Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Abstract Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …

Large language models (LLMs) learn temporal concepts from the co-occurrence of related
tokens in a sequence. Compared with conventional text generation, temporal reasoning …

The advancement of machine learning and symbolic approaches have underscored their
strengths and weaknesses in Natural Language Processing (NLP). While machine learning …

Neural networks have been rapidly expanding in recent years, with novel strategies and
applications. However, challenges such as interpretability, explainability, robustness, safety …

Symbolic artificial intelligence (AI) reflects the domain knowledge of experts and adheres to
the logic of the subject area, rules, or any relations between entities. Connectionist (neuro) …

Artificial Intelligence applications show great potential for language-related tasks that rely on
next-word prediction. The current generation of large language models have been linked to …

Brain tumor segmentation (BTS) has been studied from handcrafted engineered features to
conventional machine learning (ML) methods, followed by the cutting-edge deep learning …

This paper introduces TAM-SenticNet, a Neuro-Symbolic AI framework uniquely designed
for early depression detection through social media content analysis. Merging neural …