Category: Training

Mr Filip Radinovic defended his BSc thesis “A system for analyzing identification documents by leveraging Computer vision and Deep Learning” under co-mentorhsip of mr Stevan Cakic and prof. Tomo Popovic. The thesis focuses on the importance of identity in our digital world and how it impacts the security measures used by organizations. The main goal of the thesis is to use artificial intelligence to verify a person’s identity online. The researchers trained a model using various datasets and images, teaching it to spot even the smallest inconsistencies. The most significant discovery they made is that this model is very accurate, with a precision rate of around 90%. Additionally, the model is very efficient, taking only 3 to 4 seconds to process data, which is much faster than manual methods. Overall, the thesis highlights the potential of using AI for identity verification, making it both precise and time-saving.

ABSTRACT – Identity is one of the most sacred values and currencies in our digital era, affecting the working models of private and public institutions. This causes many strict security measures and protocols with a price of time, which is where this thesis’ goal arises. The approach of the thesis is leveraging artificial intelligence to accomplish identity verification over the web. The model was trained on a myriad of datasets and images, utilizing standard deep learning algorithms. By the end of training, it was able to detect the most subtle inconsistencies, making it quite precise. The biggest research finding is the potential that a model like this holds. Its precision varies around 90%, which is a good number by today’s standards and model’s testing conditions and hardware. The other aspect is time, in which the model excels. From the point when the model receives the data, the processing of it begins and it takes 3 to 4 seconds (on modest hardware). This implies superior efficiency than manual or alternative ways of accomplishing the same goal.

Mr Dejan Babic, a young researcher from UDG, just defended his Master thesis on the use of computer vision and artificial intelligence in medicine. This is a great example of using ICT for vertical priority domains of Monteengrin S3. This research was supported in part by HPC4S3ME project and EUROCC. Mr Babic intends to continue his research in this domain and to enroll PhD program at the UDG. Mr Babic explored the use of different tools for ML and he also experimened with the use of HPC for training prediction models that can be used in medicine. He was one of the first MSc theses defended from the Artificial Intelligence Master program created under the EuroCC project.

ABSTRACT – Artificial Intelligence is transforming the way we live, work, and communicate with the world. The proliferation of data has been the biggest driver of AI in recent years. AI in medicine is rapidly developing and holds great potential in revolutionizing healthcare systems. Its application is already producing promising results in disease detection, diagnosis and drug discovery. AI is widely used in medical facilities worldwide as a decision support tool for patient diagnosis. It is expected to bring significant benefits to healthcare sector. In this thesis, the focus is on the application of artificial intelligence and computer vision in solving real medical problems. The research is both theoretical and empirical and focuses on the application of artificial intelligence and computer vision in the detection of pneumonia, segmentation of blood vessels in the retina, and estimation of cardiovascular risk. The main goal of the research is to achieve the highest possible accuracy in specific cases and approaches, in order for these approaches to be considered applicable in medicine. Throughout the study, some of the ethical issues related to the use of this technology were also raised. At the end of the study, the results, potential challenges, and future directions of this research were discussed.

Mr Ivan Jovovic, a young researcher from UDG, just defended his Master thesis on the use of artificial intelligence and machine learining on edge devices. This research was supported in part by HPC4S3ME project and EUROCC. Mr Jovovic intends to continue his research in this domain and to enroll PhD program at the UDG. Mr Jovovic explored the use of different tools for ML and he also experimened with the use of HPC for training prediction models that can be ported onto edge devices. He was one of the first MSc theses defended from the Artificial Intelligence Master program created under EuroCC project.

ABSTRACT – This thesis explores the combination of artificial intelligence, machine learning, deep learning, and edge computing in modern applications, with a special focus on medicine and agriculture. The paper first introduces the reader to the basic terms and definitions of machine learning, deep learning, computer vision, the Internet of Things and Edge computing. After the theoretical basis, the work provides an insight into the practical applications of these technologies in medicine and agriculture, highlighting the benefits and drawbacks of their applications. In the following, the paper offers a detailed study of practical examples of edge artificial intelligence in agriculture and healthcare, as well as artificial intelligence in the field of medicine, with focus on disease classification. Through the realization and implementation of these projects, the interpretation of the results and the discussion, the paper emphasizes the importance of the integration of artificial intelligence and edge computing in various industries.