Ali Kashif Bashir

@mmu.ac.uk

Associate Professor, Department of Computing and Mathematics
Manchester Metropolitan University



                    

https://researchid.co/alik
132

Scopus Publications

5177

Scholar Citations

21

Scholar h-index

83

Scholar i10-index

Scopus Publications

  • A deep contrastive multi-modal encoder for multi-omics data integration and analysis
    Ma Yinghua, Ahmad Khan, Yang Heng, Fiaz Gul Khan, Farman Ali, Yasser D. Al-Otaibi, and Ali Kashif Bashir
    Elsevier BV

  • Q-GEV Based Novel Trainable Clustering Scheme for Reducing Complexity of Data Clustering
    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.

  • Context-Aware Prediction with Secure and Lightweight Cognitive Decision Model in Smart Cities
    Fatima Al-Quayed, Mamoona Humayun, Thanaa S. Alnusairi, Inam Ullah, Ali Kashif Bashir, and Tariq Hussain
    Springer Science and Business Media LLC
    Abstract Cognitive networks with the integration of smart and physical devices are rapidly utilized for the development of smart cities. They are explored by many real-time applications such as smart homes, healthcare, safety systems, and other unpredictable environments to gather data and process network requests. However, due to the external conditions and inherent uncertainty of wireless systems, most of the existing approaches cannot cope with routing disturbances and timely delivery performance. Further, due to limited resources, the demand for a secure communication system raises another potential research challenge to protect sensitive data and maintain the integrity of the urban environment. This paper presents a secured decision-making model using reinforcement learning with the combination of blockchain to enhance the degree of trust and data protection. The proposed model increases the network efficiency for resource utilization and the management of communication devices with the alliance of security. It provides a reliable and more adaptive paradigm by exploring learning techniques for dealing with the intrinsic uncertainty and imprecision of cognitive systems. Also, the incorporation of blockchain technology reduces the risk of a single point of failure, malicious vulnerabilities, and data leakage, ultimately fostering trust for urban sensor applications. It validates the incoming routing links and identifies any communication fault incurred due to malicious interference. The proposed model is rigorously tested and verified using simulations and its significance has been proven for network metrics in comparison to existing solutions.


  • Hyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart cites
    Yuhuan Lu, Wei Wang, Rufan Bai, Shengwei Zhou, Lalit Garg, Ali Kashif Bashir, Weiwei Jiang, and Xiping Hu
    Elsevier BV

  • Robust Wireless Distributed Learning Empowered by Thz Communications Data for Internet of Unmanned Vehicles Agents: Efficient Cluster Driving Decision-Making
    Zihong Li, Jun Wu, Ali Kashif Bashir, and Xingwang Li
    Institute of Electrical and Electronics Engineers (IEEE)

  • Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things
    Xiaoyan Yuan, Wei Wang, Junxin Chen, Kai Fang, Ali Kashif Bashir, Tapas Mondal, Xiping Hu, and M. Jamal Deen
    Institute of Electrical and Electronics Engineers (IEEE)

  • Robust Fault Diagnosis of Drilling Machinery Under Complex Working Conditions Based on Carbon Intelligent Industrial Internet of Things
    Kai Fang, Lianghuai Tong, Xiaojie Xu, Jijing Cai, Xueyuan Peng, Marwan Omar, Ali Kashif Bashir, and Wei Wang
    Institute of Electrical and Electronics Engineers (IEEE)

  • Conv-MTD: A CNN Based Multi-Label Medical Tubes Detection and Classification Model to Facilitate Resource-constrained Point-of-care Devices
    Moneeb Abbas, Wen-Chung Kuo, Khalid Mahmood, Waseem Akram, Sajid Mehmood, and Ali Kashif Bashir
    Institute of Electrical and Electronics Engineers (IEEE)
    Computer-aided detection through deep learning is becoming a prevalent approach across various fields, including detection of anomalies in medical procedures. One such medical procedure involves the placement of medical tubes to provide nutrition or other medical procedures in critically ill patients. Medical tube placement can be highly complex and prone to subjective errors. Malposition of medical tubes is often observed, and associated with significant morbidity and mortality. In addition, continuous verification using manual procedures such as capnography, pH testing, auscultation, and visual inspection through chest X-ray (CXR) imaging is required. In this paper, we propose a Conv-MTD, a medical tube detection (MTD) model that detects the placement of medical tubes using CXR images, assisting radiologists with precise identification and categorizing the tubes into normal, abnormal, and borderline placement. Conv-MTD leverages the state-of-the-art EfficientNet-B7 architecture as its backbone, enhanced with auxiliary head in the intermediate layers to mitigate vanishing gradient issues common in deep neural networks. The Conv-MTD is further optimized using post-training 16-bit floating-point (FP16) quantization, which effectively reduces memory consumption and inference latency on resource-constrained devices. Conv-MTD provided the best performance, with an average area under the receiver-operator curve AUC-ROC of 0.95. The proposed Conv-MTD has the potential to operate on resource-constrained point-of-care devices, enabling low-cost and automated assessments in various healthcare settings.

  • Knowledge-Driven Lane Change Prediction for Secure and Reliable Internet of Vehicles
    Yuhuan Lu, Zhen Zhang, Wei Wang, Yiting Zhu, Tiantian Chen, Yasser D. Al-Otaibi, Ali Kashif Bashir, and Xiping Hu
    Institute of Electrical and Electronics Engineers (IEEE)

  • Covariate-adjusted construction of gene regulatory networks using a combination of generalized linear model and penalized maximum likelihood
    Omid Chatrabgoun, Alireza Daneshkhah, Parisa Torkaman, Mark Johnston, Nader Sohrabi Safa, and Ali Kashif Bashir

    Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. This construction can be improved using the auxiliary information like gene expression profile of the related species or gene markers. To reach out this goal, we apply a generalized linear model (GLM) in first step and later a penalized maximum likelihood to construct the gene regulatory network using Glasso technique for the residuals of a multi-level multivariate GLM among the gene expressions of one species as a multi-levels response variable and the gene expression of related species as a multivariate covariates. By considering the intrinsic property of the gene data which the number of variables is much greater than the number of available samples, a bootstrap version of multi-response multivariate GLM is used. To find most appropriate related species, a cross-validation technique has been used to compute the minimum square error of the fitted GLM under different regularization. The penalized maximum likelihood under a lasso or elastic net penalty is applied on the residual of fitted GLM to find the sparse precision matrix. Finally, we show that the presented algorithm which is a combination of fitted GLM and applying the penalized maximum likelihood on the residual of the model is extremely fast, and can exploit sparsity in the constructed GRNs. Also, we exhibit flexibility of the proposed method presented in this paper by comparing with the other methods to demonstrate the super validity of our approach.

  • Federated Learning and Blockchain-Enabled Framework for Traffic Rerouting and Task Offloading in the Internet of Vehicles (IoV)
    Ganesh Gopal Devarajan, T. S, Mohammed J. F. Alenazi, K. U, Gopalakrishnan Chandran and Ali Kashif Bashir
    Institute of Electrical and Electronics Engineers (IEEE)

  • Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics
    Syed Danial Ali Shah, Ali Kashif Bashir, Yasser D. Al-Otaibi, Maryam M. Al Dabel, and Farman Ali


  • Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization
    Shailendra Pratap Singh, Naween Kumar, Gyanendra Kumar, Balamurugan Balusamy, Ali Kashif Bashir, and Maryam M. Al Dabel
    Institute of Electrical and Electronics Engineers (IEEE)

  • Sum Rate Maximization for 6G Beyond Diagonal RIS-Assisted Multi-Cell Transportation Systems
    Chi Zhang, Wali Ullah Khan, Ali Kashif Bashir, Ashit Kumar Dutta, Ateeq Ur Rehman, and Maryam M. Al Dabel
    Institute of Electrical and Electronics Engineers (IEEE)


  • Preface
    Wiley

  • Deep Federated Fractional Scattering Network for Heterogeneous Edge Internet of Vehicles Fingerprinting: Theory and Implementation
    Tiantian Zhang, Dongyang Xu, Jing Ma, Ali Kashif Bashir, Maryam M. Al Dabel, and Hailin Feng
    Institute of Electrical and Electronics Engineers (IEEE)
    With the rapid development of distributed edge intelligence (DEI) within Internet of Vehicle (IoV) network, it is required to support heterogeneous rapid, reliable and lightweight authentication which prevents eavesdropping, tampering and replay attacks. Radio frequency fingerprinting (RFF), which leverages unique and tamper-proof hardware characteristics, is an emerging deep learning-based physical layer technology poised to achieve excellent authentication within DEI enhanced heterogeneous IoV. However, centralized collection of critical datasets will bring severe privacy concerns as well as huge communication overheads toward resources-constrained IoV nodes. In this article, we propose a deep federated fractional scattering fingerprinting network (FFSFNet) which amalgamates fractional wavelet scattering and federated learning to achieve excellent identification. Particularly, we first exploit fractional wavelet scattering to extract RFF characteristics from nonstationary waveform, eliminate redundancies and enhance interpretability. To improve the training efficiency and privacy protection capability, we design a novel federated framework, which not only completes distributed training, reduces overhead but also protects privacy. Furthermore, we conducted a comprehensive comparative analysis of different model quantization schemes and validated the proposed scheme with field programmable gate array (FPGA) accelerators. Experimental results demonstrate that the proposed FFSFNet can maintain excellent identification performance with only 5.08% of original samples. The model size and inference latency can be effectively improved by quantization with limited degradation. Moreover, the identification testing accuracy of FFSFNet can eventually converge to 99.4% with 0.64 ms inference latency per sample.

  • YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8
    Junchao Yuan, Lina Wang, Tingting Wang, Ali Kashif Bashir, Maryam M. Al Dabel, Jiaxing Wang, Hailin Feng, Kai Fang, and Wei Wang
    Institute of Electrical and Electronics Engineers (IEEE)
    Pine wilt disease (PWD) poses a severe threat to the health of pine trees and has resulted in substantial losses to global pine forest resources. Due to the minute size of the pathogens and the concealed symptoms of PWD, early detection through remote sensing image technology is essential. However, in practical applications, remote sensing images are easily affected by factors, such as cloud cover and changes in illumination, resulting in significant noise and blurriness in the images. These interference factors significantly reduce the accuracy of existing object detection models. Therefore, this article presents a novel and highly robust methodology for detecting PWD, termed YOLOv8-RD. We synthesized the benefits of residual learning and fuzzy deep neural networks to develop a residual fuzzy module (ResFuzzy), which adeptly filters image noise and refines background features with enhanced smoothness. Simultaneously, we integrated a detail processing module into the ResFuzzy module to enhance the low-frequency detail features transmitted in residual learning. Furthermore, by incorporating the dynamic upsampling operator, our model can dynamically adjust the sampling step size based on the variations in the input feature map during the upsampling process, thereby effectively recovering detail from the feature map. Our model exhibited exceptional robustness to severe noise. When evaluated on a PWD dataset with 100% interference samples at an intensity of 0.07, our model achieved an average precision improvement of 4.9%, 6.3%, 7.3%, and 3.0% compared to four most representative models, making it well suited for PWD detection in interfering environments.


  • AI-optimized elliptic curve with Certificate-Less Digital Signature for zero trust maritime security
    Mohammed Al-Khalidi, Rabab Al-Zaidi, Tarek Ali, Safiullah Khan, and Ali Kashif Bashir
    Elsevier BV

  • 3D Lidar Point Cloud Segmentation for Automated Driving
    Rashid Abbasi, Ali Kashif Bashir, Amjad Rehman, and Yuan Ge
    Institute of Electrical and Electronics Engineers (IEEE)

  • Smart Steering Wheel: Design of IoMT-Based Non-Invasive Driver Health Monitoring System to Enhance Road Safety
    Muhammad Adil Khan, Mu Chen, Tahir Nawaz, Mohamed Sedky, Muhammad Sheikh, Ali Kashif Bashir, and Sohail Hassan
    Institution of Engineering and Technology (IET)
    ABSTRACTThe integration of Internet of Things (IoT) technology and medical devices in healthcare is termed the Internet of Medical Things (IoMT). This advancement holds promise for numerous applications aimed at mitigating the risk of loss of life through physiological signal monitoring. As the number of road accidents is rapidly increasing, a substantial number of car crashes occur due to medical conditions. Therefore, the need remains to develop an effective solution to enable the prevention of such accidents for enhanced road safety. Unlike existing approaches, this paper proposes a holistic IoMT‐based non‐invasive driver health monitoring system (DHMS) to monitor important vital signs for detecting abnormal health conditions. The proposed system consists of an embedded system, edge computing, cloud computing, and a mobile application with an alert system, to offer an end‐to‐end unified solution for driver physiological signal monitoring to detect abnormal health conditions that might lead to a road accident. The system is particularly suited to aid (elderly) people with medical conditions and can also be used for public transport to ensure passenger safety. A detailed experimental evaluation of the proposed system has been performed and its performance accuracy compared with standard medical devices, along with quality factors including usability, portability, and effective sensor placement.

  • Lane Change Prediction for Autonomous Driving With Transferred Trajectory Interaction
    Yuhuan Lu, Pengpeng Xu, Xinyu Jiang, Ali Kashif Bashir, Thippa Reddy Gadekallu, Wei Wang, and Xiping Hu
    Institute of Electrical and Electronics Engineers (IEEE)

  • Preface
    Wiley

RECENT SCHOLAR PUBLICATIONS

  • A deep contrastive multi-modal encoder for multi-omics data integration and analysis
    M Yinghua, A Khan, Y Heng, FG Khan, F Ali, YD Al-Otaibi, AK Bashir
    Information Sciences 700, 121864 2025

  • Q‐GEV Based Novel Trainable Clustering Scheme for Reducing Complexity of Data Clustering
    MA Elaziz, EOA Zaid, MAA Al‐qaness, A Ali, AK Bashir, AA Ewees, ...
    Expert Systems 42 (4), e70011 2025

  • TeraPRI: Homomorphic Terahertz-Empowered Joint Wireless Power and Information Transfer with Privacy-Preserving for 6G-Autonomous Vehicles
    KT Pauu, J Wu, AK Bashir
    IEEE Transactions on Consumer Electronics 2025

  • FIDSUS: Federated Intrusion Detection for Securing UAV Swarms in Smart Aerial Computing
    J Deng, W Wang, L Wang, AK Bashir, TR Gadekallu, H Feng, M Lv, ...
    IEEE Internet of Things Journal 2025

  • Robust Wireless Distributed Learning Empowered by Thz Communications Data for Internet of Unmanned Vehicles Agents: Efficient Cluster Driving Decision-Making
    Z Li, J Wu, AK Bashir, X Li
    IEEE Internet of Things Journal 2025

  • Unleashing the Potentials of IoT with Focus on Energy and Path Loss for Internet of Medical Things
    A Hussain, S Li, RW Attar, MM Al Dabel, AK Bashir, A Alhomoud, ...
    Journal of Circuits, Systems and Computers 2025

  • Lane Change Prediction for Autonomous Driving With Transferred Trajectory Interaction
    Y Lu, P Xu, X Jiang, AK Bashir, TR Gadekallu, W Wang, X Hu
    IEEE Transactions on Intelligent Transportation Systems 2025

  • Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things
    X Yuan, W Wang, J Chen, K Fang, AK Bashir, T Mondal, X Hu, MJ Deen
    IEEE Internet of Things Journal 2025

  • Conv-MTD: A CNN Based Multi-Label Medical Tubes Detection and Classification Model to facilitate resource-constrained point-of-care devices
    M Abbas, WC Kuo, K Mahmood, W Akram, S Mehmood, AK Bashir
    IEEE Journal of Biomedical and Health Informatics 2025

  • Robust Fault Diagnosis of Drilling Machinery Under Complex Working Conditions Based on Carbon Intelligent Industrial Internet of Things
    K Fang, L Tong, X Xu, J Cai, X Peng, M Omar, AK Bashir, W Wang
    IEEE Internet of Things Journal 2025

  • Knowledge-Driven Lane Change Prediction for Secure and Reliable Internet of Vehicles
    Y Lu, Z Zhang, W Wang, Y Zhu, T Chen, YD Al-Otaibi, AK Bashir, X Hu
    IEEE Transactions on Intelligent Transportation Systems 2025

  • Context-Aware Prediction with Secure and Lightweight Cognitive Decision Model in Smart Cities
    F Al-Quayed, M Humayun, TS Alnusairi, I Ullah, AK Bashir, T Hussain
    Cognitive Computation 17 (1), 1-12 2025

  • Hyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart cites
    Y Lu, W Wang, R Bai, S Zhou, L Garg, AK Bashir, W Jiang, X Hu
    Information Fusion 114, 102682 2025

  • From overfitting to robustness: Quantity, quality, and variety oriented negative sample selection in graph contrastive learning
    A Ali, J Li, H Chen, AK Bashir
    Applied Soft Computing 170, 112672 2025

  • Covariate-adjusted construction of gene regulatory networks using a combination of generalized linear model and penalized maximum likelihood
    O Chatrabgoun, A Daneshkhah, P Torkaman, M Johnston, ...
    PloS one 20 (1), e0309556 2025

  • Federated Learning and Blockchain-Enabled Framework for Traffic Rerouting and Task Offloading in the Internet of Vehicles (IoV)
    GG Devarajan, S Thangam, MJF Alenazi, U Kumaran, G Chandran, ...
    IEEE Transactions on Consumer Electronics 2025

  • Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics
    SDA Shah, AK Bashir, YD Al-Otaibi, MM Al Dabel, F Ali
    IEEE Transactions on Consumer Electronics 2025

  • Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization
    SP Singh, N Kumar, G Kumar, B Balusamy, AK Bashir, MM Al Dabel
    IEEE Transactions on Consumer Electronics 2025

  • Sum Rate Maximization for 6G Beyond Diagonal RIS-Assisted Multi-Cell Transportation Systems
    C Zhang, WU Khan, AK Bashir, AK Dutta, AU Rehman, MM Al Dabel
    IEEE Transactions on Intelligent Transportation Systems 2025

  • Smart Steering Wheel: Design of IoMT‐Based Non‐Invasive Driver Health Monitoring System to Enhance Road Safety
    MA Khan, M Chen, T Nawaz, M Sedky, M Sheikh, AK Bashir, S Hassan
    IET Intelligent Transport Systems 19 (1), e70012 2025

MOST CITED SCHOLAR PUBLICATIONS

  • Investigating the Acceptance of Mobile Library Applications with an Extended Technology Acceptance Model (TAM)
    H Rafique, AO Almagrabi, A Shamim, F Anwar, AK Bashir
    Computers & Education 145 2020
    Citations: 611

  • COVID-19 patient health prediction using boosted random forest algorithm
    C Iwendi, AK Bashir, A Peshkar, R Sujatha, JM Chatterjee, S Pasupuleti, ...
    Frontiers in public health 8, 357 2020
    Citations: 557

  • Corrauc: a malicious bot-iot traffic detection method in iot network using machine learning techniques
    M Shafiq, Z Tian, AK Bashir, X Du, M Guizani
    IEEE Internet of Things Journal 8 (5), 3242 - 3254 2021
    Citations: 546

  • DITrust Chain: Towards Blockchain-based Trust Models for Sustainable Healthcare IoT Systems
    EA Nasser, AM Iliyasu, PM Kafrawy, OH Song, AK Bashir, AAE Latif.
    IEEE Access 8, 111223-111238 2020
    Citations: 330

  • Learning-Based Context-Aware Resource Allocation for Edge Computing-Empowered Industrial IoT
    H Liao, Z Zhou, X Zhao, S Mumtaz, A Jolfaei, SH Ahmed, AK Bashir
    IEEE Internet of Things Journal 2020
    Citations: 293

  • IoT malicious traffic identification using wrapper-based feature selection mechanisms
    M Shafiq, Z Tian, AK Bashir, X Du, M Guizani
    Computers & Security 94, 101863 2020
    Citations: 284

  • A Metaheuristic Optimization Approach for Energy Efficiency in the IoT Networks.
    C Iwendi, PK Reddy, T Reddy, K Lakshmanna, AK Bashir, MJ Piran
    Software: Practice and Experience 2020
    Citations: 284

  • Securing critical infrastructures: Deep-learning-based threat detection in IIoT
    K Yu, L Tan, S Mumtaz, S Al-Rubaye, A Al-Dulaimi, AK Bashir, FA Khan
    IEEE Communications Magazine 59 (10), 76-82 2021
    Citations: 255

  • Data mining and machine learning methods for sustainable smart cities traffic classification: A survey
    M Shafiq, Z Tian, AK Bashir, A Jolfaei, X Yu
    Sustainable Cities and Society 60, 102177 2020
    Citations: 251

  • Realizing an efficient IoMT-assisted Patient Diet Recommendation System through Machine Learning Model.
    C Iweni, S Khan, JH Anajemba, AK Bashir, F Noor.
    IEEE Access 2020
    Citations: 237

  • A Survey on Resource Management in IoT Operating Systems
    A Musaddiq, Y Bin Zikria, O Hahm, H Yu, AK Bashir, SW and Kim
    IEEE Access 6, 8459-8482 2018
    Citations: 237

  • A critical cybersecurity analysis and future research directions for the internet of things: A comprehensive review
    U Tariq, I Ahmed, AK Bashir, K Shaukat
    Sensors 23 (8), 4117 2023
    Citations: 227

  • Efficient and Secure Data Sharing for 5G Flying Drones: A Blockchain-Enabled Approach
    C Feng, K Yu, AK Bashir, YD Al-Otaibi, Y Lu, S Chen, D Zhang
    IEEE Network 35 (1), 130-137 2021
    Citations: 223

  • Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN
    RMA Ujjana, Z Pervez, K Dahal, AK Bashir, R Mumtaz, J Gonzlez
    Future Generation Computer Systems 111, 763-779 2020
    Citations: 216

  • Energy-efficient random access for LEO satellite-assisted 6G internet of remote things
    L Zhen, AK Bashir, K Yu, YD Al-Otaibi, CH Foh, P Xiao
    IEEE Internet of Things Journal 8 (7), 5114-5128 2020
    Citations: 213

  • Energy-aware marine predators algorithm for task scheduling in IoT-based fog computing applications
    M Abdel-Basset, R Mohamed, M Elhoseny, AK Bashir, A Jolfaei, N Kumar
    IEEE Transactions on Industrial Informatics 17 (7), 5068-5076 2020
    Citations: 201

  • Robust spammer detection using collaborative neural network in Internet-of-Things applications
    Z Guo, Y Shen, AK Bashir, M Imran, N Kumar, D Zhang, K Yu
    IEEE Internet of Things Journal 8 (12), 9549-9558 2020
    Citations: 189

  • Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach
    L Tan, K Yu, AK Bashir, X Cheng, F Ming, L Zhao, X Zhou
    Neural Computing and Applications, 1-14 2023
    Citations: 187

  • Millimeter-Wave Communication for Internet of Vehicles: Status, Challenges and Perspectives.
    KZ Ghafoor, L Kong, S Zeadally, AS Sadiq, G Epiphniou, M Hammoudeh, ...
    IEEE Internet of Things Journal 2020
    Citations: 187

  • A Review on Classification of Imbalanced Data for Wireless Sensor Networks.
    H Patel, DS Rajput, GT Reddy, C Iwendi, AK Bashir, O Jo.
    International Journal of Distributed Sensor Networks 2020
    Citations: 186