Special Issue "Machine Learning Applications in Medical, Finance, Education and Cyber Security"

Submission Deadline: 30 August 2021 (closed)
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Guest Editors
Dr. Kamran Shaukat, The University of Newcastle, Australia.
Dr. Suhuai Luo, The University of Newcastle, Australia.
Dr. Ibrahim A. Hameed, Norwegian University of Science and Technology, Norway.
Dr. Matloob Khushi, University of Sydney, Australia.
Dr. Talha Mahboob Alam, University of Engineering and Technology, Pakistan.


Over the past decade, the rise of machine learning (ML) and deep learning (DL) evolved in various life areas, especially medical, cyber security, finance, and education. This has dramatically increased the attack surface for the vibrantly used neural network venerable to so-called adversarial attacks. On the other hand, new threats are also being discovered daily, making it harder for current solutions to cope with a large amount of data to analyse. Numerous machine learning algorithms have found their ways in the mentioned fields to identify new and unknown attacks.

While these applications of machine learning algorithms have been proven beneficial in various fields, they have also highlighted many shortcomings, such as the lack of datasets, the inability to learn from small datasets, the cost of the architecture, and imbalanced datasets name a few. On the other hand, new and emerging algorithms, such as Deep Learning, One-shot Learning, Continuous Learning and Generative Adversarial Networks, have been successfully applied to solve various tasks in these fields. Therefore, it is crucial to apply these new methods to life-critical missions and measure these less-traditional algorithms' success when used in these fields.

• Machine Learning
• Reinforcement
• Explainable Machine Learning
• Adversarial Machine Learning
• Adversarial Attacks
• Cyber Security
• Intrusion Detection Systems
• Malware
• Imbalanced Datasets
• Bioinformatics
• Medical Diagnosis
• Financial Risk Management
• Finance
• Asset Return Forecasting
• Stock Exchange
• Educational Data Mining
• Learning Analytics
• Student Performance Prediction
• Intelligent Tutoring Systems

Published Papers
  • Enhancing the Robustness of Visual Object Tracking via Style Transfer
  • Abstract The performance and accuracy of computer vision systems are affected by noise in different forms. Although numerous solutions and algorithms have been presented for dealing with every type of noise, a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing. In this paper, we have focused on the stability and robustness of one computer vision branch (i.e., visual object tracking). We have demonstrated that, without imposing a heavy computational load on a model or changing its algorithms, the drop in the performance and accuracy… More
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  • Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images
  • Abstract The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with auxiliary classifier. Each one includes… More
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  • A Hybrid Feature Selection Framework for Predicting Students Performance
  • Abstract Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions, for the improvement of quality of education and to meet the dynamic needs of society. The selection of features for student's performance prediction not only plays significant role in increasing prediction accuracy, but also helps in building the strategic plans for the improvement of students’ academic performance. There are different feature selection algorithms for predicting the performance of students, however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features. In this… More
  •   Views:451       Downloads:117        Download PDF