The concept of artificial, firstly, intelligence came up with the question asked by Alan Turing, "Can Machines Think?" John McCarthy coined the term artificial intelligence for the first time at the Dartmouth Conference in 1956. Although intelligence used to be used in a limited number of fields, it has become used in many fields such as robotics, natural language processing, computer vision and medicine. Artificial intelligence started to gain more popularity with the advent of machine learning in the 1980s. Now, we see that breathtaking artificial intelligence applications such as speech recognition, image recognition and self-driving cars are being developed as a result of the fact that large amounts of data are easily available and powerful hardware has been developed. Therefore, artificial intelligence is growing and developing like a living organism and now it enters our lives more and more every day.


As a result of research on artificial intelligence, although we have come a long way, we still have a long way to go. So much so that none of today's smart machines can reach the breadth and depth of human intelligence. Therefore, as SBTÜ family, it is our main goal to contribute to the development of algorithms that will help people live a better, safer, more productive and healthier life.



In the artificial intelligence laboratory that is planned to be established, it is planned to work on the subjects described below. With the expansion of the academic staff, it is thought that works will be done in all these areas.

1.    Machine Learning and Deep Learning

The key to machine learning is data. If we have a lot of labeled data (that is, if the data has an output), that data is evaluated by the computer and thus learns to identify and understand them. As a result, when faced with a situation that it has not learned before, it gains the ability to predict its outcome.


Deep learning has emerged as a sub-branch of machine learning. Its biggest advantage is its ability to deal with problems that researchers have been unable to solve for many years. Especially in the last 5 years, as computers have become more powerful, very large learning tools called deep neural networks can be developed.


Machine learning and deep learning will also be the main component of new generation artificial intelligence applications. Applications developed in robotics, natural language processing and video analysis will require machine learning and deep learning.

2.         Natural Language Processing

With artificial intelligence applications, very successful results can be obtained in speaking and understanding. But understanding people's spoken language (natural language) is a much more difficult process. In fact, natural language processing is one of the most active areas of artificial intelligence research. Computers can listen to a conversation and learn the meanings of the words and sentences used in that conversation. But really understanding the whole sentence and establishing dialogue is one of the problems that are still being studied.

3.         Computer Vision

People look with their eyes but see with their brains. Computer vision is the creation of concepts by processing pixels one by one and the creation of a concept by bringing them together. Computer vision has been studied by researchers for many years. Key developments in computer vision are listed below.


* With the development of ImageNet data set and the use of neural networks in 2009, objects were classified almost like humans.


* The next improvement is the detection of multiple objects in a picture. For example, the brand and model of cars in a picture can be defined up to the year of manufacture.


* The next step is for the computer to reveal the relationship in a picture. For example, being able to create sentences or stories from a picture, such as saying the cat is lying in bed next to the computer. Of course, learning 3D complex real world scenes will be possible with the development of more sophisticated algorithms.

4.         Robotics

Basically, in the field of robotics, artificial intelligence is used in applications where people try to build machines that physically move, communicate and perform human behavior. Therefore, it covers engineering fields such as mechanical engineering, electrical and electronics engineering as well as computer sciences as well as verbal fields such as psychology.


Robotic applications are used in a wide range such as defense industry, industrial industry and health. Of course, robots are also aimed to learn from their own / other robots' experiences. For example, when a robot is told to carry an object, the robot is expected to observe its environment and adapt to the change of the environment, such as people walking around.

5.         Autonomous Vehicles



Self-driving cars, planes, boats, etc. It will profoundly change people's lives and our environment over the next decades. Researchers are exploring how vehicles can operate safely in an environment that is constantly changing due to human behavior. Autonomous vehicles have hit the roads in some areas with some restrictions, but they are still not at a sufficient level to safely get on the roads in urban traffic with pedestrians and bicycles.


Autonomous vehicles require researchers interested in many fields such as robotics, computer vision, human computer interaction, machine learning and decision making. As such, controlling a robot that travels 100 km or more per hour and weighs 1-3 tons is not an easy task.

6.         Multi-Agent Autonomous Systems

Multi-agent autonomous systems can be summarized as the movement of multiple autonomous systems such as UAV swarms towards a common target. The research subject includes actions such as how multi-agent autonomous vehicles will communicate with each other, how they will act jointly, and road and route planning. Of course, all these operations and more need to be calculated and implemented in real time.

7.         Autonomous Spacecraft

Autonomous spacecraft are basically expected to perfectly fulfill many vital functions such as where the vehicle is, what it is around, planning actions, and controlling movement and balance. Autonomous spacecraft are especially used in areas such as flight planning, orbit planning, and autonomous landing on planets. In this context, machine learning techniques and reinforcement learning techniques will be researched and developed.

8.         SLAM (Simultaneous Localization and Mapping)

It is of great importance to integrate the LIDAR system, which is based on laser distance meter logic, which is widely used in applications such as mapping, imaging and obstacle diagnosis in autonomous vehicles, into our laboratory. The LIDAR system, which can perform 2D and 3D imaging, has taken today's defense technologies one step further, with many applications such as agriculture, underwater imaging, air pollution analysis and building tunnel modeling. In this context, integrating such a system into our laboratory will be instrumental in interdisciplinary applications and will enable us to develop innovative projects in defense technologies, which is our university's field of expertise. Some examples we can give to LIDAR applications:

* Military applications (detection of moving targets, detailed mapping of rural areas, air defense, air traffic control, navigation, search and rescue, fire control)

* Three-dimensional mapping

* Satellite image processing

* Obstacle detection as navigation and accident prevention for autonomous vehicles

* Agricultural practices (collecting crop harvest information and creating statistics, topographic analysis, examination of soil properties, product categorization, crop productivity control)

* Remote sensing for geographic analysis

* Mapping and viewing of closed areas such as caves and tunnels

* Air and water pollution analysis

* Climate monitoring

* Building analysis after natural disasters

* Increasing renewable energy efficiency (such as positioning of solar panels and wind turbines in the most accurate way, sensing wind direction and strength)

9.         IoT (Internet of Things)

IoT (Internet of Things) technology is the communication and communication of smart devices with each other. Today, the Internet of Things ranges from small appliances to smart cities. The data generated here appears as "big data", that is, big data. Big data is the result of all digital data being collected and accumulated in one place. The data here are generally analyzed by artificial intelligence algorithms and are useful for many of our daily lives.

10.     Data Mining and Big Data

In data mining, artificial intelligence and big data techniques are widely used in various fields such as classification, planning, forecasting, collecting and analyzing customer information. Nowadays, data is now everywhere, so it is very important to process this data produced in large quantities and to obtain meaningful information. Artificial intelligence is often used to process this type of data. Basically, Artificial Intelligence and all of its sub-branches (e.g. Machine Tendency, Deep Learning, Neural Networks) are algorithm-based. These algorithmic methods work on large amounts of data to produce desired results and find patterns or predictions. Therefore, complex analytical tasks that cannot be done with human imagination can be done quickly with the help of artificial intelligence on big data.

11.     Application of Artificial Intelligence to Fluid Mechanics and Optimal Control

Neural networks and convolution strategies are used to model and optimize flow in some specific problems in fluid mechanics such as cylinder drift and modeling. New approaches have been introduced in computational fluid dynamics, especially with the rapidly increasing computational capacities in recent years. In this research center, studies will be carried out to use machine learning algorithms in calculating flow control.

12.     Genomics and Health

Genomics is the meeting of medicine with computer science. For example, looking at diseases with genomes such as the detection of disease-causing gene sequences provides numerous contributions. Studies have shown that deviations of gene sequences from normal are associated with diseases ranging from hypertension to sleep apnea.

13.     Other Areas

Artificial intelligence applications are now used in every field. Within the scope of YZAM, which is planned to be established, studies are planned to be carried out in different areas in the future. Other topics to be studied in the future are given below.


* Decision support systems and development

* Diagnostic systems and diagnostic kit development

* Simulation studies and analysis of simulation data

* Creation of security systems and cryptology

* Information security