I tried to apply Reinforcement learning algorithm to Role Based Access Control(RBAC). Basically, RBAC is o one of the role mining processes to determine or modify user permissions by analyzing user-to-resource mapping data. My purpose was the using RBAC data to determine or modify user permissions by Artificial Intelligence. I selected Reinforcement learning method for that purpose because of the self-learning feature of it

In that journey I worked on estimating system dynamics parameters of human heart via artificial intelligence algorithms. I worked with different artificial intelligence algorithms such as 'Nonlinear Autoregressive Neural Network, MATLAB', 'Time Delay Neural Network, MATLAB', 'Nonlinear Autoregressive Neural Network, MATLAB', 'Recurrent Neural Network, Python', 'Fully Connected Neural Networks, Python, MATLAB'. I also tried to interpret 'Heart Pump' dataset which is collected from ANSYS simulation of dataset as 'Time Series Data'. I worked also on building 'Gradient Descent', 'Logistic Regression', Backpropagation' algorithms from scratch to reduce the need for a 3rd party libraries or modules.

Instant data that can be obtained with sensor fusion and observation technology is currently
processing in the industry and making sense of this data intelligently; increased production in agriculture
to meet our needs, to minimize the waste of resources and to be the most effective in agriculture.
It is essential to design a mechatronic system that will combine cultivation methods with Industry 4.0.
makes it. The design criteria of this system will be specified in the method section; measurement with sensors
receiving and collecting images via the camera, this data is embedded with a communication protocol.
to a software, the artificial intelligence and optimization algorithms of this embedded software
making autonomous decisions that will increase productivity by combining the base with processing methods
is targeted. The outputs of these autonomous decisions to be taken by the system; to the farmer
return as various improvement proposals in irrigation, fertilization, harvesting and similar areas.
is expected. The system we propose is based on the knowledge that cumulatively advances into traditional agriculture.
and a modular and innovative solution proposal for the adaptation of repetitive works to our age
offers.