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-brain-inspired-ai-2-projects-

Brain inspired AI-Boltzmann machine trained on the MNIST data and temporal difference learning model for navigating Morris water-maze task

breast_cancer_classification-_using-_ml

Fine needle aspiration is a type of biopsy procedure. In this process a thin needle is inserted into an abnormally appearing tissue or body fluid. The sample collected can help in diagnosis or rule out cancer

car-price-pred

ML Project with end to end deployment that predicts prices for second hand cars

covid-19-in-india--prediction-and-statistical-analysis-using-dl

LSTM analysis including its helper functions, Pandas Profiling, plotting of the time series, Exponential Smoothing, Simple Exp Smoothing, Holt, Augmented Dickey Fuller test.

covid19-detection-using-ct-scan-and-chest-x-rays-with-dl

Covid 19 detection using CT scans and chest Xrays with deep learning.

covid19-india-analysis-and-forecasting-using-fbprophet

Covid 19 analysis and prediction using numpy , pandaas and FbProphet

diabetes_prediction_india

Diabetes Prediction using Machine Learning. Dataset used: PIMA Indians Diabetes dataset from Kaggle

flight-fare-prediction

Usage of Machine Learning - Random Forest to predict fare of Flight ticket and then deploy the project using Flask app.

glaucoma-detetction

Glaucoma Detection and Classification using Deep Learning Glaucoma is a condition of eye in which optic nerve is damaged due to abnormally high pressure in the eye. It is a chronic and irreversible disease. It is one of the leading cause of blindness across the globe in people over the age of 60. There is no cure for glaucoma, but early detection and medical treatment can prevent from disease progression. A goal of this project was to use deep learning architecture to build a model to detect and classify glaucoma by combining multiple deep features. Keras was used to build the model. We used publicly available database Drishti-GS1. Methodology: This project was divided into two parts: Glaucoma Detection First, ROI (Region of interest) which is an area where optic disc and cup are located in the center and blood vessels of the Glaucoma fundus images were extracted using U shape convolutional neural network and then cup to disc ratio was calculated to classify if the image was glaucomatous or normal. This Paper was used for ROI extraction and disc segmentation. Glaucoma Classification Cup to disc ratio was used for glaucoma classification. VGG16 CNN model was used to distinguish between glaucoma and non-glaucoma related images from fundus images. Glaucoma severity can also be classified from cup to disc ratio: Mild ( CDR >0.3 and <0.5) Moderate (CDR >=0.5 and <0.8) Severe (CDR >=0.8)

heart_disease_prediction

Heart Disease Prediction using Machine Learning and Python

kerasapplication

malaria_detection

Detection of malaria disease using deep learning.

mask-and-social-distancing-detection-using-vgg19

Mask and social distancing measurement during covid 19 using vgg19 and haarcascade.

omicron_variant_live_worldwide

This code will give an understanding of the omicron covid 19 variant that is spreading worldwide.

pneumonia_prediction_using_dl

PDF [4 MB] Figures Save Share Reprints Request Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

python_codes

spiking-neural-networks-snns-with-pytorch-

Spiking Neural Networks (SNNs) with PyTorch where Backpropagation Engenders Spike-Timing-Dependent Plasticity (STDP)

xcode