Almas Baimagambetov

Charles Rios, Longzhen Han, Almas Baimagambetov et al.

Predicting the outcomes of professional basketball games, particularly in the National Basketball Association (NBA), has become increasingly important for coaching strategy, fan engagement, and sports betting. However, many existing prediction models struggle with concept drift, limited temporal context, and instability across seasons. To advance forecasting in this domain, we introduce a newly constructed longitudinal NBA dataset covering the 2004-05 to 2024-25 seasons and present a deep learning framework designed to model long-term performance trends. Our primary contribution is a Long Short-Term Memory (LSTM) architecture that leverages an extended sequence length of 9,840 games equivalent to eight full NBA seasons to capture evolving team dynamics and season-over-season dependencies. We compare this model against several traditional Machine Learning (ML) and Deep Learning (DL) baselines, including Logistic Regression, Random Forest, Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The LSTM achieves the best performance across all metrics, with 72.35 accuracy, 73.15 precision and 76.13 AUC-ROC. These results demonstrate the importance of long-sequence temporal modeling in basketball outcome prediction and highlight the value of our new multi-season dataset for developing robust, generalizable NBA forecasting systems.

Longzhen Han, Awes Mubarak, Almas Baimagambetov et al.

Multimodal Large Language Models (MLLMs) have rapidly evolved beyond text generation, now spanning diverse output modalities including images, music, video, human motion, and 3D objects, by integrating language with other sensory modalities under unified architectures. This survey categorises six primary generative modalities and examines how foundational techniques, namely Self-Supervised Learning (SSL), Mixture of Experts (MoE), Reinforcement Learning from Human Feedback (RLHF), and Chain-of-Thought (CoT) prompting, enable cross-modal capabilities. We analyze key models, architectural trends, and emergent cross-modal synergies, while highlighting transferable techniques and unresolved challenges. Architectural innovations like transformers and diffusion models underpin this convergence, enabling cross-modal transfer and modular specialization. We highlight emerging patterns of synergy, and identify open challenges in evaluation, modularity, and structured reasoning. This survey offers a unified perspective on MLLM development and identifies critical paths toward more general-purpose, adaptive, and interpretable multimodal systems.

Ignacio Cabrera Martin, Subhaditya Mukherjee, Almas Baimagambetov et al.

In an era defined by rapid data evolution, traditional Machine Learning (ML) models often fall short in adapting to dynamic environments. Evolving Machine Learning (EML) has emerged as a critical paradigm, enabling continuous learning and adaptation in real-time data streams. This survey presents a comprehensive analysis of EML, focusing on five core challenges: data drift, concept drift, catastrophic forgetting, skewed learning, and network adaptation. We systematically review over 100 studies, categorizing state-of-the-art methods across supervised, unsupervised, and semi-supervised approaches. The survey explores diverse evaluation metrics, benchmark datasets, and real-world applications, offering a comparative lens on the effectiveness and limitations of current techniques. Additionally, we highlight the growing role of adaptive neural architectures, meta-learning, and ensemble strategies in addressing evolving data complexities. By synthesizing insights from recent literature, this work not only maps the current landscape of EML but also identifies critical gaps and opportunities for future research. Our findings aim to guide researchers and practitioners in developing robust, ethical, and scalable EML systems for real-world deployment.