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Advanced Application of Machine Learning and Meta-Learning in Image and...
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Advanced Application of Machine Learning and Meta-Learning in Image and Text Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 30470

Special Issue Editors

Prof. Juntae Kim

E-Mail Website
Guest Editor
Department of Computer Engineering, Dongguk University, Jangchung-dong, Jung-gu, Seoul 100-715, Korea
Interests: artificial intelligence; machine learning; meta learning
Prof. Nam Ik Cho

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul 08826, Korea
Interests: computer vision; image and video signal processing; adaptive filtering
Dr. Changwon Lim

E-Mail Website
Guest Editor
Department of Applied Statistics, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 156-756, Korea
Interests: machine learning; deep learning; text mining; big data analytics

Special Issue Information

Dear Colleagues,

Machine learning is a key field of artificial intelligence and plays an important role in various image and text analysis tasks such as object recognition, motion detection, medical scan analysis, sentiment analysis, information retrieval, document clustering, image captioning, text to image generation, etc.

Unlike the existing method of applying a fixed learning algorithm to a specific task, meta-learning, learning to learn, aims to improve the learning algorithm itself based on the experience of multiple learning tasks, thereby improving the performance of learning in a task that is given only a small amount of data.

This special issue focuses on the application of machine learning for image and text analysis, and meta-learning algorithms and applications for tasks that have limitations in applying machine learning due to the limited amount of available data. The topics of interest include but are not limited to:

  • Machine Learning and Meta-Learning Theory
  • Machine Learning for Image and Text Analysis
  • Multi-Modal Machine Learning and Meta-Learning
  • Meta-Learning for Computer Vision Tasks
  • Meta-Learning for Natural Language Processing
  • Meta-Learning in Reinforcement Learning
  • Unsupervised and Self-supervised Meta-Learning
  • Practical Application of Meta-Learning

Prof. Juntae Kim
Prof. Nam Ik Cho
Prof. Changwon Lim
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. Electronics 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 2400 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 and Meta-Learning Theory
  • Machine Learning for Image and Text Analysis
  • Multi-Modal Machine Learning and Meta-Learning
  • Meta-Learning for Computer Vision Tasks
  • Meta-Learning for Natural Language Processing
  • Meta-Learning in Reinforcement Learning
  • Unsupervised and Self-supervised Meta-Learning
  • Practical Application of Meta-Learning

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Published Papers (6 papers)

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Research

15 pages, 3977 KiB  
Article
HFGNN-Proto: Hesitant Fuzzy Graph Neural Network-Based Prototypical Network for Few-Shot Text Classification
by Xinyu Guo, Bingjie Tian and Xuedong Tian
Electronics 2022, 11(15), 2423; https://doi.org/10.3390/electronics11152423 - 3 Aug 2022
Cited by 6 | Viewed by 2938
Abstract
Few-shot text classification aims to recognize new classes with only a few labeled text instances. Previous studies mainly utilized text semantic features to model the instance-level relation among partial samples. However, the single relation information makes it difficult for many models to address [...] Read more.
Few-shot text classification aims to recognize new classes with only a few labeled text instances. Previous studies mainly utilized text semantic features to model the instance-level relation among partial samples. However, the single relation information makes it difficult for many models to address complicated natural language tasks. In this paper, we propose a novel hesitant fuzzy graph neural network (HFGNN) model that explores the multi-attribute relations between samples. We combine HFGNN with the Prototypical Network to achieve few-shot text classification. In HFGNN, multiple relations between texts, including instance-level and distribution-level relations, are discovered through dual graph neural networks and fused by hesitant fuzzy set (HFS) theory. In addition, we design a linear function that maps the fused relations to a more reasonable range in HFGNN. The final relations are used to aggregate the information of neighbor instance nodes in the graph to construct more discriminative instance features. Experimental results demonstrate that the classification accuracy of the HFGNN-based Prototypical Network (HFGNN-Proto) on the ARSC, FewRel 5-way 5-shot, and FewRel 10-way 5-shot datasets reaches 88.36%, 94.45%, and 89.40%, respectively, exceeding existing state-of-the-art few-shot learning methods. Full article
(This article belongs to the Special Issue Advanced Application of Machine Learning and Meta-Learning in Image and Text Analysis)
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17 pages, 1169 KiB  
Article
A Feature-Based Approach for Sentiment Quantification Using Machine Learning
by Kashif Ayyub, Saqib Iqbal, Muhammad Wasif Nisar, Ehsan Ullah Munir, Fawaz Khaled Alarfaj and Naif Almusallam
Electronics 2022, 11(6), 846; https://doi.org/10.3390/electronics11060846 - 8 Mar 2022
Cited by 13 | Viewed by 3960
Abstract
Sentiment analysis has been one of the most active research areas in the past decade due to its vast applications. Sentiment quantification, a new research problem in this field, extends sentiment analysis from individual documents to an aggregated collection of documents. Sentiment analysis [...] Read more.
Sentiment analysis has been one of the most active research areas in the past decade due to its vast applications. Sentiment quantification, a new research problem in this field, extends sentiment analysis from individual documents to an aggregated collection of documents. Sentiment analysis has been widely researched, but sentiment quantification has drawn less attention despite offering a greater potential to enhance current business intelligence systems. In this research, to perform sentiment quantification, a framework based on feature engineering is proposed to exploit diverse feature sets such as sentiment, content, and part of speech, as well as deep features including word2vec and GloVe. Different machine learning algorithms, including conventional, ensemble learners, and deep learning approaches, have been investigated on standard datasets of SemEval2016, SemEval2017, STS-Gold, and Sanders. The empirical-based results reveal the effectiveness of the proposed feature sets in the process of sentiment quantification when applied to machine learning algorithms. The results also reveal that the ensemble-based algorithm AdaBoost outperforms other conventional machine learning algorithms using a combination of proposed feature sets. The deep learning algorithm RNN, on the other hand, shows optimal results using word embedding-based features. This research has the potential to help diverse applications of sentiment quantification, including polling, trend analysis, automatic summarization, and rumor or fake news detection. Full article
(This article belongs to the Special Issue Advanced Application of Machine Learning and Meta-Learning in Image and Text Analysis)
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11 pages, 957 KiB  
Article
Realistic Image Generation from Text by Using BERT-Based Embedding
by Sanghyuck Na, Mirae Do, Kyeonah Yu and Juntae Kim
Electronics 2022, 11(5), 764; https://doi.org/10.3390/electronics11050764 - 2 Mar 2022
Cited by 9 | Viewed by 6632
Abstract
Recently, in the field of artificial intelligence, multimodal learning has received a lot of attention due to expectations for the enhancement of AI performance and potential applications. Text-to-image generation, which is one of the multimodal tasks, is a challenging topic in computer vision [...] Read more.
Recently, in the field of artificial intelligence, multimodal learning has received a lot of attention due to expectations for the enhancement of AI performance and potential applications. Text-to-image generation, which is one of the multimodal tasks, is a challenging topic in computer vision and natural language processing. The text-to-image generation model based on generative adversarial network (GAN) utilizes a text encoder pre-trained with image-text pairs. However, text encoders pre-trained with image-text pairs cannot obtain rich information about texts not seen during pre-training, thus it is hard to generate an image that semantically matches a given text description. In this paper, we propose a new text-to-image generation model using pre-trained BERT, which is widely used in the field of natural language processing. The pre-trained BERT is used as a text encoder by performing fine-tuning with a large amount of text, so that rich information about the text is obtained and thus suitable for the image generation task. Through experiments using a multimodal benchmark dataset, we show that the proposed method improves the performance over the baseline model both quantitatively and qualitatively. Full article
(This article belongs to the Special Issue Advanced Application of Machine Learning and Meta-Learning in Image and Text Analysis)
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17 pages, 5243 KiB  
Article
PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval
by Muhammad Qasim, Danish Mahmood, Asifa Bibi, Mehedi Masud, Ghufran Ahmed, Suleman Khan, Noor Zaman Jhanjhi and Syed Jawad Hussain
Electronics 2022, 11(2), 202; https://doi.org/10.3390/electronics11020202 - 10 Jan 2022
Cited by 5 | Viewed by 2419
Abstract
This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this [...] Read more.
This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall. Full article
(This article belongs to the Special Issue Advanced Application of Machine Learning and Meta-Learning in Image and Text Analysis)
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16 pages, 1733 KiB  
Article
Optimizing Prediction of YouTube Video Popularity Using XGBoost
by Meher UN Nisa, Danish Mahmood, Ghufran Ahmed, Suleman Khan, Mazin Abed Mohammed and Robertas Damaševičius
Electronics 2021, 10(23), 2962; https://doi.org/10.3390/electronics10232962 - 28 Nov 2021
Cited by 20 | Viewed by 8777
Abstract
YouTube is a source of income for many people, and therefore a video’s popularity ultimately becomes the top priority for sustaining a steady income, provided that the popularity of videos remains the highest. Analysts and researchers use different algorithms and models to predict [...] Read more.
YouTube is a source of income for many people, and therefore a video’s popularity ultimately becomes the top priority for sustaining a steady income, provided that the popularity of videos remains the highest. Analysts and researchers use different algorithms and models to predict the maximum viewership of popular videos. This study predicts the popularity of such videos using the XGBoost model, considering features selection, fusion, min-max normalization and some precision parameters such as gamma, eta, learning_rate etc. The XGBoost gives 86% accuracy and 64% precision. Moreover, the Tuned XGboost also shows enhanced accuracy and precision. We have also analyzed the classification of unpopular videos for a comparison with our results. Finally, cross-validation methods are also used to evaluate certain combination of parameter’s values to validate our claims. Based on the obtained results, it can be said that our proposed models and techniques are very useful and can precisely and accurately predict the popularity of YouTube videos. Full article
(This article belongs to the Special Issue Advanced Application of Machine Learning and Meta-Learning in Image and Text Analysis)
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17 pages, 11821 KiB  
Article
A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images
by Junghoon Park, Il-Youp Kwak and Changwon Lim
Electronics 2021, 10(16), 1996; https://doi.org/10.3390/electronics10161996 - 18 Aug 2021
Cited by 17 | Viewed by 4372
Abstract
The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of [...] Read more.
The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance. Full article
(This article belongs to the Special Issue Advanced Application of Machine Learning and Meta-Learning in Image and Text Analysis)
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