Collaborator Organization: ICMR-National Institute of Epidemiology, (NIE -ICMR)

Title & Summary of Work:

Personalized Recommender System for Virus Research and Diagnosis Laboratory Network: Advancing Diagnostic Decision-Making through Artificial Intelligence.  The project aims to use artificial intelligence methods for Personalized Diagnostic Decision-Making & provide Optimized recommendations based on Patient's health information to identify viruses and prioritize pathological test recommendations

Collaborator Organization: AIIMS, Gorakhpur.

Title & Summary of Work:

Developing AI Based Risk Prediction Model to Gestational Diabetes Mellitus Using Anthropometric, Biochemical, and Genetic Markers. The identified primary objective of this Collaboration is to develop an ML-based predictive model for GDM using social, clinical, anthropometric, biochemical, and genetic parameters and to evaluate the predictive performance of the model in terms of sensitivity, specificity, and overall accuracy. Also to identify key predictors and their interactions that contribute most significantly to GDM risk.

Collaborator Organization: ICAR-National Institute of Biotic Stress Management, Chhattisgarh

Title & Summary of Work:

Artificial Intelligence based Strategies for Mitigating Biotic and Abiotic Stresses for Agricultural and Environmental Sustainability.
The project aims to develop AI-driven solutions to address the challenges posed by both biotic (e.g., pests, diseases) and abiotic (e.g., drought, extreme temperatures) stresses on agriculture. By utilizing machine learning models and data analytics, the project seeks to enhance crop resilience, optimize resource utilization, and promote sustainable agricultural practices, ultimately contributing to environmental sustainability and food security.
Some more problems identified for joint work are as follows:
• Deep Learning for Identifying Key Metabolites in Crop Infection and Defence.
• Predictive Modelling of Soil-Rhizosphere Interactions Using Deep Learning for Biotic Stress Management.
• Computer Vision based Real-time Detection, loss assessment and Classification of Crop Diseases in Diverse Agricultural Environments.

Collaborator Organization: CSIR- National Physical Lab, New Delhi.

Title & Summary of Work:

This joint work is to develop a deep learning model, designed to predict ambient noise levels in urban environments. Traditional noise prediction methods face challenges in accurately forecasting noise due to the complex and non-linear nature of noise data. The proposed Neural hierarchical interpolation-based model overcomes these challenges by effectively extracting deep features and learning long-term dependencies in time-series noise data. The model was tested across 30 sites from major Delhi NCR city location (Data of 3 years shared by Delhi Pollution Control Board and NPL), demonstrating superior accuracy and lowest error rates compared to existing models.

Collaborator Organization: All India Institute of Medical Sciences Kalyani, West Bengal

Title & Summary of Work:

• AI Enhanced Patient Prioritization Utilizing Diverse Physiological Parameters & ECG Data
• Precision Detection of Abdominal and Chest Haemorrhage via AI-Driven Ultrasound Imaging Analysis
• Optimizing CPR Assessment and Video Data Analysis for First Responder Evaluation

Collaborator Organization: King George's Medical University, Lucknow

Title & Summary of Work:

Predictors of Long-Term Survival Among SSPE Patients Using Artificial Intelligence Model. (Subacute Sclerosing Panencephalitis)
The objective is to develop an innovative Artificial Intelligence Model for predicting long-term survival in SSPE.  To develop an Artificial Intelligence model for the prediction of survival in SSPE and to make the Artificial Intelligence model, learn and train with a set of data parameters in SSPE. Internal and external validation of the Artificial Intelligence model. 

Collaborator Organization: ICMR - National Institute for Research in Bacterial Infections

Title & Summary of Work:

To develop an AI incorporated limited sampling strategy (LSS) to predict specific Pharmacokinetic parameters of first line Anti TB drugs. Hypothesis: AI integrated limited sampling strategy (LSS) will be able to predict plasma concentrations at non-sampled time frames (Ppredicted) of intensive sampling and derive the ‘required’ PK values to predict PK failure and possible therapeutic failure.  Will start a project funded by ICMR Titled “Development of an Artificial Intelligence-driven Pharmacokinetics-based Algorithm as an Aid for Better Management of Drug Resistance in Tuberculosis"

Collaborator Organization: CSIR Unit for Research and Development of Information Products (URDIP)

Title & Summary of Work:

Artificial Intelligence-based Patent Renewal Prediction for Enhanced Strategic Decision-Making and Resource Allocation in Intellectual Property Management
This project aims to develop AI models that predict the likelihood of patent renewals, enabling better strategic decision-making and resource allocation in intellectual property management. By analyzing historical data and various factors influencing patent renewal decisions, the AI-based approach helps organizations optimize their IP portfolios, reduce costs, and maximize the value of their intellectual assets.

Collaborator Organization: Delsys

Title & Summary of Work:

Modelling of Automated Rehabilitation Systems using Artificial Intelligence based methods
This project focuses on developing AI-driven models to enhance automated rehabilitation systems. By integrating machine learning algorithms and real-time data analytics, the project aims to create personalized and adaptive rehabilitation protocols for patients, improving the effectiveness of therapy and accelerating recovery times.

Collaborator Organization: Thales (A French Multi-national company)

Title & Summary of Work:

Adversarial Attacks and Defense on Fact Recognition Systems.
This project investigates the vulnerabilities of fact recognition systems to adversarial attacks and explores robust defense mechanisms. By analyzing how AI models can be manipulated with deceptive inputs, the project aims to develop strategies to strengthen the resilience of fact recognition systems, ensuring their accuracy and reliability in real-world applications.

Collaborator Organization: BRNO University of Technology, Czech Republic

Title & Summary of Work:

Working Jointly with this research Group from Europe in various research problems. That includes Artificial Intelligence in Healthcare, AI based Assistive Devices, Mental Health, Cancer detection, Ovarian cancer, breast cancer etc. Working in a diverse set of research Problems.

Collaborator Organization: ULPGC - Universidad de Las Palmas de Gran Canaria, Spain

Title & Summary of Work:

Working in a Joint Research Problem to make a Deep Learning Model to classify pulmonary Sounds to detect pulmonary disorders.  1D DeepRespNet and 2D DeepRespNet are developed in this work that were trained and evaluated with normalised 1-D time series and 2-D spectrograms of six types of lung sounds.   

Collaborator Organization: Norwegian University of Science and Technology Trondheim, Norway

Title & Summary of Work:

The Following Two Research Problem has been Identified for joint work.
• Synthetic Medical Imaging Generation Using Conditional Diffusion Models and Neural Radiance Fields
• A Large Language Model Approach to Personalized Survival Prediction

Collaborator Organization: University of Ljubljana, Slovenia

Title & Summary of Work:

With this University of Slovenia, 2 problems has been identified:
1. Bias in sclera recognition
2. Near-real-time generation of biometric images with diffusion models.

Collaborator Organization: CSIR-Central Scientific Instruments Organisation (CSIO), Chandigarh.

Title & Summary of Work:

The work explores the use of machine learning models to predict the electrochemical properties of supercapacitor electrodes made from two different set of data acquiring approaches that are cyclic voltammetry and galvanostatic charge discharge method. Various models, including machine learning regression models and neural networks, were developed to forecast specific capacitance, electrical conductivity, and sheet resistance. The study demonstrates that proposed integration enhances the electrode's electrochemical performance, and machine learning can efficiently optimize the composition of these materials for improved energy storage capabilities.

Collaborator Organization: ICAR-National Bureau of Agriculturally Important Microorganisms (NBAIM), Mau

Title & Summary of Work:

Design and Development of Deep Neural Network for Tracking Foliar Disease Progression in Maize Across Developmental Stages, Cropping Patterns, and Agroclimatic Zones using Mobile Application. This study proposes using advanced AI techniques, including Generative AI, deep CNNs, and Transformer-based models, for early detection of foliar diseases in maize. The findings will be used to develop an AI-enabled mobile application that allows farmers to detect diseases in real-time and offline, reducing the need for expert intervention and enabling timely management.

Collaborator Organization: Indian Institute of Soil Science, Bhopal & ICAR Research Complex for Eastern Region, Patna, Division of Land and Water Management(L&WME)

Title & Summary of Work:

"Exploring the Potential of Machine Learning, big data analytics and Remote Sensing for Soil Health Monitoring in Indian Agroecosystems". This project aims to develop and validate machine learning models for predicting soil health parameters using remote sensing and big data analytics. By integrating data from remote sensing, soil sensors, and historical agricultural records, the project will provide a comprehensive analysis of soil health. Additionally, it seeks to create a scalable framework for continuous soil health monitoring across various agroecosystems in India.

Collaborator Organization: ICAR-National Bureau of Agriculturally Important Microorganisms (NBAIM), Mau (User-Agency)

Title & Summary of Work:

"Detection and Mapping of Foliar Diseases in Wheat Fields Using UAV-Based Hyperspectral Remote Sensing and Deep Learning for Precision Agriculture." The project focuses on detecting and mapping foliar diseases in wheat fields using UAV-based hyperspectral remote sensing combined with deep learning techniques. This approach enables precise disease identification and mapping, enhancing crop management and yield. The integration of advanced remote sensing and AI facilitates timely interventions and supports precision agriculture practices.

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