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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 (31 March 2023) | Viewed by 27893

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Special Issue Editors

Prof. Dr. Hong-Jie Dai

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1. College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
2. School of Post-Baccalaureate Medicine, Kaohsiung Medical University, Kaohsiung 80778, Taiwan
3. National Institute of Cancer Research, National Health Research Institutes, Tainan, Taiwan
Interests: artificial intelligence; data mining; social media analytics; software engineering; cloud computing; natural language processing

Dr. William Yu Chung Wang

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Guest Editor

Digital Business and Supply Chain Management Department, Waikato Management School, University of Waikato, Hamilton 3210, New Zealand
Interests: business intelligence and data management; digital-heath research; enterprise systems and supply chain management
Special Issues, Collections and Topics in MDPI journals

Dr. Yung-Chun Chang

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Guest Editor

Graduate Institute of Data Science, Taipei Medical University, Taipei 110, Taiwan
Interests: artificial intelligence; natural language processing; social media analytics; biomedical text mining; AI in EHRs

Dr. Jheng-Long Wu

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Guest Editor

Department of Data Science, Soochow University, Taipei 10048, Taiwan
Interests: text mining; natural language processing; deep learning; social media analytics

Special Issue Information

Dear Colleagues,

We invite submissions to this Special Issue on Advances in Artificial Intelligence (AI)-Driven Data Analytics.

Nowadays, AI has a wide range of applications in our daily lives, playing an essential role in data-driven decision making. Industries such as manufacturing, healthcare social media and creative industries generate and manipulate a vast amount of data, and so there is an increasing need to leverage AI-driven data analytics for data interpretation. In this Special Issue, we invite submissions exploring the research and application of AI in data analysis across a variety of disciplines. In particular, we are interested in the research of applying AI to typical manufacturing problems and emerging AI technologies employed in the field of social media, healthcare and creative industries. These techniques include, but are not limited to, data preprocessing, data engineering, data visualization, machine learning, data mining and text mining.

Prof. Dr. Hong-Jie Dai
Dr. William Yu Chung Wang
Dr. Yung-Chun Chang
Dr. Jheng-Long Wu
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

artificial intelligence
healthcare
manufacturing
social media
creative industries

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Published Papers (10 papers)

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Research

17 pages, 1408 KiB

Open AccessArticle

SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing

by Qi Song and Wenjie Luo

Appl. Sci. 2023, 13(13), 7704; https://doi.org/10.3390/app13137704 - 29 Jun 2023

Cited by 2 | Viewed by 1380

Abstract

Knowledge tracing (KT) aims to model students’ knowledge levels based on their historical learning records and predict their future learning performance, which constitutes an essential component of intelligent education. Learning and forgetting are closely related, and forgetting can often interfere with the learning process. Prior research has employed diverse techniques to address the issue of interference caused by forgetting factors in predictions, yet many of these methods fail to fully leverage the forgetting information contained within learning records. This paper proposes a synthetically forgetting behavior knowledge tracing (SFBKT) model that comprehensively models a student’s knowledge level by considering both individual forgetting factors and group status. Specifically, the model initially extracts forgetting information from exercise records in the input module, then updates the student’s knowledge state through an improved continuous-time long short-term memory network (CTLSTM), and finally combines the individual state with the group state using collaborative filtering to predict the student’s ability to correctly answer the next exercise. Our predictive model has been evaluated using four public education datasets. The experimental results indicate that our model’s predictions are effective and outperform other existing methods. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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17 pages, 1666 KiB

Open AccessArticle

IWGAN: Anomaly Detection in Airport Based on Improved Wasserstein Generative Adversarial Network

by Ko-Wei Huang, Guan-Wei Chen, Zih-Hao Huang and Shih-Hsiung Lee

Appl. Sci. 2023, 13(3), 1397; https://doi.org/10.3390/app13031397 - 20 Jan 2023

Cited by 4 | Viewed by 2582

Abstract

Anomaly detection is an important research topic in the field of artificial intelligence and visual scene understanding. The most significant challenge in real-world anomaly detection problems is the high imbalance of available data (i.e., non-anomalous versus anomalous data). This limits the use of supervised learning methods. Furthermore, the abnormal—and even normal—datasets in the airport field are relatively insufficient, causing them to be difficult to use to train deep neural networks when conducting experiments. Because generative adversarial networks (GANs) are able to effectively learn the latent vector space of all images, the present study adopted a GAN variant with autoencoders to create a hybrid model for detecting anomalies and hazards in the airport environment. The proposed method, which integrates the Wasserstein-GAN (WGAN) and Skip-GANomaly models to distinguish between normal and abnormal images, is called the Improved Wasserstein Skip-Connection GAN (IWGAN). In the experimental stage, we evaluated different hyper-parameters—including the activation function, learning rate, decay rate, training times of discriminator, and method of label smoothing—to identify the optimal combination. The proposed model’s performance was compared with that of existing models, such as U-Net, GAN, WGAN, GANomaly, and Skip-GANomaly. Our experimental results indicate that the proposed model yields exceptional performance. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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25 pages, 6239 KiB

Open AccessArticle

Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting

by Emmanuel Gbenga Dada, David Opeoluwa Oyewola, Stephen Bassi Joseph, Onyeka Emebo and Olugbenga Oluseun Oluwagbemi

Appl. Sci. 2022, 12(23), 12128; https://doi.org/10.3390/app122312128 - 27 Nov 2022

Cited by 14 | Viewed by 2473

Abstract

Public health is now in danger because of the current monkeypox outbreak, which has spread rapidly to more than 40 countries outside of Africa. The growing monkeypox epidemic has been classified as a “public health emergency of international concern” (PHEIC) by the World Health Organization (WHO). Infection outcomes, risk factors, clinical presentation, and transmission are all poorly understood. Computer- and machine-learning-assisted prediction and forecasting will be useful for controlling its spread. The objective of this research is to use the historical data of all reported human monkey pox cases to predict the transmission rate of the disease. This paper proposed stacking ensemble learning and machine learning techniques to forecast the rate of transmission of monkeypox. In this work, adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE) were applied for time series forecasting of monkeypox transmission. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Additionally, the monkey pox dataset was used as test data for this investigation. Experimental results revealed that SEL outperformed other machine learning approaches considered in this work with an RMSE of 33.1075; a MSE of 1096.1068; and a MAE of 22.4214. This is an indication that SEL is a better predictor than all the other models used in this study. It is hoped that this research will help government officials understand the threat of monkey pox and take the necessary mitigation actions. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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15 pages, 2082 KiB

Open AccessArticle

A Machine Learning-Based 10 Years Ahead Prediction of Departing Foreign Visitors by Reasons: A Case on Türkiye

by Onder Tutsoy and Ceyda Tanrikulu

Appl. Sci. 2022, 12(21), 11163; https://doi.org/10.3390/app122111163 - 3 Nov 2022

Cited by 11 | Viewed by 1825

Abstract

The most important underlying reasons for marketing failures are incomplete understanding of customer wants and needs and the inability to accurately predict their future behaviors. This study develops a machine learning model to estimate the number of departing foreign visitors from Türkiye by reasons for the next 10 years to gain a deeper understanding of their future behaviors. The data between 2003 and 2021 are extensively analyzed, and a multi-dimensional model having a higher-order fractional-order polynomial structure is constructed. The resulting model can predict the 10 reasons of departing foreign visitors for the next 10 years and can update the predictions every year as new data becomes available as it has stable polynomial parameters. In addition, a batch-type genetic algorithm is modified to learn the unknown model parameters by considering the disruptions, such as the coup attempt in 2016 and the COVID-19 pandemic outbreak in 2019, termed as uncertainties. Thus, the model can estimate the overall behavior of the departing foreign visitors in the presence of uncertainties, which is the dominant character of the foreign visitors by their reasons. Furthermore, the developed model is utterly data-driven, meaning it can be trained with the data collected from different cities, regions, and countries. It is predicted that the departing foreign visitors for all reasons will increase at various rates between 2022 and 2031, while the increase in transit visitors is predicted to be higher than the others. The results are discussed, and suggestions are given considering the marketing science. This study can be helpful for global and local firms in tourism, governmental agencies, and civil society organizations. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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15 pages, 873 KiB

Open AccessArticle

Application of Generative Adversarial Networks and Shapley Algorithm Based on Easy Data Augmentation for Imbalanced Text Data

by Jheng-Long Wu and Shuoyen Huang

Appl. Sci. 2022, 12(21), 10964; https://doi.org/10.3390/app122110964 - 29 Oct 2022

Cited by 6 | Viewed by 2525

Abstract

Imbalanced data constitute an extensively studied problem in the field of machine learning classification because they result in poor training outcomes. Data augmentation is a method for increasing minority class diversity. In the field of text data augmentation, easy data augmentation (EDA) is used to generate additional data that would otherwise lack diversity and exhibit monotonic sentence patterns. Generative adversarial network (GAN) models can generate diverse sentence patterns by using the probability corresponding to each word in a language model. Therefore, hybrid EDA and GAN models can generate highly diverse and appropriate sentence patterns. This study proposes a hybrid framework that employs a generative adversarial network and Shapley algorithm based on easy data augmentation (HEGS) to improve classification performance. The experimental results reveal that the HEGS framework can generate highly diverse training sentences to form balanced text data and improve text classification performance for minority classes. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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15 pages, 3276 KiB

Open AccessFeature PaperArticle

Vickers Hardness Value Test via Multi-Task Learning Convolutional Neural Networks and Image Augmentation

by Wan-Shu Cheng, Guan-Ying Chen, Xin-Yen Shih, Mahmoud Elsisi, Meng-Hsiu Tsai and Hong-Jie Dai

Appl. Sci. 2022, 12(21), 10820; https://doi.org/10.3390/app122110820 - 25 Oct 2022

Cited by 23 | Viewed by 3677

Abstract

Hardness testing is an essential test in the metal manufacturing industry, and Vickers hardness is one of the most widely used hardness measurements today. The computer-assisted Vickers hardness test requires manually generating indentations for measurement, but the process is tedious and the measured results may depend on the operator’s experience. In light of this, this paper proposes a data-driven approach based on convolutional neural networks to measure the Vickers hardness value directly from the image of the specimen to get rid of the aforementioned limitations. Multi-task learning is introduced in the proposed network to improve the accuracy of Vickers hardness measurement. The metal material used in this paper is medium-carbon chromium-molybdenum alloy steel (SCM 440), which is commonly utilized in automotive industries because of its corrosion resistance, high temperature, and tensile strength. However, the limited samples of SCM 440 and the tedious manual measurement procedure represent the main challenge to collect sufficient data for training and evaluation of the proposed methods. In this regard, this study introduces a new image mixing method to augment the dataset. The experimental results show that the mean absolute error between the Vickers hardness value output by the proposed network architecture can be 10.2 and the value can be further improved to 7.6 if the multi-task learning method is applied. Furthermore, the robustness of the proposed method is confirmed by evaluating the developed models with an additional 59 unseen images provided by specialists for testing, and the experimental results provide evidence to support the reliability and usability of the proposed methods. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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13 pages, 2515 KiB

Open AccessArticle

A Voxel Generator Based on Autoencoder

by Bo-Cheng Huang, Yu-Cheng Feng and Tyng-Yeu Liang

Appl. Sci. 2022, 12(21), 10757; https://doi.org/10.3390/app122110757 - 24 Oct 2022

Cited by 3 | Viewed by 3218

Abstract

In recent years, 3D models have been widely used in the virtual/augmented reality industry. The traditional way of constructing 3D models for real-world objects remains expensive and time-consuming. With the rapid development of graphics processors, many approaches based on deep learning models have been proposed to reduce the time and economic cost of the generation of 3D object models. However, the quality of the generated 3D object models leaves considerable room for improvement. Accordingly, we designed and implemented a voxel generator called VoxGen, based on the autoencoder framework. It consists of an encoder that extracts image features and a decoder that maps feature values to voxel models. The main characteristics of VoxGen are exploiting modified VGG16 and ResNet18 to enhance the effect of feature extraction and mixing the deconvolution layer with the convolution layer in the decoder to enhance the feature of generated voxels. Our experimental results show that VoxGen outperforms related approaches in terms of the volumetric intersection over union (IOU) values of generated voxels. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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20 pages, 4175 KiB

Open AccessArticle

A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System

by Chung-Hong Lee, Hsin-Chang Yang, Xuan-Qi Su and Yao-Xiang Tang

Appl. Sci. 2022, 12(19), 10066; https://doi.org/10.3390/app121910066 - 7 Oct 2022

Cited by 3 | Viewed by 2439

Abstract

To achieve successful investments, in addition to financial expertise and knowledge of market information, a further critical factor is an individual’s personality. Decisive people tend to be able to quickly judge when to invest, while calm people can analyze the current situation more carefully and make appropriate decisions. Therefore, in this study, we developed a multimodal personality-recognition system to understand investors’ personality traits. The system analyzes the personality traits of investors when they share their investment experiences and plans, allowing them to understand their own personality traits before investing. To perform system functions, we collected digital human behavior data through video-recording devices and extracted human behavior features using video, speech, and text data. We then used data fusion to fuse human behavior features from heterogeneous data to address the problem of learning only one-sided information from a single modality. Through several experiments, we demonstrated that multimodal (i.e., three different signal inputs) personality trait analysis is more accurate than unimodal models. We also used statistical methods and questionnaires to evaluate the correlation between the investor’s personality traits and risk tolerance. It was found that investors with higher openness, extraversion, and lower neuroticism personality traits took higher risks, which is similar to research findings in the field of behavioral finance. Experimental results show that, in a case study, our multimodal personality prediction system exhibits high performance with highly accurate prediction scores in various metrics. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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27 pages, 3679 KiB

Open AccessArticle

Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models

by Adebola Omolaja, Abayomi Otebolaku and Ali Alfoudi

Appl. Sci. 2022, 12(18), 9305; https://doi.org/10.3390/app12189305 - 16 Sep 2022

Cited by 7 | Viewed by 3385

Abstract

Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices. Therefore, apart from using information from the traditional sensors, these systems lack the contextual information to support automatic activity recognition. In this article, we explore environmental contexts, such as illumination (light conditions) and noise level, to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) learning models. The models performed sensor fusion by augmenting low-level sensor signals with rich contextual data to improve the models’ recognition accuracy and generalization. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used triaxial inertial sensing signals to train baseline models, while the second set of experiments combined the inertial signals with contextual information from environmental sensors. The obtained results demonstrate that contextual information, such as environmental noise level and light conditions using hybrid deep learning models, achieved better recognition accuracy than the traditional baseline activity recognition models without contextual information. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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21 pages, 465 KiB

Open AccessArticle

Deep Knowledge Tracing Based on Spatial and Temporal Representation Learning for Learning Performance Prediction

by Liting Lyu, Zhifeng Wang, Haihong Yun, Zexue Yang and Ya Li

Appl. Sci. 2022, 12(14), 7188; https://doi.org/10.3390/app12147188 - 17 Jul 2022

Cited by 19 | Viewed by 2691

Abstract

Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students’ learning process, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) is proposed in this paper. DKT-STDRL extracts spatial features from students’ learning history sequence, and then further extracts temporal features to extract deeper hidden information. Specifically, firstly, the DKT-STDRL model uses CNN to extract the spatial feature information of students’ exercise sequences. Then, the spatial features are connected with the original students’ exercise features as joint learning features. Then, the joint features are input into the BiLSTM part. Finally, the BiLSTM part extracts the temporal features from the joint learning features to obtain the prediction information of whether the students answer correctly at the next time step. Experiments on the public education datasets ASSISTment2009, ASSISTment2015, Synthetic-5, ASSISTchall, and Statics2011 prove that DKT-STDRL can achieve better prediction effects than DKT and CKT. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Analytics)

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