S
Shamla Mantri
Researcher at Massachusetts Institute of Technology
Publications - 20
Citations - 129
Shamla Mantri is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Depression (differential diagnoses) & Self-organizing map. The author has an hindex of 5, co-authored 20 publications receiving 68 citations. Previous affiliations of Shamla Mantri include College of Engineering, Pune.
Papers
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Proceedings ArticleDOI
Plant Diseases Detection and Classification using Machine Learning Models
TL;DR: An overview of image segmentation using K-means clustering and HSV dependent classification for recognizing infected part of the leaf and feature extraction using GLCM is presented.
Proceedings ArticleDOI
Non invasive EEG signal processing framework for real time depression analysis
TL;DR: This work shows that linear analysis of EEG can be an efficient method for identifying depressed patients from normal subjects and it is recommended that this analysis may be a supporting aid for psychiatrists to identify severity level of depressed patients.
Proceedings ArticleDOI
Clinical Depression Analysis Using Speech Features
TL;DR: A survey of speech signal features which relates for depression analysis is presented, specially focused on adolescence speech, and it is hypothesized that many speech features are there which are responsible for depression like linear features Prosodic, cepstral, spectral and glottal features and non-linear feature Teager energy operator (TEO).
Journal ArticleDOI
Pattern recognition using neural networks
TL;DR: A recognition system for human faces is developed and illustrated using a novel Kohonen self-organizing map ( SOM) or Self-Organizing Feature Map ( SOFM ) based retrieval system that has good feature extracting property due to its topological ordering.
Proceedings ArticleDOI
Cardiovascular Disease Prediction Using Machine Learning Models
TL;DR: In this paper, the authors proposed machine learning techniques to predict cardiovascular disease using features and found that the effect of BMI on the prediction of cardiovascular disease is significant. And they concluded that BMI is a significant factor while predicting cardiovascular disease.