Omer Aydin

Omer Aydin, Enis Karaarslan

OpenAI released version GPT-4 on March 14, 2023, following the success of ChatGPT, which was announced in November 2022. In addition to the existing GPT-3 features, GPT-4 can interpret images. To achieve this, the processing power and model have been significantly improved. The ability to process and interpret images goes far beyond the applications and effectiveness of artificial intelligence. In this study, we first explored the interpretation of radiological images in healthcare using artificial intelligence (AI). Then, we experimented with the image interpretation capability of the GPT-4. In this way, we addressed the question of whether artificial intelligence (AI) can replace a healthcare professional (e.g., a medical doctor) or whether it can be used as a decision-support tool that makes decisions easier and more reliable. Our results showed that ChatGPT is not sufficient and accurate to analyze chest X-ray images, but it can provide interpretations that can assist medical doctors or clinicians.

Omer Aydin

It is obvious that fossil fuels are a limited resource and will be replaced by other energy sources in the future considering economic and en-vironmental problems. Electricity comes to the forefront among the sources that are candidates to replace fossil fuels. In the near future, electric land, air and sea vehicles will start to take more place in daily life. For this reason, systems for the charging systems of these devices and post-charge payments have been developed. There is no general standard on this issue yet. In this study, a charge and payment system, which is safe against known cyber-attacks for use in electric land ve-hicles, and which prioritizes privacy, is proposed. A system has been proposed to verify each other wired or wirelessly with an authentication protocol, where the data communication is encrypted, and the payment transactions are performed securely and invoiced to the vehicle owners.

Omer Aydin

Over the past decade, machine learning methods have given us driverless cars, voice recognition, effective web search, and a much better understanding of the human genome. Machine learning is so common today that it is used dozens of times a day, possibly unknowingly. Trying to teach a machine some processes or some situations can make them predict some results that are difficult to predict by the human brain. These methods also help us do some operations that are often impossible or difficult to do with human activities in a short time. For these reasons, machine learning is so important today. In this study, two different machine learning methods were combined. In order to solve a real-world problem, the manuscript documents were first transferred to the computer and then classified. We used three basic methods to realize the whole process. Handwriting or printed documents have been digitalized by a scanner or digital camera. These documents have been processed with two different Optical Character Recognition (OCR) operation. After that generated texts are classified by using Naive Bayes algorithm. All project was programmed in Microsoft Visual Studio 12 platform on Windows operating system. C# programming language was used for all parts of the study. Also, some prepared codes and DLLs were used.