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Applied Sciences
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Selected Papers from ISET 2022 and ISPE 2022
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Selected Papers from ISET 2022 and ISPE 2022

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 2402

Special Issue Editors

Prof. Dr. Tak-Shing Ching

E-Mail Website
Guest Editor
Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung 402, Taiwan
Interests: biomedical instrumentation design; biosensors; tissue bioimpedance; biomedical electronics; assistive technology
Prof. Dr. Po-Liang Liu

E-Mail Website
Guest Editor
Graduate Institute of Precision Engineering, National Chung Hsing University, Taichung 402, Taiwan
Interests: density functional theory and its application to the computational simulation and modeling of optical; vibrational, electronic, and thermoelastic properties of materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue discusses the latest trends in engineering and technology research, not only in the industrial field, electronics field, materials field, technology field, but also in the medical field.

The 4th International Symposium on Engineering and Technology (ISET 2022), and the 3rd International Symposium on Precision Engineering 2022 (ISPE 2022) will be held on 11-13 November 2022 in the Huisun Experimental Forest Station, Nantou County, Taiwan.

Following the same goal of the past conference of ISET and ISPE, ISET 2022 (http://www.bme.nchu.edu.tw/iset/index.htm) and ISPE 2022 (http://ispe.nchu.edu.tw/) will provide a high-level forum platform for scholars, industry experts, and researchers from all over the world to share their research achievements, explore the hot issues and exchange the new experiences in the field of engineering and technology.

Topics of interest to this Special Issue include but are not limited to:

Engineering: biomedical engineering, precision engineering, electrical and electronics engineering, civil and environmental engineering, chemical and material engineering, computer engineering, mechanical engineering, industrial engineering, and other related topics.

Technology: assistive technology, information technology, biotechnology and nanotechnology, and other related topics.

Prof. Dr. Tak-Shing Ching
Prof. Dr. Po-Liang 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

  • biomedical engineering
  • electrical and electronics engineering
  • civil and environmental engineering
  • chemical and material engineering
  • computer engineering
  • mechanical engineering
  • industrial engineering
  • assistive technology
  • information technology
  • biotechnology and nanotechnology
  • control and automation engineering
  • renewable energy
  • cloud computing
  • artificial intelligence
  • computer vision and machine learning
  • mechatronics and robotics
  • embedded system, sensors, actuators
  • optics
  • precision manufacturing
  • precision measurement
  • precision inspection
  • micro-manufacturing and assembly technologies
  • precision control
  • MEMS

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Published Papers (1 paper)

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Research

17 pages, 1097 KiB  
Article
Using Kernel Density Estimation in Knowledge Distillation to Construct the Prediction Model for Bipolar Disorder Patients
by Yu-Shiang Tseng and Meng-Han Yang
Appl. Sci. 2023, 13(18), 10280; https://doi.org/10.3390/app131810280 - 13 Sep 2023
Cited by 2 | Viewed by 1426
Abstract
Bipolar disorder is a severe mood disorder and is one of the top 20 causes of disability in the world. Although there have been numerous studies based on machine learning models for the detection of bipolar disorder patients, these works have limitations. This [...] Read more.
Bipolar disorder is a severe mood disorder and is one of the top 20 causes of disability in the world. Although there have been numerous studies based on machine learning models for the detection of bipolar disorder patients, these works have limitations. This study used a kernel density estimation algorithm to generate distributions of the input data, which can make knowledge distillation work and can improve prediction performances of the machine learning models for bipolar disorder. To the best of our knowledge, this is the first attempt to apply kernel density estimation to knowledge distillation. Another main contribution is that we used medical history information that was readily available from the electronic health record system, trying to improve the limitation of previous studies that needed to use special instruments to collect input data. Furthermore, in view of the fact that most previous studies have sample sizes of less than 1000, we collected tens of thousands of data samples to improve the representativeness of the constructed prediction models. Finally, the generated data distributions helped the decision tree algorithm to select the appropriate branching attributes to construct the prediction models. These branching attributes can be mapped back to specific diseases that are all associated with bipolar disorder. Full article
(This article belongs to the Special Issue Selected Papers from ISET 2022 and ISPE 2022)
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