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
Yuhuan Lu, Wei Wang, Rufan Bai, Shengwei Zhou, Lalit Garg, Ali Kashif Bashir, Weiwei Jiang, and Xiping Hu
Elsevier BV
Mohammed Al-Khalidi, Rabab Al-Zaidi, Tarek Ali, Safiullah Khan, and Ali Kashif Bashir
Elsevier BV
Aditya Taparia, Ali Kashif Bashir, Yaodong Zhu, Thippa Reddy Gdekallu, and Keshab Nath
Elsevier BV
Anees Baqir, Mubashir Ali, Shaista Jaffar, Hafiz Husnain Raza Sherazi, Mark Lee, Ali Kashif Bashir, and Maryam M. Al Dabel
Springer Science and Business Media LLC
AbstractThe COVID-19 pandemic has disrupted people’s lives and caused significant economic damage around the world, but its impact on people’s mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user’s PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real-world dataset, we demonstrate our model’s effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern.
Hailin Feng, Qing Li, Wei Wang, Ali Kashif Bashir, Amit Kumar Singh, Jinshan Xu, and Kai Fang
Elsevier BV
Yuhuai Peng, Jing Wang, Xiongang Ye, Fazlullah Khan, Ali Kashif Bashir, Bandar Alshawi, Lei Liu, and Marwan Omar
Elsevier BV
Junchao Yang, Ali Kashif Bashir, Zhiwei Guo, Keping Yu, and Mohsen Guizani
Elsevier BV
Ali Rohan, Muhammad Saad Rafaq, Md. Junayed Hasan, Furqan Asghar, Ali Kashif Bashir, and Tania Dottorini
Elsevier BV
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.
Kulaea Taueveeve Pauu, Qianqian Pan, Jun Wu, Ali Kashif Bashir, Mafua-'i-Vai'utukakau Maka, and Marwan Omar
Institute of Electrical and Electronics Engineers (IEEE)
The unforeseen events of natural disasters often devastate critical infrastructure and disrupt communication. The use of unmanned aerial vehicles (UAVs) in emergency response scenarios offers significant potential for delivering real-time information and assisting emergency response efforts. However, challenges such as physical barriers to communication not only hinder transmission performance by obstructing established line-of-sight (LoS) links but also pose risks to the privacy of sensitive information exchanged across these links. To address these challenges, we propose a novel IRS-aided UAV secure communications framework aimed to enhance communication efficiency while ensuring privacy preservation in emergency response scenarios. The framework consists of three stages: (i) local model training with dynamic differential privacy mechanism using stochastic gradient descent (SGD), with adaptive learning rate adjustment based on validation performance, (ii) decentralized federated learning (FL) with intelligent reflective surfaces (IRS) incorporation to improve communication and information exchange between UAV-to-UAV and UAV-to-ground station, and (iii) selection of a UAV header based on operational characteristics and connectivity to aid UAV-to-ground station communication.Furthermore, we evaluated our proposed framework through experimental simulations and achieved 0.91 accuracy after 50 federated learning rounds underscoring the efficacy of our dynamic noise and learning rate adjustment mechanism. Additionally, our integration of IRS led to lower communication latency, highlighting the effectiveness of our approach. This framework adeptly balances privacy protection with model accuracy.
Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, and Ali Kashif Bashir
Institute of Advanced Engineering and Science
Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.
Youxiang Duan, Ning Chen, Ali Kashif Bashir, Mohammad Dahman Alshehri, Lei Liu, Peiying Zhang, and Keping Yu
Institute of Electrical and Electronics Engineers (IEEE)
Multimodal retrieval has received widespread consideration since it can commendably provide massive related data support for the development of computational social systems (CSSs). However, the existing works still face the following challenges: 1) rely on the tedious manual marking process when extended to CSS, which not only introduces subjective errors but also consumes abundant time and labor costs; 2) only using strongly aligned data for training, lacks concern for the adjacency information, which makes the poor robustness and semantic heterogeneity gap difficult to be effectively fit; and 3) mapping features into real-valued forms, which leads to the characteristics of high storage and low retrieval efficiency. To address these issues in turn, we have designed a multimodal retrieval framework based on web-knowledge-driven, called unsupervised and robust graph convolutional hashing (URGCH). The specific implementations are as follows: first, a “secondary semantic self-fusion” approach is proposed, which mainly extracts semantic-rich features through pretrained neural networks, constructs the joint semantic matrix through semantic fusion, and eliminates the process of manual marking; second, a “adaptive computing” approach is designed to construct enhanced semantic graph features through the knowledge-infused of neighborhoods and uses graph convolutional networks for knowledge fusion coding, which enables URGCH to sufficiently fit the semantic modality gap while obtaining satisfactory robustness features; Third, combined with hash learning, the multimodality data are mapped into the form of binary code, which reduces storage requirements and improves retrieval efficiency. Eventually, we perform plentiful experiments on the web dataset. The results evidence that URGCH exceeds other baselines about 1%–3.7% in mean average precisions (MAPs), displays superior performance in all the aspects, and can meaningfully provide multimodal data retrieval services to CSS.
Nazik Alturki, Raed Alharthi, Muhammad Umer, Oumaima Saidani, Amal Alshardan, Reemah M. Alhebshi, Shtwai Alsubai, and Ali Kashif Bashir
Tech Science Press
Rashid Abbasi, Ali Kashif Bashir, Abdul Mateen, Farhan Amin, Yuan Ge, 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)
Ali Kashif Bashir
Springer Science and Business Media LLC
Xijian Xu, Jun Wu, Ali Kashif Bashir, and Marwan Omar
Institute of Electrical and Electronics Engineers (IEEE)
Muhammad Asad Saleem, Xiong Li, Khalid Mahmood, Salman Shamshad, Muhammad Faizan Ayub, Ali Kashif Bashir, and Marwan Omar
Institute of Electrical and Electronics Engineers (IEEE)
Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA’s assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA’s involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively.
Zirun Zhao, Yi Sun, Ali Kashif Bashir, and Zhaowen Lin
Institute of Electrical and Electronics Engineers (IEEE)
The popularity of emerging consumer electronics, such as Mobile phones, PADs, and various smart home appliances, brings unprecedented convenience to people. Currently, how to obtain targeted information without revealing personal privacy is the new challenge with the application of precision recommendation and other Artificial Intelligence technologies in consumer electronics. Several solutions, based on Federated Learning transfer and update models by intermediate parameters instead of individual data, can perform some recommending tasks with weak security. However, they still face various attacks like model inversion attacks and membership inference attacks, especially in the complex non-independent and identically distribution (non-IID) environment. In this paper, we propose AdaDpFed, an adaptive federated differential private protocol in the non-IID setting. AdaDpFed can adaptively adjust the perturbation parameters, aggregated clients and sampling size according to the changing distribution of the individual data. The convergence proof shows that AdaDpFed has $\\mathcal {O}\\left({{}{}\\frac {1}{T}}\\right)$ convergence rate. Comparative experiments demonstrate that the performance of AdaDpFed outperforms other state-of-the-art protocols in both accuracy and global privacy budget.
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)
Yi SUN Kaixiang LIN and Ali Kashif Bashir
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