Back To Projects
Predicting Mental Health Conditions Using Student Demographic Information
Ashwith Y.
With this, we will train the data to extrapolate what symptoms the user has based on their demographics and academic/social life. This can help understand what symptoms a person has of certain characteristics.
We are working with supervised data and this is classification data. With this, we will train the data to extrapolate what symptoms the user has based on their demographics and academic/social life. This can help understand what symptoms a person has of certain characteristics. Age, gender, course year, course study, cgpa, and marital status are the inputs of the data. Whether the person is more prone to depression, anxiety, or panic attacks is the output of the model.
Explore More!
Ashwith Y.
Related Projects
Structure-Based Analysis of Parkinson’s Disease Protein Mutations: Comparative Analysis of Computational and Experimental SNCA Structures
This study uses AI-based protein modeling tools such as AlphaFold and ColabFold to compare wild-type and mutant α-synuclein structures in Parkinson’s disease, evaluating their interactions with pharmacologically relevant ligands. By integrating structure prediction with molecular docking, the research highlights both the potential and limitations of computational methods for understanding mutation-specific effects and guiding drug discovery efforts.
Arya S.
Mentored by Khanh Nguyen
A Novel ML Approach to Detect Possible Protein Allosteric Sites For Drug Discovery
In this study, we investigated different machine learning models and evaluated their performance, and found that the best models were the random forest classifier and a 5-layer neural network.
Sriram S.
Mentored by
Diagnosing Brain Tumors from MRI Images Using Deep Transfer Learning
This study aims to utilize a transfer learning method in which the prior knowledge of a pretrained model is used to aid in a new classification problem.
Armita K.
Mentored by
© 2022 Inspirit AI. All Rights Reserved.