AI approaches for Microalgae growth prediction and optimization

1. Supervised Machine learning Algorithms

1.1. Classfication

1.1.1. Support Vector Machines

1.1.2. Linear Descriminant Analysis (LDA)

1.1.3. Nearest Neighbor

1.1.4. Neural Networks (NN)

1.1.4.1. Preceptron based NN

1.1.4.1.1. Multi-layer preceptron

1.1.4.1.2. Feedforward NN

1.1.4.2. Non-Preceptron based NN

1.1.4.2.1. Convolutional Neural Networks (CNN)

1.2. Regression

1.2.1. Linear Regression

1.2.2. Support Vector Regression

1.2.3. Random Forest

1.2.4. Decision Trees

1.2.5. Ensemble Methods

1.2.6. Neural Networks

1.2.6.1. Preceptron based NN

1.2.6.1.1. Multi-layer Preceptron

1.2.6.1.2. Feedforward Neural Netowrk

1.2.6.2. Non-Preceptron based NN

1.2.6.2.1. Long Short-Term Memory (LSTM)

1.2.6.2.2. Recurrent Neural Networks (RNN)

2. Unsupervised Machine learning Algorithms

2.1. Clustering

2.1.1. K-means

2.1.2. Dimensionality Reduction

2.1.3. Neural Netowrks