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: closed (30 November 2023) | Viewed by 26693

Special Issue Editors

School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: deep learning; computer vision; intelligent speech
Special Issues, Collections and Topics in MDPI journals
School of Software Technology, Dalian University of Technology, Dalian, China
Interests: artificial intelligence; medical big data; multimodal machine learning
Special Issues, Collections and Topics in MDPI journals
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

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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

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

Published Papers (13 papers)

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Research

15 pages, 14228 KiB  
Article
Eagle-YOLO: An Eagle-Inspired YOLO for Object Detection in Unmanned Aerial Vehicles Scenarios
Mathematics 2023, 11(9), 2093; https://doi.org/10.3390/math11092093 - 28 Apr 2023
Cited by 1 | Viewed by 1578
Abstract
Object detection in images taken by unmanned aerial vehicles (UAVs) is drawing ever-increasing research interests. Due to the flexibility of UAVs, their shooting altitude often changes rapidly, which results in drastic changes in the scale size of the identified objects. Meanwhile, there are [...] Read more.
Object detection in images taken by unmanned aerial vehicles (UAVs) is drawing ever-increasing research interests. Due to the flexibility of UAVs, their shooting altitude often changes rapidly, which results in drastic changes in the scale size of the identified objects. Meanwhile, there are often many small objects obscured from each other in high-altitude photography, and the background of their captured images is also complex and variable. These problems lead to a colossal challenge with object detection in UAV aerial photography images. Inspired by the characteristics of eagles, we propose an Eagle-YOLO detection model to address the above issues. First, according to the structural characteristics of eagle eyes, we integrate the Large Kernel Attention Module (LKAM) to enable the model to find object areas that need to be focused on. Then, in response to the eagle’s characteristic of experiencing dramatic changes in its field of view when swooping down to hunt at high altitudes, we introduce a large-sized feature map with rich information on small objects into the feature fusion network. The feature fusion network adopts a more reasonable weighted Bi-directional Feature Pyramid Network (Bi-FPN). Finally, inspired by the sharp features of eagle eyes, we propose an IoU loss named Eagle-IoU loss. Extensive experiments are performed on the VisDrone2021-DET dataset to compare it with the baseline model YOLOv5x. The experiments showed that Eagle-YOLO outperformed YOLOv5x by 2.86% and 4.23% in terms of the mAP and AP50, respectively, which demonstrates the effectiveness of Eagle-YOLO for object detection in UAV aerial image scenes. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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14 pages, 892 KiB  
Article
Hydraulic Rock Drill Fault Classification Using X−Vectors
Mathematics 2023, 11(7), 1724; https://doi.org/10.3390/math11071724 - 04 Apr 2023
Viewed by 1013
Abstract
Hydraulic rock drills are widely used in drilling, mining, construction, and engineering applications. They typically operate in harsh environments with high humidity, large temperature differences, and vibration. Under the influence of environmental noise and operational patterns, the distributions of data collected by sensors [...] Read more.
Hydraulic rock drills are widely used in drilling, mining, construction, and engineering applications. They typically operate in harsh environments with high humidity, large temperature differences, and vibration. Under the influence of environmental noise and operational patterns, the distributions of data collected by sensors for different operators and equipment differ significantly, which leads to difficulty in fault classification for hydraulic rock drills. Therefore, an intelligent and robust fault classification method is highly desired. In this paper, we propose a fault classification technique for hydraulic rock drills based on deep learning. First, considering the strong robustness of x−vectors to the features extracted from the time series, we employ an end−to−end fault classification model based on x−vectors to realize the joint optimization of feature extraction and classification. Second, the overlapping data clipping method is applied during the training process, which further improves the robustness of our model. Finally, the focal loss is used to focus on difficult samples, which improves their classification accuracy. The proposed method obtains an accuracy of 99.92%, demonstrating its potential for hydraulic rock drill fault classification. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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11 pages, 315 KiB  
Article
Adaptive Differential Privacy Mechanism Based on Entropy Theory for Preserving Deep Neural Networks
Mathematics 2023, 11(2), 330; https://doi.org/10.3390/math11020330 - 08 Jan 2023
Cited by 7 | Viewed by 1702
Abstract
Recently, deep neural networks (DNNs) have achieved exciting things in many fields. However, the DNN models have been proven to divulge privacy, so it is imperative to protect the private information of the models. Differential privacy is a promising method to provide privacy [...] Read more.
Recently, deep neural networks (DNNs) have achieved exciting things in many fields. However, the DNN models have been proven to divulge privacy, so it is imperative to protect the private information of the models. Differential privacy is a promising method to provide privacy protection for DNNs. However, existing DNN models based on differential privacy protection usually inject the same level of noise into parameters, which may lead to a balance between model performance and privacy protection. In this paper, we propose an adaptive differential privacy scheme based on entropy theory for training DNNs, with the aim of giving consideration to the model performance and protecting the private information in the training data. The proposed scheme perturbs the gradients according to the information gain of neurons during training, that is, in the process of back propagation, less noise is added to neurons with larger information gain, and vice-versa. Rigorous experiments conducted on two real datasets demonstrate that the proposed scheme is highly effective and outperforms existing solutions. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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15 pages, 4044 KiB  
Article
State Prediction Method for A-Class Insulation Board Production Line Based on Transfer Learning
Mathematics 2022, 10(20), 3906; https://doi.org/10.3390/math10203906 - 21 Oct 2022
Viewed by 984
Abstract
It is essential to determine the running state of a production line to monitor the production status and make maintenance plans. In order to monitor the real-time running state of an A-class insulation board production line conveniently and accurately, a novel state prediction [...] Read more.
It is essential to determine the running state of a production line to monitor the production status and make maintenance plans. In order to monitor the real-time running state of an A-class insulation board production line conveniently and accurately, a novel state prediction method based on deep learning and long short-term memory (LSTM) network is proposed. The multiple layers of the Res-block are introduced to fuse local features and improve hidden feature extraction. The transfer learning strategy is studied and the improved loss function is proposed, which makes the model training process fast and stable. The experimental results show that the proposed Res-LSTM model reached 98.9% prediction accuracy, and the average R2-score of the industrial experiments can reach 0.93. Compared with other mainstream algorithms, the proposed Res-LSTM model obtained excellent performance in prediction speed and accuracy, which meets the needs of industrial production. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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22 pages, 855 KiB  
Article
PARS: Proxy-Based Automatic Rank Selection for Neural Network Compression via Low-Rank Weight Approximation
Mathematics 2022, 10(20), 3801; https://doi.org/10.3390/math10203801 - 14 Oct 2022
Cited by 1 | Viewed by 1587
Abstract
Low-rank matrix/tensor decompositions are promising methods for reducing the inference time, computation, and memory consumption of deep neural networks (DNNs). This group of methods decomposes the pre-trained neural network weights through low-rank matrix/tensor decomposition and replaces the original layers with lightweight factorized layers. [...] Read more.
Low-rank matrix/tensor decompositions are promising methods for reducing the inference time, computation, and memory consumption of deep neural networks (DNNs). This group of methods decomposes the pre-trained neural network weights through low-rank matrix/tensor decomposition and replaces the original layers with lightweight factorized layers. A main drawback of the technique is that it demands a great amount of time and effort to select the best ranks of tensor decomposition for each layer in a DNN. This paper proposes a Proxy-based Automatic tensor Rank Selection method (PARS) that utilizes a Bayesian optimization approach to find the best combination of ranks for neural network (NN) compression. We observe that the decomposition of weight tensors adversely influences the feature distribution inside the neural network and impairs the predictability of the post-compression DNN performance. Based on this finding, a novel proxy metric is proposed to deal with the abovementioned issue and to increase the quality of the rank search procedure. Experimental results show that PARS improves the results of existing decomposition methods on several representative NNs, including ResNet-18, ResNet-56, VGG-16, and AlexNet. We obtain a 3× FLOP reduction with almost no loss of accuracy for ILSVRC-2012ResNet-18 and a 5.5× FLOP reduction with an accuracy improvement for ILSVRC-2012 VGG-16. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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20 pages, 3124 KiB  
Article
Application of Uncertain AHP Method in Analyzing Travel Time Belief Reliability in Transportation Network
Mathematics 2022, 10(19), 3637; https://doi.org/10.3390/math10193637 - 05 Oct 2022
Cited by 2 | Viewed by 985
Abstract
Because predictions of transportation system reliability can provide useful information for intelligent transportation systems (ITS), evaluation of them might be viewed as a beneficial activity for reducing traffic congestion. This evaluation procedure could include some alternatives and criteria in a discrete decision space. [...] Read more.
Because predictions of transportation system reliability can provide useful information for intelligent transportation systems (ITS), evaluation of them might be viewed as a beneficial activity for reducing traffic congestion. This evaluation procedure could include some alternatives and criteria in a discrete decision space. To handle this evaluation process in an uncertain environment, a novel uncertain multi-criteria decision-making (MCDM) method is put forward in this paper. Considering the validity of uncertainty theory as a measure of epistemic uncertainty, we first introduce it into analytic hierarchy process (AHP) and provide the whole calculation procedure of the approach. The proposed approach is employed to evaluate regional travel time belief reliability in a case study. Additionally, a comparison is performed between the results of uncertain AHP and other MCDM methods to examine the efficiency of this method. These analyses show that uncertainty theory is particularly suited to be employed combination with the AHP method. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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28 pages, 1672 KiB  
Article
ConvFaceNeXt: Lightweight Networks for Face Recognition
Mathematics 2022, 10(19), 3592; https://doi.org/10.3390/math10193592 - 01 Oct 2022
Cited by 3 | Viewed by 2439
Abstract
The current lightweight face recognition models need improvement in terms of floating point operations (FLOPs), parameters, and model size. Motivated by ConvNeXt and MobileFaceNet, a family of lightweight face recognition models known as ConvFaceNeXt is introduced to overcome the shortcomings listed above. ConvFaceNeXt [...] Read more.
The current lightweight face recognition models need improvement in terms of floating point operations (FLOPs), parameters, and model size. Motivated by ConvNeXt and MobileFaceNet, a family of lightweight face recognition models known as ConvFaceNeXt is introduced to overcome the shortcomings listed above. ConvFaceNeXt has three main parts, which are the stem, bottleneck, and embedding partitions. Unlike ConvNeXt, which applies the revamped inverted bottleneck dubbed the ConvNeXt block in a large ResNet-50 model, the ConvFaceNeXt family is designed as lightweight models. The enhanced ConvNeXt (ECN) block is proposed as the main building block for ConvFaceNeXt. The ECN block contributes significantly to lowering the FLOP count. In addition to the typical downsampling approach using convolution with a kernel size of three, a patchify strategy utilizing a kernel size of two is also implemented as an alternative for the ConvFaceNeXt family. The purpose of adopting the patchify strategy is to reduce the computational complexity further. Moreover, blocks with the same output dimension in the bottleneck partition are added together for better feature correlation. Based on the experimental results, the proposed ConvFaceNeXt model achieves competitive or even better results when compared with previous lightweight face recognition models, on top of a significantly lower FLOP count, parameters, and model size. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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19 pages, 7919 KiB  
Article
Deep Learning Approaches to Automatic Chronic Venous Disease Classification
Mathematics 2022, 10(19), 3571; https://doi.org/10.3390/math10193571 - 30 Sep 2022
Cited by 4 | Viewed by 2497
Abstract
Chronic venous disease (CVD) occurs in a substantial proportion of the world’s population. If the onset of CVD looks like a cosmetic defect, over time, it might be transformed into serious problems that will require surgical intervention. The aim of this work is [...] Read more.
Chronic venous disease (CVD) occurs in a substantial proportion of the world’s population. If the onset of CVD looks like a cosmetic defect, over time, it might be transformed into serious problems that will require surgical intervention. The aim of this work is to use deep learning (DL) methods for automatic classification of the stage of CVD for self-diagnosis of a patient by using the image of the patient’s legs. The images of legs with CVD required for DL algorithms were collected from open Internet resources using the developed algorithms. For image preprocessing, the binary classification problem “legs–no legs” was solved based on Resnet50 with accuracy of 0.998. The application of this filter made it possible to collect a dataset of 11,118 good-quality leg images with various stages of CVD. For classification of various stages of CVD according to the CEAP classification, the multi-classification problem was set and resolved by using four neural networks with completely different architectures: Resnet50 and transformers such as data-efficient image transformers (DeiT) and a custom vision transformer (vit-base-patch16-224 and vit-base-patch16-384). The model based on DeiT without any tuning showed better results than the model based on Resnet50 did (precision = 0.770 (DeiT) and 0.615 (Resnet50)). vit-base-patch16-384 showed the best results (precision = 0.79). To demonstrate the results of the work, a Telegram bot was developed, in which fully functioning DL algorithms were implemented. This bot allowed evaluating the condition of the patient’s legs with fairly good accuracy of CVD classification. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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30 pages, 11470 KiB  
Article
CNN-Hyperparameter Optimization for Diabetic Maculopathy Diagnosis in Optical Coherence Tomography and Fundus Retinography
Mathematics 2022, 10(18), 3274; https://doi.org/10.3390/math10183274 - 09 Sep 2022
Cited by 13 | Viewed by 2203
Abstract
Diabetic Maculopathy (DM) is considered the most common cause of permanent visual impairment in diabetic patients. The absence of clear pathological symptoms of DM hinders the timely diagnosis and treatment of such a critical condition. Early diagnosis of DM is feasible through eye [...] Read more.
Diabetic Maculopathy (DM) is considered the most common cause of permanent visual impairment in diabetic patients. The absence of clear pathological symptoms of DM hinders the timely diagnosis and treatment of such a critical condition. Early diagnosis of DM is feasible through eye screening technologies. However, manual inspection of retinography images by eye specialists is a time-consuming routine. Therefore, many deep learning-based computer-aided diagnosis systems have been recently developed for the automatic prognosis of DM in retinal images. Manual tuning of deep learning network’s hyperparameters is a common practice in the literature. However, hyperparameter optimization has shown to be promising in improving the performance of deep learning networks in classifying several diseases. This study investigates the impact of using the Bayesian optimization (BO) algorithm on the classification performance of deep learning networks in detecting DM in retinal images. In this research, we propose two new custom Convolutional Neural Network (CNN) models to detect DM in two distinct types of retinal photography; Optical Coherence Tomography (OCT) and fundus retinography datasets. The Bayesian optimization approach is utilized to determine the optimal architectures of the proposed CNNs and optimize their hyperparameters. The findings of this study reveal the effectiveness of using the Bayesian optimization for fine-tuning the model hyperparameters in improving the performance of the proposed CNNs for the classification of diabetic maculopathy in fundus and OCT images. The pre-trained CNN models of AlexNet, VGG16Net, VGG 19Net, GoogleNet, and ResNet-50 are employed to be compared with the proposed CNN-based models. Statistical analyses, based on a one-way analysis of variance (ANOVA) test, receiver operating characteristic (ROC) curve, and histogram, are performed to confirm the performance of the proposed models. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Machine Learning)
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28 pages, 807 KiB  
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
Viewed by 1070
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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24 pages, 3512 KiB  
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
Cited by 10 | Viewed by 2296
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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27 pages, 787 KiB  
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
Cited by 1 | Viewed by 1371
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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17 pages, 2148 KiB  
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
Cited by 4 | Viewed by 1207
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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