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
Rashid Abbasi, Ali Kashif Bashir, Abdul Mateen, Farhan Amin, Yuan Ge, and Marwan Omar
Institute of Electrical and Electronics Engineers (IEEE)
Ali Kashif Bashir
Springer Science and Business Media LLC
Senthil Murugan Nagarajan, Ganesh Gopal Devarajan, Ramana T.V., Asha Jerlin M., Ali Kashif Bashir, and Yasser D. Al-Otaibi
Elsevier BV
Jian Chen, Yuzhu Hu, Qifeng Lai, Wei Wang, Junxin Chen, Han Liu, Gautam Srivastava, Ali Kashif Bashir, and Xiping Hu
Elsevier BV
Heyi Zhang, Jun Wu, Xi Lin, Ali Kashif Bashir, and Yasser D. Al-Otaibi
Institute of Electrical and Electronics Engineers (IEEE)
Muhammad Ahmed Hassan, Muhammad Usman Ghani Khan, Razi Iqbal, Omer Riaz, Ali Kashif Bashir, and Usman Tariq
Springer Science and Business Media LLC
Heyi Zhang, Jun Wu, Qianqian Pan, Ali Kashif Bashir, and Marwan Omar
Institute of Electrical and Electronics Engineers (IEEE)
Rajesh R, Hemalatha S, Senthil Murugan Nagarajan, Ganesh Gopal Devarajan, Marwan Omar, and Ali Kashif Bashir
Institute of Electrical and Electronics Engineers (IEEE)
V. D. Ambeth Kumar, Sowmya Surapaneni, D. Pavitra, R. Venkatesan, Marwan Omar, and A. K. Bashir
World Scientific Pub Co Pte Ltd
In the colloquy concerning human rights, equality, and human health, mental illness and therapy regarding mental health have been condoned. Mental disorder is a behavioral motif that catalyzes the significant anguish or affliction of personal functioning. The symptoms of a mental disorder may be tenacious, degenerative, or transpire as a single episode. Brain sickness is often interpreted as a combination of how a person thinks, perceives, contemplates and reacts. This may be analogous to a specific region or workings of the brain frequently in a social context. Anxiety disorders, psychotic disorders, personality disorders, mood disorders, eating disorders, and many more are examples of mental disorders, while complications include social problems, suicides, and cognitive impairment. These days, mental disorders are quotidian worldwide, and clinically consequential levels of derangement rise adversely. The purpose of this paper is to aid in prognosis of the type of mental disorder by analyzing the brainwaves such as Alpha ([Formula: see text]), Beta ([Formula: see text]), Gamma ([Formula: see text]), Theta ([Formula: see text]), Delta ([Formula: see text]) with the help of big data analysis and the Internet of Medical Things (IoMT). IoMT helps in gathering the required data and data transmission, while big data analysis helps in predicting the type of disorder.
Xijian Xu, Jun Wu, Ali Kashif Bashir, and Marwan Omar
Institute of Electrical and Electronics Engineers (IEEE)
Ali Kashif Bashir, Nancy Victor, Sweta Bhattacharya, Thien Huynh-The, Rajeswari Chengoden, Gokul Yenduri, Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, Thippa Reddy Gadekallu, and Madhusanka Liyanage
Institute of Electrical and Electronics Engineers (IEEE)
Recent technological advancements have considerably improved healthcare systems to provide various intelligent services, improving life quality. The Metaverse, often described as the next evolution of the Internet, helps the users interact with each other and the environment, thus offering a seamless connection between the virtual and physical worlds. Additionally, the Metaverse, by integrating emerging technologies, such as artificial intelligence (AI), cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, can potentially transform many vertical domains in general and the healthcare sector (healthcare Metaverse) in particular. The healthcare Metaverse holds huge potential to revolutionize the development of intelligent healthcare systems, thus presenting new opportunities for significant advancements in healthcare delivery, personalized healthcare experiences, medical education, collaborative research, and so on. However, various challenges are associated with the realization of the healthcare Metaverse, such as privacy, interoperability, data management, and security. Federated learning (FL), a new branch of AI, opens up enormous opportunities to deal with the aforementioned challenges in the healthcare Metaverse by exploiting the data and computing resources available at the distributed devices. This motivated us to present a survey on adopting FL for the healthcare Metaverse. Initially, we present the preliminaries of IoT-based healthcare systems, FL in conventional healthcare, and the healthcare Metaverse. Furthermore, the benefits of the FL in the healthcare Metaverse are discussed. Subsequently, we discuss the several applications of FL-enabled healthcare Metaverse, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight the significant challenges and potential solutions toward realizing FL in the healthcare Metaverse.
Muhammad Umer, Turki Aljrees, Hanen Karamti, Abid Ishaq, Shtwai Alsubai, Marwan Omar, Ali Kashif Bashir, and Imran Ashraf
Springer Science and Business Media LLC
AbstractIntelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.
Bilal Hassan, Hafiz Husnain Raza Sherazi, Mubashir Ali, and Ali K. Bashir
Springer Science and Business Media LLC
AbstractAs the number of passengers at border entry points such as airports and rail stations increases, so does the demand for seamless, secure, and fast biometric technologies for verification purposes. Although fingerprints are currently useful biometric technologies, they are intrusive and slow down the end-to-end verification process, increasing the chances of tampering. Emerging as an alternative technology, soft biometrics have proven successful for non-intrusive and rapid verification. Soft biometrics consists of a large set of features from three different modalities of the human body, including the face, body, and essential & auxiliary attachments. This paper proposes a multi-channel soft biometrics framework that leverages soft biometrics technology over traditional biometrics. The framework encapsulates four distinct components: ApparelNet, which verifies essential and auxiliary attachments; A-Net, which measures anthropometric soft biometrics; OneDetect, which predicts global soft biometrics; and RSFS, which develops a set of highly relevant and supportive soft biometrics for verification. The proposed framework addresses several critical limitations of existing biometrics technologies during the verification process at border entry points, such as intrusive behavior, response time, biometric tampering, and privacy issues. The proposed multi-channel soft biometrics framework has been evaluated using several benchmark datasets in the field, such as Front-view Gait (FVG), Pedestrian Attribute Recognition At Far Distance (PETA), and Multimedia and Vision (MMV) Pedestrian. Using heterogeneous datasets enables the testing of each framework component or channel against numerous constrained and unconstrained scenarios. The outcome of the envisioned multi-channel soft biometrics framework is presented based on distinct outcomes from each channel, but it remains focused on determining a single cumulative verification score for verification at border control. In addition, this multi-channel soft biometrics framework has extended applications in several fields, including crowd surveillance, the fashion industry, and e-learning.
Mahdi Jemmali, Ali Kashif Bashir, Wadii Boulila, Loai Kayed B. Melhim, Rutvij H. Jhaveri, and Jawad Ahmad
Institute of Electrical and Electronics Engineers (IEEE)
Nowadays, developing environmental solutions to ensure the preservation and sustainability of natural resources is one of the core research topics for providing a better life quality. Using renewable energy sources, such as solar energy, is one of the solutions that can reduce the overuse of natural resources. This research aims to boost the efficiency of solar energy plants by proposing a novel approach to optimize the total flying time of battery-based drone systems to enhance the performance of solar plant systems. The contribution of the proposed approach is to solve scheduling problems based on timing constraints to monitor the solar plant. The main objective of the proposed approach is to maximize the drone’s minimum total flying time, which will increase the availability and reliability of the solar plant monitoring system. Time to empty values is calculated based on battery degradation rates. This problem is proven to be NP-hard. Four categories of enhanced algorithms were developed to solve drones’ scheduling problems in handling various tasks within multiple errands in the extent of solar parks in the monitored power plant to achieve the desired objective. Experimental results of the presented algorithms showed that the $M2S$ algorithm has a stable performance behavior in all conducted experiments.
Yuxin Qi, Jun Wu, Ali Kashif Bashir, Xi Lin, Wu Yang, and Mohammad Dahman Alshehri
Institute of Electrical and Electronics Engineers (IEEE)
Traffic forecasting is essential in improving and maintaining safety and orderliness in intelligent transportation systems (ITS). As a deep learning approach, graph neural networks (GNN) based spatial-temporal association mining methods are promising in traffic forecasting. However, current GNN-based methods usually require a high number of training data, and when the sample volume is small, the performance of the model drops dramatically. The existing transfer methods can solve this problem by leveraging knowledge from other data-rich areas, but the domain adaption method with access to source data still faces the non-neglectable problem of private information leakage in the source area. A solution that can solve cross-area transfer without access to source data is still missing. In this paper, to fill the gap, we propose a Transferable Federated Inductive Spatial-Temporal Graph Neural Network (T-ISTGNN) framework to transfer spatial-temporal dependency information in cross-area data to accomplish traffic state forecasting. First, we introduce a multi-source model aggregation scheme based on federated learning to retain the traffic information of the source areas. Second, we propose a transfer method between source and target areas based on hypothesis transfer learning to achieve domain adaption under source domain data protection. Third, we propose a GNN-based method called Inductive Spatial-Temporal Graph Neural Network (ISTGNN) for traffic forecasting. Experiments on real-world datasets demonstrate that T-ISTGNN is capable of cross-area traffic state forecasting under the restriction of preserving the privacy of source areas.
Sharma, Shalli Rani, Ali Kashif Bashir, Moez Krichen, and Abdulaziz Alshammari
Institute of Electrical and Electronics Engineers (IEEE)
Yi Zhou, Jun Wu, Xi Lin, Ali Kashif Bashir, Yasser D. Al-Otaibi, and Hansong Xu
Institute of Electrical and Electronics Engineers (IEEE)
Digital twin (DT) technology is being applied increasingly in the Internet of Vehicles environment, but it still faces many challenges in terms of efficiency and security. In the field of DT-based autonomous driving, many previous works have been done to study the efficient migration methods of DT models. But these works consider the migration process as a blackbox. We study the efficient migration method of the DT model between the edge computing nodes inside the blackbox. We propose three different migration strategies depending on the source of the initial data and the source of the updated data, and evaluate the efficiency of these strategies in terms of migration time in different network environments using the autonomous driving simulation platform CARLA. We then derive methods for selecting migration strategies under different network conditions. During the migration process, there may be external attacks on participating elements or networks. We analyze the security problems that may arise during the migration process and propose corresponding defense methods against such cyberattacks.
V. D. Ambeth Kumar, S. Sharmila, Abhishek Kumar, A. K. Bashir, Mamoon Rashid, Sachin Kumar Gupta, and Waleed S. Alnumay
Springer Science and Business Media LLC
Wenru Zeng, Zhiwei Guo, Yu Shen, Ali Kashif Bashir, Keping Yu, Yasser D. Al-Otaibi, and Xu Gao
Springer Science and Business Media LLC
Marwan Omar, Rebet Jones, Darrell Norman Burrell, Maurice Dawson, Calvin Nobles, Derek Mohammed, and Ali Kashif Bashir
IGI Global
Due to its simple installation and connectivity, the internet of things (IoT) is susceptible to malware attacks. As IoT devices have become more prevalent, they have become the most tempting targets for malware. In this chapter, the authors propose a novel detection and analysis method that harnesses the power and simplicity of decision trees. The experiments are conducted using a real word dataset, MaleVis, which is a publicly available dataset. Based on the results, the authors show that this proposed approach outperforms existing state-of-the-art solutions in that it achieves 97.23% precision and 95.89% recall in terms of detection and classification. A specificity of 96.58%, F1-score of 96.40%, an accuracy of 96.43%, and an average processing time per malware classification of 789 ms.
Muhammad Ali Naeem, Yousaf Bin Zikria, Rashid Ali, Usman Tariq, Yahui Meng, and Ali Kashif Bashir
Elsevier BV
Muhammad Ismail, Hamza Qadir, Farrukh Aslam Khan, Sadeeq Jan, Zahid Wadud, and Ali Kashif Bashir
Elsevier BV
Peng He, Chunhui Lan, Ali Kashif Bashir, Dapeng Wu, Ruyan Wang, Rupak Kharel, and Keping Yu
Institute of Electrical and Electronics Engineers (IEEE)
Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this paper, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.
Xiaoyu Yi, Jun Wu, Gaolei Li, Ali Kashif Bashir, Jianhua Li, and Ahmad Ali AlZubi
Institute of Electrical and Electronics Engineers (IEEE)