Exploiting the Spatio-Temporal Patterns in IoT Data to Establish a Dynamic Ensemble of Distributed Learners by Mehdi Mohammadi (5024105)

Traffic Transformer: Transformer-based framework for temporal traffic accident prediction by Mansoor G. Al-Thani (19237204)

“…Moreover, it facilitates a more flexible and comprehensive learning of diverse hidden patterns within the sequences. …”

Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil (11513178)

“…Our method successfully captures temporal patterns, making it robust against subtle anomalies and structural changes. …”

A temporal–spatial deep learning framework leveraging dynamic 3D attention maps for violence detection by Elizabeth B. Varghese (19198018)

“…By learning where, when, and for how long to attend within a video, using dynamic three-dimensional attention prediction networks, the model captures complex patterns of violent behavior more effectively. …”

Temporal self-attention for risk prediction from electronic health records using non-stationary kernel approximation by Rawan AlSaad (14159019)

Identifying patterns of plant “waste accumulation” in House 169, Elephantine Island, Egypt (1773–1650 BCE) using Machine Learning by Malleson, Claire

“…However, the results of our two-step approach in data analysis revealed some patterns. A hierarchical clustering strategy applied to the composition of the samples yielded 2 to 4 reasonably equally sized clusters among the collected samples; subsequently, evolutionary prediction trees – our additional step in Machine Learning – served to relate spatial, temporal, and functional variables to cluster membership. …”
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Continuous Arabic Sign Language Recognition in User Dependent Mode by Assaleh, Khaled

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Sensor-based Continuous Arabic Sign Language Recognition by Tubaiz, Noor Ali

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Learning-Based Space-Time Adaptive Processing by El Khatib, Alaa

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Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment by Ammar Abulibdeh (15785928)

“…This study aims to assess the environmental impact of the 2023–2024 war on air quality in the Gaza Strip by examining temporal and spatial changes in key atmospheric pollutants. …”

A New Flow-Based Approach for Enhancing Botnet Detection Efficiency Using Convolutional Neural Networks and Long Short-Term Memory by Mehdi Asadi (12566741)

“…CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. …”

Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet by Vineet Dhanawat (22361395)

LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysis by Saeif Alhazbi (17058108)

“…Our method leverages the intrinsic autoregressive generation nature of language models, which generate text one token at a time based on all previously generated tokens, creating a unique temporal pattern-like a rhythm or heartbeat-that persists even when the output is streamed over a network. …”

Optimizing energy efficiency through precise occupancy detection: A tailored CNN architecture for smart buildings and beyond by Aya Nabil Sayed (17317006)

“…This transformation captures spatial and temporal characteristics of the data, allowing the network to learn more expressive occupancy-related patterns from raw 1D input. …”

MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model by Md Abrar Jahin (20108252)

“…MCDFN utilizes Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) to extract spatial and temporal features from time series data. Comparative benchmarking against seven other deep-learning models validates MCDFN’s efficacy, showing it outperforms its counterparts across key metrics with a mean squared error (MSE) of 23.5738, root mean squared error (RMSE) of 4.8553, mean absolute error (MAE) of 3.9991, and mean absolute percentage error (MAPE) of 20.1575%. …”

A Diffusion-Based Probabilistic Ultra-Short-Term Solar Power Prediction Using the Sky Image Sequences by Razieh Rastgoo (22457767)