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Applied Sciences
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Machine Learning on Various Data Sources in Smart Applications
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Machine Learning on Various Data Sources in Smart Applications

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 (20 January 2024) | Viewed by 5606

Special Issue Editors

Prof. Dr. Rung-Ching Chen

E-Mail Website
Guest Editor
Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: network technology; pattern recognition and machine learning; IoT and data analysis; applications of artificial intelligence; deep learning; generative adversarial networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Goutam Chakraborty

E-Mail Website
Guest Editor
Faculty of Software Information Science, Iwate Prefectural University, Takizawa 020-0693, Japan
Interests: soft computing; pattern recognition; data prediction; scheduling and optimization; wired and wireless Networks
Special Issues, Collections and Topics in MDPI journals
Dr. Christine Dewi

E-Mail Website
Guest Editor
Department of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia
Interests: database programming; advanced machine learning; feature selection; artificial neural networks; computer vision; object detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning combines high-performance computing, leading to unusual solutions for multi-model data analysis problems. Machine learning-empowered systems can today achieve performance levels in various data analysis tasks comparable to, or even exceeding, those of humans. These advancements have the potential to open new high-impact applications in different environments. In this Special Issue, the authors will use theoretical, methodological, and experimental contributions to fully exploit machine learning solutions in smart applications. The topics will include, but are not limited to:

  1. Lightweight machine learning models for visual and audio data analysis and applications.
  2. Machine learning models for efficient multimodal data analysis and fusion.
  3. Sensor data analysis based on machine learning.
  4. Efficient deep learning methodologies for the Internet of Things.
  5. Machine learning for applications in smart homes, smart lighting.
  6. Machine learning for smart city applications.
  7. Machine learning and deep learning for intelligent transportation systems.
  8. Machine learning and deep learning for natural language processing and applications.
  9. Machine learning and deep learning for medical sciences applications.
  10. Machine learning for virtual reality applications.
  11. Machine learning for Metaverse applications.

Prof. Dr. Rung-Ching Chen
Prof. Dr. Goutam Chakraborty
Dr. Christine Dewi
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

  • deep learning
  • machine learning
  • internet of things
  • smart city
  • smart home
  • natural language processing
  • virtual reality

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Published Papers (3 papers)

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Research

16 pages, 2793 KiB  
Article
Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning
by Huaiwen Wang, Yang Hu, Honghuan Yin and Yue Cui
Appl. Sci. 2024, 14(5), 2049; https://doi.org/10.3390/app14052049 - 29 Feb 2024
Viewed by 970
Abstract
To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of [...] Read more.
To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments. Full article
(This article belongs to the Special Issue Machine Learning on Various Data Sources in Smart Applications)
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15 pages, 2694 KiB  
Article
Learning Hierarchical Representations for Explainable Chemical Reaction Prediction
by Jingyi Hou and Zhen Dong
Appl. Sci. 2023, 13(9), 5311; https://doi.org/10.3390/app13095311 - 24 Apr 2023
Viewed by 1850
Abstract
This paper aims to propose an explainable and generalized chemical reaction representation method for accelerating the evaluation of the chemical processes in production. To this end, we designed an explainable coarse-fine level representation model that incorporates a small amount of easily available expert [...] Read more.
This paper aims to propose an explainable and generalized chemical reaction representation method for accelerating the evaluation of the chemical processes in production. To this end, we designed an explainable coarse-fine level representation model that incorporates a small amount of easily available expert knowledge (i.e., coarse-level annotations) into the deep learning method to effectively improve the performances on reaction representation related tasks. We also developed a new probabilistic data augmentation strategy with contrastive learning to improve the generalization of our model. We conducted experiments on the Schneider 50k and the USPTO 1k TPL datasets for chemical reaction classification, as well as the USPTO yield dataset for yield prediction. The experimental results showed that our method outperforms the state of the art by just using a small-scale dataset annotated with both coarse-level and fine-level labels to pretrain the model. Full article
(This article belongs to the Special Issue Machine Learning on Various Data Sources in Smart Applications)
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19 pages, 4358 KiB  
Article
A Hybrid Deep Learning Model for Multi-Station Classification and Passenger Flow Prediction
by Lijuan Liu, Mingxiao Wu, Rung-Ching Chen, Shunzhi Zhu and Yan Wang
Appl. Sci. 2023, 13(5), 2899; https://doi.org/10.3390/app13052899 - 23 Feb 2023
Cited by 4 | Viewed by 1770
Abstract
Multiple station passenger flow prediction is crucial but challenging for intelligent transportation systems. Recently, deep learning models have been widely applied in multi-station passenger flow prediction. However, flows at the same station in different periods, or different stations in the same period, always [...] Read more.
Multiple station passenger flow prediction is crucial but challenging for intelligent transportation systems. Recently, deep learning models have been widely applied in multi-station passenger flow prediction. However, flows at the same station in different periods, or different stations in the same period, always present different characteristics. These indicate that globally extracting spatio-temporal features for multi-station passenger flow prediction may only be powerful enough to achieve the excepted performance for some stations. Therefore, a novel two-step multi-station passenger flow prediction model is proposed. First, an unsupervised clustering method for station classification using pure passenger flow is proposed based on the Transformer encoder and K-Means. Two novel evaluation metrics are introduced to verify the effectiveness of the classification results. Then, based on the classification results, a passenger flow prediction model is proposed for every type of station. Residual network (ResNet) and graph convolution network (GCN) are applied for spatial feature extraction, and attention long short-term memory network (AttLSTM) is used for temporal feature extraction. Integrating results for every type of station creates a prediction model for all stations in the network. Experiments are conducted on two real-world ridership datasets. The proposed model performs better than unclassified results in multi-station passenger flow prediction. Full article
(This article belongs to the Special Issue Machine Learning on Various Data Sources in Smart Applications)
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