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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Image Steganography algorithm based on edge detection

A novel steganography algorithm is proposed based on local reference edge detection technique and exclusive disjunction (XOR) property is proposed. Human eyes are less sensitive towards intensity changes in the sharp edge region compared to the uniform region of the image. Because of this, the secret message bits have been embedded in the sharp regions by local reference pixels that are located in the edge blocks. The predefined sets of pixels are easily identified with less computational complexity in the stego image. The embedding algorithm improved in terms of security…

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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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Liver Disease Prediction through machine learning and deep learning

OBJECTIVES Objective of study is to implement Liver Disease prediction application based on machine learning techniques. The major objective is to predict the disease with higher accuracy. To propose more than one machine learning algorithm and compare to find the best one. METHODOLOGY USED Data Visualization Data Splitting Machine Learning Liver disease prediction Python Demo Video

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