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Mathematics
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Data Mining and Machine Learning with Applications, 2nd Edition
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Data Mining and Machine Learning with Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2334

Special Issue Editors

Prof. Dr. Wei Fang

E-Mail Website
Guest Editor
Jiangsu Engineering Center of Network Monitoring, School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: data mining; big data analytics; knowledge discovery; cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Victor S. Sheng

E-Mail Website
Guest Editor
Department of Computer, Texas Tech University, Lubbock, TX 79409, USA
Interests: data science; machine learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the emergence of big data and the advances made in computing services, artificial intelligence (AI) has attracted increasing attention around the world. The role of AI is becoming more and more important in our daily lives in applications such as machine learning, pattern recognition, computer vision, data mining, human–machine interfaces, information retrieval, and natural language processing. To this end, an increasing number of researchers and engineers are already or will be involved in the AI field.

This topic aims to bring together leading scientists in deep learning and related areas within artificial intelligence, data mining, and machine learning with applications. Papers using advanced mathematical methods and statistical approaches in these areas are particularly welcome for publication in this Special Issue.

Prof. Dr. Wei Fang
Dr. Victor S. Sheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • data mining
  • statistical machine learning
  • statistical classification
  • statistical inference
  • Bayesian methods
  • algorithms and architectures for big data searches, mining, and processing
  • deep learning
  • computer vision and image processing
  • evolutionary computation
  • knowledge discovery
  • industrial and medical applications
  • security applications
  • applications of unsupervised learning
  • industrial and medical applications

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Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 2065 KiB  
Article
Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors
by Felipe Emiliano Arévalo-Cordovilla and Marta Peña
Mathematics 2024, 12(20), 3272; https://doi.org/10.3390/math12203272 - 18 Oct 2024
Viewed by 899
Abstract
Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network [...] Read more.
Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning with Applications, 2nd Edition)
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20 pages, 24161 KiB  
Article
Deep Embedding Koopman Neural Operator-Based Nonlinear Flight Training Trajectory Prediction Approach
by Jing Lu, Jingjun Jiang and Yidan Bai
Mathematics 2024, 12(14), 2162; https://doi.org/10.3390/math12142162 - 10 Jul 2024
Viewed by 1015
Abstract
Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the [...] Read more.
Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the unconstrained airspace maps, and the randomization of driving patterns. In this work, a deep learning model based on data-driven modern koopman operator theory and dynamical system identification is proposed. The model does not require the manual selection of dictionaries and can automatically generate augmentation functions to achieve nonlinear trajectory space mapping. The model combines stacked neural networks to create a scalable depth approximator for approximating the finite-dimensional Koopman operator. In addition, the model uses finite-dimensional operator evolution to achieve end-to-end adaptive prediction. In particular, the model can gain some physical interpretability through operator visualization and generative dictionary functions, which can be used for downstream pattern recognition and anomaly detection tasks. Experiments show that the model performs well, particularly on flight training trajectory datasets. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning with Applications, 2nd Edition)
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