omnayak27199

B-WBCIS

Blokchain enabled weather based crop insurance services

Blockchain-based-crop-index-insurance-solution

blockchain-book

blockchain-developer-bootcamp-final-project

DAPP Crop Insurance. Consensys final bootcamp project

Blockchain-Enabled-EHR-B-EHR-

under development

energy-market-b2b

A fully decentralized platform to trade energy between suppliers and generators.

Heart-Disease-Prediction-Using-Ensemble-learning-framwork-

Nowadays cardiovascular disease is amidst of the leading ten sources for the growing rate of morbidity and mortality worldwide, affecting roughly 50% of the adult age group in the health care sector. Heart disease claims the lives of about one person per minute in this modern era. Accurate detection methods for the timely identification of cardiovascular disorders are essential because there is rapid growth in the number of patients with this disease. The dataset contains quantitative and structured data on heart disease indicators in patients. Our goal is to understand risk factors by analyzing the dataset using exploratory data analysis. Heart disease is a long-term problem with a greater risk of becoming worse over time. In this paper, one of the ensemble learning techniques soft voting was used for the identification of disease and it achieved an accuracy of 90.21%.

Hyperspectral-image-classification-using--Hybrid-CNN-method

The Hyperspectral Images (HSI) are now being widely popular due to the evolution of satellite imagery and camera technology. Remote sensing has also gained popularity and it is also closely related to HSI. HSI possesses a wide variety of spatial and spectral features. However, HSI also has a consider-able amount of useless or redundant data. This redundant data causes a lot of trouble during classifications as it possesses a huge range in contrast to RGB. Traditional classification techniques do not apply efficiently to HSI. Even if somehow the traditional techniques are applied to it, the results produced are inefficient and undesirable. The Convolutional Neural Network (CNN), which are widely famous for the classification of images, have their fair share of trouble when dealing with HSI. 2D CNNs is not very efficient and 3D CNNs increases the computational complexity. To overcome these issues a new hybrid CNN approach is used which uses sigmoid activation function at the output layer, using a 2D CNN with 3D CNN to generate the desired output. Here, we are using HSI classification using hybrid CNN i.e., 2D and 3D. The dataset used is the Indian pines dataset sigmoid classifier for classification. And we gain the Overall accuracy 99.34 %, average accuracy 99.27%, kappa 99.25%.

instagram

Create an Instagram-like DApp with IPFS

survey-on-IBM-watson

omnayak27199/Heart-Disease-Prediction-Using-Ensemble-learning-framwork-

Nowadays cardiovascular disease is amidst of the leading ten sources for the growing rate of morbidity and mortality worldwide, affecting roughly 50% of the adult age group in the health care sector. Heart disease claims the lives of about one person per minute in this modern era. Accurate detection methods for the timely identification of cardiovascular disorders are essential because there is rapid growth in the number of patients with this disease. The dataset contains quantitative and structured data on heart disease indicators in patients. Our goal is to understand risk factors by analyzing the dataset using exploratory data analysis. Heart disease is a long-term problem with a greater risk of becoming worse over time. In this paper, one of the ensemble learning techniques soft voting was used for the identification of disease and it achieved an accuracy of 90.21%.

omnayak27199/Hyperspectral-image-classification-using--Hybrid-CNN-method

The Hyperspectral Images (HSI) are now being widely popular due to the evolution of satellite imagery and camera technology. Remote sensing has also gained popularity and it is also closely related to HSI. HSI possesses a wide variety of spatial and spectral features. However, HSI also has a consider-able amount of useless or redundant data. This redundant data causes a lot of trouble during classifications as it possesses a huge range in contrast to RGB. Traditional classification techniques do not apply efficiently to HSI. Even if somehow the traditional techniques are applied to it, the results produced are inefficient and undesirable. The Convolutional Neural Network (CNN), which are widely famous for the classification of images, have their fair share of trouble when dealing with HSI. 2D CNNs is not very efficient and 3D CNNs increases the computational complexity. To overcome these issues a new hybrid CNN approach is used which uses sigmoid activation function at the output layer, using a 2D CNN with 3D CNN to generate the desired output. Here, we are using HSI classification using hybrid CNN i.e., 2D and 3D. The dataset used is the Indian pines dataset sigmoid classifier for classification. And we gain the Overall accuracy 99.34 %, average accuracy 99.27%, kappa 99.25%.

omnayak27199/instagram

Create an Instagram-like DApp with IPFS

omnayak27199/survey-on-IBM-watson

omnayak27199/B-WBCIS

Blokchain enabled weather based crop insurance services

omnayak27199/Blockchain-based-crop-index-insurance-solution

omnayak27199/blockchain-book

omnayak27199/blockchain-developer-bootcamp-final-project

DAPP Crop Insurance. Consensys final bootcamp project

omnayak27199/Blockchain-Enabled-EHR-B-EHR-

under development

omnayak27199/energy-market-b2b

A fully decentralized platform to trade energy between suppliers and generators.

omnayak27199/Energy-Trading-from-house-to-powergrid

This project is related to sell and buy energy from the the centralized powerhub(government) in smart city

omnayak27199/Exploratory-data-analysis-and-prediction-of-chronic-kidney-disease

In this project, I focused to analyze and visualize the dataset using python language to understand what the data looks like, show the relationship between the data, and choose the best way to clean the dataset. Also, we applied a different machine learning algorithm to predict the test data set.

omnayak27199/Hacktoberfest-2022

omnayak27199/hactoberfest

omnayak27199/jQuery-animation

omnayak27199/p2p-energy-dapp

An Ethereum DApp for peer-to-peer energy trading in a community micro-grid.

omnayak27199/Water-Leakage-Detection-Using-Genetic-Algorithm