Fahad Ayaz

Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art

Braiton Mukhalela, Serestina Viriri, David Ndzi, Michael Gebreslasie et autres

Extreme rainfall events (ERES) are among the most challenging hydro-meteorological phenomena to forecast because the complex, nonlinear atmospheric processes involved span multiple spatial and temporal scales and are further intensified by rising climate variability. While Numerical Weather Prediction (NWP) models offer physically …

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Machine Learning for Heatwave Prediction: A Global Scoping Review of Environmental Predictors and Modelling Practices

Adam Ashford, Fahad Ayaz, Muhammad Zeeshan Shakir, Naeem Ramzan et autres

As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in parts of the world that are already subject to …

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Environmental predictors and machine learning models for climate-health risk prediction from extreme weather events: a global systematic review

Fahad Ayaz, Adam Ashford, Muhammad Zeeshan Shakir, Naeem Ramzan et autres

Climate change is increasing the frequency, severity, and duration of extreme weather events, including heatwaves, floods, and heavy rainfall, posing substantial risks to population health worldwide. Traditional epidemiological approaches capture retrospective associations but are limited in modeling the non-linear, multivariable relationships between …

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Machine Learning for Heatwave Prediction: A Global Scoping Review of Environmental Predictors and Modelling Practices

Adam Ashford, Fahad Ayaz, Muhammad Zeeshan Shakir, Naeem Ramzan et autres

As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in the hottest parts of the world. In recent years, …

Non-Invasive Driver Activity Recognition using mmWave Radar

Basim Alhumaily, Fahad Ayaz, Sajjad Hussain, Lina Mohjazi et autres

Driver monitoring systems are crucial for automotive safety, yet conventional camera-based solutions suffer from lighting variability and significant privacy concerns. This paper presents a privacy-preserving, illumination-invariant alternative using a single Frequency Modulated Continuous Wave (FMCW) mmWave radar sensor to classify subtle driver …

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Benchmarking Radar Preprocessing Techniques and Transfer Learning Models for FMCW-based Human Activity Recognition

Fahad Ayaz, Basim Alhumaily, Ahsan Raza Khan, Sajjad Hussain et autres

Human Activity Recognition (HAR) using radar signals has gained significant attention due to its non-intrusive nature and robustness in various environments. However, the impact of radar signal preprocessing techniques on the performance of deep learning (DL) models remains an active area of …

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Multimodal Fusion of Heart Rate and Gaze Data For Real-Time Driver Monitoring in Naturalistic Driving

Basim Alhumaily, Fahad Ayaz, Lina Mohjazi, Sajjad Hussain et autres

Driver distraction and diminished alertness remain key contributors to road‑traffic accidents. This study introduces a real‑time multimodal driver‑monitoring framework that fuses heart rate (HR) signals with gaze‑based metrics. Five licensed drivers undertook 480 minutes of naturalistic driving in both urban and motorway …

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Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition

Fahad Ayaz, Basim Alhumaily, Sajjad Hussain, Muhammad Ali Imran et autres

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to …

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Evaluating Radar Signal Preprocessing Techniques for Improved Transfer Learning in Human Activity Recognition

Fahad Ayaz, Basim Alhumaily, Ahsan Raza Khan, Sajjad Hussain et autres

Human Activity Recognition (HAR) using radar signals has gained significant attention due to its non-intrusive nature and robustness in various environments. However, the impact of radar signal preprocessing techniques on the performance of deep learning (DL) models remains an active area of …

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Hybrid Neuromorphic‐Federated Learning for Activity Recognition Using Multi‐modal Wearable Sensors

Ahsan Raza Khan, Habib Ullah Manzoor, Fahad Ayaz, Muhammad Ali Imran et autres

In this chapter, the authors proposed an hybrid neuromorphic federated learning framework that synergizes the computational efficiency of spiking neural networks with the dynamic temporal learning capabilities of long short-term memory (LSTM) networks for human activity recognition (HAR) using multi-model data from …

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Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition

Fahad Ayaz, Basim Alhumaily, Sajjad Hussain, Muhammad Ali Imran et autres

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to …

Real-Time Lightweight Deep Learning Models for Human Activity Recognition Using FMCW Radar

Fahad Ayaz, Basim Alhumaily, Sajjad Hussain, Muhammad Ali Imran et autres

Radar-based Human Activity Recognition (HAR) has attracted much attention in various fields such as smart security, medical monitoring, and human computer interaction. Integrating Convolutional Neural Networks (CNNs) with radar spectrum techniques for HAR is becoming increasingly popular. However, traditional network models usually …

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