Learning Vector Quantization ( or LVQ ) is a type of Artificial Neural Network which also inspired by biological models of neural systems. It is based on a prototype supervised learning classification algorithm and trained its network through a competitive learning algorithm similar to Self Organizing Map.
In mathematics, a Voronoi diagram is a partition of a plane into regions close to each of a given set of objects. In the simplest case, these objects are just finitely many points in the plane (called seeds, sites, or generators).

GMDH is a sorting-out of gradually complicated models generated from base function. The best model is indicated by the minimum of the external criterion characteristic. Multilayered procedure is equivalent to the Artificial Neural Network with polynomial activation function of neurons.
To forecast the values of future time steps of a sequence, specify the targets as the training sequences with values shifted by one time step. In other words, at each time step of the input sequence, the GMDH neural network learns to predict the value of the next time step.

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques, machine learning, and deep learning. Researchers employ predictive analytics to find patterns in this data to identify risks and opportunities.
Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In classification, the model is fully trained using the training data, and then it is evaluated on test data before being used to perform prediction on new unseen data.
LSTM stands for long short-term memory networks, used in the field of Deep Learning. It is a variety of recurrent neural networks (RNNs) that are capable of learning long-term dependencies, especially in sequence prediction problems.
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A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of convolutional neural networks which operate directly on graphs.
To run this program, we use the Google Colab environment. You can also use Jupyter Notebook.

Super Dynamic Virtual Assistant Using ChatGPT
This project is implemented on Pycharm.
First, it is necessary to install the libraries used in the program.
Then you need to create an account on the OpenAI website. And get an openai.api_key through your user account and put it in the appropriate place in the program.
Now you can easily talk about anything you like with your ChatGPT-Based voice assistant.

Generate Music/Melody with Genetic Algorithm
First, you need to install the libraries. then just run the code.

A convolutional neural network (CNN) is a type of artificial neural network used primarily for image recognition and processing, due to its ability to recognize patterns in images.
VGG stands for Visual Geometry Group; it is a standard deep Convolutional Neural Network (CNN) architecture with multiple layers. The “deep” refers to the number of layers with VGG-16 or VGG-19 consisting of 16 and 19 convolutional layers.
Residual Network (ResNet) is a deep learning model used for computer vision applications. It is a Convolutional Neural Network (CNN) architecture designed to support hundreds or thousands of convolutional layers.
TensorFlow is an end-to-end open-source platform for machine learning. TensorFlow is a rich system for managing all aspects of a machine learning system; however, this class focuses on using a particular TensorFlow API to develop and train machine learning models.

The neural gas is a simple algorithm for finding optimal data representations based on feature vectors. The algorithm was coined "neural gas" because of the dynamics of the feature vectors during the adaptation process, which distribute themselves like a gas within the data space.
Neural gas is an artificial neural network, inspired by the self-organizing map. The neural gas is a simple algorithm for finding optimal data representations based on feature vectors.
Growing Neural Gas (GNG) was introduced by Fritzke as an incremental unsupervised neural network for non-temporal data. Starting with two neurons GNG grows in regular time intervals up to a maximal size. Connections between neurons are created by topology preserving Competitive Hebbian Learning.

The traveling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?" It is an NP-hard problem in combinatorial optimization, important in theoretical computer science and operations research.
the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.