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Future Internet
Special Issues
Deep Learning Techniques Addressing Data Scarcity
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Deep Learning Techniques Addressing Data Scarcity

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 3539

Special Issue Editors

Dr. Laith Alzubaid

E-Mail Website
Guest Editor
School of Mechanical, Medical, and Process Engineering The Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: deep learning; machine learning; medical imaging; pattern recognition; transfer learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. YuanTong Gu

E-Mail Website
Guest Editor
School of Mechanical, Medical, and Process Engineering, The Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: numerical modeling; computational mechanics; computational biomechanics; nanotechnology; machine learning; physics-informed neural network

Special Issue Information

Dear Colleagues,

Deep learning, which has overcome many drawbacks of traditional machine learning techniques, is an alternative approach applied to many challenging real-world environments, ranging from medical imaging to agriculture to bioinformatics. Despite its great efficiency in dealing with challenging tasks, it still has some pitfalls that must be addressed, such as the lack of training data and imbalanced data. Deep learning requires a large number of data to perform well.

However, many fields suffer from a lack of sufficient data for training deep learning models because this requires a long collection process and manual labeling by human annotators, which are time-consuming and expensive procedures.

This Special Issue aims to present novel works proposing new tools and techniques to deal with data scarcity in several research areas, including different transfer learning types, physics-informed neural networks, generative adversarial networks, deep synthetic minority oversampling techniques, and model complexity.

High-quality reviews and survey papers are welcome. Papers may focus on, but are limited to, the following areas:

  • Deep learning;
  • Data scarcity;
  • Machine learning;
  • Convolutional neural network (CNN);
  • Deep neural network architectures;
  • Lack of training data;
  • Small datasets;
  • Transfer learning;
  • Physics-informed neural network;
  • Generative adversarial networks;
  • Deep synthetic minority oversampling technique;
  • Model complexity;
  • Deep learning applications;
  • Image classification;
  • Image segmentation;
  • Image registration;
  • Supervised learning;
  • Unsupervised learning;
  • Hardware solutions;
  • Overfitting;
  • Imbalanced data.

Dr. Laith Alzubaid
Prof. Dr. YuanTong Gu
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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.

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Published Papers (2 papers)

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Research

20 pages, 552 KiB  
Article
SBNNR: Small-Size Bat-Optimized KNN Regression
by Rasool Seyghaly, Jordi Garcia, Xavi Masip-Bruin and Jovana Kuljanin
Future Internet 2024, 16(11), 422; https://doi.org/10.3390/fi16110422 - 14 Nov 2024
Viewed by 309
Abstract
Small datasets are frequent in some scientific fields. Such datasets are usually created due to the difficulty or cost of producing laboratory and experimental data. On the other hand, researchers are interested in using machine learning methods to analyze this scale of data. [...] Read more.
Small datasets are frequent in some scientific fields. Such datasets are usually created due to the difficulty or cost of producing laboratory and experimental data. On the other hand, researchers are interested in using machine learning methods to analyze this scale of data. For this reason, in some cases, low-performance, overfitting models are developed for small-scale data. As a result, it appears necessary to develop methods for dealing with this type of data. In this research, we provide a new and innovative framework for regression problems with a small sample size. The base of our proposed method is the K-nearest neighbors (KNN) algorithm. For feature selection, instance selection, and hyperparameter tuning, we use the bat optimization algorithm (BA). Generative Adversarial Networks (GANs) are employed to generate synthetic data, effectively addressing the challenges associated with data sparsity. Concurrently, Deep Neural Networks (DNNs), as a deep learning approach, are utilized for feature extraction from both synthetic and real datasets. This hybrid framework integrates KNN, DNN, and GAN as foundational components and is optimized in multiple aspects (features, instances, and hyperparameters) using BA. The outcomes exhibit an enhancement of up to 5% in the coefficient of determination (R2 score) using the proposed method compared to the standard KNN method optimized through grid search. Full article
(This article belongs to the Special Issue Deep Learning Techniques Addressing Data Scarcity)
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16 pages, 4967 KiB  
Article
A Novel Machine Learning Prediction Model for Aerosol Transport in Upper 17-Generations of the Human Respiratory Tract
by Mohammad S. Islam, Shahid Husain, Jawed Mustafa and Yuantong Gu
Future Internet 2022, 14(9), 247; https://doi.org/10.3390/fi14090247 - 24 Aug 2022
Cited by 4 | Viewed by 2029
Abstract
The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, [...] Read more.
The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, developing an innovative tool that accurately predicts transport behaviour and reduces computational time is essential. This study aims to develop a novel and innovative machine learning (ML) model to predict particle deposition to the lower airways. The first-ever study uses ML techniques to explore the pulmonary aerosol TD in a digital 17-generation airway model. The ML model uses the computational data for a 17-generation airway model and four standard ML regression models are used to save the computational cost. Random forest (RF), k-nearest neighbour (k-NN), multi-layer perceptron (MLP) and Gaussian process regression (GPR) techniques are used to develop the ML models. The MLP regression model displays more accurate estimates than other ML models. Finally, a prediction model is developed, and the results are significantly closer to the measured values. The prediction model predicts the deposition efficiency (DE) for different particle sizes and flow rates. A comprehensive lobe-specific DE is also predicted for various flow rates. This first-ever aerosol transport prediction model can accurately predict the DE in different regions of the airways in a couple of minutes. This innovative approach and accurate prediction will improve the literature and knowledge of the field. Full article
(This article belongs to the Special Issue Deep Learning Techniques Addressing Data Scarcity)
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