Undergraduate Projects​

Students:
Ms. Jamyang Tshomo, Ms. Sujitha Rajkumar, Ms. Rainy Varshney


Faculty Name:
Dr. Utkarsh Raj, Ms. Shivani Varshney


Short Description:
BTech Biotechnology students at NIIT University are harnessing the power of Artificial Intelligence to address critical healthcare challenges. Ms. Jamyang Tshomo presented the research paper, “Integrative Bioinformatics and ML Strategy for Identifying Lung Cancer Biomarkers,” at the 12th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO 2025), an IEEE & ANRF-sponsored conference held at Amity University, Noida.

The research integrates bioinformatics with machine learning to identify potential lung cancer biomarkers, demonstrating the growing role of AI in precision medicine. Co-authored by Ms. Sujitha Rajkumar and Ms. Rainy Varshney, the work has been published in the IEEE Xplore (Scopus-indexed) conference proceedings.

The project was carried out under the guidance of Dr. Utkarsh Raj, Associate Professor, and Ms. Shivani Varshney, Assistant Professor, Department of Biotechnology & Bioinformatics, NIIT University, showcasing the University’s commitment to AI-enabled interdisciplinary research with real-world impact.

Students:
Adarsh Bajpai, Alok Singh, Arvind Kumar, Diptadeep Dam, Kanishak Sharma, Kelzang Dorji, Nakul Yadav, Rishi Raj Singh


Faculty Name:
Dr. Jetendra Joshi


Short Description:
The innovative solutions using IoT and deep learning technologies showcases how global health challenges and plant health can be monitored worldwide. To find the innovative approach that leverages the Internet of Things (IoT) technology and machine learning techniques. By integrating IoT capabilities into a web-based application for early detection of Human diseases. To find the automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming

Students:

Banju Shriya, Anushka Kushwaha, Rasuti


Faculty Name:

Dr. Utkarsh Raj


Short Description:

This project will identify and validates key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC) through integrated bioinformatics analysis. It will explore gene expression, mutations, and drug interactions to uncover potential biomarkers and therapeutic targets, contributing to early diagnosis, improved understanding of NSCLC, and targeted drug discovery.

Students:

Priyanshu Chatterjee, Khushali Agarwal


Faculty Name:

Dr. Utkarsh Raj


Short Description:

This project will explore the discovery of novel inhibitors targeting the MLK4 protein, a key factor in colorectal cancer progression. Utilizing chemoinformatic approaches, molecular docking, pharmacokinetic analysis, and molecular dynamics simulations, it aims to identify effective and stable drug candidates, providing a foundation for developing targeted therapies for colorectal cancer.

Students:

Sujitha Rajkumar, Jamyang Ugyen Tshomo, Rainy Varshney


Faculty Name:

Dr. Utkarsh Raj


Short Description:

This project focuses on identifying potential biomarkers for lung cancer through an integrative analytical approach. The study aims to enhance early diagnosis, improve prognosis, and facilitate the development of targeted therapies, ultimately contributing to better patient outcomes and personalized treatment strategies.

Students:

Nikunj Sharma, Abhi Bhardwaj, Vartika Singh, T Somnath, Arjav Jhamb, Hardik Batra, Praveen, Puneet Shrivastava


Faculty Name:

Dr. Jetendra Joshi


Short Description:

In the SEM system the major challenge is still because of the fluctuation of environmental conditions and efficiency variables, even though solar energy is a crucial renewable source for environmentally friendly power generation To find the approach by combining the HCI, IoT and Generative Adversarial Networks (GANs) to design a SEM systems which is adaptive and optimize energy output.

Students:

Ishvit Khajuria, Khushi Aggarwal, Krishna Sharma, Manav Rohatgi, Parth Pahwa, Pranav Sahni, Rithesh V Reddy, Yashwanth Yalavali


Faculty Name:

Dr. Jetendra Joshi


Short Description:

Implementation of intelligence and ML models for safeguarding from attacks. The real world approach for monitoring and protection of computer infrastructure of organisation , detecting and alerting about threats to the infrastructure and using it for speeding up the process for cybersecurity analyst .AI-based intrusion detection systems (IDSs) to address cybercrimes, demonstrating their efficacy in detecting and preventing cyberattacks

Students:

Ajay Kumar, Anuvrat Gautam, Dhruv Temura, Guni Sharma, K Harshavardan Babu, Kartikeya Attrey, Krishan Rathi, Raajit J Singh, Rithik Sharma


Faculty Name:

Dr. Jetendra Joshi


Short Description:

Cybersecurity breaches targeting electrical substations constitute a significant threat to the integrity of the power grid, necessitating comprehensive defense and mitigation strategies. Any anomaly in information and communication technology (ICT) should be detected for secure communications in smart grid To propose the Large Models, for anomaly detection that can bridge the gap between raw data and actionable insights.

Students:

Ritupriya Chakraborty, Ananya Singh, Akasapu Suma Sri, Lavanya Pramod


Faculty Name:

Dr. Sanjeev Kumar


Short Description:

Anogeissus spp, popularly known as Dhok is a native tree of the Aravali region. It is documented for its contribution to arid ecosystems, medicinal properties and strength of timber. The development an in-vitro regeneration protocol of this tree will not only make its mass production feasible but will allow scientists to further research on this tree and explore its potential.

Students:

Arman Raghav, Abhi Chowdary, Sachin, Indurthi Sai Sowgandh, Saketh Sri ram b, Aryaman Singh Patwal


Faculty Name:

Dr. Ayan Nandy


Short Description:

Graph Burning problem is NP-complete when restricted to the trees of maximum degree three. This implies that the burning graph problem is NP-complete for binary trees, chordal graphs, bipartite graphs, planar graphs, and disconnected graphs. There is a polynomial time approximation algorithm with approximation factor 3 for general graphs. The project will focus on discovering a complete characterization of the degree sequences of trees that satisfy the inequality in Theorem 12 of Mohamed Omar et al’s Burning Graph Classes (https://arxiv.org/abs/2111.01328).

Students:

Karma Tshering Yangden, Sarabnoor Singh, Devansh Arora, Harshwardhan Saini


Faculty Name:

Dr. Ayan Nandy


Short Description:

In 2018 Bessy et al. provided (https://arxiv.org/pdf/1511.06023) a bound on burning number of trees which was improved by Das et al in 2023 (https://arxiv.org/pdf/2308.02825). The project will focus on further improving the bound on burning number of trees.

Students:

Abhay Jadli, chhavi Sharma, Jyotir Aditya Parida, Mannat Ashish Nayak, Mohit Khandal, Priyanshi Sharma, Ram Venugopal Menon, Suhana Mishra


Faculty Name:

Mr. Surender Singh


Short Description:

Threat detection through honeypot data involves identifying intrusion attempts, malware behaviors, and emerging threat trends. Students:

Karma Tshering Yangden, Sarabnoor Singh, Devansh Arora, Harshwardhan Saini


Faculty Name:

Dr. Ayan Nandy


Short Description:

Threat detection through honeypot data involves identifying intrusion attempts, malware behaviors, and emerging threat trends.

Students:

Khushi Jha, Aditya Chawla, Raunak Shailesh Sinha, Vineet Sahi


Faculty Name:

Mr. Surender Singh


Short Description:

DAISY proactively defends SDN against Denial-of-Service attacks by leveraging SDN’s centralized control and programmability for real-time detection, mitigation, and prevention of network disruptions.

Students:

Swapneel Ghosh, Abhishek Madaan, Gaourang Baria, Ishaan Joshi


Faculty Name:

Mr. Surender Singh


Short Description:

A SOAR Engine dynamically deploys honeypots based on attacker behavior, detecting botnet traffic, DDoS attacks, and malware, significantly enhancing attacker engagement time and providing actionable threat intelligence.

Students:

Vigneshwar Naganathan, Ayush Ranjan, Lakshya Devjani, Himanshu Rathi, Ayush Ranjan, Mayank Kohli, Vikhyat


Faculty Name:

Prof. Ajay Kumar Singh


Short Description:

Innovation in agriculture is critical to accomplishing long-term food security and increasing productivity. Smart farming technologies for sustainable agricultural development are a key driver in innovation and tech in agriculture.

Students:

Etikala Sathvika, G Niranjan Reddy, Jagadeesh Chandra Duggirala, Jakka Poojitha, K. Sai Priya, Kambam Mythri, Lokeshreddy Devarapalli, C S Vighnesh


Faculty Name:

Dr. Jetendra Joshi


Short Description:

The IoT network systems transmit diverse and heterogeneous data in dispersed environments. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. To propose a hybrid model for Intrusion Detection (ID) with different learning techniques to tackle these limitations.

Students:

Adwit Panigrahi, Ishika Sahu, Shruti Sharma, Yash Rustagi


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

Safety and security in modern surveillance systems largely depend on the ability of detection in real time of abnormal human behavior. Still, the traditional monitoring system approaches rely mainly on human factors, which have inefficiencies, oversight, and high cost. This project tries to solve the problems by conceptualizing the automatic detection of anomaly in human activities by a computer vision system from video feeds. This is to identify between normal and abnormal behavior, such as sudden aggressive movements or suspicious loitering around or any unruly actions not part of standard functioning within different environments using real-time, scalable, and efficient surveillance.

Students:

Drishti Joshi, Lovedeep, Deepro Chatterjee, Digvijay Wairagade


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

This study addresses the need for ethical integration of generative AI in education to support personalized learning pathways, foster critical thinking, and ensure data privacy, aiming to provide a balanced framework that mitigates the risks of dependency, bias, and accessibility disparities in AI-driven educational tools.

Students:

Abhinav Singh, Ahana Nair, Archana Anto, Sambhav Jain


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

Facial Presentation Attack Detection Presentation attack is impersonating someone to get access to confidential information. We are working on a computer vision based facial presentation attack detection where we intend to enhance existing technology that classifies the input images into spoofed or genuine images.

Students:

Agnishwar Raychaudhuri, Geetika Agrawal, Jagriti Singh, Khushi Singh


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

Developing an AI-driven machine learning model for the early detection of infectious diseases. The model will leverage real-time data, including patient symptoms, lab test results, and demographic information, to identify signs of emerging infections with high accuracy. By analyzing these diverse data sources, the model aims to recognize disease patterns and classify potential cases, enabling timely diagnosis. This approach will support healthcare providers in making faster, data-driven decisions, improving patient outcomes, and aiding in the effective management of infectious disease outbreaks.

Students:

Avijith Manikandan, Gupta Devesh Yash, Jatin Sharma, Sarabjoth Bhatia


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

A technology to convert spoken language and text into Indian Sign Language (ISL) for effective communication between the Deaf community and non-signers. Using speech and text recognition with computer vision, this tool would provide real-time ISL translation for better accessibility in settings like transportation and mobile applications.

Students:

Abhilaksh Sharma, Anmol Ranjan, Ugyen Jigme Rangdrel, Yash Kuletha


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

The objective of this research is to explore and optimize pre-existing deep learning algorithms and architectures which have been designed specifically for deployment on resource-constrained embedded systems. It aims to explore and enhance pre-existing techniques such as model compression, quantization, and adaptive learning, and to address challenges related to real-time inference, on-device training, and long-term model adaptability. The results of this research could contribute to the practical application of deep learning on embedded systems for fields such as Internet of Things (IoT), wearable technology, autonomous systems, and edge computing, thereby extending the reach of intelligent systems to low-power, decentralized environments.

Students:

Ankita Kapoor, Gautam Sharma, Samarth Tanay, Shubham Garg


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

We aim to develop a reinforcement learning-based traffic signal control system that dynamically adjusts traffic light timings based on real-time traffic flow, optimizing vehicle throughput and reducing congestion.

Students:

Aastha Kumar, Ananya Gupta, Humaira Khan, Kaustav Karan, Jay Juneja


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

How different fonts effect social media interaction, what kind of impact they make on day to day content consumption.

Students:

Himanshu, Nainmeet Kaur Bhatia, Akshay, Sajal Pandey


Faculty Name:

Dr. Supratik Banerjee


Short Description:

MEC is an edge computing framework that moves storage and processing capabilities closer to the mobile users. Thus, it helps to fulfil the requirements of 5G and beyond networks as it provides anytime, anywhere connectivity with ultra low delay. ICN on the other hand overcomes the host-centric routing and operational shortcomings by implementing content-centric networks. Integration of ICN and MEC is quite applicable for future internet including 5G and Beyond (B5G) networks. However, there are various challenges in integration of ICN and MEC. Very few standardizations are available of integration of ICN and MEC. Secondly, integration of ICN and MEC in B5G still needs attention. Thirdly, careful installation of privacy and security measures is ICN based MEC systems is must. Fourthly, there are ICN naming challenges in ICN based MEC systems.

Students:

Aayush Dutta, Ankit Yadav, Divyansh Mishra Kartik Mudgal, Khushi Poonia, Krish Patel, Nidharshana P, Ranil Bazira


Faculty Name:

Dr. Sujata


Short Description:

Use of AIML in fashion Industry for generation of new cloths

Students:

Karthik Jonnalagadda, Adi Gupta, Arpit Deewan, Avick Saha, Muskaan Thakur, Samar Sparsh, Vishwas Kaushik, Yuvraj Chawla


Faculty Name:

Dr. Sujata


Short Description:

Empowering Rural Healthcare Through AI Solutions

Students:

Aditya V Krishnan, Kanwar Jiten Singh, Prakhyat Singh, Aryan Jain, Shaik Arman Ali, Yenugu Bhavesh Chandra, Srachet Rai, Vaibhav Malhotra


Faculty Name:

Dr. Anand Kumar Mishra


Short Description:

The project aims to address the unique challenges of digital forensics in cloud computing. With the increasing adoption of virtualized environments, traditional forensic techniques often fall short in identifying, preserving, and analyzing evidence during data breach investigations. This project proposes a comprehensive forensic framework tailored for cloud-based systems, focusing on integration of virtualization-specific artifacts, such as hypervisor logs and VM snapshots, into forensic workflows.

Students:

Sainath Nukam, Kunala Sashank, Shikhar Sharma, Siddharth Saini, Nitesh Jha, Swastik Tripathy, Toril Jain, Aryan Vats


Faculty Name:

Dr. Anand Kumar Mishra


Short Description:

The project explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in addressing the unique challenges of securing cloud environments. As cloud computing continues to evolve, traditional digital forensic and incident response methods face limitations in handling the scale, complexity, and dynamic nature of cloud-based systems. This project proposes leveraging AI and ML technologies to enhance evidence collection and analysis processes in multi-tenant, distributed cloud environments.

Students:

Prakriti Negi, Bhavya Yadav, Sumit Kumar Khadanga, Ojas Dasarwar, Baddela Muni Sai Dolith, MD Kaif Khan, Sachin Tvs Warrier


Faculty Name:

Dr. Anand Kumar Mishra


Short Description:

The project focuses on uncovering and analyzing the digital footprints left by social media applications across various operating systems and devices. With the growing reliance on social media for communication and information sharing, these platforms have become critical sources of evidence in digital investigations. This study aims to identify and categorize digital artifacts generated by popular social media platforms and analyze how these artifacts vary across operating systems (Windows, macOS, Linux) and devices (mobile and desktop).

Students:

Yash Upadhyay


Faculty Name:

Dr. Vikas Upadhyaya & Dr. Neha Tiwari


Short Description:

Detection of cataract at early stage. Exploring the ML/DL models for early-stage detection

Students:

Rakshit Sawarn, Amruta Vivek Saharkar


Faculty Name:

Dr. Vikas Upadhyaya


Short Description:

We are going to do research on how to detect the counterfeit products by hardware. We have already achieved this in software version and filed a patent on this.

Students:
Aditya Joshi, Jitender Reddy Kalli, Shravya Gupta

Faculty Name:
Dr Gaurav Gupta

Objective:
To find evidence of correlation between probiotics and gut health.


Methodology:
Isolating novel LAB bacteria and applying it as a probiotic with anti-cancer properties. Testing the probiotic’s anti-toxic and anti-cancer properties to investigate whether it can act as a mediator for treating cancers.

Students:
Deergha Borker

Faculty Name:
Dr Sudeep Goswami

Objective:
To broaden the spectrum of antimicrobial activity of a powerful bacterial membrane targeting antimicrobial peptide ‘Nisin’ at varying pH range simply by conjugating it with organic acids followed by developing Nisin-Nanoparticle composite


Methodology:
Many food-borne drug resistant pathogens have evolved to evade antibiotics meant to kill them by changing physical parameters such as pH. Nisin being an FDA-approved food preservative inactivated at higher pH. The nanocomposites can be characterised and synergistic antimicrobial interaction of nanomaterial with Nisin is further tested on targeted microbial strains.

Students:
Aditya Joshi, Jitender Reddy Kalli, Shravya Gupta

Faculty Name:
Dr Narayan Kumar

Objective:
To specifically identify APC-represented peptides unique to lung cancer to be used for therapeutic purposes.
Methodology: Identify and eliminate all peptides that show similarity above the degree of similarity (DoS) threshold values so as to prevent any off-target cyto-toxicity. Use multiple computational techniques and novel strategies to mitigate current challenges associated with existing strategies to develop an improved version of the same.

Students:
Amit Ladha, Soumya Shah

Faculty Name:
Dr Gaurav Gupta

Objective:
To find evidence of correlation between pro-biotics and gut health.


Methodology:
Isolating novel LAB bacteria and applying it as a pro-biotic with anti-cancer properties. Testing the probiotic’s anti-toxic and anti-cancer properties to investigate whether it can act as a mediator for treating cancers.

Students:
Tekuru Vishnu Koushik and Anushka Chakraborty

Faculty Name:
Dr Utkarsh Raj

Objective:
To identify potential plasma microRNAs (miRNAs) for the early detection of oral cancer using RNA-Seq data analysis.

Methodology:
Oral cancer is among the three main kinds of tumours found in the Indian subcontinent. Oral squamous cell carcinoma (OSCC) is often diagnosed at a late stage and may be malignantly transformed from oral leukoplakia (OL).

Students:
Akash Hirgond

Faculty Name:
Dr Utkarsh Raj

Objective:
To examine the inhibition proficiency of well-known kinase inhibitors as well as natural compounds from flavonoids library for the control of HPV16/18-E7 function, through molecular modelling, virtual screening and MD simulation studies.

Theory:
HPV16/18-E7 participates in the process of cell proliferation and metastasis of cancer, indicating its functional similarity with human cellular kinases.

Students:
Mousumi Saha, Navelli Gupta, Sushmita Nandy

Faculty Name:
Prof Ratna Sanyal

Objective:
To examine the inhibition proficiency of well-known kinase inhibitors as well as natural compounds from flavonoids library for the control of HPV16/18-E7 function, through molecular modelling, virtual screening and MD simulation studies.

Objective:
To select only important parts in a document to reduce the number of lines to summarise any document.

Students:
Pratyaksh Mathur Prerak Anand Om Ahire Suman Dey

Faculty Name:
Dr Chandan Medatwal and Prof Ratna Sanyal
(Accepted and presented in ISDSI-Global 2021)

Objective:
To investigate the effectiveness of appraisal systems and its effect on employee morale.

Various modifications are presented on an extractive graph-based summarisation technique called TextRank to show how these modifications improve upon the original algorithm in different aspects. State-of-the-art text vectorisation and anaphora resolution techniques are used. The performance of modifications with the original algorithm are shown using ROUGE scores as a metric. Two sentence vectorisation approaches, using GloVe embedding and SBERT are explored in this research work. The quality of sentence vectors generated using these two models are evaluated. This has been done by assessing the quality of summaries produced using them against reference summaries from CNN’s news article dataset.

Students:
Akshat Jain, Dishank Kalra, Varun Bisht

Faculty Name:
Dr Ratna Sanyal
(1^st prize in Project Innovation Contest (PIC 2022))

Objective:
To monitor and study the class participation of students in online lectures.

Methodology:
Without face-to-face interaction, it has become harder to keep track of the overall progress of a class. Analysis of chats during online classes is vital and essential to the online method of learning, to gauge the level of effective participation of the students.

Students:
Ritav Das and Sristi Agrawal

Faculty Name:
Dr Ratna Sanyal

Objective:
To deploy an ensemble learning model to solve the fine-grained sentiment classification task.



Sentiment classification is an important process to analyse social conversations online and determine deeper context as they apply to a topic, brand or theme. Four classification algorithms were implemented for the purpose of this study. Implementing k-fold cross validation on each improved their accuracies by 40 per cent. The final ensemble model was created by calculating the correlation between each of the models and combining them carefully using results generated. Out of the four classification algorithms, Multinomial Naives Bayes and Support Vector Machine were found to produce the best results.

Students:
Kouthagouni Nikhil Reddy

Faculty Name:
Dr Sanjay K Biswas

Objective:
To deploy an ensemble learning model to solve the fine-grained sentiment classification task.

• To establish pre-requisite understanding of the 5G network, its architectural builds, and components to get a proper know-how of the 5th generation of cellular network.
• To set up basic groundwork leading to device-to-device communication-related research work that is a prominent feature of 5G.
• To address the issue of suitable handover criteria under QoS aware D2D handover schemes which is a challenge with respect to mobility management in D2D communications.

Students:
Anushree Krishania, Rajat Kaushik, Pranay Kumar Shrivastava, Mohd. Hamza Ahsan

Faculty Name:
Surender Singh

Objective:
To examine the security vulnerabilities in Bluetooth technology used in IoT devices. This paper lines up the following objectives:

• Describing the overview of Bluetooth Low Energy with a focus on its security in IoT devices.
• Discussing major threat to Just Work Pairing Method
• Proposing authentication method in Just Works and OOB as alternatives.

Students:
Tanuj Pandey, Anup Mishra, Tripti Lamba, Kanishya Mohan

Faculty Name:
Surender Singh

Objective:
To create a feasible detection system with low false positives to identify phishing attacks.

Students:
Vishnu Sharma, Siddhant Sharma, Sahil Singh Rawat, Ali Khan,
Priyanshu Joshi, Richard Tony, Siddhi Lad, Yashas Grover

Faculty Name:
Prof Debashis Sengupta and Prof Nagendra Nyamgondalu

Objective:
To find an effective solution for mass vaccination using blockchain technology combined with IoT.

Students:
Mayuri Mane, Harsh Parmar, Hritik Dalal

Faculty Name:
Dr Thota Sivasankar

Objective:
To find an effective solution for mass vaccination using blockchain technology combined with IoT.

Methodology:
To build a machine-learning model to classify different crops using multi-temporal data from Sentinel-2 and different machine learning techniques.

Methodology: Using Random Forest algorithm for better accuracy.

Students:
Akshay Sharma,Aman Kumar Singh, Vyoman Jain

Faculty Name:
Dr Ayan Nandy

Objective:
To solve bin-packing problems in polynomial time by a non-deterministic Turing machine.

Students:
Sai Praneeth Diddigam

Faculty Name:
Manish Hurkat

Objective:
To make games lighter to store and run with low-level programming and easier usability.

Students:
Yash Agarwal, Varshith Muthya Vankamamidi, Vishal Shukla

Faculty Name:
Prof Kumar Nitesh

Objective:
To create an energy-efficient clustering algorithm using PSO.

Students:
Charitha Madala, Svayam K Gopal

Faculty Name:
Dr Vikas Upadhyaya

Objective:
Object detection has been an important issue in computer vision with applications involved with locations and movements of the target object. The rising use of digital cameras for applications like security purposes leads to the need for an automatic method that will yield efficient detection of objects. As computer vision and visual recognition is advancing, the industry has created a wide variety of computer vision products and services like video surveillance, medical image analysis, face detection and recognition and many more.

Students:
NC Mohit, Mudit Dixit, Yuvraj Singh

Faculty Name:
Dr Vikas Upadhyaya

Objective:
To analyse pre-existing GAIT recognition methods, determine a methodology to calculate accuracy of the GAIT recognition methods, suggest modifications and optimisations for better accuracy and computational efficiency, with respect to time and memory.

Methodology:
GAIT data acquisition, pre-processing, period extraction, feature extraction and classification.

Students:
Deena Nath Srivastava, Pranay Mokida

Faculty Name:
Dr Yogendra Pal

Objective:
To make the case for the effectiveness of YouTube in advancing student comprehension of learning concepts. The findings of this study are expected to contribute new knowledge to the existing body of education management, instruction and learning literature.

Students:
Abhinav Gupta, Srikeerthana Reddy, Yukta Sharma

Faculty Name:
Prof Eswaran Narasimhan

Objective:
To investigate effective procedures to minimise the instances of crew scheduling by looking ahead and cutting costs.

Students:
Yash Jain

Faculty Name:
Prof Sudip Sanyal

Objective:
To align the agent-based Model and Lotka-Volterra model to analyse ecological systems. The agent-based Model will perform realistic simulations while the Lotka-Volterra model will provide a mathematical analysis of the model. The models will work hand-in-hand to provide superior results.

Students:
Nitish Ranjan

Faculty Name:
Prof Sudip Sanyal

Objective:
To analyse the electoral process as the whole and determine the effect of different propaganda mechanisms (election campaigns) to influence the decision of the voters. The researchers aimed to design a model to predict the vote share of each political party in a particular state/constituency using real world data.

Students:
Pearl Miglani

Faculty Name:
Prof Sudip Sanyal

Objective:
To use the concept of random forest to train uncorrelated models by randomising the observations of the training set with replacement.

Students:
Md Zubair Hussain Emon, Samarth Gangwal, Sagar Makar and Manikanta Reddy

Faculty Name:
Jetendra Joshi and Dr Prashant Srivastava

Objective:

• To study various methods/techniques of IOT systems and its implementation used in retail advertisement.
• To provide a solution for virtual fitting room, with cloud integration and computer vision for image capturing.
• To enhance user experience with the resultant display of virtually fitted clothes on the smartphone with an integrated payment option.

Students:
Gaurav Sengar, Harsh Vardhan, Muskaan Mahindrakar, Sarthak Khabiya

Faculty Name:
Meera S Datta

Objective:
To develop a QR-code based system to provide universities with an end-to-end solution from registration to campus tour and feedback to visitors, including back-end integration with university ERP system.


Methodology:
Examining existing solutions, providing a complete design for the QR-code based visitor self-navigation system and demonstrate a prototype that will cover at least six sites of interest on the campus.