Research

20 pages, 15351 KiB

Open AccessArticle

Intelligent Analysis System for Teaching and Learning Cognitive Engagement Based on Computer Vision in an Immersive Virtual Reality Environment

by Ce Li, Li Wang, Quanzhi Li and Dongxuan Wang

Appl. Sci. 2024, 14(8), 3149; https://doi.org/10.3390/app14083149 - 09 Apr 2024

Viewed by 263

Abstract

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive [...] Read more.

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive characteristics, which are an important way of virtual learning and are also one of the important ways in which to promote the development of smart education. Based on the above background, this article proposes an intelligent analysis system for Teaching and Learning Cognitive engagement in an IVR environment based on computer vision. By automatically analyzing the cognitive investment of students in the IVR environment, it is possible to better understand their learning status, provide personalized guidance to improve learning quality, and thereby promote the development of smart education. This system uses Vue (developed by Evan You, located in Wuxi, China) and ECharts (Developed by Baidu, located in Beijing, China) for visual display, and the algorithm uses the Pytorch framework (Developed by Facebook, located in Silicon Valley, CA, USA), YOLOv5 (Developed by Ultralytics, located in Washington, DC, USA), and the CRNN model (Convolutional Recurrent Neural Network) to monitor and analyze the visual attention and behavioral actions of students. Through this system, a more accurate analysis of learners’ cognitive states and personalized teaching support can be provided for the education field, providing certain technical support for the development of smart education. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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17 pages, 5902 KiB

Open AccessArticle

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

by Shinya Watanuki, Yumiko Nomura, Yuki Kiyota, Minami Kubo, Kenji Fujimoto, Junko Okada and Katsue Edo

Appl. Sci. 2024, 14(1), 378; https://doi.org/10.3390/app14010378 - 31 Dec 2023

Viewed by 558

Abstract

Although a multimodal data analysis, comprising physiological and questionnaire survey data, provides better insights into addressing management science concerns, such as challenging the predictions of consumer choice behavior, studies in this field are scarce because of two obstacles: limited sample size and information [...] Read more.

Although a multimodal data analysis, comprising physiological and questionnaire survey data, provides better insights into addressing management science concerns, such as challenging the predictions of consumer choice behavior, studies in this field are scarce because of two obstacles: limited sample size and information privacy. This study addresses these challenges by synthesizing multimodal data using deep generative models. We obtained multimodal data by conducting an electroencephalography (EEG) experiment and a questionnaire survey on the prediction of skilled nurses. Subsequently, we validated the effectiveness of the synthesized data compared with real data regarding the similarities between these data and the predictive performance. We confirmed that the synthesized big data were almost equal to the real data using the trained models through sufficient epochs. Conclusively, we demonstrated that synthesizing data using deep generative models might overcome two significant concerns regarding multimodal data utilization, including physiological data. Our approach can contribute to the prevailing combined big data from different modalities, such as physiological and questionnaire survey data, when solving management issues. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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23 pages, 18068 KiB

Open AccessArticle

Prediction of Internal Temperature in Greenhouses Using the Supervised Learning Techniques: Linear and Support Vector Regressions

by Fabián García-Vázquez, Jesús R. Ponce-González, Héctor A. Guerrero-Osuna, Rocío Carrasco-Navarro, Luis F. Luque-Vega, Marcela E. Mata-Romero, Ma. del Rosario Martínez-Blanco, Celina Lizeth Castañeda-Miranda and Germán Díaz-Flórez

Appl. Sci. 2023, 13(14), 8531; https://doi.org/10.3390/app13148531 - 24 Jul 2023

Cited by 1 | Viewed by 1008

Abstract

Agricultural greenhouses must accurately predict environmental factors to ensure optimal crop growth and energy management efficiency. However, the existing predictors have limitations when dealing with dynamic, non-linear, and massive temporal data. This study proposes four supervised learning techniques focused on linear regression (LR) [...] Read more.

Agricultural greenhouses must accurately predict environmental factors to ensure optimal crop growth and energy management efficiency. However, the existing predictors have limitations when dealing with dynamic, non-linear, and massive temporal data. This study proposes four supervised learning techniques focused on linear regression (LR) and Support Vector Regression (SVR) to predict the internal temperature of a greenhouse. A meteorological station is installed in the greenhouse to collect internal data (temperature, humidity, and dew point) and external data (temperature, humidity, and solar radiation). The data comprises a one year, and is divided into seasons for better analysis and modeling of the internal temperature. The study involves sixteen experiments corresponding to the four models and the four seasons and evaluating the models’ performance using R

^{2}

, RMSE, MAE, and MAPE metrics, considering an acceptability interval of ±2

^{°}

C. The results show that LR models had difficulty maintaining the acceptability interval, while the SVR models adapted to temperature outliers, presenting the highest forecast accuracy among the proposed algorithms. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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24 pages, 2932 KiB

Open AccessArticle

C-MWCAR: Classification Based on Multiple Weighted Class Association Rules

by Gui Li, Fan Liu, Cheng Wu, Yuan Yao, Guangxin Wu, Zhu Wang and Yanchun Zhang

Appl. Sci. 2023, 13(14), 8082; https://doi.org/10.3390/app13148082 - 11 Jul 2023

Cited by 1 | Viewed by 659

Abstract

Classification is a very important task in data mining and pattern analysis, which have been widely used to solve various real-world problems. To obtain better classification performance, in this paper, we propose a novel classification framework based on multiple weighted class association rules [...] Read more.

Classification is a very important task in data mining and pattern analysis, which have been widely used to solve various real-world problems. To obtain better classification performance, in this paper, we propose a novel classification framework based on multiple weighted class association rules (C-MWCAR), whose key idea is to transform the association among features into a set of class association rules (CARs), then classify unknown instances based on the CARs obtained. Concretely, C-MWCAR consists of a dictionary order-based CAR mining algorithm (DOCMA), a branch-based CAR selection algorithm (BCSA), and a multiple weighted CARs-based classifier (MWCC). Specifically, DOCMA mines the complete set of CARs, from which BCSA further selects a representative and concise set of CARs based on the distribution, coverage, and redundancy of the mined CARs. When classifying an unknown instance, MWCC picks out a set of CARs that are most similar to the given instance and computes the weighted importance of those CARs. Finally, the class label of the given instance will be determined by the similarities between the instance and the CARs and the weighted importance of the CARs. Furthermore, we apply the proposed C-MWCAR to a real-world classification task, i.e., hypertension diagnosis, based on a real dataset of 128 subjects. Experimental results indicate that C-MWCAR outperforms four baseline methods and achieves 93.3%, 93.8%, and 92.7% in terms of accuracy, sensitivity, and specificity, respectively. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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19 pages, 818 KiB

Open AccessArticle

Secure Convolution Neural Network Inference Based on Homomorphic Encryption

by Chen Song and Ruwei Huang

Appl. Sci. 2023, 13(10), 6117; https://doi.org/10.3390/app13106117 - 16 May 2023

Cited by 1 | Viewed by 1673

Abstract

Today, the rapid development of deep learning has spread across all walks of life, and it can be seen in various fields such as image classification, automatic driving, and medical imaging diagnosis. Convolution Neural Networks (CNNs) are also widely used by the public [...] Read more.

Today, the rapid development of deep learning has spread across all walks of life, and it can be seen in various fields such as image classification, automatic driving, and medical imaging diagnosis. Convolution Neural Networks (CNNs) are also widely used by the public as tools for deep learning. In real life, if local customers implement large-scale model inference first, they need to upload local data to the cloud, which will cause problems such as data leakage and privacy disclosure. To solve this problem, we propose a framework using homomorphic encryption technology. Our framework has made improvements to the batch operation and reduced the complexity of layer connection. In addition, we provide a new perspective to deal with the impact of the noise caused by the homomorphic encryption scheme on the accuracy during the inference. In our scheme, users preprocess the images locally and then send them to the cloud for encrypted inference without worrying about privacy leakage during the inference process. Experiments show that our proposed scheme is safe and efficient, which provides a safe solution for users who cannot process data locally. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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20 pages, 3369 KiB

Open AccessArticle

Exploring the Impact of AI-Based Cyber Security Financial Sector Management

by Shailendra Mishra

Appl. Sci. 2023, 13(10), 5875; https://doi.org/10.3390/app13105875 - 10 May 2023

Cited by 5 | Viewed by 9331

Abstract

Cyber threats are attempts to secure unauthorized access to, change, or delete private information, to demand money from victims, or to disrupt business. Cybercrime includes everything from identity theft, malware threats, email and online fraud, to bank fraud. Businesses and individuals use this [...] Read more.

Cyber threats are attempts to secure unauthorized access to, change, or delete private information, to demand money from victims, or to disrupt business. Cybercrime includes everything from identity theft, malware threats, email and online fraud, to bank fraud. Businesses and individuals use this method to guard their data centers and other digital systems. The lack of scalability, sluggish response times, and inability to spot advanced and insider threats are among some of the problems with conventional approaches to network security. These flaws highlight the need for research to build more efficient and all-encompassing security methods to guard against the expanding variety of network attacks. Cybercriminals use AI and data poisoning, as well as model theft strategies to automate their attacks. A cyber security technique based on artificial intelligence is presented in this study for financial sector management (CS-FSM). In order to map and prevent unexpected risks from devouring a business, artificial intelligence is one of the best technologies. Using the proposed technique, cyberattack problems can be classified and solved. To ensure the security of financial sector information, algorithms such as the Enhanced Encryption Standard (EES) encrypt and decrypt data. By learning from the training data, the K-Nearest Neighbor (KNN) algorithm produces predictions. In the financial sector, it is used to detect and stop malware attacks. The proposed method increases cyber security systems’ performance by increasing their defense against cyberattacks. CS-FSM enhances data privacy (18.3%), scalability (17.2%), risk reduction (13.2%), data protection (16.2%), and attack avoidance (11.2%) ratios. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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19 pages, 2961 KiB

Open AccessArticle

Identifying Indoor Objects Using Neutrosophic Reasoning for Mobility Assisting Visually Impaired People

by Saad M. Darwish, Mohamed A. Salah and Adel A. Elzoghabi

Appl. Sci. 2023, 13(4), 2150; https://doi.org/10.3390/app13042150 - 07 Feb 2023

Cited by 4 | Viewed by 1176

Abstract

Indoor object detection is a fundamental activity for the development of applications of mobility-assistive technology for visually impaired people (VIP). The challenge of seeing interior objects in a real indoor environment is a challenging one since there are numerous complicated issues that need [...] Read more.

Indoor object detection is a fundamental activity for the development of applications of mobility-assistive technology for visually impaired people (VIP). The challenge of seeing interior objects in a real indoor environment is a challenging one since there are numerous complicated issues that need to be taken into consideration, such as the complexity of the background, occlusions, and viewpoint shifts. Electronic travel aids that are composed of the necessary sensors may assist VIPs with their navigation. The sensors have the ability to detect any obstacles, regardless of whether they are static or dynamic, and offer information on the context of an interior scene. The characteristics of an interior scene are not very clear and are subject to a great deal of variation. Recent years have seen the emergence of methods for dealing with issues of this kind, some of which include the use of neural networks, probabilistic methods, and fuzzy logic. This study describes a method for detecting indoor objects using a rotational ultrasonic array and neutrosophic logic. A neutrosophic set has been seen as the next evolution of the fuzzy set because of its indeterminate membership value, which is absent from conventional fuzzy sets. The suggested method is constructed to reflect the position of the walls (obstacle distance) and to direct the VIP to move freely (ahead, to the right, or to the left) depending on the degree of truthiness, the degree of indeterminacy, and the degree of falsity for the reflected distance. The results of the experiments show that the suggested indoor object detecting system has good performance, as its accuracy rate (a mean average precision) is 97.2 ± 1%. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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17 pages, 2007 KiB

Open AccessArticle

Multi-Angle Fast Neural Tangent Kernel Classifier

by Yuejing Zhai, Zhouzheng Li and Haizhong Liu

Appl. Sci. 2022, 12(21), 10876; https://doi.org/10.3390/app122110876 - 26 Oct 2022

Viewed by 1249

Abstract

Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional [...] Read more.

Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional perspective of the data: the neural tangent kernel (NTK)-based multi-kernel learning method is proposed, where the NTK kernel regressor is shown to be equivalent to an infinitely wide neural network predictor, and the NTK with deep structure is used as the base kernel function to enhance the learning ability of multi-kernel models; and a parallel computing kernel model based on data partitioning techniques. An RBF, POLY-based multi-kernel model is also proposed. All models use historical memory-based PSO (HMPSO) for efficient search of parameters within the model. Since NTK has a multi-layer structure and thus has a significant computational complexity, the use of a Monotone Disjunctive Kernel (MDK) to store and train Boolean features in binary achieves a 15–60% training time compression of NTK models in different datasets while obtaining a 1–25% accuracy improvement. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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18 pages, 2670 KiB

Open AccessArticle

Sales Forecasting, Market Analysis, and Performance Assessment for US Retail Firms: A Business Analytics Perspective

by Chih-Hsuan Wang and Yu-Wei Gu

Appl. Sci. 2022, 12(17), 8480; https://doi.org/10.3390/app12178480 - 25 Aug 2022

Cited by 1 | Viewed by 3599

Abstract

Retail firms are the best representatives of a developed country’s economic condition because they sell many of the necessary goods used for daily consumption, including food, clothes, shoes, electric appliances, and office supplies. This study presents a novel framework to help retail practitioners [...] Read more.

Retail firms are the best representatives of a developed country’s economic condition because they sell many of the necessary goods used for daily consumption, including food, clothes, shoes, electric appliances, and office supplies. This study presents a novel framework to help retail practitioners achieve the following goals: (1) predict sales revenues by identifying significant economic indicators, (2) estimate stable equilibriums by capturing interactive dynamics between competing firms, and (3) derive operational efficiencies and indicate required improvements by conducting performance assessments. To verify the validity of the research, data pertaining to Walmart, Costco, and Kroger are collected. Specifically, the least absolute shrinkage and selection operator (Lasso) is adopted in order to identify significant economic indicators. Consumer price index and regular wage are two common indicators that affect the the three firms’ sales numbers. In sales forecasting, support vector regression (SVR) and multivariate adaptive regression splines (MARS), respectively, perform the best in the training set and the testing set. Finally, the Lotka–Volterra model (LVM) and data envelopment analysis (DEA) are used for competitive analysis and performance assessment. A relationship of economic mutualism has been identified between the three firms. Furthermore, research findings show that Kroger performs inefficiently, though it can expect to increase sales more than the others in stable equilibriums. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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20 pages, 3210 KiB

Open AccessArticle

Big Data ETL Process and Its Impact on Text Mining Analysis for Employees’ Reviews

by Laura Gabriela Tanasescu, Andreea Vines, Ana Ramona Bologa and Claudia Antal Vaida

Appl. Sci. 2022, 12(15), 7509; https://doi.org/10.3390/app12157509 - 26 Jul 2022

Cited by 3 | Viewed by 1778

Abstract

Big data analysis is challenging in the current context for enterprises that would like to apply these capabilities in the human resource sector. This paper will show how an organization can take advantage of the current or former employees’ reviews that are provided [...] Read more.

Big data analysis is challenging in the current context for enterprises that would like to apply these capabilities in the human resource sector. This paper will show how an organization can take advantage of the current or former employees’ reviews that are provided on a constant basis on different sites, so that the management can adjust or change business decisions based on employees’ wishes, dissatisfaction or needs. Considering the previously mentioned challenge on big data analysis, this research will first provide the best practice for the collection and transformation of the data proposed for analysis. The second part of this paper presents the extraction of two datasets containing employee reviews using data scraping techniques, the analysis of data by using text mining techniques to retrieve business insights and the comparison of the results for these algorithms. Experimental results with Naïve Bayes, Logistic Regression, K-Nearest Neighbor and Support Vector Machine for employee sentiment prediction showed much better performances for Logistic Regression. Three out of the four analyzed algorithms performed better for the second, triple-size dataset. The final aim of the paper is to provide an end-to-end solution with high performance and reduced costs. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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15 pages, 5911 KiB

Open AccessArticle

Risk Assessment in Supplier Selection for Intelligent Manufacturing Systems Based on PLS-SEM

by Li Shi, Ke Huang, Yuan Liu, Fangzhen Ge and Sheng Liu

Appl. Sci. 2022, 12(8), 3998; https://doi.org/10.3390/app12083998 - 15 Apr 2022

Cited by 5 | Viewed by 1720

Abstract

With the in-depth reform of intelligent manufacturing, selecting high-quality intelligent manufacturing system solution suppliers has become a key force to promote the intelligent transformation of manufacturing enterprises. However, manufacturing enterprises have hidden risks in the selection process of many intelligent manufacturing system solution [...] Read more.

With the in-depth reform of intelligent manufacturing, selecting high-quality intelligent manufacturing system solution suppliers has become a key force to promote the intelligent transformation of manufacturing enterprises. However, manufacturing enterprises have hidden risks in the selection process of many intelligent manufacturing system solution suppliers, so it is urgent to carry out the research on the risk evaluation of intelligent manufacturing system solution suppliers. Based on the current situation in China’s intelligent manufacturing industry, this paper constructs the evaluation index system of intelligent manufacturing system solution suppliers, uses the PLS-SEM method to establish the risk evaluation model of intelligent manufacturing system solution suppliers, collects data through a questionnaire survey, uses a PLS algorithm to fit the index and test the model, and uses power BI software to visualize the risky impact. The conclusions are as follows: (1) The primary indicators have hidden risks for the system solution suppliers. (2) The higher the achievement of secondary indicators, the lower the implied risk, and the more conducive to the intelligent upgrading of manufacturing enterprises. According to the visualization results, management suggestions are given to provide useful reference for manufacturing enterprises to select high-quality intelligent manufacturing system solution suppliers and promote the transformation and upgrading of manufacturing enterprises, from digitization and networking to intelligent stage. Full article

(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)

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