Deep learning became an ubiquitous paradigm due to its extraordinary effectiveness and applicability in numerous domains. However, the approach suffers from the high demand for data required to achieve the potential of this type of model. An ever-increasing subfield of Artificial Intelligence, Image Synthesis, aims to address this limitation through the design of intelligent…
Decision trees rank among the most popular classification tools, employed in practical applications due to their known efficiency. A Nash equilibrium-based decision tree splits node data using the Nash equilibrium concept. Boosting is a technique that is used to enhance the performance of a classifier by allowing an in-depth exploration of the data. This…
Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection crucial for improving survival rates. Deep learning-based approaches have shown remarkable potential in automating tumor detection from mammographic images; however, their effectiveness largely depends on the choice of data augmentation strategies and model architecture. In this study, we…
Breast cancer is the most commonly diagnosed cancer among women worldwide, with early detection playing an essential role in improving survival rates. Detection of breast abnormalities at an early stage is best performed using mammography. This paper presents a new approach integrating advanced preprocessing techniques based on Cellular Automaton. It was applied on the…
Background: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have…
Automatic diacritic restoration is crucial for text processing in languages with rich diacritical marks, such as Romanian. This study evaluates the performance of several large language models (LLMs) in restoring diacritics in Romanian texts. Using a comprehensive corpus, we tested models including OpenAI’s GPT-3.5, GPT-4, GPT-4o, Google’s Gemini 1.0 Pro, Meta’s Llama 2 and…
This study presents our findings on estimating the horizontal rotation angle (yaw) of traffic signs from 2D images using deep learning techniques. The aim is to introduce novel approaches for accurately estimating a traffic sign’s orientation, with applications in automatic map generation. The primary goal is to associate a traffic sign with a road…
The identification of elements in user interfaces is a problem that can generate great interest in current times due to the significant interaction between users and machines. Digital technologies are increasingly used to carry out almost any daily task. Computer vision can be helpful in different applications, such as accessibility, testing, or automatic code…
Can we control the writing style of large language models (LLMs) by specifying desired linguistic features? We address this question by investigating the impact of handcrafted linguistic feature (HLF) instructions on LLM-generated text. Our experiment evaluates various state-of-the-art LLMs using prompts incorporating HLF statistics derived from corpora of CNN articles and Yelp reviews. We…
As virtual assistants (VAs) become essential to contemporary interactions, it is imperative to understand how to evaluate their functionalities. This study offers a comparison framework for assessing the design and execution of Amazon Alexa and Microsoft Copilot Studio, emphasizing their capabilities in question-answering activities. Through the examination of their deterministic and probabilistic approaches, we…
