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

Submission Deadline: 30 August 2021 (closed)
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
  • User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework
  • Abstract As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with… More
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  • An Ensemble Methods for Medical Insurance Costs Prediction Task
  • Abstract The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs. The open dataset is used for data analysis methods development. The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients’ health. Machine learning is associated with many expectations, but its quality is determined by choosing a good algorithm and the proper steps to plan, develop, and implement the model. The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy. Pierson coefficient and Boruta algorithm are used… More
  •   Views:33       Downloads:19        Download PDF

  • Engagement Detection Based on Analyzing Micro Body Gestures Using 3D CNN
  • Abstract This paper proposes a novel, efficient and affordable approach to detect the students’ engagement levels in an e-learning environment by using webcams. Our method analyzes spatiotemporal features of e-learners’ micro body gestures, which will be mapped to emotions and appropriate engagement states. The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames. We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset. The adopted C3D model was used based on two different approaches; as a feature extractor with linear classifiers and… More
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  • Epilepsy Radiology Reports Classification Using Deep Learning Networks
  • Abstract The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing technique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given… More
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  • 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:467       Downloads:128        Download PDF