gugudexiatian

HSI_Classification

Classification for hyperspectral imagery

segmentation_models

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

Multimodal-autoencoder-for-breast-cancer

Prognostically Relevant Subtypes and Survival Prediction for Breast Cancer Based on Multimodal Genomics Data

HSI-Classification

HyperSpectral Image Classification With DeepLearning

Auto-CNN-HSI-Classification

Code for the paper "Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification"

HSI-SVM

高光谱遥感影像识别与分类,HSI_SVM,Indian Pines

mySparseAutoencoder-

Plant-Seedlings-Classification

Jupyter notebooks for Kaggle Plant Seedlings Classification

LSTM-Autoencoders

Anomaly detection for streaming data using autoencoders

Fault-Detection-in-Ball-Bearing-Systems

To detect fault in ball-bearing systems. Dataset from CWRU university

Place-Recognition-using-Autoencoders-and-NN

Place recognition with WiFi fingerprints using Autoencoders and Neural Networks

autoencoder_classifier

Autoencoder model for rare event classification

RobustAutoencoder

A combination of Autoencoder and Robust PCA

Credit-Card-Fraud-Detection-using-Autoencoders-in-Keras

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

timeseries-clustering-vae

Variational Recurrent Autoencoder for timeseries clustering in pytorch

Hyperspectral-Classification

Hyperspectral-Classification Pytorch

Unsupervised-hyperspectral-image-classification

Rice-Disease-Classification

Classify images of Diseased Rice Leaves using Convolutional Neural Networks

ROhsi

Source code of "Hyperspectral Image Classification Using Random Occlusion Data Augmentation"

Hyperspectral

Deep Learning for Land-cover Classification in Hyperspectral Images.

Feature-Selection-Hybrid

Intrusion Detection is a technique to identify the abnormal behavior of system due to attack. The unusual behavior of the environment is then identified and steps are taken and methods are formed to classify and recognize attacks. Data set containing a number of records sometimes may decrease the classifiers performance due to redundancy of data. The other problems may include memory requirements and processing power so we need to either reduce the number of data or the number of records. Feature Selection techniques are used to reduce the vertical largeness of data set. This project makes a comparative study of Particle Swarm Optimization, Genetic Algorithm and a hybrid of the two where we see that PSO being simpler swarm algorithm works for feature selection problems but since it is problem dependent and more over its stochastic approach makes it less efficient in terms of error reduction compared to GA. In standard PSO, the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on sub optimal solutions that are not even guaranteed to be local optimum. A further drawback is that stochastic approaches have problem-dependent performance. This dependency usually results from the parameter settings in each algorithm. The different parameter settings for a stochastic search algorithm result in high performance variances. In this project the modification strategies are proposed in PSO using GA. Experimental results show that GA performs better than PSO for the feature selection in terms of error reduction problems whereas hybrid outperforms both the model in terms of error reduction.

-Crop-classification

在有少量样本的情况下，对含有三种农作物和背景的一幅多光谱图像进行农作物分类

ForecastCropYield

农作物产量预测-天池

Bearing-Detection

bearing detection by conv1d

linearregression-lasso-ridge-elasticnet

基于波士顿房屋租赁价格数据，使用lasso回归算法做特征选择后，分别使用线性回归、Lasso回归、Ridge回归、Elasitic Net四类回归算法构建模型（分别测试1,2,3阶）

feature_selection_GAAlgorithm

基于遗传算法的特征选择

hyperspectral-soilmoisture-dataset

Hyperspectral and soil-moisture data from a field campaign based on a soil sample. Karlsruhe (Germany), 2017.

AD_Classification_VAE

Deep spectral-based shape features for Alzheimer’s Disease classification

DeepMultiSurveyClassificationOfVariableStars

Implementation of the project done in Deep Multi-Survey classification of variable stars.

ML-Precision-Agriculture-Web-App

This web application uses Machine Learning to recommend crop, fertilizer, pesticide and storage process based on various variables. Algorithm used is SVM for multi-classification