Syed Kumayl Raza Moosavi

• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Bukhari, Syed Muhammad Salman; Hadi, Shahzaib Farooq & Sanfilippo, Filippo (2026). Exploring the Synergies between Federated Learning, Collaborative Robotics, and Autonomous Systems: Shaping the path to Industry 5.0. Proceedings - European Council for Modelling and Simulation (ECMS). ISSN 2522-2414. 2026, p. 92–101. doi: 10.7148/2026-0092. Full text in Research Archive Show summary

  The advent of Industry 5.0 marks a transformative era in industrial development, where advanced Technologies converge to create more intelligent, efficient, and humancentric systems. This paper examines the synergistic integration of Federated Learning (FL), Collaborative Robotics (cobots), and Autonomous Systems (AS), exploring their combined potential in shaping the trajectory toward Industry 5.0. We present a comprehensive analysis of how these technologies collectively enhance the capabilities of industrial systems, with emphasis on humanmachine collaboration, resilience, adaptability, and sustainability. A case study centred on a disaster response scenario is introduced to illustrate the practical benefits of this technological convergence in real-world, high-stress environments. Our findings clarify that the convergence of these technologies not only propels the industrial sector toward the visionary goals of Industry 5.0 but also opens new avenues for addressing complex challenges across various industrial and societal domains.

• Murtaza, Aitzaz Ahmed; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Aftab, Muhammad Faisal & Sanfilippo, Filippo (2026). Tool wear prediction using multi-domain features and quantised attention embedded CNN. Measurement: Sensors. 47. doi: 10.1016/j.measen.2026.102013. Full text in Research Archive Show summary

  In precision manufacturing, accurate prediction of tool wear is pivotal for maintaining production quality and efficiency. This paper introduces a novel predictive model that employs a multi-branch convolutional neural network (multi-CNN) enhanced by an attention mechanism and Bayesian optimisation to forecast tool wear with high accuracy. The proposed approach is evaluated using the publicly available dataset, which comprises force and torque measurements acquired from machining processes. By incorporating time-domain (TD), frequency-domain (FD), and discrete wavelet transform (DWT) features, the model comprehensively analyses signals from machining processes, enabling robust predictions. The attention mechanism strategically emphasises significant features, augmenting the model’s predictive capability. Demonstrated results show superior performance with a mean absolute error (MAE) of 2.8, root mean squared error (RMSE) of 3.4, and an R2 score of 0.986, significantly outperforming conventional models. Additionally, post-training quantisation optimises the model for deployment on edge computing devices, maintaining effectiveness while being lightweight, ideal for real-time applications in resource-constrained environments. This study exemplifies how advanced machine learning techniques can improve predictive maintenance in manufacturing which not only enhances operational efficiency but also reduces downtime.

• Masood, Muhammad Zeeshan; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Demrozi, Florenc; Shaukat, Furqan & Sanfilippo, Filippo (2026). Towards robust neuromorphic/event vision-based fall detection: Cross-dataset generalisation benchmarking. Expert Systems With Applications. ISSN 0957-4174. 325. doi: 10.1016/j.eswa.2026.132675. Full text in Research Archive Show summary

  Fall detection is a critical safety concern in many real-world environments, including smart homes, healthcare and assisted living facilities, workplaces, and other indoor/outdoor environments where individuals may be exposed to situations that increase the risk of accidental falls. Vision-based systems have emerged as a promising solution due to their non-intrusive nature and strong recognition performance. However, traditional RGB-based approaches suffer from limitations, including sensitivity to lighting conditions, occlusions, and privacy concerns. In contrast, event cameras capture asynchronous brightness changes with high temporal resolution and low latency, making them more robust in challenging conditions. Despite this potential, progress in event-based fall detection is limited by the lack of large-scale datasets and standardized evaluation protocols. In this paper, we present a benchmarking study based on four widely used RGB fall detection datasets–CAUCA Fall, GMDCSA24, Fall Vision, and Multiple Cameras Fall Dataset–converted into event data using the Metavision Simulator. These datasets are used to train and evaluate nine different deep learning models (i.e., I3D, 3DCNN, ConvLSTM, MobileNetV3-LSTM, TCN, SwinTransformer, TimeSFormer, EfficientViT, and TemporalMotionNet). To evaluate cross-domain generalization, the trained models are tested on a real-world event dataset, the Pedestrian Detection, Action Recognition, and Fall Detection (PAF) dataset. Our study highlights the challenges of simulation-to-real transfer in event-based fall detection and provides a comparative evaluation across synthetic and real event datasets. The results show that while TemporalMotionNet achieves high performance on synthetic data (98.65% accuracy), performance drops significantly on real data, with a 17.25% gap (81.4% accuracy on PAF). The converted datasets, together with the evaluation protocol, are publicly released (https://github.com/AIBCRG/EventFallDetection) to foster further research in this domain.

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2026). CurFed-CHARM: Curriculum sequential federated learning with a channel-hierarchical action recognition model for non-IID heterogeneous data in neuromorphic/event cameras. Computer Vision and Image Understanding. ISSN 1077-3142. 265. doi: 10.1016/j.cviu.2026.104696. Full text in Research Archive
• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2026). MATRIX-HAR: Lightweight temporal motion-guided feature network with adaptive temporal resolution based transfer learning for resource-constrained event camera-based human action recognition. Expert Systems With Applications. ISSN 0957-4174. 303. doi: 10.1016/j.eswa.2025.130628. Full text in Research Archive Show summary

  Event-based cameras have emerged as a promising alternative to traditional sensors for human action recognition due to their high temporal resolution, low latency, and robustness to lighting variations. However, existing methods struggle to effectively capture the complex spatio-temporal dynamics inherent in event streams, particularly under challenging environmental conditions. In this paper, we propose Lightweight Temporal Motion-guided Feature Network with Adaptive Temporal Resolution Based Transfer Learning for Resource-Constrained Event Camera-based Human Action Recognition (MATRIX-HAR), a novel deep learning architecture specifically designed for event-based human action recognition that incorporates three key innovations: an Adaptive Temporal Resolution (ATR) module that dynamically adjusts temporal processing granularity based on input characteristics, a Motion Guided Feature Module (MGFM) that leverages intrinsic motion information encoded in event streams, and a comprehensive hierarchical transfer learning framework that enables effective knowledge transfer from simple to challenging environmental settings. We systematically evaluate four distinct transfer learning strategies, ranging from early feature transfer to comprehensive multi-level transfer, demonstrating their effectiveness in adapting models trained on simple scenarios to complex real-world conditions. To ensure practical deployment on resource-constrained edge devices, we implement post-training quantization that maintains performance while achieving 4 ×  compression ratios. Extensive experiments on four benchmark datasets (HARDVS, PAF, THU-E-ACT-50, and THU-E-ACT-50 CHL) demonstrate that MATRIX-HAR achieves state-of-the-art performance, surpassing existing methods by significant margins: 0.71 % on HARDVS (97.82 %), 0.74 % on PAF (97.23 %), 3.03 % on THU-E-ACT-50 (95.73 %), and 7.3 % on THU-E-ACT-50 CHL (65.8 %). Ablation studies confirm the individual contributions of each proposed module, while transfer learning analysis reveals that comprehensive feature transfer (Strategy 4) provides the most robust adaptation with 22.3 % relative performance improvement over baseline approaches. The proposed framework demonstrates superior learning dynamics, achieving faster convergence and enhanced stability compared to training from scratch, making it particularly suitable for real-world deployment scenarios where environmental conditions are variable and computational resources are limited.

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Applications of Neuromorphic/Event Camera in Robotics With Human in Loop: A Systematic Review, Datasets, and Challenges. IEEE Transactions on Human-Machine Systems. ISSN 2168-2291. 56(1), p. 32–47. doi: 10.1109/thms.2025.3628064. Full text in Research Archive Show summary

  The evolution of industrial robotics has advanced from isolated, caged systems through basic human–robot interaction (HRI) to sophisticated human–robot collaboration (HRC). However, conventional vision systems based on red, green, blue (RGB) cameras remain a significant limiting factor in realizing the full potential of collaborative automation. This comprehensive review examines the transformative role of event cameras in advancing HRC capabilities and addressing current limitations in industrial settings. Event cameras, with their microsecond-level temporal resolution and robust performance under challenging lighting conditions, offer substantial advantages over traditional RGB cameras that are constrained by fixed frame rates and ambient lighting dependencies. We present a systematic framework for leveraging event cameras to enhance the human state understanding in collaborative robotics, encompassing real-time detection of poses, gestures, facial expressions, and emotional states. This framework addresses fundamental challenges in workplace safety and collaborative efficiency while enabling more sophisticated and responsive HRC systems. Our review synthesizes recent research developments in event camera applications specific to HRC, providing a detailed comparative analysis of their advantages over conventional vision systems. We identify emerging opportunities and potential research directions for advancing event-based vision in industrial robotics. In addition, we examine integration challenges and propose strategies for implementing event camera technology in existing industrial infrastructure. This work contributes valuable insights into the future trajectory of adaptive and intuitive HRC systems, offering a roadmap for researchers and practitioners in the field of industrial automation.

• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2025). Snake robots: A state-of-the-art review on design, locomotion, control, and real-world applications. Mechatronics (Oxford). ISSN 0957-4158. 112. doi: 10.1016/j.mechatronics.2025.103418. Full text in Research Archive Show summary

  Snake robots have emerged as a transformative class of bio-inspired robotic systems, offering unparallelled adaptability in navigating complex unstructured terrains. Their limbless design, inspired by biological snakes, enables efficient movement across diverse environments, including rough terrains, confined spaces, and hazardous conditions where traditional wheeled or legged robots struggle. This review provides a comprehensive analysis of the design and development of snake robots, covering their locomotion strategies, mechanical configurations, sensor integration, control schemas, and actuation mechanisms. The review further explores the evolution of mechanical structures from rigid, soft, and hybrid designs, emphasising advancements in actuation and sensor technologies in enhancing adaptability and navigation. Applications of snake robots extend across various domains, including search and rescue (SAR), industrial inspection, and exploration of extreme environments. Despite significant progress, challenges such as optimising energy efficiency, improving environmental perception, and achieving real-time adaptability remain open research areas. This review serves as a foundational reference for researchers and engineers working towards advancing the next generation of snake robots, paving the way for their integration into real-world applications

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Neuromorphic Event Camera-Based Object Recognition and Grasping Position Detection Using a Transfer Learning-Enhanced Multi-Task Model. IEEE Transactions on Automation Science and Engineering. ISSN 1545-5955. 22, p. 20169–20181. doi: 10.1109/tase.2025.3598695. Full text in Research Archive Show summary

  Object recognition and grasping position detection are critical tasks in robotic manipulation, particularly when operating in dynamic and unstructured environments. This paper presents the Channel Sharpening Attention-based Adaptive Inception Network (CSA-AInceptNet), a novel multi-task learning model designed for these tasks using event camera data. The proposed architecture integrates channel sharpening attention with adaptive inception networks to enhance feature extraction and improve robustness. The model’s performance is evaluated on two state-of-the-art event camera datasets, E-Grasp and Neuro-Grasp. On the E-Grasp dataset, CSA-AIncepNet achieves a remarkable accuracy of 99.47% and a mean Intersection over Union (IoU) of 0.9370, significantly surpassing existing methods. On the Neuro-Grasp dataset, leveraging transfer learning, the model attains 98.58% accuracy and a mean IoU of 0.4897, demonstrating strong generalization capabilities across datasets. Comparative analyses and ablation studies further validate the effectiveness of the proposed architecture, highlighting its superiority over conventional models like ConvNeXt, DarkNet, DenseNet, and VGG16. The results establish CSA-AIncepNet as a robust solution for event-based object recognition and grasping detection, paving the way for advancements in human-robot collaboration and dynamic robotic manipulation. Note to Practitioners—This work provides a practical solution for improving object recognition and grasping position detection in robotic systems, particularly in unpredictable and fast-changing real-world environments. By leveraging event camera data, the proposed approach enables robots to efficiently identify objects and determine optimal grasping positions, even under challenging conditions. The results highlight the model’s ability to outperform existing methods, making it highly suitable for applications such as human-robot collaboration and precise object handling. This advancement has significant implications for industries like manufacturing, logistics, and healthcare, where robots must interact with objects quickly and accurately. Practitioners can adopt this method to enhance robotic performance, reduce errors, and improve operational efficiency. Future work could focus on testing the model in more complex environments and adapting it for real-time deployment in dynamic settings.

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Federated Learning-Enhanced Edge Deep Learning Model for EMG-Based Gesture Recognition in Real-Time Human-Robot Interaction. IEEE Sensors Journal. ISSN 1530-437X. 25(5), p. 9139–9151. doi: 10.1109/JSEN.2025.3529841. Full text in Research Archive Show summary

  Electromyography (EMG)-based gesture detection plays a crucial role in human-robot interaction (HRI), providing a seamless interface for controlling robotic systems through muscle activity. Despite its potential, EMG systems face significant challenges related to the security and privacy of sensitive biometric data, as well as the computational limitations of deploying deep learning (DL) models on edge devices. To address these issues, we propose a federated learning (FL)-based DL model for EMG gesture recognition, specifically designed for edge devices. Our model utilizes a custom dataset collected using a Mindrove eight-channel EMG armband, capturing eight distinct hand gestures—rest, move left, move right, move down, move up, open fingers, close fist, and twist hand—from ten subjects with seven repetitions each, ensuring diverse and robust data for training. During preprocessing, a bandpass filter (50–450 Hz) was applied to remove noise and enhance signal quality, followed by a short-term frequency transform (STFT) with a 200-ms sample time and 50% overlap to extract relevant features from the EMG signals. The dataset was segmented into training and testing sets with a 70/30 split for evaluation. We evaluate several FL techniques, including FedAvg, FedProx, and FedSGD, demonstrating that FedAvg achieves the highest accuracy of 96.92% without quantization with Scenario 9 (15 epochs, 20 rounds) with minimal communication overhead. Additionally, our model is quantized, resulting in an 89% reduction in size and a high accuracy of 95.99%, representing a minimal loss of 0.93%, making it ideal for edge deployment without compromising performance. A comparative analysis with other DL models, such as multiconvolutional residual networks (MCRNs), multiconvolutional neural networks (MCNNs), temporal convolutional networks (TCNs), and InceptionNet, shows that our approach outperforms these models in both accuracy and efficiency. Experimental results validate the high accuracy of our model in both training/testing and real-time disaster scenario simulations using the Spot robot. The proposed solution provides a secure, efficient, and highly accurate framework for EMG-based gesture recognition on edge devices, ideal for HRI and assistive technologies such as in search and rescue operations.

• Zafar, Muhammad Hamza; Poursina, Mohammad; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Trajectory Optimisation of a Robotic Manipulator Using a Novel Evolutionary Intelligence Algorithm. In Scarpa, Marco; Cavalieri, Salvatore; Serrano, Salvatore & Vita, Fabrizio De (Ed.), Proceedings of the 39th ECMS International Conference on Modelling and Simulation (ECMS 2025), June 24th - June 27th, 2025, Catania, Italy. ECMS European Council for Modelling and Simulation. ISSN 9783937436869. p. 93–99. doi: 10.7148/2025-0093. Full text in Research Archive Show summary

  In this paper, we present a novel approach for Proportional–Integral–Derivative (PID) tuning of a robotic manipulator, modeled using the Lagrange method and validated through comprehensive modeling and simulation. To optimize the PID gains, we developed a hybrid algorithm that combines Greylag Goose Optimization (GGO) with the Sine Cosine Algorithm (SCA), leveraging the strengths of both optimization techniques. The proposed hybrid algorithm, termed GGOSCA, was tested against GGO and Particle Swarm Optimization (PSO) using three objective functions: Lyapunov Based Function (LBF), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE). The results demonstrated that GGOSCA outperforms both GGO and PSO across all objective functions. Specifically, GGOSCA achieved the lowest costs of 0.1018, 0.2484, and 0.6601 for LBF, IAE, and ITAE, respectively, compared to 0.1022, 0.2580, and 0.6939 for GGO, and 0.1023, 0.2660, and 0.7273 for PSO. The superior performance of GGOSCA highlights its effectiveness in balancing exploration and exploitation, making it well-suited for complex control tasks such as PID tuning in robotic systems. This novel combination of GGO and SCA provides a robust and efficient solution for optimizing control parameters in dynamic environments, demonstrating its potential through rigorous modeling and simulation.

• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Emotion recognition with a Randomized CNN-multihead-attention hybrid model optimized by evolutionary intelligence algorithm. Array. 26. doi: 10.1016/j.array.2025.100401. Full text in Research Archive Show summary

  Emotion recognition systems are vital for various applications, yet existing models often face limitations in computational efficiency and accuracy, especially when handling complex emotional expressions in sequential data. To address these challenges, we propose an innovative emotion recognition framework that integrates a Randomised Convolutional Neural Network (RCNN) with a Multi-Head Attention model, further optimized by the Football Team Training Algorithm (FTTA) metaheuristic to enhance network parameters effectively. The RCNN, characterized by fixed random weights in its convolutional layers, efficiently extracts features from facial landmarks, enabling robust and diverse feature extraction while reducing computational load. This structure is complemented by a multi-head attention mechanism that processes temporal dynamics in emotion data, with both components optimized through FTTA to balance exploration and exploitation. Our hybrid model undergoes rigorous testing on a widely recognized emotion recognition dataset, outperforming conventional fully trainable models and alternative architectures. The results indicate a substantial improvement in classification accuracy, with an overall accuracy of 99%, and a significant reduction in computational demands, achieving a 65% faster training time on average compared to state-of-the-art models. These enhancements confirm the model’s efficiency and robustness across various emotional classifications. The synergy between the RCNN’s fixed-weight feature extraction and FTTA’s optimization capabilities demonstrates a powerful solution for emotion recognition systems. The combination of accuracy and efficiency renders our model suitable for real-world applications, particularly in fields like healthcare and mental health monitoring, where real-time emotion detection can have significant impacts.

• Sanfilippo, Filippo; Moosavi, Syed Kumayl Raza & Zafar, Muhammad Hamza (2025). Autonomy Uprising: How Civilians, First Responders, and Robots Are Redefining the Future of Search-and-Rescue (SAR). In NN, NN (Eds.), 2025 9th International Conference on Robotics and Automation Sciences: AI‑Powered Robotics Advancements (ICRAS 2025). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350392487. p. 1–8. doi: 10.1109/ICRAS65818.2025.11108899. Full text in Research Archive Show summary

  This paper explores the dynamics of human-robot interaction (HRI) in emergency response scenarios, focusing on how autonomous robots engage with civilians and first responders in search-and-rescue (SAR) operations. We examine the role of trust-building and emotional engagement in crisis situations, where human lives depend on effective HRI. The potential for autonomous systems to support decision-making while maintaining human oversight is investigated. Using the Autonomy Levels for Unmanned Systems (ALFUS) framework, we assess SAR robots' autonomy in terms of mission complexity (MC), environmental complexity (EC), and external system independence (ESI). Additionally, the Autonomy and Technology Readiness Assessment (ATRA) method supports gradual capability enhancement, providing a roadmap to higher autonomy while ensuring safety and reliability. Based on this established methodology, we introduce the Emergency Response Interaction Framework (ERIF), a novel conceptual framework defining the roles of actors involved, to connect theoretical insights with real-world applications. This work highlights the significance of human-robot trust in crisis scenarios and proposes directions for future research on collective decision-making in emergency response HRI.

• Sanfilippo, Filippo; Salvietti, Gionata; Blažauskas, Tomas; Gabriele, Guido; Zafar, Maria & Hua, Minh Tuan [Show all 14 contributors for this article] (2025). Integrating VR, AR, and Haptics in Basic Surgical Skills Training: A Review and Perspective. IEEE Access. 13, p. 99203–99220. doi: 10.1109/ACCESS.2025.3574265. Full text in Research Archive Show summary

  Traditional surgical training faces challenges related to sustainability, cost, and ethical considerations. Limited access to resources, ethical concerns about cadaver-based learning, limited availability of surgical cases, and the high costs associated with hands-on training impede the effectiveness and accessibility of surgical education. Despite these challenges, the need for direct tactile experience remains critical, as it fosters the development of essential motor skills and tactile abilities vital for surgical practice. However, advancements in immersive technologies such as virtual reality (VR), augmented reality (AR), and haptics offer a promising solution to address these challenges. VR and AR technologies offer realistic, controlled environments where learners can visualise anatomical structures and practice surgical procedures without risks. The integration of haptic feedback in these immersive simulations further enriches the learning process by adding the sense of touch and physical interaction, aiding in mastering critical surgical skills. The incorporation of immersive technologies in surgical training presents an opportunity to address the constraints of conventional methods. These technologies offer the possibility of creating platforms that replicate real-life surgical scenarios, allowing learners to hone their skills and build confidence in a safe and controlled environment. Moreover, immersive learning offers an ethical alternative to cadaver-based training and extends access to training materials irrespective of geographical limitations. Such an approach allows for step-based training, where students may begin acquiring skills through immersive learning, then move to special surgical simulators, and finally to patients. This article deals with the impact of immersive technologies such as VR, AR, and haptics on reshaping conventional surgical training, by focusing specifically on basic surgical skills. Targeting educators, medical experts, and involved parties, the article aims to stimulate additional exploration and adoption of immersive technologies to elevate surgical competencies and patient care standards.

• Das, Opy; Dahlioui, Dounia; Zafar, Muhammad Hamza; Akhtar, Naureen; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2025). Ultra-short term PV power forecasting under diverse environmental conditions: A case study of Norway. Energy Conversion and Management: X. 27. doi: 10.1016/j.ecmx.2025.101072. Full text in Research Archive Show summary

  Accurate short-term solar forecasting is critical for power plant operations, grid balancing, real-time dispatching, automatic generation control, and energy trading. In Norway, where solar radiation is limited in winter and highly variable in summer, accurate predictions are essential. This study focuses on ultra-short-term forecasting of solar radiation and power output from a 37.8 kWp solar photovoltaic (PV) power plant at the University of Agder (UiA), Grimstad (58.335322°N, 8.577718°E), in southern Norway. We propose a novel forecasting model that integrates Spatial Attention, Temporal Attention, Self-Attention, CNN, and BiLSTM architectures to enhance prediction accuracy. Using a custom dataset collected from the UiA PV plant, the model’s effectiveness was validated through comprehensive ablation studies and comparative analysis with state-of-the-art methods. The proposed model achieved a low RMSE of 0.162 kW using seven days of data, demonstrating its superiority in predicting short-term PV power outputs and associated uncertainties, outperforming conventional forecasting techniques.

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2024). Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection. Frontiers in Robotics and AI. 11, p. 01–16. doi: 10.3389/frobt.2024.1356345. Full text in Research Archive
• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2024). Collaborative robots (cobots) for disaster risk resilience: a framework for swarm of snake robots in delivering first aid in emergency situations. Frontiers in Robotics and AI. 11, p. 1–12. doi: 10.3389/frobt.2024.1362294. Full text in Research Archive
• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Mansoor, Majad & Sanfilippo, Filippo (2024). An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease. Healthcare Analytics. 7. doi: 10.1016/j.health.2024.100374. Full text in Research Archive Show summary

  Cardiovascular disease is the leading cause of death worldwide, including critical conditions such as blood vessel blockage, heart failure, and stroke. Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. The SCNN provides robust feature extraction, while LFGOA enhances the model by optimizing hyperparameters, improving classification accuracy, and reducing overfitting. The proposed approach is evaluated using four publicly available heart disease datasets, each representing diverse clinical and demographic features. Compared to traditional classifiers, including Regression Trees, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and standard Neural Networks, the SCNN-LFGOA consistently outperforms these methods. The results highlight that the SCNN-LFGOA achieves an average accuracy of 99%, with significant improvements in specificity, sensitivity, and F1-Score, showcasing its adaptability and robustness across datasets. This study highlights the SCNN-LFGOA’s potential as a transformative tool for early and accurate heart disease prediction, contributing to improved patient outcomes and more efficient healthcare resource utilization. By combining deep learning with an advanced optimization technique, this research introduces a scalable and effective solution to a critical healthcare problem.

• Murtaza, Aitzaz Ahmed; Saher, Amina; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Aftab, Muhammad Faisal & Sanfilippo, Filippo (2024). Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study. Results in Engineering (RINENG). 24. doi: 10.1016/j.rineng.2024.102935. Full text in Research Archive
• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2024). Hierarchical Recurrent-Inception Residual Transformer (HRIRT) for Multidimensional Hand Force Estimation Using Force Myography Sensor. IEEE Sensors Letters. 8(9). doi: 10.1109/LSENS.2024.3431433. Full text in Research Archive
• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Khan, Noman Mujeeb & Sanfilippo, Filippo (2024). FireNet: A Hybrid Deep Learning Approach for Enhanced Fire Detection in Remote Sensing Imagery. In NN, NN (Eds.), Intelligent Systems Conference (IntelliSys). Springer. ISSN 9783031663291. p. 1–15. doi: 10.1007/978-3-031-66329-1_1. Full text in Research Archive Show summary

  In this study, we address the critical task of early bushfire detection in remote areas through UAV imaging, a vital effort for protecting inhabitants, infrastructure, and ecosystems within the smart cities paradigm. We introduce FireNet, a deep learning framework that employs a hybrid Inception Residual Neural Network (Inception-ResNet) model, specifically designed for the rapid and precise classification of fire and non-fire regions in real-time image processing scenarios. FireNet synergises the strengths of both Inception and ResNet architectures, enhancing the model’s feature extraction capabilities while significantly reducing computational overhead. A distinctive aspect of FireNet is the adoption of the HardSwish activation function, demonstrating superior performance over the conventional Rectified Linear Unit (ReLU) in our fire detection cases. Through rigorous evaluation of a robust dataset, FireNet achieves an impressive accuracy of 96%, with a corresponding AUC of approximately 96%. These results not only affirm FireNet’s efficacy in accurately identifying fire and non-fire areas but also underscore its importance as a crucial tool for real-time fire monitoring and rescue operations, especially in the context of smart cities. With its outstanding efficiency and accuracy, FireNet represents a significant leap forward in the domain of fire detection technologies, highlighting the role of deep learning in enhancing urban resilience against fires.

• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Khan, Noman Mujeeb & Sanfilippo, Filippo (2024). Enhancing Alzheimer’s Disease Detection and Classification Through Federated Learning-Optimized Deep Convolutional Neural Networks on MRI Data. In NN, NN (Eds.), Intelligent Systems Conference (IntelliSys). Springer. ISSN 9783031663291. p. 693–712. doi: 10.1007/978-3-031-66428-1_44. Full text in Research Archive Show summary

  Early and accurate detection of Alzheimer’s Disease (AD) is vital for effective management and intervention. However, the subtle nuances of AD progression and the limited availability of comprehensive datasets pose significant diagnostic challenges. Our study introduces an innovative Deep Convolutional Neural Network (DCNN) model, optimised using Federated Learning (FL) for AD detection and staging from MRI images. This approach respects data privacy by enabling decentralised, multi-institutional model training, and demonstrates exceptional adaptability and precision. On the Open Access Series of Imaging Studies (OASIS) database, our FL-DCNN model achieved a remarkable 97% accuracy rate, suggesting significant potential for enhancing AD diagnostics and patient care.

• Moosavi, Syed Kumayl Raza; Sanfilippo, Filippo & Zafar, Muhammad Hamza (2024). Collaborative robots (cobots) for emergency situations: a snake robot as a team member for delivering first aid in emergency situations. In Bui, Tung X. (Eds.), Proceedings of the 57th Hawaii International Conference on System Sciences. The Hawaii International Conference on System Sciences (HICSS). ISSN 9780998133171. p. 443–452. Full text in Research Archive
• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Saadat, Ahsan; Abaid, Zainab; Ni, Wei & Jamalipour, Abbas [Show all 7 contributors for this article] (2024). Transductive Transfer Learning-Assisted Hybrid Deep Learning Model for Accurate State of Charge Estimation of Li-Ion Batteries in Electric Vehicles. IEEE Transactions on Intelligent Transportation Systems (T-ITS). ISSN 1524-9050. 25(10), p. 14445–14459. doi: 10.1109/TITS.2024.3403518. Full text in Research Archive
• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Houran, Mohamad Abou; Moosavi, Syed Kumayl Raza; Mansoor, Majad & Muaaz, Muhammad [Show all 7 contributors for this article] (2024). Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability. Ad hoc networks. ISSN 1570-8705. 155. doi: 10.1016/j.adhoc.2024.103407. Full text in Research Archive
• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Houran, Mohamad Abou; Qadir, Zakria; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2024). Enhancing cybersecurity in Edge IIoT networks: An asynchronous federated learning approach with a deep hybrid detection model. Internet of Things: Engineering Cyber Physical Human Systems. 27. doi: 10.1016/j.iot.2024.101252. Full text in Research Archive
• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Sanfilippo, Filippo; Akhter, Malik Naveed & Hadi, Shahzaib Farooq (2023). Early Mental Stress Detection Using Q-Learning Embedded Starling Murmuration Optimiser-Based Deep Learning Model. IEEE Access. 11, p. 116860–116878. doi: 10.1109/ACCESS.2023.3326129. Full text in Research Archive Show summary

  Stress affects individual of all ages as a regular part of life, but excessive and chronic stress can lead to physical and mental health problems, decreased productivity, and reduced quality of life. By identifying stress at an early stage, individuals can take steps to manage it effectively and improve their overall well-being. Feature selection is a critical aspect of early stress detection because it helps identify the most relevant and informative features that can differentiate between stressed and non-stressed individuals. This paper firstly proposes a variance based feature selection technique that uses q-learning embedded Starling Murmuration Optimiser (QLESMO) to choose relevant features from a publicly available dataset in which stresses experienced by nurses working during the Covid’19 Pandemic is recorded using bio-signals and user surveys. Furthermore, a comparative study with other metaheuristic based feature selection techniques have been demonstrated. Next, to evaluate the efficacy of the proposed algorithm, 10 benchmark test functions have been used. The reduced feature subset is then classified through a 1D convolutional neural network (CNN) model (QLESMO-CNN) and is seen to perform well in terms of the evaluation metrics in comparison to other competitive algorithms. Finally, the proposed technique is compared with the State-of-the-Art methodologies present in literature. The experiments provide a strong basis to determine features that are most relevant for early mental stress classification using a hybrid model combining CNN, Reinforcement Learning and metaheuristic algorithms.

• Zafar, Muhammad Hamza; Bukhari, Syed Muhammad Salman; Houran, Mohamad Abou; Moosavi, Syed Kumayl Raza; Mansoor, Majad & Al-Tawalbeh, Nedaa [Show all 7 contributors for this article] (2023). Step towards secure and reliable smart grids in Industry 5.0: A federated learning assisted hybrid deep learning model for electricity theft detection using smart meters. Energy Reports. 10, p. 3001–3019. doi: 10.1016/j.egyr.2023.09.100. Full text in Research Archive Show summary

  The integration of Smart Grid technology and conceptual Industry 5.0 has paved the way for advanced energy management systems that enhance efficiency and revolutionized the parallel integration of power sources in a sustainable manner. However, this digitization has opened a new stream of the threat and opportunities of electricity theft posing a significant challenge to the security and reliability of Smart Grid networks. In this paper, we propose a secure and reliable theft detection technique using deep federated learning (FL) mechanism. The technique leverages the collaborative power of FL to train a Convolutional Gated Recurrent Unit (ConvGRU) model on distributed data sources without compromising data privacy. The training deep learning model backbone consists of a ConvGRU model that combines convolutional and gated recurrent units to capture spatial and temporal patterns in electricity consumption data. An improvised preprocessing mechanism and hyperparameter tuning is done to facilitate FL mechanism. The halving randomized search algorithm is used for hyperparameters tuning of the ConvGRU model. The impact of hyperparameters involved in the ConvGRU model such as number of layers, filters, kernel size, activation function, pooling, GRU layers, hidden state dimension, learning rate, and the dropout rate is elaborated. The proposed technique achieves promising results, with high accuracy, precision, recall, and F1 score, demonstrating its efficacy in detecting electricity theft in Smart Grid networks. Comparative analysis with existing techniques reveal the superior performance of the deep FL-based ConvGRU model. The findings highlight the potential of this approach in enhancing the security and efficiency of Smart Grid systems while preserving data privacy.

• Mohyuddin, Hassan; Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2023). A comprehensive framework for hand gesture recognition using hybrid-metaheuristic algorithms and deep learning models. Array. 19. doi: 10.1016/j.array.2023.100317. Full text in Research Archive Show summary

  This paper presents a novel methodology that utilizes gesture recognition data, which are collected with a Leap Motion Controller (LMC), in tandem with the Spotted Hyena-based Chimp Optimization Algorithm (SSC) for feature selection and training of deep neural networks (DNNs). An expansive tabular database was created using the LMC for eight distinct gestures and the SSC algorithm was used for discerning and selecting salient features. This refined feature subset is then utilized in the subsequent training of a DNN model. A comprehensive comparative analysis is conducted to evaluate the performance of the SSC algorithm in comparison with established optimization techniques, such as Particle Swarm Optimization(PSO), Grey Wolf Optimizer(GWO), and Sine Cosine Algorithm(SCA), specifically in the context of feature selection. The empirical findings decisively establish the efficacy of the SSC algorithm, consistently achieving a high accuracy rate of 98% in the domain of gesture recognition tasks. The feature selection approach proposed emphasizes its intrinsic capacity to enhance not only the accuracy of gesture recognition systems and its wider suitability across diverse domains that require sophisticated feature extraction techniques.

• Zafar, Muhammad Hamza; Houran, Mohamad Abou; Mansoor, Majad; Khan, Noman Mujeeb; Moosavi, Syed Kumayl Raza & Khan, Muhammad Kamran [Show all 7 contributors for this article] (2023). A Novel MPPT Controller Based on Mud Ring Optimization Algorithm for Centralized Thermoelectric Generator under Dynamic Thermal Gradients. Applied Sciences. 13(7). doi: 10.3390/app13074213. Full text in Research Archive Show summary

  Most industrial processes generate raw heat. To enhance the efficiency of industrial operations, this raw heat is recovered. Thermoelectric generators (TEG), as solid state devices, provide an excellent application of heat recovery in the form of most manageable electrical power. This work presents a novel MPPT controller based on the Mud Ring Optimization algorithm for a centralized Thermoelectric Generator (TEG) under dynamic thermal gradients. The existing stochastic optimization algorithm for Maximum Power Point Tracking (MPPT) control in renewable energy systems exhibits several limitations that affect its performance in MPPT control. The convergence speed, local minima trap, hyper parameters’ sensitivity toward the population size, acceleration coefficients, and the stopping criterion all impact the convergence stability. In addition to these limitations, sensor noise sensitivity in measurement fluctuates the control system leading to reduced performance. Therefore, the careful design and implementation of the MRO algorithm is crucial to overcome these limitations and achieve a satisfactory performance in MPPT control. The results of this study contribute to developing more efficient MPPT control of TEG systems and implementing renewable energy technologies. The algorithm effectively tracks the maximum power point in dynamic thermal environments and increases the power output compared to conventional MPPT methods. The findings illustrate the efficacy of the proposed controller providing a higher power output (Avg. 99.95%) and faster response time (220 ms) under dynamic thermal conditions achieving 38–70% faster tracking of the GM in dynamic operating conditions.

• Khan, Muhammad Kamran; Zafar, Muhammad Hamza; Rashid, Saad; Mansoor, Majad; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2023). Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems. Applied Sciences. 13(2). doi: 10.3390/app13020945. Full text in Research Archive Show summary

  The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique.

• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Mirjalili, Seyedali & Sanfilippo, Filippo (2023). Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems. In NN, NN (Eds.), Artificial Intelligence and Soft Computing. Springer. ISSN 9783031425059. p. 427–438. doi: 10.1007/978-3-031-42505-9_36. Full text in Research Archive Show summary

  A better understanding of natural behavior modeling in mathematical systems has enabled a new class of stochastic optimization algorithms that can estimate optimal solutions using reasonable computational resources for problems where exact algorithms show poor performance. The position up-dating mechanism in various optimization algorithms utilizes similar chaotic random behavior which impedes the performance of the search for a globally optimum solution in monotonic nonlinear search space. In this work, an approach is proposed that tackles these issues on an already established algorithm; Improved Barnacle Mating Optimization (IBMO) Algorithm, inspired by the movement and mating of Gooseneck Barnacles. The algorithm introduces the mimicry of the movement and mating behavior in nature to model an optimization process. Several benchmark functions are employed to gauge the performance of the proposed optimization technique. Results are compared with several meta-heuristics and conventional optimization algorithms. It is observed that the IBMO algorithm performs generally better and provides a huge potential for solving real-world problems.

• Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2023). Inverse Kinematic Modelling of a 3-DOF Robotic Manipulator using Hybrid Deep Learning Models. Procedia CIRP. 120, p. 213–218. doi: 10.1016/j.procir.2023.08.038. Full text in Research Archive Show summary

  As the degrees of freedom (DOF) for a manipulator rise, so does the complexity of inverse kinematic modeling. This research provides an inverse kinematic model mapped with the aid of a Multilayer Deep Neural Network (DNN) trained using a unique meta-heuristic approach, namely the Gannet Optimization Algorithm (GOA), to decrease the computational weight and time lag for desired output transformation. The suggested design can automatically pick up on the kinematic characteristics of the manipulator. The sole observational basis for repeated learning is the link between input and output. Using the Robot Operating System (ROS), related simulations on a 3-DOF manipulator are performed. The simulation-generated dataset is split 65:35 for the purpose of training and testing the suggested model. Cost, time for the training data, mean relative error, normal mean square error, and mean absolute error for the test data are the metrics utilized for model validation. The efficacy and superiority of the suggested method are demonstrated by a comparison of the GOA-DNN model with the particle swarm optimization (PSO)-DNN and Grey Wolf Optimization (GWO)-DNN meta-heuristic DNN models.

• Murtaza, Aitzaz Ahmed; Amina, Saher; Mohyuddin, Hassan; Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2023). Enhancing Cardiovascular Disease Prediction via Hybrid Deep Learning Architectures: A Step Towards Smart Healthcare. In NN, NN (Eds.), 2nd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350305654. p. 1–6. doi: 10.1109/ETECTE59617.2023.10396716. Full text in Research Archive Show summary

  Cardiovascular disease presents a serious and increasing global health challenge, making a substantial contribution to morbidity and mortality rates on a global scale. This research study presents a novel methodology for predicting Cardiovascular Diseases by employing a, recently developed, metaheuristic optimisation algorithm within a neural network framework. The Coati Optimisation Algorithm (COA) is employed in an artificial neural network (ANN) to enhance the predictive accuracy of outcomes related to Cardiovascular Diseases. The enhanced performance of the COA can be ascribed to its adept utilisation of both exploration and exploitation phenomena. This research employs publicly available datasets pertaining to heart and stroke disorders, integrating two datasets focused on heart disease and one dataset focused on stroke disease. A comparison analysis is undertaken between the proposed COA-ANN and existing approaches, namely Particle Swarm Optimizer based ANN (PSO-ANN), Grey Wolf Optimizer based ANN (GWO-ANN), and backpropagation based ANN (BP-ANN). The findings of the study indicate that the COA-ANN model exhibits the highest level of predictive accuracy. The COAANN outperformed the other three networks, namely GWOANN, PSO-ANN, and BP-ANN, with an average accuracy of 98.43%. As a result, the utilisation of the COA-ANN leads to an improvement in predictive accuracy for these datasets, with an increase of up to 2.64%. Additional assessment metrics, such as F1-Score, Precision, and Recall, provide more insight into the balanced performance of the COA-ANN architecture when applied to imbalanced class datasets. These results prove that the integration of nature-inspired algorithms with cardiovascular diseases (CVDs) is a promising direction for future research.

• Zafar, Muhammad Hamza; Younus, Hassaan Bin; Moosavi, Syed Kumayl Raza; Mansoor, Majad & Sanfilippo, Filippo (2023). Online PID Tuning of a 3-DoF Robotic Arm Using a Metaheuristic Optimisation Algorithm: A Comparative Analysis. In NA, NA (Eds.), Communications in Computer and Information Science. Springer. ISSN 9783031489815. p. 25–37. doi: 10.1007/978-3-031-48981-5_3. Full text in Research Archive
• Zafar, Muhammad Hamza; Younis, Hassaan Bin; Mansoor, Majad; Moosavi, Syed Kumayl Raza; Khan, Noman Mujeeb & Akhtar, Naureen (2022). Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys. Materials. 15(18). doi: 10.3390/ma15186198. Full text in Research Archive
• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2022). A Review of the State-of-the-Art of Sensing and Actuation Technology for Robotic Grasping and Haptic Rendering, 2022 5th International Conference on Information and Computer Technologies (ICICT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665469609. p. 182–190. doi: 10.1109/ICICT55905.2022.00039. Full text in Research Archive Show summary

  In this paper, a survey of the state of the art, chal-lenges, and possibilities with sensing and actuation technology for robotic grasping and haptic rendering is presented. To this end, a survey and classification of robotic grippers and grasping methods is first outlined. Then, haptic rendering is surveyed by focusing on different challenges and approaches, such as rigid body haptic interaction, deformable/rigid haptic interaction, fluid haptic interaction, image/video based haptic interaction, and virtual reality (VR) based haptic interaction. Successively, the current sensing technology is reviewed by considering sensor development for robotic hands/grippers, such as tactile sensors, and visual sensors. Finally, the current actuation technology is addressed by considering soft robotic grippers, micro and nano grippers, multi-fingered grippers, and under actuated grippers. The main objective of this study is to boost worldwide efforts toward achieving the vast variety of applications that robotic grasping and haptics may give, as well as to provide an up-to-date reference as a baseline for future research and development in this sector.

• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2022). Forward Kinematic Modelling with Radial Basis Function Neural Network Tuned with a Novel Meta-Heuristic Algorithm for Robotic Manipulators. Robotics. 11(2). doi: 10.3390/robotics11020043. Full text in Research Archive Show summary

  The complexity of forward kinematic modelling increases with the increase in the degrees of freedom for a manipulator. To reduce the computational weight and time lag for desired output transformation, this paper proposes a forward kinematic model mapped with the help of the Radial Basis Function Neural Network (RBFNN) architecture tuned by a novel meta-heuristic algorithm, namely, the Cooperative Search Optimisation Algorithm (CSOA). The architecture presented is able to automatically learn the kinematic properties of the manipulator. Learning is accomplished iteratively based only on the observation of the input–output relationship. Related simulations are carried out on a 3-Degrees of Freedom (DOF) manipulator on the Robot Operating System (ROS). The dataset created from the simulation is divided 65–35 for training–testing of the proposed model. The metrics used for model validation include spread value, cost and runtime for the training dataset, and Mean Relative Error, Normal Mean Square Error, and Mean Absolute Error for the testing dataset. A comparative analysis of the CSOA-RBFNN model is performed with an artificial neural network, support vector regression model, and with with other meta-heuristic RBFNN models, i.e., PSORBFNN and GWO-RBFNN, that show the effectiveness and superiority of the proposed technique.

• Moosavi, Syed Kumayl Raza; Mansoor, Majad; Zafar, Muhammad Hamza; Khan, Noman Mujeeb; Mirza, Adeel Feroz & Akhtar, Naureen (2022). Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions. Energy Reports. 8, p. 13529–13543. doi: 10.1016/j.egyr.2022.10.011. Full text in Research Archive
• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Akhter, Malik Naveed; Hadi, Shahzaib Farooq; Khan, Noman Mujeeb & Sanfilippo, Filippo (2021). A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification. In Unknown, Unknown (Eds.), International Conference on Digital Futures and Transformative Technologies (ICoDT2). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665430746. p. 1–6. doi: 10.1109/ICoDT252288.2021.9441473. Full text in Research Archive
• Zafar, Muhammad Hamza; Khan, Noman Mujeeb; Mirza, Adeel Feroz; Mansoor, Majad; Akhtar, Naureen & Qadir, Muhammad Usman [Show all 8 contributors for this article] (2021). A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustainable Energy Technologies and Assessments. ISSN 2213-1388. 47. doi: 10.1016/j.seta.2021.101367. Full text in Research Archive

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• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Das, Opy & Sanfilippo, Filippo (2024). Human-Robot Teaming for Intelligent Transportation Systems. Full text in Research Archive
• Moosavi, Syed Kumayl Raza & Sanfilippo, Filippo (2024). How to design, build, and test a haptic-enabled immersive tool for surgical education. Full text in Research Archive

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