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Applied Sciences
Special Issues
Machine Learning and Artificial Intelligence for Human Information Analysis
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Machine Learning and Artificial Intelligence for Human Information Analysis

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 6944

Special Issue Editors

Prof. Dr. Sang-Woong Lee

E-Mail Website
Guest Editor
School of Computing, Gachon University, Seongnam 13120, Republic of Korea
Interests: deep learning; computer vision; image processing; brain science; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. O-Joun Lee

E-Mail Website
Guest Editor
Department of Artificial Intelligence, The Catholic University of Korea, Bucheon 14662, Republic of Korea
Interests: computational narrative; artificial intelligence; knowledge representation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning and artificial intelligence have revolutionized the way data are analyzed, processed, and utilized in various industries. The field of human information analysis has particularly benefited from the advancements in machine learning and artificial intelligence, where it has enabled the analysis of large datasets and made it possible to extract meaningful insights from the data. The incorporation of biometric and action analysis information as well as medical information in humans further enhances the accuracy of the insights and predictions made.

The development and application of ML and AI have shown great potential in improving the understanding of complex domains in human beings. This Special Issue aims to highlight the latest research and advancements in this field, with a specific focus on understanding human beings from sensing data from several devices. The articles in this Special Issue will provide valuable insights into the potential applications of ML and AI in healthcare, biometrics, etc., as well as the challenges and opportunities in this rapidly evolving field.

In addition to the above topics, this Special Issue welcomes submissions on biometric and action analysis, as well as medical information analysis. Biometric analysis involves the measurement and analysis of physiological or behavioral characteristics, such as facial features, voice patterns, or gait, to identify individuals or detect changes in their health status. Action analysis, on the other hand, involves analyzing patterns of movement and behavior to identify changes in physical or mental health.

This Special Issue aims to highlight the latest research in the application of machine learning and artificial intelligence for medical data analysis, human information analysis, biometric analysis, and action analysis. The issue will cover various topics including but not limited to:

Topics of Interest:

  • Application of ML and AI in medical diagnosis and treatment;
  • Predictive modeling using ML and AI for disease outbreak and spread;
  • Human information analysis using ML and AI for mental health diagnosis and treatment;
  • Biometric and action analysis for remote patient monitoring;
  • Medical image analysis using machine learning and artificial intelligence;
  • Predictive analytics for medical diagnosis and prognosis using machine learning;
  • Human behavior analysis using biometric and action analysis;
  • Human image/signal analysis;
  • Biomedical image/signal processing;
  • Biomedical image reconstruction
  • Sensing, detection, and recognition in images/signals;
  • Human emotion recognition and affective computing using machine learning;
  • Personalized healthcare using machine learning and artificial intelligence;
  • Human information analyses not mentioned above.

Prof. Dr. Sang-Woong Lee
Dr. O-Joun Lee
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 image/signal processing
  • automated/computer-aided diagnosis
  • domain adaptation
  • transfer learning
  • human information
  • deep learning/artificial intelligence

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

20 pages, 2802 KiB  
Article
A Machine Learning-Based Forecast Model for Career Planning in Human Resource Management: A Case Study of the Turkish Post Corporation
by Hakan Gülten and Hayri Baraçlı
Appl. Sci. 2024, 14(15), 6679; https://doi.org/10.3390/app14156679 - 31 Jul 2024
Viewed by 445
Abstract
In sustainable and competitive business management, it is crucial for organizations to consider organizational change and transformational leadership in human resource (HR) management to adapt to the changes in their environment. This capability enables large-scale enterprises to maintain their presence in an increasingly [...] Read more.
In sustainable and competitive business management, it is crucial for organizations to consider organizational change and transformational leadership in human resource (HR) management to adapt to the changes in their environment. This capability enables large-scale enterprises to maintain their presence in an increasingly competitive environment through enhanced management capacity. Enterprises that adopt transformational leadership in HR management must equip leadership candidates with competencies such as creating a shared vision, providing appropriate role models, encouraging the adoption of group goals, meeting high performance expectations, and providing individual support and the development of intellectual stimulation. By identifying potential leadership candidates using a decision support model, the necessary competencies can be developed through in-service training and experiential learning during their careers. Innovative and effective approaches to identifying leadership candidates can be developed by analyzing complex big data using advanced artificial intelligence (AI) techniques. In this article, a forecast model using machine learning (ML) algorithms for a human resource management career planning approach was developed for the Turkish Post Corporation (PTT) and it was tested to predict potential leadership candidates by analyzing the big data of 5000 employees. The Turkish Post Corporation ML algorithms were applied to 100 randomly selected data points using the k-Nearest Neighbor (kNN), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) algorithms to predict the types of titles held by the staff employed at PTT. The kNN, GB, RF, and SVM algorithms achieved accuracy rates of 96%, 91%, 73%, and 41%, respectively. The case study results indicate that promotion decisions in large-scale and rooted enterprises can be effectively modeled using big data and ML algorithms, highlighting significant potential for HR management and leadership development practices in the public sector. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Human Information Analysis)
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16 pages, 37260 KiB  
Article
Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network
by Viet Dung Nguyen, Thinh Nguyen-Quang, Minh Duc Nguyen, Dang Hung Phan and Ngoc Dung Bui
Appl. Sci. 2024, 14(8), 3491; https://doi.org/10.3390/app14083491 - 20 Apr 2024
Viewed by 956
Abstract
Motivated by the increasing interest in clinical studies focused on infant movements and poses, this research addresses the limited emphasis on speed and efficiency in existing 2D and 3D pose estimation methods, particularly concerning infant datasets. The scarcity of publicly available infant data [...] Read more.
Motivated by the increasing interest in clinical studies focused on infant movements and poses, this research addresses the limited emphasis on speed and efficiency in existing 2D and 3D pose estimation methods, particularly concerning infant datasets. The scarcity of publicly available infant data poses a significant challenge. In response, we aim to develop a lightweight pose estimation model tailored for edge devices and CPUs. Drawing inspiration from the OpenPose-2016 approach, we refine the algorithm’s architecture, focusing on 2D image training. The resulting model, with 4.09 million parameters, features a single-branch structure. During execution, it achieves an algorithmic complexity of 8.97 giga floating-point operations per second (GFLOPS), enabling operation at approximately 23 frames per second on a Core i5-10400f processor.Notably, this approach balances compact dimensions with superior performance on our self-collected infant dataset. We anticipate that this pragmatic methodology establishes a robust foundation, addressing the need for speed and efficiency in infant pose estimation and providing favorable conditions for future research in this application. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Human Information Analysis)
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12 pages, 1502 KiB  
Article
Comparing In Silico Fungi Toxicity Prediction with In Vitro Cytotoxicity Assay for Indoor Airborne Fungi
by Sung-Yoon Ahn, Mira Kim, Hye-Won Jeong, Wonsuck Yoon, Iel-Soo Bang and Sang-Woong Lee
Appl. Sci. 2024, 14(3), 1265; https://doi.org/10.3390/app14031265 - 2 Feb 2024
Viewed by 814
Abstract
Technological advancements have shifted human living and working environments from outdoor to indoor. Although indoor spaces offer protection from unfavorable weather conditions, they also present new health challenges. Stale, humid, and warm indoor air creates an ideal breeding ground for bacteria and fungi, [...] Read more.
Technological advancements have shifted human living and working environments from outdoor to indoor. Although indoor spaces offer protection from unfavorable weather conditions, they also present new health challenges. Stale, humid, and warm indoor air creates an ideal breeding ground for bacteria and fungi, leading to health issues such as asthma and bacterial infections. Although proper ventilation is crucial, a comprehensive inspection of local indoor air quality is necessary to prevent widespread diseases. In vitro experiments involving bacteria and fungi collected from indoor air yield accurate results but are time- and cost-intensive. In silico methods offer faster results and provide valuable insights for guiding further in vitro experiments. In this study, we conduct an in vitro cytotoxicity assay on 32 fungi species and compare its results with a memory-efficient in silico modeling method using parameter-efficient fine-tuning (PEFT) and ProtBERT. This study suggests a potential methodology for predicting the toxicity of indoor airborne fungi when their identities are known. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Human Information Analysis)
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17 pages, 870 KiB  
Article
Authentication by Keystroke Dynamics: The Influence of Typing Language
by Najwa Altwaijry
Appl. Sci. 2023, 13(20), 11478; https://doi.org/10.3390/app132011478 - 19 Oct 2023
Viewed by 2562
Abstract
Keystroke dynamics is a biometric method that uses a subject’s typing patterns for authentication or identification. In this paper we investigate typing language as a factor influencing an individual’s keystroke dynamics. Specifically, we discern whether keystroke dynamics is contingent on the spatial arrangement [...] Read more.
Keystroke dynamics is a biometric method that uses a subject’s typing patterns for authentication or identification. In this paper we investigate typing language as a factor influencing an individual’s keystroke dynamics. Specifically, we discern whether keystroke dynamics is contingent on the spatial arrangement of letters on the keyboard, or alternatively, whether it is influenced by the linguistic characteristics inherent to the language being used. For this purpose, we construct a new dataset called the Bilingual Keystroke Dynamics Dataset in two languages: English and Arabic. The results show that the authentication system is not contingent on the spatial arrangement of the letters, and is primarily influenced by the language being used, and a system that is used by bilingual users must take into account that each user should have two profiles created, one for each language. An average equal error rate of 0.486% was achieved when enrolling in English and testing on Arabic, and 0.475% when enrolling in Arabic and testing on English. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Human Information Analysis)
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27 pages, 5232 KiB  
Article
Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
by Najla Z. Alhazzani, Isra M. Al-Turaiki and Sarah A. Alkhodair
Appl. Sci. 2023, 13(18), 10305; https://doi.org/10.3390/app131810305 - 14 Sep 2023
Cited by 1 | Viewed by 1193
Abstract
Improving the quality of healthcare services is of the utmost importance in healthcare systems. Patient experience is a key aspect that should be gauged and monitored continuously. However, the measurement of such a vital indicator typically cannot be carried out directly, instead being [...] Read more.
Improving the quality of healthcare services is of the utmost importance in healthcare systems. Patient experience is a key aspect that should be gauged and monitored continuously. However, the measurement of such a vital indicator typically cannot be carried out directly, instead being derived from the opinions of patients who usually express their experience in free text. When it comes to patient comments written in the Arabic language, the currently used strategy to classify Arabic comments is totally reliant on human annotation, which is time-consuming and prone to subjectivity and error. Thus, fully using the value of patient feedback in a timely manner is difficult. This paper addresses the problem of classifying patient experience (PX) comments written in Arabic into 25 classes by using deep learning- and BERT-based models. A real-world data set of patient comments is obtained from the Saudi Ministry of Health for this purpose. Features are extracted from the data set, then used to train deep learning-based classifiers—including BiLSTM and BiGRU—for which pre-trained static word embedding and pre-training vector word embeddings are utilized. Furthermore, we utilize several Arabic pre-trained BERT models, in addition to building PX_BERT, a customized BERT model using the PX unlabeled database. From the experimental results for the 28 classifiers built in this study, the best-performing models (based on the F1 score) are found to be PX_BERT and AraBERTv02. To the best of our knowledge, this is the first study to tackle PX comment classification for the Arabic language. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Human Information Analysis)
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