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Research Article

BEST PAPER 2021

Machine Learning Applications in Medical, Finance, Education and Cyber Security


Submission Deadline: 30 August 2021 (closed)

Abstract

This article has no abstract.

Keywords

• 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
  • Research Article

    BEST PAPER 2021

    ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

    Talha Imtiaz Baig1,*, Yaser Daanial Khan1, Talha Mahboob Alam2, Bharat Biswal3, Hanan Aljuaid4, Durdana Qaiser Gillani5 CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 215-230, 2022, DOI:10.32604/cmc.2022.021849
    Abstract Lysine Lipoylation is a protective and conserved Post Translational Modification (PTM) in proteomics research like prokaryotes and eukaryotes. It is connected with many biological processes and closely linked with many metabolic diseases. To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level, the computational methods and several other factors play a key role in this purpose. Usually, most of the techniques and different traditional experimental models have a very high cost. They are time-consuming; so, it is required to construct a predictor model to extract lysine lipoylation sites. This study proposes a model that… More >

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  • Research Article

    BEST PAPER 2021

    Educational Videos Subtitles’ Summarization Using Latent Dirichlet Allocation and Length Enhancement

    Sarah S. Alrumiah*, Amal A. Al-Shargabi CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6205-6221, 2022, DOI:10.32604/cmc.2022.021780
    Abstract Nowadays, people use online resources such as educational videos and courses. However, such videos and courses are mostly long and thus, summarizing them will be valuable. The video contents (visual, audio, and subtitles) could be analyzed to generate textual summaries, i.e., notes. Videos’ subtitles contain significant information. Therefore, summarizing subtitles is effective to concentrate on the necessary details. Most of the existing studies used Term Frequency–Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) models to create lectures’ summaries. This study takes another approach and applies Latent Dirichlet Allocation (LDA), which proved its effectiveness in document summarization. Specifically, the proposed… More >

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  • Research Article

    BEST PAPER 2021

    Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

    Talha Mahboob Alam1,*, Kamran Shaukat2,6, Adel Khelifi3, Wasim Ahmad Khan4, Hafiz Muhammad Ehtisham Raza5, Muhammad Idrees6, Suhuai Luo2, Ibrahim A. Hameed7 CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5305-5319, 2022, DOI:10.32604/cmc.2022.020344
    Abstract Disease diagnosis is a challenging task due to a large number of associated factors. Uncertainty in the diagnosis process arises from inaccuracy in patient attributes, missing data, and limitation in the medical expert's ability to define cause and effect relationships when there are multiple interrelated variables. This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things (IoT) empowered by the fuzzy inference system (FIS) to diagnose various diseases. The Fuzzy System is one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties, and fuzzy logic is… More >

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  • Research Article

    BEST PAPER 2021

    User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

    Rahmat Budiarto1,*, Ahmad A. Alqarni1, Mohammed Y. Alzahrani1, Muhammad Fermi Pasha2, Mohamed Fazil Mohamed Firdhous3, Deris Stiawan4 CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2679-2698, 2022, DOI:10.32604/cmc.2022.019847
    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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  • Research Article

    BEST PAPER 2021

    An Ensemble Methods for Medical Insurance Costs Prediction Task

    Nataliya Shakhovska1, Nataliia Melnykova1,*, Valentyna Chopiyak2, Michal Gregus ml3 CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3969-3984, 2022, DOI:10.32604/cmc.2022.019882
    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 >

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  • Research Article

    BEST PAPER 2021

    Engagement Detection Based on Analyzing Micro Body Gestures Using 3D CNN

    Shoroog Khenkar1,*, Salma Kammoun Jarraya1,2 CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2655-2677, 2022, DOI:10.32604/cmc.2022.019152
    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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  • Research Article

    BEST PAPER 2021

    Epilepsy Radiology Reports Classification Using Deep Learning Networks

    Sengul Bayrak1,2, Eylem Yucel2,*, Hidayet Takci3 CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3589-3607, 2022, DOI:10.32604/cmc.2022.018742
    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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  • Research Article

    BEST PAPER 2021

    Enhancing the Robustness of Visual Object Tracking via Style Transfer

    Abdollah Amirkhani1,*, Amir Hossein Barshooi1, Amir Ebrahimi2 CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 981-997, 2022, DOI:10.32604/cmc.2022.019001
    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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  • Research Article

    BEST PAPER 2021

    Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

    Mohamed Esmail Karar1,2, Marwa Ahmed Shouman3, Claire Chalopin4,* CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1683-1697, 2022, DOI:10.32604/cmc.2022.018564
    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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  • Research Article

    BEST PAPER 2021

    A Hybrid Feature Selection Framework for Predicting Students Performance

    Maryam Zaffar1,2,*, Manzoor Ahmed Hashmani1, Raja Habib2, KS Quraishi3, Muhammad Irfan4, Samar Alqhtani5, Mohammed Hamdi5 CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1893-1920, 2022, DOI:10.32604/cmc.2022.018295
    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 >

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