Computer Science & Engineering and Information Technology (CSE&IT)

Dr. Aman Sharma is currently working as Assistant Professor (SG) in the Department of Computer Science & Engineering at Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh. He has around 7 years of teaching and research experience. He has completed his Ph.D. thesis with title “Machine Learning based Framework for Drug Prediction of Cancerous Genomic Profiles” in Computer Science & Engineering Department from Thapar Institute of Engineering and Technology, Patiala, Punjab. He obtained his M.E. with thesis title “Community detection and analysis of Twitter social data” in Computer Science & Engineering from Thapar Institute of Engineering and Technology, Patiala, Punjab. He has obtained his B.Tech. in Computer Science & Engineering from R.B.I.E.B.T, Mohali, Punjab. He has been a rank holder throughout his studies. He has also obtained A.I.R-1800 in GATE 2013.

Research Interests:
Machine Learning
Bioinformatics
Artificial Intelligence
Deep Learning
Data Analytics

STARTUPS:

Major Project Open Titles:

AI for Heart Disease Prediction
Machine Learning can play an essential role in predicting presence/absence of Locomotor disorders, Heart diseases and more. Such information, if predicted well in advance, can provide important insights to doctors who can then adapt their diagnosis and treatment per patient basis.

AI for Plant Disease Prediction
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis.

Phycological Chatbot
Mental health has become one of the most overlooked, yet crucial, aspects of our entire well-being in today's environment. In this work, we propose a system for a virtual mental health assistant due to cost, time, and space constraints, as well as a lack of resources associated with inperson counselling. Disrupted mental health is typically the consequence of a snowball effect that necessitates continual attention and deliberate efforts to remediate. This is made possible with the help of a virtual mental health chatbot. The recommended chatbot will have a chat feature, many language voice input options, and a recommendation tool to improve the user's mood.

AI for Social communication: Speech and Language Deficits in Neurological Disorders
Certainly! Speech and language deficits in neurological disorders encompass a range of challenges, affecting communication abilities due to conditions like aphasia, dysarthria, and apraxia. These disorders can arise from strokes, traumatic brain injuries, or neurodegenerative diseases, impacting speech production, comprehension, and overall communication skills. AI interventions aim to assist in therapy, offering innovative tools for rehabilitation and communication support.

AI for Social Good: Social Media Analytics for Mental Health
AI-driven social media analytics for mental health involve using algorithms to analyze online behavior, language patterns, and sentiment to detect potential mental health concerns. By monitoring social media posts, AI can identify signs of depression, anxiety, or other issues, providing early intervention opportunities and support for individuals in need, ultimately contributing to mental health awareness and care.

Federated Learning for Natural Language Processing (NLP)
Explore federated learning applications in NLP tasks like sentiment analysis or language translation. Develop a system that learns from decentralized text data sources while preserving linguistic nuances and privacy.

Personalized Federated Recommender System
Create a personalized recommendation system using federated learning techniques. Tailor recommendations to individual preferences while ensuring user privacy across multiple data sources.

AI for Diabetic Prediction
Diabetes Mellitus is among critical diseases and lots of people are suffering from this disease. Age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, high blood pressure, etc. can cause Diabetes Mellitus. People having diabetes have high risk of diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. Current practice in hospital is to collect required information for diabetes diagnosis through various tests and appropriate treatment is provided based on diagnosis. Big Data Analytics plays an significant role in healthcare industries. Healthcare industries have large volume databases. Using big data analytics one can study huge datasets and find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly.

Minor Project Open Titles:

AI for Social communication: Speech and Language Deficits in Neurological Disorders
Certainly! Speech and language deficits in neurological disorders encompass a range of challenges, affecting communication abilities due to conditions like aphasia, dysarthria, and apraxia. These disorders can arise from strokes, traumatic brain injuries, or neurodegenerative diseases, impacting speech production, comprehension, and overall communication skills. AI interventions aim to assist in therapy, offering innovative tools for rehabilitation and communication support.

AI for Social Good: Social Media Analytics for Mental Health
AI-driven social media analytics for mental health involve using algorithms to analyze online behavior, language patterns, and sentiment to detect potential mental health concerns. By monitoring social media posts, AI can identify signs of depression, anxiety, or other issues, providing early intervention opportunities and support for individuals in need, ultimately contributing to mental health awareness and care.

Federated Learning for Natural Language Processing (NLP)
Explore federated learning applications in NLP tasks like sentiment analysis or language translation. Develop a system that learns from decentralized text data sources while preserving linguistic nuances and privacy.

Personalized Federated Recommender System
Create a personalized recommendation system using federated learning techniques. Tailor recommendations to individual preferences while ensuring user privacy across multiple data sources.

AI for Kidney Stone Prediction
Using AI for kidney stone prediction involves leveraging machine learning algorithms to analyze various patient parameters like medical history, dietary habits, and urine composition to forecast the likelihood of kidney stone formation. By integrating these factors, AI models can assist in early detection, allowing for proactive measures to prevent kidney stone development and provide personalized medical guidance for at-risk individuals.

AI for Diabetic Prediction
AI in diabetic prediction utilizes machine learning models to analyze patient data including blood sugar levels, lifestyle factors, family history, and other health indicators to forecast the risk of developing diabetes. These AI algorithms aid in early identification of at-risk individuals, enabling proactive interventions, personalized care, and lifestyle modifications to prevent or manage diabetes effectively.

AI for Cancer Prediction
AI in cancer prediction involves leveraging machine learning algorithms to analyze medical imaging, genetic data, patient records, and biomarkers to identify patterns indicative of cancer risk or early-stage tumors. These models assist in early detection, improving prognosis, treatment planning, and potentially saving lives through timely intervention and targeted therapies.

Minor Project Groups guided (2023):

Minor Project Groups guided (2022):

Major Project Groups guided (2023):

Major Project Groups guided (2022):

Output of Projects Guided:

1. A. Sharma and S. Chaturvedi, "Mouse-Less Cursor Control for Quadriplegic and Autistic Patients Using Artificial Intelligence" In Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder, IGI Global. [DOI: 10.4018/978-1-7998-7460-7.ch008]

2. A. Sharma, A. Bansal and A. Bhardwaj, "Forecasting the Trend of Covid-19 Epidemic", In Proceedings of 6th International Conference on Parallel and Distributed Computing. (PDGC-2020) [DOI: 10.1109/PDGC50313.2020.9315795]

3. A. Sharma, K. Shah, S. Verma, "Face Recognition using Haar Cascade and Local Binary Pattern Histogram in OpenCV", In Proceedings of 2021 Sixth International Conference on Image Information Processing (ICIIP-2021) [DOI: 10.1109/ICIIP53038.2021.9702579]

4. Mohana R, Sharma P, Sharma A. Ensemble Framework for Red Wine Quality Prediction. Food Analytical Methods. 2022 Aug 19:1-5.

5. Joshi A, Rana V, Sharma A. Brain Tumor Classification using Machine Learning and Deep Learning Algorithms: A Comparison. In Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing 2022 Aug 4 (pp. 15-21).

6. Tiwari A, Chugh A, Sharma A. Ensemble framework for cardiovascular disease prediction. Computers in Biology and Medicine. 2022 May 17:105624.

7. Agarwal P, Rastogi D, Sharma A. Face Mask Detection Alert System for COVID Prevention Using Deep Learning. InApplications of Machine Learning and Deep Learning on Biological Data 2023 Mar 13 (pp. 57-74). Auerbach Publications.

Patents: