Ali Kashif Bashir
@mmu.ac.uk
Associate Professor, Department of Computing and Mathematics
Manchester Metropolitan University
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Fida Muhammad Khan, Asim Zeb, Taj Rahman, Mahmoud Ahmad Al-Khasawneh, Yousef Ibrahim Daradkeh, Isma Farah Siddiqui, Ali Kashif Bashir, and Inam Ullah
Springer Science and Business Media LLC
Nehal F. Al-Otaiby, Mohammad Hammoudeh, Jameleddine Hassine, and Ali Kashif Bashir
Springer Science and Business Media LLC
Gyanendra Kumar, Varun Kumar Sharma, Vinay Chamola, and Ali Kashif Bashir
Institute of Electrical and Electronics Engineers (IEEE)
Muhammad Ajmal Azad, Ali Kashif Bashir, R. Muhammad Atif Azad, and Syed Attique Shah
Institute of Electrical and Electronics Engineers (IEEE)
Md Israfil Biswas, Muhammad Atif Ur Rehman, Mohammed Al-Khalidi, and Ali Kashif Bashir
Springer Nature Switzerland
A. Saranya, Kottilingam Kottursamy, Ahmad Ali AlZubi, and Ali Kashif Bashir
Springer Science and Business Media LLC
Jiaming Pei, Minghui Dai, R. R. Venkatesha Prasad, Norah Saleh Alghamdi, Yasser D. Al-Otaibi, and Ali Kashif Bashir
Institute of Electrical and Electronics Engineers (IEEE)
Iqra Adnan, Tariq Umer, Ahmad Arsalan, Maryam M. Al Dabel, Ali Kashif Bashir, and Arooj Ansif
Elsevier BV
Junaid Akram, Awais Akram, Ali Anaissi, Rutvij H. Jhaveri, Ali Kashif Bashir, and Maryam M. Al Dabel
Institute of Electrical and Electronics Engineers (IEEE)
Jiaming Pei, Valerio Frascolla, Anwer Al-Dulaimi, Wei Liu, Theyazn H. H. Aldhyani, Ali Kashif Bashir, and Shahid Mumtaz
Institute of Electrical and Electronics Engineers (IEEE)
Haotian Wu, Jiwei Zhang, Minxi Feng, Minghui Dai, Samra Mohiuddin, Ali Kashif Bashir, Shahid Mumtaz, and Jiaming Pei
Institute of Electrical and Electronics Engineers (IEEE)
Haewon Byeon, Azzah AlGhamdi, Ismail Keshta, Mukesh Soni, Mohammad Shabaz, Muhammad Attique Khan, Ali Kashif Bashir, and Nazeeruddin Mohammad
Springer Nature Switzerland
Ziyu Song, Jing Yang, Lei Fang, Muhammad Umair Ali, Gyanendra Kumar, Ali Kashif Bashir, Nazik Alturki, Lip Yee Por, and Seung-Won Lee
Institute of Electrical and Electronics Engineers (IEEE)
Fida Muhammad Khan, Asim Zeb, Taj Rahman, Inam Ullah, Nazik Alturki, Ali Kashif Bashir, Yamen El Touati, Nidhal Ben Khedher, and Khalid Mahmood Awan
Wiley
ABSTRACT The Internet of Vehicles (IoV) is revolutionising transportation by connecting vehicles, infrastructure and devices, enabling more intelligent and safer mobility. One key challenge is ensuring efficient and secure communication among vehicles with varying capabilities, including different sizes, speeds and sensor configurations. This research introduces a Federated Learning‐Driven Deep Learning (FLDL) approach to intelligent collision avoidance, designed to address the heterogeneity of vehicles in the IoV ecosystem. The system integrates real‐time data from vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communications, while considering factors like vehicle type, road conditions, driver behaviour and Digital Twins. Our approach leverages multiple Federated Learning strategies, which enhance privacy protection, reduce communication overhead and enable real‐time decision‐making without the need for centralised data storage. Experimental results show that the GNN + FedGC model achieves the highest performance with an accuracy of 98.8%, outperforming other models such as MLP with FedLU (98.5%), DRL with FedPPO (98.3%) and LSTM with FedSGD (97.65%). The integration of Digital Twins further enhances model accuracy by simulating real‐time vehicle behaviour and environmental conditions. This FL‐based system not only improves collision prediction but also enhances safety, reduces accident rates and supports scalable decision‐making in smart city transportation systems.
Xiaoshan Bai, Baode Li, Inam Ullah, Zongze Wu, Shakila Basheer, and Ali Kashif Bashir
Elsevier BV
Zihong Li, Jun Wu, Ali Kashif Bashir, and Xingwang Li
Institute of Electrical and Electronics Engineers (IEEE)
Daniyal Yousaf, Muhammad Bilal Khan, Hazrat Bilal, Abdul Basit Khattak, Hamna Baig, Shujaat Ali Khan Tanoli, Muhammad Shamrooz Aslam, Inam Ullah, Shakila Basheer, and Ali Kashif Bashir
Springer Science and Business Media LLC
Altaf Hussain, Shuaiyong Li, Razaz Waheeb Attar, Maryam M. Al Dabel, Ali Kashif Bashir, Ahmed Alhomoud, and Tariq Hussain
World Scientific Pub Co Pte Ltd
The Internet of Things (IoT) encompasses a broad platform of sensor networks incorporating independent wireless networks. With advancements in sensor technology and IoT-enabled networks, their applications in the medical field have led to the development of the Internet of Medical Things (IoMT). In IoMT, sensor nodes monitor and evaluate patient conditions such as heartbeat, blood sugar levels, blood pressure and temperature, and can also remotely track patient activities through remote analysis. These IoMT systems utilize tiny sensors with limited communication ranges to gather essential patient information. Wireless devices are equipped with a short range and need a direct communication path. However, transmitting data from the source node to the destination node ultimately results in energy consumption and path loss. Path loss models and energy consumption models are essential to address these issues. In this paper, we propose a novel routing protocol named Energy Efficient and Path Loss Preserving (EEPLP) for IoMT. The EEPLP protocol focuses on energy efficiency and path loss preservation based on the relay approach. Two models are being proposed, one for path loss and the other for energy consumption. Finally, both models are merged since the major contribution is to avoid path loss and enhance the protocol’s energy efficiency. The EEPLP evaluates the state-of-the-art existing approaches of IoMT. The protocol is evaluated by simulating conditions and compared with other similar routing protocols already deployed in the IoMT; it has been observed that the EEPLP scheme has the potential to be maneuvered in IoMT structures with core targets of energy efficiency as well as path loss preservation techniques.
Jian Chen, Shaorui Zhou, Wei Wang, Yuzhu Hu, Jianqing Li, Ben-guo He, Junxin Chen, Marwan Omar, Ali Kashif Bashir, and Xiping Hu
Association for Computing Machinery (ACM)
Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this article proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
Muhammad Ali Naeem, Ali Kashif Bashir, and Yahui Meng
Elsevier BV
Heyi Zhang, Jun Wu, Qianqian Pan, Ali Kashif Bashir, and Marwan Omar
Institute of Electrical and Electronics Engineers (IEEE)
Ma Yinghua, Ahmad Khan, Yang Heng, Fiaz Gul Khan, Farman Ali, Yasser D. Al-Otaibi, and Ali Kashif Bashir
Elsevier BV
Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, and Ali Kashif Bashir
Institute of Advanced Engineering and Science
Deep learning (DL), a pivotal artificial intelligence (AI) innovation, has dramatically transformed biosciences, aligning with the surge in complex data volumes to foster notable progress across disciplines such as genomics, genetics, and drug discovery. DL's precision and efficiency outmatch conventional methods, propelling advancements in biomedical imaging and disease marker identification. Despite its success, DL's integration into broader bioscience areas encounters hurdles including data scarcity, interpretability challenges, computational demands, and the necessity for ethical and regulatory considerations. Overcoming these obstacles is vital for DL to achieve its transformative potential fully. This review explores into DL's expanding role in biosciences, critically examining areas ripe for DL application and highlighting underexplored opportunities. It provides an insightful analysis of the algorithms that form the backbone of DL in biosciences, offering a thorough understanding of their capabilities. Ultimately, this paper aims to equip biotechnologists and researchers with the knowledge to leverage DL effectively, thereby enhancing the analysis of complex bioscience data and contributing to the field's future advancements.
Mohamed Abd Elaziz, Esraa Osama Abo Zaid, Mohammed A. A. Al‐qaness, Amjad Ali, Ali Kashif Bashir, Ahmed A. Ewees, Yasser D. Al‐Otaibi, and Ala Al‐Fuqaha
Wiley
ABSTRACTThis paper presents a new data clustering technique aimed at enhancing the performance of the trainable path‐cost algorithm and reducing the computational complexity of data clustering models. The proposed method facilitates the discovery of natural groupings and behaviours, which is crucial for effective coordination in complex environments. It identifies natural groupings within a set of features and detects the best clusters with similar behaviour in the data, overcoming the limitations of traditional state‐of‐the‐art methods. The algorithm utilises a density peak clustering method to determine cluster centers and then extracts features from paths passing through these peak points (centers). These features are used to train the support vector machine (SVM) to predict the labels of other points. The proposed algorithm is enhanced using two key concepts: first, it employs Q‐Generalised Extreme Value (Q‐GEV) under power normalisation instead of traditional generalised extreme value distributions, thereby increasing modelling flexibility; second, it utilises the random vector functional link (RVFL) network rather than the SVM, which helps avoid overfitting and improves label prediction accuracy. The effectiveness of the proposed clustering algorithm is evaluated through various experiments, including those on UCI benchmark datasets and real‐world data, demonstrating significant improvements across multiple performance metrics, including F1 measure, Jaccard index, purity, and accuracy, highlighting its capability in accurately identifying paths between similar clusters. Its average F1 measure, Jaccard index, purity, and accuracy is measured 76.87%, 56.29%, 80.29%, and 79.64%, respectively.