171 citations · 323 across the 14 of their papers we have counts for
16 papers
Cluster-Specific Localized Drift Detection for Efficient Batch Model Adaptation under Controlled Distribution Shift
Ignacio Cabrera Martin, Marcello Trovati, Almas Baimagambetov +1
Machine learning systems deployed in dynamic environments frequently operate under nonstationary data distributions, where controlled distribution shift can progressively degrade p…
Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Xuanyan Liu, Ignacio Cabrera Martin, Marcello Trovati +2
The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread availability of machine learning…
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…
Mobile recommender systems: Identifying the major concepts
Elias Pimenidis, Nikolaos Polatidis, Haralambos Mouratidis
This paper identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applic…
From product recommendation to cyber-attack prediction: Generating attack graphs and predicting future attacks
Nikolaos Polatidis, Elias Pimenidis, Michalis Pavlidis +2
Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the…
A multi-level collaborative filtering method that improves recommendations
Nikolaos Polatidis, Christos K. Georgiadis
Collaborative filtering is one of the most used approaches for providing recommendations in various online environments. Even though collaborative recommendation methods have been…
A dynamic multi-level collaborative filtering method for improved recommendations
Nikolaos Polatidis, Christos K. Georgiadis
One of the most used approaches for providing recommendations in various online environments such as e-commerce is collaborative filtering. Although, this is a simple method for re…
Mobile recommender systems: An overview of technologies and challenges
Nikolaos Polatidis, Christos K. Georgiadis
The use of mobile devices in combination with the rapid growth of the internet has generated an information overload problem. Recommender systems is a necessity to decide which of…
Factors Influencing the Quality of the User Experience in Ubiquitous Recommender Systems
Nikolaos Polatidis, Christos K. Georgiadis
The use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using Ubiquitous recommender systems. However in mobile de…
Chatbot for admissions
Nikolaos Polatidis
The communication of potential students with a university department is performed manually and it is a very time consuming procedure. The opportunity to communicate with on a one-t…
SFA Referee Allocation Scheme
Nikolaos Polatidis
For many sports, the allocation of officials to matches is performed manually and is a very time consuming procedure. For the Scottish Football Association (SFA), the allocation of…