Towards an interpretable breast cancer detection and diagnosis system (2024)

According to the World Health Organization, breast cancer becomes fatal only if it spreads throughout the body. Therefore, regular screening is essential. Whilst mammography is the most frequently used technique, its interpretation can be challenging and time-consuming. For this reason, computer-aided detection and diagnosis systems are increasingly being used for second opinion. However, in…

Quiz-Ifying Education: Exploring the Power of Virtual Assistants (2024)

Technology offers transformative potential for educational innovation. This paper introduces a novel approach by harnessing virtual assistants, specifically through an Alexa quiz skill tailored for university students, to enhance learning experiences. Supported by a preliminary evaluation, our solution demonstrates significant user satisfaction, indicating its effectiveness and areas for further refinement. Our tailored skill dynamically…

Significance of Training Images and Feature Extraction in Lesion Classification (2024)

Proper treatment of breast cancer is essential to increase survival rates. Mammography is a widely used, non-invasive screening method for breast cancer. A challenging task in mammogram analysis is to distinguish between tumors. In the current study, we address this problem using different feature extraction and classification methods. In the literature, numerous feature extraction…

Using Chat GPT for Malicious Web Links Detection (2024)

Over the last years, the Internet has monopolized most businesses and industries. These outstanding advancements lead to the dangerous development of specialized threats employed to outsmart everyday users, collect personal data and financial benefits. One of the most relevant attacks is malicious web links, which can be inserted into private messages, emails, social media…

Evaluating cooperative-competitive dynamics with deep Q-learning (2023)

We model cooperative-competitive social group dynamics with multi-agent environments, specialized in cases with a large number of agents from only a few distinct types. The multi-agent optimization problems are addressed in turn with multi-agent reinforcement learning algorithms to obtain flexible and robust solutions. We analyze the effectiveness of centralized and decentralized algorithms using three…