171 citations · 323 across the 14 of their papers we have counts for
6 papers · 1 filter
Long-Sequence LSTM Modeling for NBA Game Outcome Prediction Using a Novel Multi-Season Dataset
Charles Rios, Longzhen Han, Almas Baimagambetov +1
Predicting the outcomes of professional basketball games, particularly in the National Basketball Association (NBA), has become increasingly important for coaching strategy, fan en…
Ethical AI prompt recommendations in large language models using collaborative filtering
Jordan Nelson, Almas Baimagambetov, Konstantinos Avgerinakis +1
As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability c…
Utilizing Large Language Models for Machine Learning Explainability
Alexandros Vassiliades, Nikolaos Polatidis, Stamatios Samaras +5
This study explores the explainability capabilities of large language models (LLMs), when employed to autonomously generate machine learning (ML) solutions. We examine two classifi…
LLM-Generated Samples for Android Malware Detection
Nik Rollinson, Nikolaos Polatidis
Android malware continues to evolve through obfuscation and polymorphism, posing challenges for both signature-based defenses and machine learning models trained on limited and imb…
A Survey of Generative Categories and Techniques in Multimodal Generative Models
Longzhen Han, Awes Mubarak, Almas Baimagambetov +2
Multimodal generative models (MGMs) have rapidly expanded beyond text generation to encompass images, music, video, human motion, and 3D objects. This survey categorises six primar…
Evolving Machine Learning in Non-Stationary Environments: A Unified Survey of Drift, Forgetting, and Adaptation
Ignacio Cabrera Martin, Subhaditya Mukherjee, Almas Baimagambetov +2
In an era defined by rapid data evolution, traditional Machine Learning (ML) models often struggle to adapt to dynamic environments. Evolving Machine Learning (EML) has emerged as…