1. Sponsoring Agency: Dhristi CPS Foundation

• Title: Agri-Genomics Data Management Platform.
• Principal Investigator: Prof. Aruna Tiwari.
• Co-PI: Dr. Milind Ratnparkhe, ICAR_IISR Indore.
• Co-PI: Mr. Arun Sharma, CEO, Devaditya Technocrates Pvt. Ltd. Indore.
• Start Details: November-2023.
• End Details: November-2024.
• Funding Amount: 10 Lakhs INR
• Current Status: Ongoing;

2. Sponsoring Agency: Centre for Rural Development and Technology (CRDT)

• Title: Development of Mobile app for farmers to help them in Agricultural activities: Krishi Sewa (Approved in Dec 2022).
• Collaborator: Dr. Abhishek M. Waghaye, CIAE Bhopal.
• Funding Amount: 2 Lakhs INR
• Current Status: Ongoing;

3. Sponsoring Agency: National Supercomputing Mission, HPC Applications Development Funded Research Project by DST in collaboration with the Ministry of Electronics and Information Technology (MeiTY).

• Title: A Novel Scalable High-Performance Machine Learning Algorithms for NGS Analysis of Genomics Data at Exascale Level (Approved in March 2021 for two years).
• Collaborator: Dr Milind Ratnaparkhe, Senior Scientist (Biotechnology) ICAR-Indian Institute of Soybean Research (ICAR-IISR) Indore, Dr Neha Bharill, Assistant Professor, Department of Computer Science and Engineering, Mahindra University, Ecole Centrale School of Engineering, Hyderabad, and Dr Om Prakash Patel, Assistant Professor, Department of Computer Science and Engineering, Mahindra University, Ecole Centrale School of Engineering, Hyderabad.
• Funding Amount: 25 Lakhs INR (for first year);
• Current Status: Completed;

4. Sponsoring Agency: Council of Scientific and Industrial Research (CSIR), Delhi, Govt. of India.

• Title: A novel integrated scalable system for Protein sequence and SNP data analysis using clustering and classification (Approved in December 2020 for three years).
• Collaborator: Dr Milind Ratnaparkhe, Senior Scientist (Biotechnology) ICAR-Indian Institute of Soybean Research (ICAR-IISR) Indore and Dr Neha Bharill, Assistant Professor, Department of Computer Science and Engineering, Mahindra University, Ecole Centrale School of Engineering, Hyderabad.
• Funding Amount: 25 Lakhs INR;
• Current Status: Ongoing;

5. Sponsoring Agency: Indo-Norwegian collaboration in intelligent offshore mechatronics systems (INMOST), Norwegian Research Council (RCN) under INTPART Scheme

• Title: Mobility Project (Approved in 2020 for three years).
• Collaborator : Lead Partner in Norway: Prof. Jing Zhou and Prof. Linga Reddy Cenkeramaddi, University of Agder (UiA), Norway, Participants from other institutes: Prof. Houxiang Zhang from NTNU, Norway, Dr. Jan Einar Gravdal from NORSE, Norway, Dr. Saidi Reddy Parne from NIT Goa, India, Dr. Santosh Kumar Vishvakarma, Co-Principal Investigator: Prof. Ram Bilas Pachori, Dr. Aruna Tiwari and Dr. Abhinoy Singh from IIT Indore, Dr. Rama Krishna Sai Gorthi from IIT Tirupati and Dr. Shaikshavali Chitraganti from IIT Palakkad.
• Funding Amount: 56,20,000 NOK (42, 000 NOK for IIT Indore);
• Current Status: Ongoing;

6. Sponsoring Agency: The Ministry of Electronics and Information Technology (MeitY), Union Government of the Republic of India

• Title: Resource Constrained Artificial Intelligence:"Design of Novel Algorithms for Action recognition using Generative Adversarial Networks", (Approved on March, 2020).
• Funding Amount: Two Crore Forty Four Lakh INR;
• Collaborator :CSIR CEERI Pilani (Chief PI), IIT Indore (Myself as Co-PI), IIT Jodhpur (Co-PI), IIIT Allahabad (Co-PI).
• Current Status: Ongoing;

7. Sponsoring Agency: Council of Scientific and Industrial Research (CSIR), Delhi, Govt. of India.

• Title: Development of an Efficient Scalable Clustering Algorithms for Big Data and investigation of Integrated system for Protein Sequence Classification (Approved on October 10, 2017 for three years).
• Collaborator : Dr. Milind Ratnaparkhe, Senior Scientist (Biotechnology) ICAR-Indian Institute of Soybean Research (ICAR-IISR) Indore
• Funding Amount: 30 Lakhs INR;
• Current Status: Completed;

8. Sponsoring Agency: Joint sponsors CSIR - Central Electronics Engineering Research Institute (CSIR-CEERI)Pilani & Indian Institute of Technology Indore India. (Approved in March 2017 for one year).

• Title 1: Extreme Learning Machine & Its Hardware Realization
• Title 2: Quantum based learning algorithm & Its Hardware Realization for signature verification
• Collaborator : Dr. Santanu Chaudhary, Director, CSIR-CEERI Pilani; Dr. Sanjay Singh, Scientist, CSIR-CEERI Pilani
• Funding Amount: 6 Lakhs INR;
• Current Status: Completed;

1. Sponsoring Agency: GIAN Short Term Course Scheme of Ministry of Human Resources and Development (MHRD). Approved on 30th December 2015

• Title: Advanced Neural Network Learning Theory
• Course Conducted: July 6 to July 15 2016
• Funding Amount: 8 Lakhs INR; 12000 USD (Sponsored visit of Prof. Suresh Sundaram from NTU Singapore to IIT Indore)

2. For conduction of first NTU-India Connect Program of our country: an International Symposium on Computational Mathematics, Optimization, and Computational Intelligence (CMOCI 2017) during July 17-19, 2017. Grants received from following Sponsoring agencies :

(i) Science and Engineering Research Board (SERB), New Delhi (2 Lakhs INR)
(ii) National Board for Higher Mathematics (NBHM), DAE, Mumbai ( 30,000 INR)
(iii) Council of Scientific and Industrial Research (CSIR), New Delhi (1 Lakh INR)
(iv) Indian National Science Academy (INSA), New Delhi (1.5 Lakhs INR)
(v) State Bank of India (SBI), Simrol Branch, Indore (1 Lakh INR)

• 3-D Sign Language Gesture Recognition and Leap Motion, an aid for the Dumb: The aim of the project is to develop a Gesture Recognition system for practices of human machine interaction. This project would involve the use of a leap motion 3-D control infrared camera to detect hand gestures. These gestures first mapped to the text than to the voice signals. The system is trained with the standard sign language gesture and acts as an automatic voice system for the dumb to recognize the voice form their gestures.
• TSP using Ant Colony Optimization: The aim of the project is to develop system that solve the travelling salesman problem using Ant colony optimization method where a user has to travel a list of cities and the distance between each pair of cities and come out with the set of approximate solutions which have a polynomial time complexity.
• Face Detection using Neural Networks: The aim of the project is to develop the face detection system using back propagation neural networks and to analyze the performance of various artificial neural networks in terms of error and rate of convergence with the change of input parameter.
• Neuro-Fuzzy Classifier for Protein Classification Using New Technique for Feature Extraction: The aim of the project is to develop a Neuro-Fuzzy classifier for protein classification using new techniques of feature extraction and to enhance the performance of classifier at every stage to validate the classification of amino acid.
• Gender Identification Using Neural Networks: The aim of the system is to develop the gender identification system using neural networks with hybrid approach which combine the template and feature based approach to develop a robust and an efficient solution for gender identification problem.
• License Plate Recognition: The aim of the project is to develop to an automated license plate recognition system using image processing, feature extraction and neural networks. This system utilizes color based license plate localization for finding and isolating the plate on the picture, which further goes through multiple levels of image pre-processing before final stage of character segmentation and neural network based recognition. Hopfield neural network is selected as a powerful tool to perform the recognition process.
• Face Recognition based on Neural Networks: The aim of the project is to develop the face recognition system using neural networks based on support vector machines and Haar classifiers. To develop the system we used Matlab Neural Network toolkit and Abrosoft face feature extractor.
• Number Plate Detection Using Neural NetworksL: The aim of the project is to develop a license plate recognition system with three integrated segment which include license plate detection, character segmentation and recognition where neural network is used for recognition, different wavelets are used for license plate detection and feature extraction are used for license character recognition.

• Designing a parking lot: The aim of the project is to get the whole information about the vehicle by detecting and analyzing its license plate using MACHINE LEARNING techniques. With the help of license plate number, we will get information regarding its size like whether it’s a bike, car or heavy truck. According to it, we will assign an available parking slot for the vehicle using STACK data structure. It will produce a slip for parking having name of the vehicle owner from the data collected from license plate, and details like parking fee and parking slot number according to its size, using HASHING data structure.
• Face Recognition System Using Genetic Algorithm and SVM:The aim of the project is to develop a face recognition system using genetic algorithm and support vector machines and to compare the recognition rates with PCA (Principal Component Analysis) and LDA (Linear Discriminate Analysis) algorithms using available databases.
• Object Presence Notification (time-based) Algorithm: The aim of the project is to develop an object presence notification (time-based) algorithm that detect certain objects on real-time video stream and generate a notification if they are present for more than specified time.
• Speed Detection: The aim of the project is to use a deep learning based algorithm that takes a video as an input, processes and gives the list of coordinates of all vehicles and their speed.
• Text Summarization: Text summarization (TS) is the process of identifying the most salient information in a document or set of related documents and conveying it in less space than the original text. In principle, TS is possible because of the naturally occurring redundancy in text and because important (salient) information is spread unevenly in textual documents. Identifying the redundancy is a challenge that hasn’t been fully resolved yet. Auto-summarization is a technique used to generate summaries of electronic documents.
• Simulation of Pacman Game: The aim of the project is to simulate Pacman Game. In this game we create a maze with alleys having coins in their way. Player has to collect all the coins in the maze without getting caught by the ghost bot, moves of which are processed using an algorithm..
• Simulation of Car Parking Game with Android base Mobile: Given a 6x6 grid which is divided into 36 small units, and this grid is made up of 36 small units. We have a car parked in the third row in such a way that it is blocked and cannot come out of the area without moving other cars which are also parked in the grid. The cars can only move in forward or backward direction. Our aim is to make a way for the target car to come out of the grid.
• Battleship Game: The game is played on four grids, two for each player. The grids are typically square – usually 10×10 – and the individual squares in the grid are identified by letter and number. On one grid the player arranges ships and records the shots by the opponent.
• Natural Language Processor: It is meant to be interface through which any user without knowing the peculiarities of the system can use it with their understanding and their own language. It can work as a word editor in addition to that if its programing mode is fired up.
• Simulation of Chinese Checkers Game: A basic strategy is to create or find the longest hopping path that leads closest to home, or immediately into it. Since either player can make use of any hopping 'ladder' or 'chain' created, a more advanced strategy involves hindering an opposing player in addition to helping oneself make jumps across the board.
• Tower of Hanoi puzzle: This is a web application to solve the puzzle of Tower of Hanoi. The application will provide the user a choice to solve the puzzle for N (N>=3) disks and also if user wants to see the solution then he/she can also see direct solution for a particular N.