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cat_dog-classification

detection-and-classification-of-alzheimers-disease

The purpose of this paper is to detect Alzheimer’s Disease using Deep Learning and Machine Learning algorithms on the early basis which is being further optimized using CSA(Crow Search Algorithm). Alzheimer’s is one of kind and fatal. The early detection of Alzheimer’s Disease because of it’s progressive risk and patients all around the world. Early detection of AD is promising as it can help lot of patients to predetermine the condition they may face in future. AD being progressive, can be prevented if detected early. On worse stage, the curing of this disease is very difficult and expensive. So, by analyzing the consequences of AD, we can make use of Artificial intelligence technology by using MRI scanned images to classify the patients if they may or may not have AD in future. Using of Bio-inspired algorithm can maximize the result and accuracy for this purpose. After comparing the results of the various AI technologies, CSA came to be the best approach, using it with ML algorithms.

image-augmentation

linear-binary-pattern-feature-extraction-

mnist-ann-

okta-sso-integration

Integration of OKTA SSO with web app

pytorch-yolo2

Convert https://pjreddie.com/darknet/yolo/ into pytorch

sentiment-analysis

simple-linear-regression

svm-mnist-digits

test-serverless

various-forms-of-unet

wave2lip

yolo-object-detection

This repository gives an implementation of object-detection algorithm,YOLO(v2 and v3) using VOC or COCO data