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Applied Sciences
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New Trends on Machine Learning Based Pattern Recognition and Classification
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New Trends on Machine Learning Based Pattern Recognition and Classification

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 13735

Special Issue Editors

Dr. Tsung-Jung Liu

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan
Interests: visual quality assessment; perceptual image/video processing; pattern recognition/machine learning; user experience; big data; computer vision; artificial intelligence
Dr. Kuan-Hsien Liu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung 40401, Taiwan
Interests: signal and image processing; big data analytics; computer vision; machine learning and deep learning; facial age estimation/ facial expression recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, because of the rapid development of computer hardware (e.g., GPU), the machine learning (ML) technique has been used in many areas, including visual quality assessment, image classification, image segmentation, object detection and recognition, age estimation, image super-resolution, image restoration, image denoising, image compression, image retrieval, and satellite image related applications. All of them can be regarded as one branch of computer vision and can also be extened to artificial intelligence (AI) tasks.

In this Special Issue, we are expecting articles presenting new ideas, algorithms, and experimental results in the new trends on ML-based pattern recognition and classification. Specific topics of interest include, but are not limited to:

  • Image and video quality assessment;
  • Image classification, recognition, and segmentation;
  • Object detection and tracking;
  • Human age estimation and age synthesis;
  • Image restoration, denoising, super-resolution, and enhancement;
  • Image compression and video coding;
  • Multimedia retrieval;
  • Facial expression recognition and synthesis;
  • Image style transfer;
  • ML-based remote-sensing image related applications;
  • ML-based autonomous driving related applications;
  • ML-based agriculture related applications;
  • ML-based fashion related applications (e.g., virtual clothes try-on);
  • ML-based AR, VR related applications;
  • ML-based information security related applications.

We are looking forward to your contributions.

Dr. Tsung-Jung Liu
Dr. Kuan-Hsien Liu
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. Applied Sciences 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
  • deep learning
  • artificial intelligence
  • computer vision
  • image, video, and audio processing

Published Papers (9 papers)

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Research

11 pages, 5011 KiB  
Article
Using Auto-ML on Synthetic Point Cloud Generation
by Moritz Hottong, Moritz Sperling and Christoph Müller
Appl. Sci. 2024, 14(2), 742; https://0-doi-org.brum.beds.ac.uk/10.3390/app14020742 - 15 Jan 2024
Viewed by 548
Abstract
Automated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper, we introduce a novel approach that applies Auto-ML methods [...] Read more.
Automated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper, we introduce a novel approach that applies Auto-ML methods to the process of generating synthetic datasets for training machine learning models. Our approach addresses the problem that generating synthetic datasets requires a complex data generator, and that developing and tuning a data generator for a specific scenario is a time-consuming and expensive task. Being able to reuse this data generator for multiple purposes would greatly reduce the effort and cost, once the process of tuning it to the specific domains of each task is automated. To demonstrate the potential of this idea, we have implemented a point cloud generator for simple scenes. The scenes from this generator can be used to train a neural network to semantically segment cars from the background. The simple composition of the scene allows us to reuse the generator for several different semantic segmentation tasks. The models trained on the datasets with the optimized domain parameters easily outperform a model without such optimizations, while the optimization effort is minimal due to our Auto-ML approach. Although the development of such complex data generators requires considerable effort, we believe that using Auto-ML for dataset creation has the potential to speed up the development of machine learning applications in domains where high-quality labeled data is difficult to obtain. Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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23 pages, 4837 KiB  
Article
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
by Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa and Fernando Morgado-Dias
Appl. Sci. 2023, 13(18), 10221; https://0-doi-org.brum.beds.ac.uk/10.3390/app131810221 - 12 Sep 2023
Viewed by 847
Abstract
Wind factors significantly influence air travel, and extreme conditions can cause operational disruptions. Machine learning approaches are emerging as a valuable tool for predicting wind patterns. This research, using Madeira International Airport as a case study, delves into the effectiveness of feature creation [...] Read more.
Wind factors significantly influence air travel, and extreme conditions can cause operational disruptions. Machine learning approaches are emerging as a valuable tool for predicting wind patterns. This research, using Madeira International Airport as a case study, delves into the effectiveness of feature creation and selection for wind nowcasting, focusing on predicting wind speed, direction, and gusts. Data from four sensors provided 56 features to forecast wind conditions over intervals of 2, 10, and 20 min. Five feature selection techniques were analyzed, namely mRMR, PCA, RFECV, GA, and XGBoost. The results indicate that combining new wind features with optimized feature selection can boost prediction accuracy and computational efficiency. A strong spatial correlation was observed among sensors at different locations, suggesting that the spatial-temporal context enhances predictions. The best accuracy for wind speed forecasts yielded a mean absolute percentage error of 0.35%, 0.53%, and 0.63% for the three time intervals, respectively. Wind gust errors were 0.24%, 0.33%, and 0.38%, respectively, while wind direction predictions remained challenging with errors above 100% for all intervals. Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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19 pages, 3184 KiB  
Article
MSCSA-Net: Multi-Scale Channel Spatial Attention Network for Semantic Segmentation of Remote Sensing Images
by Kuan-Hsien Liu and Bo-Yen Lin
Appl. Sci. 2023, 13(17), 9491; https://0-doi-org.brum.beds.ac.uk/10.3390/app13179491 - 22 Aug 2023
Cited by 3 | Viewed by 858
Abstract
Although deep learning-based methods for semantic segmentation have achieved prominent performance in the general image domain, semantic segmentation for high-resolution remote sensing images remains highly challenging. One challenge is the large image size. High-resolution remote sensing images can have very high spatial resolution, [...] Read more.
Although deep learning-based methods for semantic segmentation have achieved prominent performance in the general image domain, semantic segmentation for high-resolution remote sensing images remains highly challenging. One challenge is the large image size. High-resolution remote sensing images can have very high spatial resolution, resulting in images with hundreds of millions of pixels. This makes it difficult for deep learning models to process the images efficiently, as they typically require large amounts of memory and computational resources. Another challenge is the complexity of the objects and scenes in the images. High-resolution remote sensing images often contain a wide variety of objects, such as buildings, roads, trees, and water bodies, with complex shapes and textures. This requires deep learning models to be able to capture a wide range of features and patterns to segment the objects accurately. Moreover, remote sensing images can suffer from various types of noise and distortions, such as atmospheric effects, shadows, and sensor noises, which can also increase difficulty in segmentation tasks. To deal with the aforementioned challenges, we propose a new, mixed deep learning model for semantic segmentation on high-resolution remote sensing images. Our proposed model adopts our newly designed local channel spatial attention, multi-scale attention, and 16-piece local channel spatial attention to effectively extract informative multi-scale features and improve object boundary discrimination. Experimental results with two public benchmark datasets show that our model can indeed improve overall accuracy and compete with several state-of-the-art methods. Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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20 pages, 2152 KiB  
Article
On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
by André Moura, Pedro Lima, Fábio Mendonça, Sheikh Shanawaz Mostafa and Fernando Morgado-Dias
Appl. Sci. 2023, 13(8), 5178; https://0-doi-org.brum.beds.ac.uk/10.3390/app13085178 - 21 Apr 2023
Cited by 3 | Viewed by 1882
Abstract
Chatbots are becoming increasingly popular and require the ability to interpret natural language to provide clear communication with humans. To achieve this, intent detection is crucial. However, current applications typically need a significant amount of annotated data, which is time-consuming and expensive to [...] Read more.
Chatbots are becoming increasingly popular and require the ability to interpret natural language to provide clear communication with humans. To achieve this, intent detection is crucial. However, current applications typically need a significant amount of annotated data, which is time-consuming and expensive to acquire. This article assesses the effectiveness of different text representations for annotating unlabeled dialog data through a pipeline that examines both classical approaches and pre-trained transformer models for word embedding. The resulting embeddings were then used to create sentence embeddings through pooling, followed by dimensionality reduction, before being fed into a clustering algorithm to determine the user’s intents. Therefore, various pooling, dimension reduction, and clustering algorithms were evaluated to determine the most appropriate approach. The evaluation dataset contains a variety of user intents across different domains, with varying intent taxonomies within the same domain. Results demonstrate that transformer-based models perform better text representation than classical approaches. However, combining several clustering algorithms and embeddings from dissimilar origins through ensemble clustering considerably improves the final clustering solution. Additionally, applying the uniform manifold approximation and projection algorithm for dimension reduction can substantially improve performance (up to 20%) while using a much smaller representation. Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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18 pages, 5117 KiB  
Article
Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
by Maya Genisa, Johari Yap Abdullah, Bazli MD Yusoff, Erry Mochamad Arief, Maman Hermana and Chandra Prasetyo Utomo
Appl. Sci. 2023, 13(8), 5094; https://0-doi-org.brum.beds.ac.uk/10.3390/app13085094 - 19 Apr 2023
Viewed by 1382
Abstract
Machine learning (ML) and artificial intelligence (AI) are widely applied in many disciplines including medicine. Pattern recognition or automatization has been successfully implemented in various field studies. Similarly, multiple efforts have been made in medicine to implement AI/ML technology to solve medical problems, [...] Read more.
Machine learning (ML) and artificial intelligence (AI) are widely applied in many disciplines including medicine. Pattern recognition or automatization has been successfully implemented in various field studies. Similarly, multiple efforts have been made in medicine to implement AI/ML technology to solve medical problems, for example, for automating osteoporosis detection. In general, the success of AI/ML technology is highly dependent on the amount of available data, especially during the training stage. Feature generation is a common technique that allows the manipulation of available data for the training stages. This paper aims to study the feasibility of adopting signal-processing techniques for feature generation in medical image processing. Signal attributes from signal processing workflow were adopted and applied to image processing of CT and DEXA scanning data to differentiate between normal and osteoporotic bone. Five attributes, namely amplitude, frequency, instantaneous phase, roughness, and first derivative or contrast attributes, have been tested. An attribute index number is formulated to indicate the attribute’s strength at the selected region of interest (ROI). A case study applying these attributes to the CNN model is presented. More than five hundred CT scan images of normal and osteoporosis bone were used during the training stage to test classification performance with and without developed attributes as an input. From the ten selected CT scan images used to test the CNN model, 90% were well predicted in the scenario only utilizing the grayscale as input. However, when including the developed attributes, the CNN can predict all the images well (100% were well predicted). In conclusion, the technique adopted from the signal-processing technique has the potential to enhance feature generation in image processing, whereby the results can be used for the early application of AI/ML in osteoporosis identification. Further research testing this proposed method in different image modalities needs to be conducted to verify the robustness of the proposed method. Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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24 pages, 7761 KiB  
Article
WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform
by Ahmed Sedik, Mohamed Marey and Hala Mostafa
Appl. Sci. 2023, 13(5), 2785; https://0-doi-org.brum.beds.ac.uk/10.3390/app13052785 - 21 Feb 2023
Cited by 6 | Viewed by 2308
Abstract
As the number of road accidents increases, it is critical to avoid making driving mistakes. Driver fatigue detection is a concern that has prompted researchers to develop numerous algorithms to address this issue. The challenge is to identify the sleepy drivers with accurate [...] Read more.
As the number of road accidents increases, it is critical to avoid making driving mistakes. Driver fatigue detection is a concern that has prompted researchers to develop numerous algorithms to address this issue. The challenge is to identify the sleepy drivers with accurate and speedy alerts. Several datasets were used to develop fatigue detection algorithms such as electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and electromyogram (EMG) recordings of the driver’s activities e.g., DROZY dataset. This study proposes a fatigue detection system based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) with machine learning and deep learning classifiers. The FFT and DWT are used for feature extraction and noise removal tasks. In addition, the classification task is carried out on the combined EEG, EOG, ECG, and EMG signals using machine learning and deep learning algorithms including 1D Convolutional Neural Networks (1D CNNs), Concatenated CNNs (C-CNNs), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), k-Nearest Neighbor (KNN), Quadrature Data Analysis (QDA), Multi-layer Perceptron (MLP), and Logistic Regression (LR). The proposed methods are validated on two scenarios, multi-class and binary-class classification. The simulation results reveal that the proposed models achieved a high performance for fatigue detection from medical signals, with a detection accuracy of 90% and 96% for multiclass and binary-class scenarios, respectively. The works in the literature achieved a maximum accuracy of 95%. Therefore, the proposed methods outperform similar efforts in terms of detection accuracy. Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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14 pages, 1438 KiB  
Article
Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
by Sergiu Zaharia, Traian Rebedea and Stefan Trausan-Matu
Appl. Sci. 2022, 12(23), 12463; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312463 - 06 Dec 2022
Cited by 5 | Viewed by 1788
Abstract
Software developers represent the bastion of application security against the overwhelming cyber-attacks which target all organizations and affect their resilience. As security weaknesses which may be introduced during the process of code writing are complex and matching different and variate skills, most applications [...] Read more.
Software developers represent the bastion of application security against the overwhelming cyber-attacks which target all organizations and affect their resilience. As security weaknesses which may be introduced during the process of code writing are complex and matching different and variate skills, most applications are launched intrinsically vulnerable. We have advanced our research for a security scanner able to use automated learning techniques based on machine learning algorithms to recognize patterns of security weaknesses in source code. To make the scanner independent on the programming language, the source code is converted to a vectorial representation using natural language processing methods, which are able to retain semantical traits of the original code and at the same time to reduce the dependency on the lexical structure of the program. The security flaws detection performance is in the ranges accepted by software security professionals (recall > 0.94) even when vulnerable samples are very low represented in the dataset (e.g., less than 4% vulnerable code for a specific CWE in the dataset). No significant change or adaptation is needed to change the source code language under scrutiny. We apply this approach on detecting Common Weaknesses Enumeration (CWE) vulnerabilities in datasets provided by NIST (Test suites–NIST Software Assurance Reference Dataset). Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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20 pages, 22590 KiB  
Article
Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network
by Tsung-Jung Liu and Yu-Zhang Chen
Appl. Sci. 2022, 12(23), 12311; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312311 - 01 Dec 2022
Cited by 8 | Viewed by 990
Abstract
With the gradually increasing demand for high-resolution images, image super-resolution (SR) technology has become more and more important in our daily life. In general, high resolution is often accomplished by increasing the accuracy and density of the sensor. However, such an approach is [...] Read more.
With the gradually increasing demand for high-resolution images, image super-resolution (SR) technology has become more and more important in our daily life. In general, high resolution is often accomplished by increasing the accuracy and density of the sensor. However, such an approach is too expensive on the design and equipment. Particularly, increasing the sensor density of satellites incurs great risks. Inspired by EEGAN, some parts of networks: Ultra-Dense Subnet (UDSN) and Edge-Enhanced Subnet (EESN) are modified. The UDSN is used to extract features and obtain high-resolution images which look clear but are deteriorated by artifacts in the intermediate stage, while the EESN is used to purify, enhance and extract the image contours and uses mask processing to eliminate the image corrupted by noise. Then, the restored intermediate image and the enhanced edge are combined to become a high-resolution image with clear contents and high authenticity. Finally, we use Kaggle open source, AID, WHU-RS19, and SpaceWill datasets to perform the test and compare the SR results among different models. It shows that our proposed approach outperforms other state-of-the-art SR models on satellite images. Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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17 pages, 11428 KiB  
Article
Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel Segmentation
by Yassir Edrees Almalki, Nisar Ahmed Jandan, Toufique Ahmed Soomro, Ahmed Ali, Pardeep Kumar, Muhammad Irfan, Muhammad Usman Keerio, Saifur Rahman, Ali Alqahtani, Samar M. Alqhtani, Mohammed Awaji M. Hakami, Alqahtani Saeed S, Waleed A. Aldhabaan and Abdulrahman Samir Khairallah
Appl. Sci. 2022, 12(16), 8243; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168243 - 17 Aug 2022
Cited by 5 | Viewed by 1650
Abstract
Analyzing medical images has always been a challenging task because these images are used to observe complex internal structures of the human body. This research work is based on the study of the retinal fundus and magnetic resonance images (MRI) for the analysis [...] Read more.
Analyzing medical images has always been a challenging task because these images are used to observe complex internal structures of the human body. This research work is based on the study of the retinal fundus and magnetic resonance images (MRI) for the analysis of ocular and cerebral abnormalities. Typically, clinical quality images of the eyes and brain have low-varying contrast, making it challenge to diagnose a specific disease. These issues can be overcome, and preprocessing or an image enhancement technique is required to properly enhance images to facilitate postprocessing. In this paper, we propose an iterative algorithm based on the McCann Retinex algorithm for retinal and brain MRI. The foveal avascular zone (FAZ) region of retinal images and the coronal, axial, and sagittal brain images are enhanced during the preprocessing step. The High-Resolution Fundus (HRF) and MR brain Oasis images databases are used, and image contrast and peak signal-to-noise ratio (PSNR) are used to assess the enhancement step parameters. The average PSNR enhancement on images from the Oasis brain MRI database was about 3 dB with an average contrast of 57.4. The average PSNR enhancement of the HRF database images was approximately 2.5 dB with a contrast average of 40 over the database. The proposed method was also validated in the postprocessing steps to observe its impact. A well-segmented image was obtained with an accuracy of 0.953 and 0.0949 on the DRIVE and STARE databases. Brain tumors were detected from the Oasis brain MRI database with an accuracy of 0.97. This method can play an important role in helping medical experts diagnose eye diseases and brain tumors from retinal images and Oasis brain images. Full article
(This article belongs to the Special Issue New Trends on Machine Learning Based Pattern Recognition and Classification)
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