Creations
Sentimental analysis of reviewers' feedback using BERT vs. Machine learning
To predict sentiment (postive, neutral, negeative) of customer feedback using tweet texts of differnt airline companies and compare different models'performace on text classification.
Enhanced fraud detection using ML and PySpark framework with feature selection
To develop a generalized model to deal with big and imblance data prediction that suitable for real-time fraud detection at the PySpark framework
Fraud detection using ML and PySpark framework
To develop a generalized model to deal with big and imblance data prediction that suitable for real-time fraud detection at the PySpark framework
Malaria detection using CNN and data augmentation
The aim of this project is to distingulish if a person was infeacted with the Malaria from a microscopic image, and provide support for the lab examination results to quickly diagnosis the Malaria parasites.
Ensemble models in PySpark
To examplify the uses of ensemble models in PySpark as the ensemble models in [previous project using sklearn and keras](https://github.com/tankwin08/ensemble-models-ML-DL-) and predict if the client will subscribe (yes/no) a term deposit (variable y) using market campaign data.
Bayesian Uncertainty for time series data (EVI) prediction using LSTM and autoencoder
To investigate the trend and pattern of time seriese data (MODIS data) using the Long Short Term Memory (LSTM) networks and quantify the uncertianty of the time series prediction of target variables.
Time series analysis using ARIMA & LSTM - MODIS
To investigate the trend and pattern of time seriese data (MODIS data) using the Autoregressive Integrated Moving Averages (ARIMA) and Long Short Term Memory (LSTM) networks and further to check if we can use the current model to predict further values of target variables.
Sentiment analysis for review classification using SWIVEL and a small datasets
To retrain the pretrained model (Submatrix-wise Vector Embedding Learner (SWIVEL) using using a small collected review datasets and classify the reviews of customer feedback as either positive or negative.
Ensemble models for classification (combine deep learning with machine learning)
To develop a robust approach to conduct classification on data (a person is wearing glasses or not) using a ensemble of models, which include machine learning models (random forest,Gradient Boosting and Extra Trees) and deep learning model (optimized NN using Bayesian optimization).
Bayesian optimization deep learning
To construct the architecture of Nentural Network (NN) and conduct paramter optimization of the NN.
waveform decomposition vs. deconvolution
Bayesian decompostion of waveform lidar and uncertaitny analysis
