Special Issue "Recent Advances in Artificial Intelligence and Machine Learning"

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

Deadline for manuscript submissions: 31 August 2022 | Viewed by 1957

Special Issue Editors

Prof. Dr. Liang Zou
E-Mail Website
Guest Editor
School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: deep learning; computer vision; intelligent speech
Prof. Dr. Liang Zhao
E-Mail Website
Guest Editor
School of Software Technology, Dalian University of Technology, Dalian 116024, China
Interests: artificial intelligence; big data analysis
Dr. Yonghui Xu
E-Mail Website
Guest Editor
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, 639798, Singapore
Interests: artificial intelligence; big data analysis

Special Issue Information

Dear Colleagues,

With the rapid development of artificial intelligence (AI) and machine learning (ML), various intelligent models have been developed to solve practical problems in every imaginable domain, including but not limited to healthcare, engineering, finance, agriculture, and remote sensing. Currently, the application of intelligent systems for real-world applications is feasible and sound. AI and ML have a significant impact on human life and are helping to transform life for the better in general. However, the implementation of AI and ML technologies faces several challenges, such as limited labeled samples, class imbalance, privacy issues, and model interpretability. There is a critical need for the development of advanced AL and ML methods to mitigate these challenges.

This Special Issue focuses on state-of-the-art research related to the development and application of AI and ML technologies to enhance people’s lives. Topics of interest include, but are not limited to:

1) The applications of artificial intelligence and machine learning models in various domains, such as smart health, smart cities, and smart factories;

2) Novel artificial intelligence and machine learning methods and algorithms;

3) Interpretable artificial intelligence and machine learning for big data understanding;

4) Artificial intelligence and machine learning for computer vision, such as image classification, object detection, segmentation, understanding and generation;

5) Deep learning artificial intelligence and machine learning for intelligent speech (e.g., speech recognition, speaker verification, speech enhancement and speech synthesis);

6) Artificial intelligence and machine learning for natural language processing.

7) Deepfake and anti-spoofing techniques.

Prof. Dr. Liang Zou
Prof. Dr. Liang Zhao
Dr. Yonghui Xu
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 1800 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

  • artificial intelligence
  • machine learning
  • computer vision
  • natural language processing
  • intelligent speech
  • interpretable algorithms
  • deepfake

Published Papers (4 papers)

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Research

Article
An Ensemble and Iterative Recovery Strategy Based kGNN Method to Edit Data with Label Noise
Mathematics 2022, 10(15), 2743; https://doi.org/10.3390/math10152743 - 03 Aug 2022
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Abstract
Learning label noise is gaining increasing attention from a variety of disciplines, particularly in supervised machine learning for classification tasks. The k nearest neighbors (kNN) classifier is often used as a natural way to edit the training sets due to its [...] Read more.
Learning label noise is gaining increasing attention from a variety of disciplines, particularly in supervised machine learning for classification tasks. The k nearest neighbors (kNN) classifier is often used as a natural way to edit the training sets due to its sensitivity to label noise. However, the kNN-based editor may remove too many instances if not designed to take care of the label noise. In addition, the one-sided nearest neighbor (NN) rule is unconvincing, as it just considers the nearest neighbors from the perspective of the query sample. In this paper, we propose an ensemble and iterative recovery strategy-based kGNN method (EIRS-kGNN) to edit data with label noise. EIRS-kGNN first uses the general nearest neighbors (GNN) to expand the one-sided NN rule to a binary-sided NN rule, taking the neighborhood of the queried samples into account. Then, it ensembles the prediction results of a finite set of ks in the kGNN to prudently judge the noise levels for each sample. Finally, two loops, i.e., the inner loop and the outer loop, are leveraged to iteratively detect label noise. A frequency indicator is derived from the iterative processes to guide the mixture approaches, including relabeling and removing, to deal with the detected label noise. The goal of EIRS-kGNN is to recover the distribution of the data set as if it were not corrupted. Experimental results on both synthetic data sets and UCI benchmarks, including binary data sets and multi-class data sets, demonstrate the effectiveness of the proposed EIRS-kGNN method. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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Article
Mispronunciation Detection and Diagnosis with Articulatory-Level Feedback Generation for Non-Native Arabic Speech
Mathematics 2022, 10(15), 2727; https://doi.org/10.3390/math10152727 - 02 Aug 2022
Viewed by 205
Abstract
A high-performance versatile computer-assisted pronunciation training (CAPT) system that provides the learner immediate feedback as to whether their pronunciation is correct is very helpful in learning correct pronunciation and allows learners to practice this at any time and with unlimited repetitions, without the [...] Read more.
A high-performance versatile computer-assisted pronunciation training (CAPT) system that provides the learner immediate feedback as to whether their pronunciation is correct is very helpful in learning correct pronunciation and allows learners to practice this at any time and with unlimited repetitions, without the presence of an instructor. In this paper, we propose deep learning-based techniques to build a high-performance versatile CAPT system for mispronunciation detection and diagnosis (MDD) and articulatory feedback generation for non-native Arabic learners. The proposed system can locate the error in pronunciation, recognize the mispronounced phonemes, and detect the corresponding articulatory features (AFs), not only in words but even in sentences. We formulate the recognition of phonemes and corresponding AFs as a multi-label object recognition problem, where the objects are the phonemes and their AFs in a spectral image. Moreover, we investigate the use of cutting-edge neural text-to-speech (TTS) technology to generate a new corpus of high-quality speech from predefined text that has the most common substitution errors among Arabic learners. The proposed model and its various enhanced versions achieved excellent results. We compared the performance of the different proposed models with the state-of-the-art end-to-end technique of MDD, and our system had a better performance. In addition, we proposed using fusion between the proposed model and the end-to-end model and obtained a better performance. Our best model achieved a 3.83% phoneme error rate (PER) in the phoneme recognition task, a 70.53% F1-score in the MDD task, and a detection error rate (DER) of 2.6% for the AF detection task. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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Article
Enhancing the Performance of Software Authorship Attribution Using an Ensemble of Deep Autoencoders
Mathematics 2022, 10(15), 2572; https://doi.org/10.3390/math10152572 - 24 Jul 2022
Viewed by 267
Abstract
Software authorship attribution, defined as the problem of software authentication and resolution of source code ownership, is of major relevance in the software engineering field. Authorship analysis of source code is more difficult than the classic task on literature, but it would be [...] Read more.
Software authorship attribution, defined as the problem of software authentication and resolution of source code ownership, is of major relevance in the software engineering field. Authorship analysis of source code is more difficult than the classic task on literature, but it would be of great use in various software development activities such as software maintenance, software quality analysis or project management. This paper addresses the problem of code authorship attribution and introduces, as a proof of concept, a new supervised classification model AutoSoft for identifying the developer of a certain piece of code. The proposed model is composed of an ensemble of autoencoders that are trained to encode and recognize the programming style of software developers. An extension of the AutoSoft classifier, able to recognize an unknown developer (a developer that was not seen during the training), is also discussed and evaluated. Experiments conducted on software programs collected from the Google Code Jam data set highlight the performance of the proposed model in various test settings. A comparison to existing similar solutions for code authorship attribution indicates that AutoSoft outperforms most of them. Moreover, AutoSoft provides the advantage of adaptability, illustrated through a series of extensions such as the definition of class membership probabilities and the re-framing of the AutoSoft system to address one-class classification. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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Article
DeepDA-Ace: A Novel Domain Adaptation Method for Species-Specific Acetylation Site Prediction
Mathematics 2022, 10(14), 2364; https://doi.org/10.3390/math10142364 - 06 Jul 2022
Viewed by 261
Abstract
Protein lysine acetylation is an important type of post-translational modification (PTM), and it plays a crucial role in various cellular processes. Recently, although many researchers have focused on developing tools for acetylation site prediction based on computational methods, most of these tools are [...] Read more.
Protein lysine acetylation is an important type of post-translational modification (PTM), and it plays a crucial role in various cellular processes. Recently, although many researchers have focused on developing tools for acetylation site prediction based on computational methods, most of these tools are based on traditional machine learning algorithms for acetylation site prediction without species specificity, still maintained as a single prediction model. Recent studies have shown that the acetylation sites of distinct species have evident location-specific differences; however, there is currently no integrated prediction model that can effectively predict acetylation sites cross all species. Therefore, to enhance the scope of species-specific level, it is necessary to establish a framework for species-specific acetylation site prediction. In this work, we propose a domain adaptation framework DeepDA-Ace for species-specific acetylation site prediction, including Rattus norvegicus, Schistosoma japonicum, Arabidopsis thaliana, and other types of species. In DeepDA-Ace, an attention based densely connected convolutional neural network is designed to capture sequence features, and the semantic adversarial learning strategy is proposed to align features of different species so as to achieve knowledge transfer. The DeepDA-Ace outperformed both the general prediction model and fine-tuning based species-specific model across most types of species. The experiment results have demonstrated that DeepDA-Ace is superior to the general and fine-tuning methods, and its precision exceeds 0.75 on most species. In addition, our method achieves at least 5% improvement over the existing acetylation prediction tools. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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