabling intelligent retrieval and filtering of similar, duplicate, and low-quality images. Using deep embeddings and mobile-optimized models, the system allows for real-time image clustering and quality assessment, improving photo organization and storage efficiency. Both solutions exemplify how cutting-edge AI research can be successfully deployed on edge devices, bridging academic innovation with industry-grade mobile AI applications.

• Societal Impact

The collaborative projects between Prof. C. Krishna Mohan and OPPO R&D India have received widespread national and international recognition across prominent media platforms. The innovative outcomes, particularly in the areas of mobile AI for video bokeh enhancement and intelligent image management. Recognized by government agencies like the Ministry of Human Resource Development and the Department of Science and Technology, the work demonstrates how academic-industry partnerships can drive meaningful advancements in AI and 5G technologies, enhancing everyday mobile experiences at scale.

• Media Links

• IIT Hyderabad-OPPO Partnership in Advancing AI and 5G-Enabled Technologies

1. The Hindu [Link]
2. Financial Express [Link]
3. The Hans India [Link]
4. NDTV [Link]
5. Business Standard [Link]
6. Economic Times Telecom [Link]
7. National Geographic TV Channel’s Superfactories [Video Link] [Time Stamp : 33:23 To 34:17]
8. Acknowledged by Government Bodies - Ministry of Human Resource Development [Link]

Research Supervision

• PhDs Graduated - 20    PhDs Ongoing - 2

PhD Alumni

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PhDs Ongoing

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-<<< PUBLICATIONS>>>-

Book

1. S.S. Iyengar , Seyedsina Nabavirazavi , Yashas Hariprasad , Prasad HB , C. Krishna Mohan, "Artificial Intelligence in Practice", Theory and Application for Cyber Security and Forensics, 2025. ISBN 978-3-031-89326-1 ISBN 978-3-031-89327-8 https://doi.org/10.1007/978-3-031-89327-8

Book Chapters [3]

1. Earnest Paul Ijjina and C. Krishna Mohan, "Human behavioural analysis using evolutionary algorithms and deep learning", Hybrid Intelligence for Image Analysis and Understanding, John Wiley, UK, pp. 165-186, 2017. ISBN101119242924, ISBN13 9781119242925
2. Shyju Wilson, C. Krishna Mohan, and K. Srirama Murthy, "Event Based Sports Videos Classification using HMM Framework", Computer Vision in Sports, Springer International Publishing, pp. 229-244, 2014. DOI: 10.1007/978-3-319-09396-3_11
3. C. Krishna Mohan, "Handbook of Multimodal Transport for Smart City" [Link]

Journals []

1. K. Naveen Kumar, Srinivasa Rao Chalamala, Ajeet Kumar Singh, C. Krishna Mohan, “Optimal Transport Barycentric Aggregation for Byzantine-Resilient Federated Learning,” Accepted in IEEE Transactions on Big Data, 2025, (Impact Factor = 5.1)
2. G Swetha, Rajeshreddy Datla, Sobhan Babu, C Krishna Mohan, "CPL-PL: Contrapositive Learning-Based Pseudo-Labeling for Semi-Supervised Scene Classification in Remote Sensing Images", IEEE Geoscience and Remote Sensing Letters, vol. 22, pp. 1-5, 2025, (Impact Factor = 4.4) [Link]
3. Aveen Dayal, S Shrusti, Linga Reddy Cenkeramaddi, C Krishna Mohan, Abhinav Kumar, "Leveraging Mixture Alignment for Multi-Source Domain Adaptation", IEEE Transactions on Image Processing, vol. 34, pp. 885-898, 2025, (Impact Factor = 10.8) [Link]
4. K Naveen Kumar, C Krishna Mohan, and Linga Reddy Cenkeramaddi, "Federated Learning Minimal Model Replacement Attack Using Optimal Transport: An Attacker Perspective", IEEE Transactions on Information Forensics and Security, vol. 20, pp. 478-487, 2025, (Impact Factor = 6.3) [Link]
5. K Naveen Kumar, C Krishna Mohan, Linga Reddy Cenkeramaddi, and Navchetan Awasthi, "Minimal Data Poisoning Attack in Federated Learning for Medical Image Classification: An Attacker Perspective", Artificial Intelligence In Medicine (Elsevier), vol. 159, pp. 103024, 2024, (Impact Factor = 6.1) [Link]
6. Anushka Joshi, Balasubramanian Raman, and C Krishna Mohan, "Real-Time Earthquake Magnitude Prediction Using Designed Machine Learning Ensemble Trained on Real and CTGAN Generated Synthetic Data", Geodesy and Geodynamics Journal (Elsevier), vol. 16, pp. 350-368, 2024, (Impact Factor = 8.0) [Link]
7. K. Naveen Kumar, Debaditya Roy, Thakur Ashutosh Suman, Chalavadi Vishnu, C. Krishna Mohan, "TSANet: Forecasting Traffic Congestion Patterns from Aerial Videos using Graphs and Transformers", Pattern Recognition (Elsevier), vol. 155, pp. 110-117, 2024, (Impact Factor = 8.0) [Link]
8. Rajeshreddy Datla, Nazil Perveen, C Krishna Mohan, "Learning scene-vectors for remote sensing image scene classification", Neurocomputing (Elsevier), vol. 587, pp. 157-167, 2024, (Impact Factor = 5.779) [Link]
9. Rajeshreddy Datla, G Swetha, C Gayathri, "EGANet: Elevation-guided attention network for scene classification in panchromatic remote sensing images", Neural Computing and Applications (Springer), vol. 36, pp. 18215-18262, 2024, (Impact Factor = 6.0) [Link]
10. Yenduri Sravani, Chalavadi Vishnu, and C Krishna Mohan, "Adaptive temporal aggregation for table tennis shot recognition", Neurocomputing (Elsevier), vol. 587, pp. 127-137, 2024, (Impact Factor = 5.779) [Link]
11. K Naveen Kumar, C Krishna Mohan, Linga Reddy Cenkeramaddi, "The Impact of Adversarial Attacks on Federated Learning: A Survey", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 2672-2691, 2023, (Impact Factor = 24.314) [Link]
12. Peketi Divya, Yenduri Sravani, Chalavadi Vishnu, C Krishna Mohan, Yen Wei Chen, "Memory Guided Transformer with Spatio-Semantic Visual Extractor for Medical Report Generation", IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 5, pp. 3079-3089, 2024, (Impact Factor = 7.7) [Link]
13. Madhavi Kondapally, K Naveen Kumar, Chalavadi Vishnu, and C Krishna Mohan, "Towards a Transitional Weather Scene Recognition Approach for Autonomous Vehicles", IEEE Transactions on Intelligent Transportation Systems, vol. 25, pp. 5201-5210, 2023, (Impact Factor = 8.5) [Link]
14. Anushka Joshi, Linga Reddy Cenkeramaddi, Balasubramanian Raman, C Krishna Mohan, Linga Reddy Cenkeramaddi, "A new machine learning approach for estimating shear wave velocity profile using borelog data", Soil Dynamics and Earthquake Engineering (Elsevier), vol. 177, pp. 108424, 2023, (Impact Factor = 4.0) [Link]
15. Soumya. A, Krishna Mohan C, and Linga Reddy Cenkeramaddi, "Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review", MDPI Sensors Journal (MDPI Sensors), vol. 23, pp. 8901, 2023, (Impact Factor = 3.9) [Link]
16. Rambabu Damalla, Rajeshreddy Datla, Chalavadi Vishnu, C Krishna Mohan, "Self-supervised embedding for generalized zero-shot learning in remote sensing scene classification", Applied Remote Sensing (SPIE), vol. 17, pp. 032405-032405, 2023, (Impact Factor = 1.568) [Link]
17. Anushka Joshi, Balasubramanian Raman, C Krishna Mohan, Linga Reddy Cenkeramaddi, "Application of a New Machine Learning Model to Improve Earthquake Ground Motion Predictions", Natural Hazards (Springer), vol. 120, pp. 729-753, 2023, (Impact Factor = 3.7) [Link]
18. Sai Harsha Yelleni, Kumari Deepshikha, Srijith P.K, Krishna Mohan Chalavadi, "Monte Carlo DropBlock for Modelling Uncertainty in Object Detection", Pattern Recognition (Elsevier), vol. 146, pp. 110003, 2023, (Impact Factor = 8) [Link]
19. G Swetha, Rajeshreddy Datla, C Vishnu, C Krishna Mohan, "M2-APNet: A multimodal deep learning network to predict major air pollutants from temporal satellite images", Applied Remote Sensing (SPIE), vol. 18, pp. 012005-012005, 2023, (Impact Factor = 1.568) [Link]
20. Suresh Samudrala, C Krishna Mohan, "Semantic Segmentation of Breast Cancer Images Using DenseNet with Proposed PSPNet", Multimedia Tools and Applications (Springer), vol. 83, pp. 46037-46063, 2023, (Impact factor = 3.6) [Link]
21. Anushka Joshi, Chalavadi Vishnu, C Krishna Mohan, Balasubramanian Raman, "XGBoost model for early prediction of earthquake magnitude from waveform data", Earth System Science (Springer), vol. 133, pp. 5, 2023, (Impact factor = 2.0) [Link]
22. Chalavadi Vishnu, Linga Reddy Cenkeramaddi, C Krishna Mohan, Jayesh Khandelwal, "EVAA - Exchange Vanishing Adversarial Attack on LiDAR Point Clouds in Autonomous Vehicles", IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 032405-032405, 2023, (Impact factor = 8.2) [Link]
23. Chalavadi Vishnu, Vineel Abhinav, Debaditya Roy, Sobhan Babu, and C. Krishna Mohan, "Improving Multi-agent Trajectory Prediction using Dynamic Traffic States on Interactive Driving Scenarios", IEEE Robotics and Automation Letters, vol. 8, no.5, pp. 2708-2715, 2023, (Impact factor = 5.2) [Link]
24. Aveen Dayal, M. Aishwarya, S. Abhilash, C Krishna Mohan, Abhinav Kumar, Linga Reddy Cenkeramaddi, "Adversarial Unsupervised Domain Adaptation for Hand Gesture Recognition using Thermal Images", IEEE Sensors Journal, vol. 23, pp. 3493-3504, 2022, (Impact factor = 4.323) [Link]
25. Anushka Joshi, Chalavadi Vishnu, Krishna Mohan, "Early detection of earthquake magnitude based on stacked ensemble model", Asian Earth Sciences (Elsevier), vol. 8, pp. 100122, 2022, (Impact factor = 3.0) [Link]
26. Onkar Susladkar, Gayatri Deshmukh, Subhrajit Nag, Ananya Mantravadi, Dhruv Makwana, Sujitha Ravichandran, Sai Chandra Teja R, Gajanan H Chavhan, C Krishna Mohan, and Sparsh Mittal, "ClarifyNet: A High-Pass and Low-Pass Filtering Based CNN for Single Image Dehazing", Systems Architecture (Elsevier), vol. 132, pp. 102736, 2022, (Impact factor = 4.5) [Link]
27. Subhrajit Nag, Dhruv Makwana, Sai Chandra Teja R, Sparsh Mittal, and C Krishna Mohan, "WaferSegClassNet - A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects", Computers in Industry journal (Elsevier), vol. 142, pp. 1-10, 2022, (Impact factor = 11.245) [Link]
28. Dhruv Makwana, Subhrajit Nag, Onkar Susladkar, Gayatri Deshmukh, Sai Chandra Teja R, Sparsh Mittal, C Krishna Mohan, "ACLNet: An Attention and Clustering-based Cloud Segmentation Network", International Journal of Remote Sensing, vol. 13, no. 9, pp.865-875, 2022, (Impact factor = 3.531) [Link]
29. Prudviraj Jeripothula, Chalavadi Vishnu, C Krishna Mohan, "AAP-MIT: Attentive Atrous Pyramid Network and Memory Incorporated Transformer for Multi-Sentence Video Description", IEEE Transactions on Image Processing, vol. 31, pp. 5559-5569, 2022, (Impact factor = 10.6) [Link]
30. Chalamala Srinivasa R., K Naveen Kumar, Singh Ajeet, Saibewar Aditya, and C Krishna Mohan, "Federated learning to comply with data protection regulations", CSI Transactions on ICT (Springer Nature), vol. 10, pp.47-60, 2022 [Link]
31. Prudviraj Jeripothula, Chalavadi Vishnu and C Krishna Mohan, "M-FFN: Multi-Scale Feature Fusion Network for Image Captioning", Applied Intelligence (Springer), vol. 52, pp. 1-21, 2022, (Impact factor = 5.019) [Link]
32. Chalavadi, Vishnu, Prudviraj Jeripothula, Rajeshreddy Datla, Sobhan Babu Ch. and C Krishna Mohan, "mSODANet: A Network for Multi-Scale Object Detection in Aerial Images using Hierarchical Dilated Convolutions", Pattern Recognition (Elsevier), vol. 126, pp. 108548, 2022, (Impact factor = 8) [Link]
33. Prudviraj Jeripothula, Yenduri Sravani, and C. Krishna Mohan, "Incorporating attentive multi-scale context information for image captioning", Multimedia Tools and Applications (Springer), vol. 82, pp. 1-21, 2022, (Impact factor = 3.6) [Link]
34. Yenduri, Sravani, Nazil Perveen, Vishnu Chalavadi, and C. Krishna Mohan, "Fine-grained action recognition using dynamic kernels", Pattern Recognition (Elsevier), vol. 122, pp.108282, 2021, (Impact factor = 8.0) [Link]
35. Chalavadi Vishnu, Rajeshreddy Datla, Debaditya Roy, Sobhan Babu, and C. Krishna Mohan, "Human Fall Detection in Surveillance Videos using Fall Motion Vector Modeling", IEEE Sensors Journal, vol. 21, no. 15, pp. 17162-17170, 2021, (Impact factor = 4.325) [Link]
36. Deepak, K., Chandrakala, S. and C. Krishna Mohan, "Residual spatiotemporal autoencoder for unsupervised video anomaly detection", Signal, Image and Video Processing (Springer), vol. 15, no. 1, pp.215-222, 2021, (Impact factor = 2.3) [Link]
37. Debaditya Roy, Tetsuhiro Ishizaka, C Krishna Mohan, Atsushi Fukuda, "Detection of Collision-Prone Vehicle Behavior at Intersections using Siamese Interaction LSTM", IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3137-3147, 2020, (Impact factor = 8.5) [Link]
38. Rajeshreddy Datla, C Krishna Mohan, "A novel framework for seamless mosaic of Cartosat-1 DEM scenes", Computers & Geosciences (Elsevier), vol. 146, pp. 104619, 2020, (Impact factor = 4.4) [Link]
39. Nazil Perveen, Debaditya Roy and C Krishna Mohan, "Facial Expression Recognition in Videos using Dynamic Kernels", IEEE Transactions on Image Processing, vol. 29, pp. 8316-8325, 2020, (Impact factor = 10.6) [Link]
40. Inayathullah Ghori, & Debaditya Roy, Renu John, and C Krishna Mohan, "Echocardiogram Analysis using Motion Profile Modeling", IEEE Transactions on Medical Imaging, vol. 39, no. 5, pages 1767-1774, 2019, (Impact factor = 10.6) [Link]
41. D. Roy, S. R. M. K and C. K. Mohan, "Unsupervised Universal Attribute Modelling for Action Recognition", IEEE Transactions on Multimedia, vol. 21, no. 7, pp. 1672-1680, 2018, (Impact factor = 7.3) [Link]
42. Dinesh Singh and C. Krishna Mohan, "Deep spatio-temporal representation for detection of road accident using stacked autoencoder", IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 879-887, 2019, (Impact factor = 8.5) [Link]
43. Nazil Perveen, Debaditya Roy, and C. Krishna Mohan, "Spontaneous expression recognition using universal attribute model", IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5575-5584, 2018, (Impact factor = 10.6) [Link]
44. Debaditya Roy and C. Krishna Mohan, "Snatch Theft Detection in Unconstrained Surveillance Videos Using Action Attribute Modeling", Pattern Recognition Letters (Elsevier), Vol. 108, 2018, Pages 56-61, 2018, (Impact factor = 5.1) [Link]
45. Shyju Wilson and C. Krishna Mohan, "An information bottleneck approach to optimize the dictionary of visual data", IEEE Transactions on Multimedia, vol. 20, no. 1, pp. 96-106, 2018, (Impact factor = 7.3) [Link]
46. Earnest Paul Ijjina and C. Krishna Mohan, "Human action recognition in RGB-D using motion sequence and deep learning", Pattern Recognition (Elsevier), vol. 72, pp. 504-516, 2017, (Impact factor = 8) [Link]
47. Shyju Wilson and C. Krishna Mohan, "Coherent and non-coherent dictionaries for action recognition", IEEE Signal Processing Letters, vol.24, no.5, pp.698-702, 2017, (Impact factor = 3.9) [Link]
48. Dinesh Singh, Debaditya Roy, and C. Krishna Mohan, "DiP-SVM : Distribution preserving kernel support vector machine for big data", IEEE Transactions on Big Data, vol.3, no.1, pp.79-90, 2017, (Impact factor = 7.2) [Link]
49. Dinesh Singh and C. Krishna Mohan, "Graph formulation of video activities for abnormal activity recognition", Pattern Recognition (Elsevier), vol. 65, pp. 265-273, 2017, (Impact factor = 8) [Link]
50. Earnest Paul Ijjina and C Krishna Mohan, "Classification of human actions using pose-based features and stacked auto encoder", Pattern Recognition Letters (Elsevier), vol. 83, pp. 268- 277, 2016, (Impact factor = 5.1) [Link]
51. Debaditya Roy, M. Srinivas, and C. Krishna Mohan, "Sparsity-inducing dictionaries for effective action classification", Pattern Recognition (Elsevier), vol. 59, pp. 55-62, 2016, (Impact factor = 8) [Link]
52. Earnest Paul Ijjina and C. Krishna Mohan, "Human action recognition using genetic algorithms and convolutional neural networks", Pattern Recognition (Elsevier), vol. 59, pp. 199-212, 2016, (Impact factor = 8) [Link]
53. Earnest Paul Ijjina and C. Krishna Mohan, "Hybrid deep neural network model for human action recognition", Applied Soft Computing (Elsevier), vol. 46, pp. 936-952, 2016, (Impact factor = 8.7) [Link]
54. M. Srinivas, R. Ramu Naidu, C.S. Sastry, and C. Krishna Mohan, "Content based medical image retrieval using dictionary learning", Neurocomputing (Elsevier), vol. 168, Pages 880-895, 2015, (Impact factor = 6) [Link]
55. M. Srinivas, Tony Basil, and C. Krishna Mohan, "Adaptive learning based heartbeat Classification", Bio-Medical Materials and Engineering, vol. 26, no. 1, pp. 49-55, 2015, (Impact factor = 1.234) [Link]
56. N. Pattabhi Ramaiah and C. Krishna Mohan, "Iris classification based on sparse representations using online dictionary learning for large-scale de-duplication applications", SpringerPlus (Springer), vol. 4, no. 1, pp.238, 2015 [Link]
57. C. Krishna Mohan and B. Yegnanarayana, "Classification of sport videos using edge-Based features and autoassociative neural network models", Signal, Image and Video Processing (Springer), vol. 4, no. 1, pp. 61-73, 2008, (Impact factor = 2.3) [Link]
58. A. Tirupathi Rao, N. Pattabhi Ramaiah and C. Krishna Mohan, "Fingerprint Recognition on Various Authentication Sensors", Journal of Electronic Science and Technology (ScienceDirect), vol.12, no.1, pp. 139-143, 2014 [Link]
59. N. Pattabhi Ramaiah and C. Krishna Mohan, "De-duplication Complexity of Fingerprint Data in Large-scale Applications", Journal of Electronic Science and Technology (ScienceDirect), vol.12, no.2 pp. 1-5, 2014 [Link]

Conferences []

1. K Naveen Kumar, Jha RR, C Krishna Mohan, Tallamraju RB. “Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution”, in Proc. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), pp. 4999-5009, 2025. [Link]
2. Peketi Divya, C Krishna Mohan, Sumanth Yenduri, Sobhan Babu, "FSF3A: Federated Spatial Feature Alignment and Adaptive Aggregation for Heterogeneous Brain Tumor Segmentation", Accepted in British Machine Vision Conference (BMVC), 2025
3. Khin Cho Win, Zahid Akhtar, and C. Krishna Mohan, “Orthogonality-Aware Projection-Based Feature Enhancement for Fine-Grained Facial Expression Recognition in the Wild”, Accepted in Int. Conf. on Machine Vision (ICMV), 2025.
4. Khin Cho Win, Zahid Akhtar and C Krishna Mohan, "CE-KD: Class-Wise Expert-based Knowledge Distillation for Facial Expression Recognition", Accepted in IEEE Int. Conf. on Advanced Visual and Signal-Based Systems (AVSS), 2025.
5. Khin Cho Win, Zahid Akhtar, and C. Krishna Mohan, “CFE-FER: Channel-Guided Feature Enhancement for Robust Facial Expression Recognition”, Accepted in Int. Conf. on Machine Vision (ICMV), 2025
6. A Soumya, C Krishna Mohan and Linga Reddy Cenkeramaddi, “PointBi-FPN: An Extension to Point Pillars for LiDAR 3D Object Detection in Autonomous Vehicles Using Bi-Directional Feature Pyramid Network”, in Proc. IEEE Int. Conf. TENCON, pp. 1950-1953, 2024. [Link]
7. Soumya A , Krishna Mohan C , and Linga Reddy Cenkeramaddi, "High Precision Single Shot Object Detection in Automotive Scenarios", in Proc. 19th Int. Conf. on Computer Vision Theory and Applications (VISAPP), pp. 604-611, 2024. [Link]
8. Khin Cho Win, Zahid Akhtar and C Krishna Mohan, “Robust Facial Emotion Recognition System via De-Pooling Feature Enhancement and Weighted Exponential Moving Average”, in Proc. IEEE Int. Conf. TENCON, pp. 116-119, 2024. [Link]
9. Ishu Priya, C Krishna Mohan, "FL-PSeC: Federated Learning-Pseudo Labeled Medical Image Segmentation with Personalized Class Balancing Semi-supervised Approach", in Proc. Int. Conf on Pattern Recognition (ICPR), pp. 227-241, 2024 [Link]
10. Kondapally Madhavi, K Naveen Kumar, C Krishna Mohan, “Object Detection in Transitional Weather Conditions for Autonomous Vehicles”, in Proc. Int. Joint Conf. on Neural Networks (IJCNN), IEEE, pp. 1-8, 2024. [Link]
11. Damalla Rambabu, Swetha G, Datla Rajesh Reddy, Vishnu Chalavadi , C Krishna Mohan, “RSZero-CSAT: Zero-Shot Scene Classification in Remote Sensing Imagery Using a Cross Semantic Attribute-Guided Transformer”, in Proc. Int. Joint Conf. on Neural Networks (IJCNN), IEEE, pp. 1-8, 2024. [Link]
12. Aveen Dayal, Rishabh Lalla, Vineeth Balasubramanian, Linga Reddy Cenkeramaddi, C. Krishna Mohan, Abhinav Kumar, "Improving Unsupervised Domain Adaptation: A Pseudo-Candidate Set Approach", in Proc. European Conference on Computer Vision (ECCV), pp. 127-144, 2024. [Link]
13. K Naveen Kumar, Reshmi Mitra, C Krishna Mohan, “Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space”, in Proc. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), pp. 24387-24397, 2024. [Link]
14. Khin Cho Win, Zahid Akhtar, and C. Krishna Mohan, "Does Hard-Negative Contrastive Learning Improve Facial Emotion Recognition?", in Proc. Int. Conf. on Machine Vision and Applications (ICMVA), pp. 162–168, 2024. [Link]
15. K Naveen Kumar, C Krishna Mohan, Aravind Machiry, “Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning”, in Proc. ACM Conference on Data and Application Security and Privacy (ACM CODASPY), pp. 233-244, 2024. [Link]
16. Aveen Dayal, Vimal K B, Linga Reddy Cenkeramaddi, C Krishna Mohan, Abhinav Kumar, Vineeth N. Balasubramanian, “Margin-based Adversarial Learning for Domain Generalization”, in Proc. Int. Conf. on Neural Information Processing Systems (NeurIPS), pp. 58938-58952, 2023. [Link]
17. Peketi Divya, Vishnu Chalavadi, C. Krishna Mohan, Yen Wei Chen, "FLWGAN: Federated Learning with Wasserstein Generative Adversarial Network for Brain Tumor Segmentation", in Proc. Int. Joint Conf. on Neural Networks (IJCNN), IEEE, pp. 1-8, 2023. [Link]
18. Soumya. A, Linga Reddy Cenkeramaddi, Chalavadi Vishnu, and Krishna Mohan C, "Multi-Class Object Classification using Deep Learning Models in Automotive Object Detection Scenarios"; in Proc. 16th Int. Conf. on Machine Vision (ICMV), pp.48-55, 2023. [Link]
19. Soumya. A, Linga Reddy Cenkeramaddi, Chalavadi Vishnu, Yash Vinod, and Krishna Mohan C, "Multi-Target Classification using Deep Learning Models for Automotive Applications", in Proc. 11th Int. Conf. on Control, Mechatronics, and Automation (ICCMA), pp.31-36, 2023. [Link]
20. Mrinmay Sen, C Krishna Mohan, Kai Qin, “FopLAHD: Federated optimization using Locally Approximated Hessian Diagonal”, in Proc. Int. Conf. Big Data Analytics (BDA), pp. 235-245, 2023. [Link]
21. Mrinmay Sen, C Krishna Mohan, Kai Qin, "Federated Optimization with Linear-time approximated Hessian diagonal", in Proc. Int. Conf. on Pattern Recognition and Machine Intelligence (PReMI2023), pp. 106-113, 2023. [Link]
22. Mrinmay Sen and C. Krishna Mohan, “NOAH: Newton method of Optimization with Approximated Hessian”, in Proc. IEEE Int. Conf. on High Performance Computing & Communications (HPCC), Melbourne, Australia, Dec. 17-21, 2023.
23. Mrinmay Sen, C. Krishna Mohan and Kai Qin, “Nys-FL: A Communication Efficient Federated Learning with Nyström Approximated Global Newton Direction”, in Proc. IEEE Int. Conf. on High Performance Computing & Communications (HPCC), Melbourne, Australia, Dec. 17-21, 2023.
24. Mrinmay Sen and C. Gayatri, “FReNG: Federated Optimization by using Regularized Natural Gradient Descent”, in Proc. Int. Conf. on Machine Learning and Applications (ICMLA), Florida, USA, Dec. 15-17, 2023.
25. P Harinadha, C. Krishna Mohan, “Leaf Based Tomato Plant Disease Detection Using Generated Images from WGP-ESR GAN”, in Proc. IEEE Int. Conf. on Data Science and Network Security (ICDSNS-2023), pp. 1-6, 2023. [Link]
26. Suresh Samudrala, C. Krishna Mohan, “MRI Brain Tumor Detection and Classification Using U-NET CNN", in Proc. IEEE Int. Conf. on Data Science and Network Security (ICDSNS-2023), pp. 1-5, 2023. [Link]
27. P Harinadha, C. Krishna Mohan, “Tomato Plant Leaf Disease Detection Using Transfer Learning-based ResNet110”, in Proc. IEEE Int. Conf. on Data Science and Network Security (ICDSNS-2023), pp. 1-8, 2023. [Link]
28. Swetha G, Rajesh Datla, C Krishna Mohan, “MS-VACSNet: A Network for Multi-scale Volcanic Ash Cloud Segmentation in Remote Sensing Images”, in Proc. Int. Conf. on Machine Vision Applications (ICMVA), pp. 1-6, 2023. [Link]
29. C. Nagaraju, Mrinmay Sen, C. Krishna Mohan, "Handling Data Heterogeneity in Federated Learning with Global Data Distribution", in Proc. Int. Conf. on Image Processing and Vision Engineering (IPVE), pp. 121-125, 2023. [Link]
30. Nagaraju C, Ramesh Yenda and Krishna Mohan C, “A Data Parallel Approach for Distributed Neural Networks to Achieve Faster Convergence”, in Proc. Int. Conf. on Machine Vision (ICMV 2023), pp. 380-389, 2023. [Link]
31. C. Nagaraju, Mrinmay Sen, and C. Krishna Mohan, "FONN: Federated Optimization with Nys-Newton." in Proc. IEEE Int. Conf. TENCON, Chiang Mai, Thailand, pp. 530-534, Oct. 31 - Nov. 3, 2023.
32. Sharma J, Rajesh Reddy Datla, Yenduri Sravani, Chalavadi Vishnu, and C Krishna Mohan. "Aircraft type recognition in remote sensing images using mean interval kernel", in Proc. Int. Conf. on Image Processing and Vision Engineering (IPVE), pp. 166-173,2022. [Link]
33. Amit R.A., C Krishna Mohan, "Federated Learning: Dataset Management for Airport Object Representations Using Remote Sensing Images", in Proc. IEEE Aerospace Conference (AERO), pp. 1-14, 2022.
34. Amit R.A., C Krishna Mohan, “GeoMask: Foreign Object Debris Instance Segmentation Using Geodesic Representations”, in Proc. IEEE Aerospace Conference (AERO), pp. 1-9, 2022.
35. Amit R.A., C Krishna Mohan, “Geodesic Based Image Matching Network for the Multi-scale Ground to Aerial Geo-localization”, in Proc. IEEE Aerospace Conference (AERO), pp. 1-9, 2022.
36. Amit R.A., C Krishna Mohan, “Quantitative Analysis to find the optimum scale range for object representations in Remote Sensing Images”, in Proc. Int. Conf. on Computer Vision Theory and Applications (VISSAP), 2022.
37. J. Sharma., P. Divya., V. Chalavadi., C. L. Reddy., B. H. Shekar., and Krishna Mohan C. "Deformable and structural representative network for remote sensing image captioning ", in Proc. Int. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications.(VISAPP), pp. 56-64, 2022. [Link]
38. Sharma J, Peketi Divya, Sravani Yenduri, Shekar B H and C Krishna Mohan, “Structural Representative Network for Remote Sensing Image Captioning”, in Proc. Int. Conf. on Machine Vision (ICMV), pp. 519-527, 2022. [Link]
39. Yenduri Sravani, Vishnu Chalavadi, and C. Krishna Mohan. "STIP-GCN: Space-time interest points graph convolutional network for action recognition", in Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN), pp. 1-8, 2022. [Link]
40. Sravani Yenduri, Chalavadi Vishnu, and C. Krishna Mohan. , "Adaptive spatial and temporal aggregation for table tennis shot recognition", in Proc. Int. Conf. on Machine Vision (ICMV), pp. 132-139, 2022. [Link]
41. B H Shekar, Smannan, Habtuhailu, C Krishna Mohan, Linga Cenkeramaddi. “Performance Analysis of Deep Neural Networks for Covid-19 Detection from Chest Radiographs”, in Proc. Int. Conf. on Machine Vision (ICMV), 2022.
42. Yenduri Sravani, Nazil Perveen, Chalavadi Vishnu, and C Krishna Mohan, “Fine-grained Action Recognition using Attribute Vectors", in Proc. 17th Int. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), pp. 134-143, 2022. [Link]
43. Dandpati Kumar Bhargav, Sai Chandra Teja R, Sparsh Mittal, Biswabandan Panda, and C. Krishna Mohan. "Inferring DNN layer-types through a Hardware Performance Counters based Side Channel Attack", in Proc. Int. Conf. on AI-ML-Systems, pp. 1-7, 2021. [Link]
44. Subhrajit Nag, Yash Khandelwal, Sparsh Mittal, C Krishna Mohan, and A. Kai Qin. "ARCN: A Real-time Attention-based Network for Crowd Counting from Drone Images", in Proc. IEEE Int. Conf. India Council (INDICON), pp. 1-6, 2021. [Link]
45. Rajeshreddy Datla, Chalavadi Vishnu, and C. Krishna Mohan. "A multimodal semantic segmentation for airport runway delineation in panchromatic remote sensing images", in Proc. Int. Conf. on Machine Vision (ICMV), Vol. 12084, pp. 46-52, 2021. [Link]
46. Rajeshreddy Datla, Chalavadi Vishnu, and C. Krishna Mohan. "A framework to derive geospatial attributes for aircraft type recognition in large-scale remote sensing images", in Proc. Int. Conf. on Machine Vision (ICMV), Vol. 12084, pp. 172-179, 2021. [Link]
47. Rajeshreddy Datla, Chalavadi Vishnu, and C Krishna Mohan. "Scene Classification in Remote Sensing Images using Dynamic Kernels", in Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN), pp. 1-8, 2021. [Link]
48. Prudviraj Jeripothula, Chalavadi Vishnu, and C. Krishna Mohan. "Attentive Contextual Network for Image Captioning", in Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN), pp. 1-8, 2021. [Link]
49. Debaditya Roy, K Naveen Kumar, C. Krishna Mohan, "Defining Traffic States using Spatio-Temporal Traffic Graphs", in Proc. IEEE Intelligent Transport Systems Conference (ITSC), pp. 1-6, 2020. [Link]
50. Dinesh Singh, C. Vishnu, C. Krishna Mohan, "Real-Time Detection of Motorcyclist without Helmet using Cascade of CNNs on Edge-device", in Proc. IEEE Intelligent Transport Systems Conference (ITSC), pp. 1-8, 2020. [Link]
51. Nandan Kumar Jha, Rajat Kumar Saini, Bedant Das, Sparsh Mittal, C Krishna Mohan, "ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks", in Proc. IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1627-1636, 2020. [Link]
52. Nazil Perveen and C. Krishna Mohan, "Configural representation of facial action units for spontaneous facial expression recognition in the wild", in Proc. 15th Int. Joint conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), pp. 93-102, 2020. [Link]
53. Nazil Perveen, C. Krishna Mohan, and Yen. Wei Chen, "Quantitative Analysis of Facial Paralysis Using GMM and Dynamic Kernels", in Proc. 15th Int. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), pp. 173-184, 2020. [Link]
54. Debaditya Roy, Tetsuhiro Ishizaka, Atsushi Fukuda, and C. Krishna Mohan, "Vehicle Trajectory Prediction at Intersections using Interaction based Generative Adversarial Networks'', in Proc. IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, pp. 2318-2323. DOI: 10.1109/ITSC.2019.8916927, 2019. [Link]
55. Dinesh Singh and C. Krishna Mohan, "Projection-SVM: Distributed Kernel Support Vector Machine for Big Data using Subspace Partitioning", in Proc. IEEE Int. Conf. on Big Data (Big Data), Seattle, WA, USA, pp. 74-83. DOI: 10.1109/BigData.2018.8622478, 2018. [Link]
56. Dinesh Singh, Abhijeet Bhure, Sumit Mamtani and C. Krishna Mohan, “Fast-BoW: Scaling Bag-of-Visual-Words Generation", in Proc. British Machine Vision Conference (BMVC), p.287, 2018. [Link]
57. Debaditya Roy, G.S. Sanjay Bharath, Manikumar T., and C. Krishna Mohan. “Tracking rapid body deformation using sparse representation of deep features”, in Proc. of the 11th Indian Conf. on Computer Vision, Graphics and Image Processing (ICVGIP), ACM, Article 70, pp. 17, 2018.
58. Debaditya Roy, C. Krishna Mohan, and K. Sri Rama Murty, “Action recognition based on discriminative embedding of actions using Siamese networks”, in Proc. 2018 IEEE Int. Conf. on Image Processing (ICIP), 2018. [Link]
59. C. Vishnu, Dinesh Singh, C. Krishna Mohan and Ch. Sobhan Babu, “Detection of Motorcyclists without Helmet in Videos using Convolutional Neural Network”, in Proc. IEEE Int. Joint Conf. on Neural Network (IJCNN), pp. 3036-3041, 2017. [Link]
60. Debaditya Roy, K. Sri Rama Murty and C. Krishna Mohan, “Action-vectors: Unsupervised Movement Modeling for Action Recognition”, in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1602-1606, 2017. [Link]
61. Dinesh Singh, C. Vishnu, and C. Krishna Mohan, “Visual Big Data Analytics for Traffic Monitoring in Smart City”, in Proc. IEEE Int. Conf. on Machine Learning and Applications (ICMLA), pp. 886-891, 2016. [Link]
62. Nazil Perveen, Dinesh Singh and C. Krishna Mohan, “Spontaneous Facial Expression Recognition: A Part Based Approach”, in Proc. IEEE Int. Conf. on Machine Learning and Applications (ICMLA), pp. 819-824, 2016. [Link]
63. Dinesh Singh and C. Krishna Mohan, “Distributed Quadratic Programming Solver for Kernel SVM using Genetic Algorithm”, in Proc. IEEE Int. Congress on Evolutionary Computation (IEEE CEC), pp. 152-159, 2016. [Link]
64. Kunal Dahiya, Dinesh Singh and C. Krishna Mohan, “Automatic Detection of Bike-riders without Helmet using Surveillance Videos in Real-time”, in Proc. Int. Joint Conf. on Neural Networks (IJCNN), pp. 3046-3051, 2016. [Link]
65. M. Srinivas, Debaditya Roy and C. Krishna Mohan, “Discriminative feature extraction of X-ray images using deep neural networks”, in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 917-921, 2016. [Link]
66. M. Srinivas and C. Krishna Mohan, “Classification of medical images using edge based features and sparse representation”, in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, pp. 912-916, 2016. [Link]
67. A Tirupathi Rao, N. Pattabhi Ramaiah and C. Krishna Mohan, “Nearest neighbor minutia quadruplets based fingerprint matching with reduced time and space complexity”, in Proc. IEEE Int. Conf. on Machine Learning and Applications (ICMLA), pp. 378-381, 2015. [Link]
68. M. Srinivas and C. Krishna Mohan, “Multi-level classification: A generic classification method for medical data sets”, in Proc. IEEE Int. Conf. on E-health Networking, Application Services (Healthcom), pp. 262-267, 2015. [Link]
69. N. Pattabhi Ramaiah, N. Srilatha, C. Krishna Mohan, “Sparsity-based iris classification using iris fiber structures”, in Proc. IEEE Int. Conf. of Biometrics Special Interest Group (BIOSIG), pp. 1-4, 2015. [Link]
70. Debaditya Roy, K. Murty, and C. Krishna Mohan, “Feature selection using deep neural networks”. in Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN), pp. 1-6, 2015. [Link]
71. M. Srinivas, R. Bharath, Pachamuthu Rajalakshmi and C. Krishna Mohan, “Sparseland model for speckle suppression of B-mode ultrasound images”, in Proc. IEEE Nat. Conf. on Communication (NCC), pp. 1-6, 2015. [Link]
72. Debaditya Roy, M. Srinivas, and C. Krishna Mohan, “Sparsifying dense features for action classification”, in Proc. ACM Int. Conf. on Perception and Machine Intelligence (PerMIn), pp. 211-217, 2015. [Link]
73. N. Pattabhi Ramaiah, Earnest Paul Ijjina, C. Krishna Mohan, “Illumination invariant face recognition using convolutional neural networks”, in Proc. IEEE Int. Conf. on Signal Processing, Informatics, Communication and Energy Systems (SPICES), pp. 1-4, 2015. [Link]
74. Earnest Paul Ijjina, and C. Krishna Mohan, “Human action recognition based on motion capture information using fuzzy convolution neural networks”. in Proc. IEEE Int. Conf. on Advances in Pattern Recognition (ICAPR), pp. 1-6, 2015. [Link]
75. Earnest Paul Ijjina and C. Krishna Mohan, “Facial expression recognition using kinect depth sensor and convolutional neural networks”, in Proc. IEEE Int. Conf. on Machine Learning and Applications (ICMLA), Detroit, USA, pp. 392-396, 2014. [Link]
76. Earnest Paul Ijjina and C. Krishna Mohan, “One-shot periodic activity recognition using convolutional neural networks”, in Proc. IEEE Int. Conf. on Machine Learning and Applications (ICMLA), Detroit, USA, pp. 388-391, 2014. [Link]
77. Earnest Paul Ijjina and C. Krishna Mohan, “Human action recognition based on recognition of linear patterns in action bank features using convolutional neural networks”, in Proc. IEEE Int. Conf. on Machine Learning and Applications (ICMLA), Detroit, USA, pp. 178-182, 2014. [Link]
78. Earnest Paul Ijjina and C. Krishna Mohan, “Human action recognition based on MOCAP information using convolution neural networks”, in Proc. IEEE Int. Conf. on Machine Learning and Applications (ICMLA), Detroit, USA, pp. 159-164, 2014. [Link]
79. M. Srinivas, Debaditya Roy, and C. Krishna Mohan, "Learning sparse dictionaries for music and speech classification", in Proc. 19th IEEE Int. Conf. on Digital Signal Processing (DSP), pp. 673-675, 2014. [Link]
80. M. Srinivas and C. Krishna Mohan, "Medical images modality classification using multi-scale dictionary learning", in Proc. 19th IEEE Int. Conf. on Digital Signal Processing (DSP), pp. 621-625, 2014. [Link]
81. Shyju Wilson, M. Srinivas and C. Krishna Mohan, "Dictionary based action video classification with action bank", in Proc. 19th IEEE Int. Conf. on Digital Signal Processing (DSP), pp. 597-600, 2014. [Link]
82. M. Srinivas, Debaditya Roy, and C. Krishna Mohan, "Music genre classification using On-line dictionary learning", in Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN), pp. 1937-1941, 2014. [Link]
83. A. Tirupathi Rao, N. Pattabhi Ramaiah, Ahmed Babu I.A.S, and C. Krishna Mohan, "Biometrics in e-governance and academia using hand-held fingerprint terminals", in Proc. Elsevier Int. Conf. on Advances in Communication, Network, and Computing (CNC), pp. 653-658, 2014. [Link]
84. N. Pattabhi Ramaiah, and C. Krishna Mohan, "De-noising slap fingerprint images for accurate slap fingerprint segmentation", in Proc. IEEE Int. Conf. on Machine Learning and Applications and Workshops (ICMLA), vol. 1, pp. 208-211, 2011. [Link]
85. N. Pattabhi Ramaiah, and C. Krishna Mohan, "ROI-based tissue type extraction and volume estimation in 3D brain anatomy", in Proc. Int. Conf. on Image Information Processing (ICIIP), pp. 1-5, 2011. [Link]
86. N. Pattabhi Ramaiah, and C. Krishna Mohan, "De-duplication of photograph images using histogram refinement", in Proc. IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 391-395, 2011. [Link]
87. M. Srinivas, and C. Krishna Mohan, "Efficient clustering approach using incremental and Hierarchical clustering methods", in Proc. IEEE Int. Joint Conf. on Neural Networks (IJCNN), pp. 1-7, 2010. [Link]
88. C. Krishna Mohan, N. Dhananjaya, and B. Yegnanarayana, "Video shot segmentation using late fusion techniques", in Proc. Int. Conf. on Machine Learning and Applications (ICMLA), pp. 267-270, 2008. [Link]
89. C. Krishna Mohan and B. Yegnanarayana, “Edge-based classification of sports videos using HMM,” in Proc. Indian Int. Conf. on Artificial Intelligence (IICAI), pp. 559-564, 2007.
90. C. Krishna Mohan, Suryakanth V. Gangashetty, and B.Yegnanarayana, “Categorization of sports videos using SVMs”, in Proc. Int. Conf. on Cognitive and Neural Systems (ICCNS), pp. 47, 2007.
91. C. Krishna Mohan, N. Dhananjaya, Suryakanth V. Gangashetty, and B.Yegnanarayana, “Sports video classification using autoassociative neural network models”, in Proc. Int. Conf. on cognitive and neural systems (ICCNS), pp. 30, 2006.
92. Vakkalanka Suresh, C. Krishna Mohan, R. Kumara Swamy, and B. Yegnanarayana, “Combining multiple evidence for video classification”. in Proc. IEEE Int. Conf. on Intelligent Sensing and Information Processing (ICISIP), 2005. [Link]
93. Vakkalanka Suresh, C. Krishna Mohan, R. Kumara Swamy, and B. Yegnanarayana, “Content-based video classification using support vector machines”, in Proc. Int. Conf. on Neural Information Processing (ICONIP), pp. 726-731, 2004. [Link]

Workshops [6]

1. K. Madhavi, K. Naveen Kumar, and C. Krishna Mohan, "TransWardX: An Explainable Black-box Object Detection Attack for Autonomous Driving in Transitional Weather Conditions", in Pattern Recognition. ICPR vol 15619, pp. 275-290, Springer, Cham. doi:10.1007/978-3-031-88223-4_20, 2024. [Link]
2. K. Naveen Kumar, Digvijay S. Pawar, and C. Krishna Mohan. "Open-air Off-street Vehicle Parking Management System Using Deep Neural Networks: A Case Study", In Proc. 14th IEEE Int. Conf. on COMmunication Systems & NETworkS (COMSNETS), pp. 800-805, doi: 10.1109/COMSNETS53615.2022.9668364, 2022. [Link]
3. K Naveen Kumar, C Vishnu, Reshmi Mitra, C Krishna Mohan, "Black-box Adversarial Attacks in Autonomous Vehicles", in IEEE Applied Imagery Pattern Recognition (AIPR), pp. 1-7, 2020. [Link]
4. Earnest Paul Ijjina and C. Krishna Mohan, "View and illumination invariant object classification based on 3D color histogram using convolutional neural networks", IEEE 12th Asian Conference on Computer Vision (ACCV), Workshops, Singapore, pp. 316-327, Nov. 2014. DOI: 10.1007/978-3-319-16628-5_23 [Link]
5. Earnest Paul Ijjina and C. Krishna Mohan, "Human action recognition using action bank features and convolutional neural networks", IEEE 12th Asian Conference on Computer Vision (ACCV), Workshops, Singapore, pp. 328-339, Nov. 2014. DOI: 10.1007/978-3-319-16628-5_24 [Link]