Plant Leaf Disease Detection using Deep learning algorithm

MODULES The modules included in our implementation are as follows Dataset collection Data pre-processing Training and prediction using Regression Models DATASET COLLECTION The dataset is downloaded from kaggle.com with two classes ‘healthy’ and ‘diseased’. The dataset contains plant leaf image with training set and test set folders. The dataset variable names are described below Variable name Attribute Description Class Binary class ‘healthy’ and ‘diseased’ Training set 364 images in diseased 388 images in healthy Test set 60 images in diseased 60 images in healthy Project Demo Video

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Heart Disease Prediction Using Hybrid Algorithm

IMPLEMENTATION METHODOLOGY The proposed work is implemented in Python 3.6.4 with libraries scikit-learn, pandas, matplotlib and other mandatory libraries. We downloaded dataset from uci.edu. The data downloaded contains binary classes of heart disease. Machine learning algorithm is applied such as decision tree and random forest along with hybrid model. DATA DICTIONARY The dataset collected with attributes age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slop, ca, thal, pred_attribute. Modules The modules included in our implementation are as follows Decision Tree Random forest Hybrid RF & Linear model Python…

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Convolutional Neural Networks for Diabetic Retinopathy

Convolutional Neural Networks for Diabetic Retinopathy The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a dicult and time consuming task. In this paper, we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and haemorrhages…

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Face detection and recognition and attendance using machine learning and deep learning

This project is proposed for real time face detection and recognition. The project is implemented in both machine learning and deep learning. Implementation step: Face is detected in real time, detected face is trained with atleast 1000 frames for good accuracy. The training the collected data Face recognition with input and mark attendance Software used: Python Python Project Demo

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