Muhammad Hamza Zafar

• 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. ISSN 2296-9144. 11, s. 01–16. doi: 10.3389/frobt.2024.1356345.
• Muhammad Salman Bukhari, Syed; Zafar, Muhammad Hamza; Houran, Mohamad Abou; Qadir, Zakria; Kumayl Raza Moosavi, Syed & 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. ISSN 2542-6605. 27. doi: 10.1016/j.iot.2024.101252.
• Mansoor, Majad; Abou Houran, Mohamad; Al-Tawalbeh, Nedaa; Zafar, Muhammad Hamza & Akhtar, Naureen (2024). Thermoelectric power generation system intelligent Runge Kutta control: A performance analysis using processor in loop testing. Energy Conversion and Management: X. 23. doi: 10.1016/j.ecmx.2024.100612.
• Khan, Noman Mujeeb; Khan, Umer Amir; Asif, Mansoor & Zafar, Muhammad Hamza (2024). Analysis of deep learning models for estimation of MPP and extraction of maximum power from hybrid PV-TEG: A step towards cleaner energy production. Energy Reports. ISSN 2352-4847. 11, s. 4759–4775. doi: 10.1016/j.egyr.2024.04.035.
• Zafar, Muhammad Hamza; Langås, Even Falkenberg & Sanfilippo, Filippo (2024). Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: A state-of-the-art review. Robotics and Computer-Integrated Manufacturing. ISSN 0736-5845. 89. doi: 10.1016/j.rcim.2024.102769.
• Al-Tawalbeh, Nedaa; Zafar, Muhammad Hamza; Radzi, Mohd Amran Mohd; Zainuri, Muhammad Ammirrul Atiqi Mohd & Al-Wesabi, Ibrahim (2024). Novel initialization strategy: Optimizing conventional algorithms for global maximum power point tracking. Results in Engineering (RINENG). ISSN 2590-1230. 22. doi: 10.1016/j.rineng.2024.102067.
• Ahmad, Nisar; Yi, Xu; Tayyab, Muhammad; Zafar, Muhammad Hamza & Akhtar, Naureen (2024). Water resource management and flood mitigation: hybrid decomposition EMD-ANN model study under climate change. Sustainable Water Resources Management. ISSN 2363-5037. 10(2). doi: 10.1007/s40899-024-01048-9.
• 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. ISSN 2296-9144. 11, s. 1–12. doi: 10.3389/frobt.2024.1362294.
• Zafar, Muhammad Hamza; Khan, Noman Mujeeb; Houran, Mohamad Abou; Mansoor, Majad; Akhtar, Naureen & Sanfilippo, Filippo (2024). A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature. Energy. ISSN 0360-5442. 292, s. 1–22. doi: 10.1016/j.energy.2024.130584.
• Bukhari, Syed Muhammad Salman; Zafar, Muhammad Hamza; Houran, Mohamad Abou; Moosavi, Syed Kumayl Raza; Mansoor, Majad & Muaaz, Muhammad [Vis alle 7 forfattere av denne artikkelen] (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.
• Khan, Muhammad Kamran; Zafar, Muhammad Hamza; Riaz, Talha; Mansoor, Majad & Akhtar, Naureen (2024). Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm. Energy Conversion and Management: X. 21, s. 1–17. doi: 10.1016/j.ecmx.2023.100509.
• Zafar, Muhammad Hamza; Khan, Noman Mujeeb; Mansoor, Majad & Sanfilippo, Filippo (2023). Optimal Tuning of PID Controller for Boost Converter using Meta-Heuristic Algorithm for Renewable Energy Applications. I NN, NN (Red.), International Conference on Mechanical, Automotive and Mechatronics Engineering (ICMAME 2023). ICMAME. ISSN 9786250015261. s. 1–6.
• 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. I NN, NN (Red.), 2nd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE). IEEE conference proceedings. ISSN 9798350305654. s. 1–6. doi: 10.1109/ETECTE59617.2023.10396716. Fulltekst i vitenarkiv
• Langås, Even Falkenberg; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2023). Harnessing digital twins for human-robot teaming in industry 5.0: Exploring the ethical and philosophical implications. I Yu, Wen (Red.), 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE conference proceedings. ISSN 978-1-6654-3065-4. s. 1788–1793. doi: https:/doi.org/10.1109/SSCI52147.2023.10372069. Fulltekst i vitenarkiv
• Hua, Tuan; Langås, Even Falkenberg; Zafar, Muhammad Hamza & Sanfilippo, Filippo (2023). From rigid to hybrid/soft robots: Exploration of ethical and philosophical aspects in shifting from caged robots to human-robot teaming. I Yu, Wen (Red.), 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE conference proceedings. ISSN 978-1-6654-3065-4. s. 1794–1799. doi: %2010.1109/SSCI52147.2023.10372032. Fulltekst i vitenarkiv
• 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. I NA, NA (Red.), Communications in Computer and Information Science. Springer. ISSN 978-3-031-48981-5. s. 25–37. doi: 10.1007/978-3-031-48981-5_3.
• Zafar, Muhammad Hamza; Sanfilippo, Filippo & Blažauskas, Tomas (2023). Harmony unleashed: Exploring the ethical and philosophical aspects of machine learning in human-robot collaboration for industry 5.0. I Yu, Wen (Red.), 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE conference proceedings. ISSN 978-1-6654-3065-4. s. 1775–1780. doi: 10.1109/SSCI52147.2023.10371798.
• Moosavi, Syed Kumayl Raza; Zafar, Muhammad Hamza; Mirjalili, Seyedali & Sanfilippo, Filippo (2023). Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems. I NN, NN (Red.), Artificial Intelligence and Soft Computing. Springer. ISSN 978-3-031-42505-9. s. 427–438. doi: 10.1007/978-3-031-42505-9_36. Fulltekst i vitenarkiv
• 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. ISSN 2212-8271. 120, s. 213–218. doi: 10.1016/j.procir.2023.08.038. Fulltekst i vitenarkiv
• Khan, Umer Amir; Khan, Noman Mujeeb & Zafar, Muhammad Hamza (2023). Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0. Energy Conversion and Management: X. 20. doi: 10.1016/j.ecmx.2023.100486. Fulltekst i vitenarkiv
• Zafar, Muhammad Hamza; Langås, Even Falkenberg & Sanfilippo, Filippo (2023). Empowering human-robot interaction using sEMG sensor: Hybrid deep learning model for accurate hand gesture recognition. Results in Engineering (RINENG). ISSN 2590-1230. 20. doi: 10.1016/j.rineng.2023.101639. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, a novel approach using a Henry Gas Solubility-based Stacked Convolutional Neural Network (HGS-SCNN) for hand gesture recognition using surface electromyography (sEMG) sensors is proposed. The stacked architecture of the CNN model helps to capture both low-level and high-level features, enabling effective representation learning. To begin, we generated a dataset comprising 600 samples of hand gestures. Next, we applied the Discrete Wavelet Transform (DWT) technique to extract features from the filtered sEMG signal. This step allowed us to capture both spatial and frequency information, enhancing the discriminative power of the extracted features. Extensive experiments are conducted to evaluate the performance of the proposed HGS-SCNN model. In addition, the obtained results are compared with state-of-the-art techniques, namely AOA-SCNN, GWO-SCNN, and WOA-SCNN. The comparative analysis demonstrates that the HGS-SCNN outperforms these existing methods, achieving an impressive accuracy of 99.3%. The experimental results validate the effectiveness of our proposed approach in accurately detecting hand gestures. The combination of DWT-based feature extraction and the HGS-SCNN model offers robust and reliable hand gesture recognition, thereby opening new possibilities for intuitive human-machine interaction and applications requiring gesture-based control.

• Salman Bukhari, Syed Muhammad; Raza Moosavi, Syed Kumayl; Zafar, Muhammad Hamza; Mansoor, Majad; Mohyuddin, Hassan & Sajid Ullah, Syed [Vis alle 8 forfattere av denne artikkelen] (2023). Federated transfer learning with orchard-optimized Conv-SGRU: A novel approach to secure and accurate photovoltaic power forecasting. Renewable Energy Focus. ISSN 1755-0084. 48. doi: 10.1016/j.ref.2023.100520. Fulltekst i vitenarkiv
• 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. ISSN 2169-3536. 11, s. 116860–116878. doi: 10.1109/ACCESS.2023.3326129. Fulltekst i vitenarkiv Vis sammendrag

  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; Abou Houran, Mohamad; Moosavi, Syed Kumayl Raza; Mansoor, Majad & Al-Tawalbeh, Nedaa [Vis alle 7 forfattere av denne artikkelen] (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. ISSN 2352-4847. 10, s. 3001–3019. doi: 10.1016/j.egyr.2023.09.100. Fulltekst i vitenarkiv
• 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. ISSN 2590-0056. 19. doi: 10.1016/j.array.2023.100317. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Abou Houran, Mohamad; Salman Bukhari, Syed M.; Zafar, Muhammad Hamza; Mansoor, Majad & Chen, Wenjie (2023). COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications. Applied Energy. ISSN 0306-2619. 349. doi: 10.1016/j.apenergy.2023.121638. Vis sammendrag

  Power prediction is now a crucial part of contemporary energy management systems, which is important for the organization and administration of renewable resources. Solar and wind powers are highly dependent upon environmental factors, such as wind speed, temperature, and humidity, making the forecasting problem extremely difficult. The suggested composite model incorporates Long Short-Term Memory (LSTM) and Swarm Intelligence (SI) optimization algorithms to produce a framework that can precisely estimate offshore wind output in the short term, addressing the discrepancies and limits of conventional estimation methods. The Coati optimization algorithm enhances the hyper parameters CNN-LSTM. Optimum hyper parameters improvise learning rate and performance. The day-ahead and hour-ahead short-term predictions RMSE can be decreased by 0.5% and 5.8%, respectively. Compared to GWO-CNN-LSTM, LSTM, CNN, and PSO-CNN-LSTM models, the proposed technique achieves an nMAE of 4.6%, RE 27% and nRMSE of 6.2%. COA-CNN-LSTM outperforms existing techniques in terms of the Granger causality test and Nash-Sutcliffe metric analysis for time series forecasting performance, scores are 0.0992 and 0.98, respectively. Experimental results show precise and definitive wind power-making predictions for the management of renewable energy conversion networks. The presented model contributes positively to the body of knowledge and development of clean energy.

• Zafar, Muhammad Hamza; Mansoor, Majad; Abou Houran, Mohamad; Khan, Noman Mujeeb; Khan, Kamran & Raza Moosavi, Syed Kumayl [Vis alle 7 forfattere av denne artikkelen] (2023). Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles. Energy. ISSN 0360-5442. 282. doi: 10.1016/j.energy.2023.128317. Vis sammendrag

  State of charge (SoC) estimation is critical for the safe and efficient operation of electric vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN)-based approach for SoC estimation in EVs. This HMDNN uses Mountain Gazelle Optimizer (MGO) as a training algorithm for the deep neural network. Our method leverages the intrinsic relationship between the SoC and the voltage/current measurements of the EV battery to accurately estimate the SoC in real time. We evaluate our approach on a large dataset of real-world EV charging data and demonstrate its effectiveness in comparison to traditional SoC estimation methods. Four diverse Li-ion battery datasets of electric vehicles are employed which are the dynamic stress test (DST), Beijing dynamic stress test (BJDST), federal urban driving schedule (FUDS), and highway driving schedule (US06) with different temperatures of . The comparison is made with Mayfly Optimization Algorithm based DNN, Particle Swarm Optimization based DNN and Back-Propagation based DNN. The evaluation indices used are normalized mean square error (NMSE), root mean square error (RMSE), mean absolute error (MAE), and relative error (RE). The proposed algorithm achieves 0.1% NMSE and 0.3% RMSE on average on all datasets, which validates the effective performance of the proposed model. The results show that the proposed neural network-based approach can achieve higher accuracy and faster convergence than existing methods. This can enable more efficient EV operation and improved battery life.

• Muqeet, Abdul; Israr, Asif; Zafar, Muhammad Hamza; Mansoor, Majad & Akhtar, Naureen (2023). A novel optimization algorithm based PID controller design for real-time optimization of cutting depth and surface roughness in finish hard turning processes. Results in Engineering (RINENG). ISSN 2590-1230. 18. doi: 10.1016/j.rineng.2023.101142. Fulltekst i vitenarkiv Vis sammendrag

  This paper proposes a novel method to improve surface finish in turning processes by effectively controlling the cutting depth. A metaheuristic algorithm based PID control system, in combination with a piezoelectric vibration sensor for feedback, is introduced to regulate the position of the servo motor that controls the cutting tool. The PID controller is optimized using Q-learning based Sand Cat Optimization algorithm to achieve the best performance in terms of cutting depth accuracy and surface finish quality. The piezoelectric sensor provides realtime feedback information about the cutting process and allows for precise adjustments to the cutting depth. The results demonstrate the proposed system’s ability to handle variations in cutting conditions and tool’s wear and tear. Compared to highly optimized standard PID control, improved robustness and stability has been achieved in experimental results by proposed framework. Experimental results demonstrate improved robustness and stability compared to standard PID control. Several materials with high hardness of 20–65 HRC including Phenolic Bakelite, Copper, Thermoplastic, and Stainless Steel (AISI-420, AA6061-T6, and AISI-316) are tested. Minimum value of Vibration amplitude achieved is 0.598 μm for cutting depth of 0.25 mm. The sustained minimum amplitude of vibration and Ra value of the surface finish is found comparable to standard models with less than 10% error.

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