This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …

Abstract The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural
Language Processing (NLP) during the past decade. However, the demands of long …

This study proposes a method for classifying economic activity descriptors to match
Nomenclature of Economic Activities (NACE) codes, employing a blend of machine learning …

As precision agriculture (PA) advances, the demand for accurate and high-resolution
weather forecasts becomes critical for optimizing agricultural management practices …

Effectively analyzing the comments to uncover latent intentions holds immense value in
making strategic decisions across various domains. However, several challenges hinder the …

Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP)
poses significant challenges due to the domain gap and limited data availability. This study …

This paper investigates the application of pre-trained large language models (LLMs) for
spam email classification using zero-shot prompting. We evaluate the performance of both …

Artificial Intelligence (AI) chatbots leveraging Large Language Models (LLMs) are gaining
traction in healthcare for their potential to automate patient interactions and aid clinical …

The existing course recommendation systems around the world rely on student's past
performances, grades and CGPA when giving personalized suggestions. The goal is to …

Denna studie, finansierad av Myndigheten för samhällsskydd och beredskap (MSB),
undersöker hur artificiell intelligens (AI) och i synnerhet generativ AI kan påverka samhällets …