Image Steganography algorithm based on edge detection

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 and capacity using bit plane dependent XOR coding technique that makes least possible alterations in LSB bits of edge pixels. The existing edge-based steganography techniques provide better imperceptibility but relatively limits the embedding capacity. The proposed method efficiently improves the embedding capacity with an acceptable range of imperceptibility and robustness.

image

JAVA DEMO

Heart Disease Prediction Using Hybrid Algorithm

Heart Disease Prediction Using Hybrid Algorithm

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System Architecture – Heart Disease Prediction

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 – Demo