Ebook: Modern Management based on Big Data III
Data is the basic ingredient of all Big Data applications, and Big Data technologies are constantly deploying new strategies to maximise efficiency and reduce the time taken to process information.
This book presents the proceedings of MMBD2022, the third edition of the conference series Modern Management based on Big Data (MMBD). The conference was originally scheduled to take place from 15 to 18 August 2022 in Seoul, South Korea, but was changed to a virtual event on the same dates. Some 200 submissions were received for presentation at the conference, 52 of which were ultimately accepted after exhaustive review by members of the programme committee and peer-reviewers, who took into account the breadth and depth of the research topics and the scope of MMBD. Topics covered include data analytics, modelling, technologies and visualization, architectures for parallel processing systems, data mining tools and techniques, machine learning algorithms, and big data for engineering applications. There are also papers covering modern management, including topics such as strategy, decision making, manufacturing and logistics-based systems, engineering economy, information systems and law-based information treatment, and papers from a special session covering big data in manufacturing, retail, healthcare, accounting, banking, education, global trading, and e-commerce. Big data analysis and emerging applications were popular topics.
The book includes many innovative and original ideas, as well as results of general significance, all supported by clear and rigorous reasoning and compelling evidence and methods, and will be of interest to all those working with Big Data.
Data is the basic ingredient of Big Data (BiD) applications. BiD technologies are deploying new strategies to maximise efficiency and reduce the time taken to process information. Currently, we continue to work against the COVID-19 pandemic and the emerging COVID-22 wave.
The third edition of the conference series Modern Management based on Big Data (MMBD) has taken place as MMBD2022.
The general topics covered at the MMBD2022 conference fell into three main categories: big data, modern management and big-data-driven manufacturing and service industry supply chain (sc) management. Among other things, the first of these includes data analytics; big data modelling; big data technologies; data visualization; architectures for massively parallel processing systems; data mining tools and techniques; machine learning algorithms for big data; and big data for engineering applications. The second, modern management, encompasses modern management and strategy; management decision making; manufacturing and logistics-based systems; engineering economy; management information systems; human gender engineering; and law-based information treatment. The last group includes big data in manufacturing sc; big data in retail and healthcare; big data in accounting and banking sc; big data in educational sc; big data in global trading; and big data in e-commerce.
Apart from these general topics, as a follow-up to MMBD2021, MMBD2022 has attracted a number of papers related to COVID-19, including some hot topics but not limited to platform economy; innovations in online education management with big data; innovation and technology management; blockchain management and technology; green supply chain management; big data analytics in supply chains; policy and strategy for new energy and environment; smart grid load and energy management; sustainable traffic management; decision making on sustainable transport policies; modern healthcare management; modern food security management; social strategies for managing human relationships; and detecting fake news with mathematics and big data.
The MMBD2022 conference was originally scheduled to be held from 15–18 August 2022 in Seoul, South Korea. The most popular topics in this book concern big data analysis and emerging applications.
All papers were exhaustively reviewed by members of the programme committee and peer-reviewers, who took into account the breadth and depth of the research topics falling under the scope of MMBD. The 52 most promising and FAIA-mainstream-relevant contributions of some 200 submissions are included in this book. They present innovative and original ideas or results of general significance supported by clear and rigorous reasoning and compelling new light in terms of evidence, as well as method.
I would like to thank all the keynote and invited speakers, authors and anonymous reviewers whose efforts have resulted in MMBD becoming a top-level conference. I am also very grateful to those people, particularly the members of the programme committee and reviewers, who devoted their time to the assessment of the papers. It has been an honour to work from the outset on the publication of these proceedings in the prestigious series Frontiers in Artificial Intelligence and Applications (FAIA) from IOS Press. Our particular thanks also go to J. Breuker, N. Guarino, J.N. Kok, R. López de Mántaras, J. Liu, R. Mizoguchi, M. Musen, S.K. Pal and N. Zhong, the FAIA series editors, for their support for this conference.
Last but not least, any inconvenience caused due to the change of format from face to face to virtual is sincerely regretted. Hopefully we will meet face to face at the MMBD2023 conference next year, the venue of which may be any convenient city in mainland China.
Antonio J. Tallón-Ballesteros
University of Huelva (Spain)
Huelva city, Spain
Today, the Internet trust is a critical framework that forms a fundamental premise for network security. Several identity federations are deployed to embody the classic theory of trust framework. However, in an emerging blockchain-based decentralized system, we need to model dynamically grown/shrunken trust. This paper models the trust elasticity by formalizing PDP(policy decision point)s of enrolling entities and introducing Trust PDP. In the proposed formalization, assertions are exchanged among participating nodes to express the knowledge of the PDP of nodes. The knowledge may grow by exchanging assertions. Furthermore, we propose exchanging assertions on the trust relationship to express the elasticity of trust. The judgment on trust is operated by Trust PDP that affects the elasticity of trust. The proposed theory is applied to the trust establishment of blockchain and an authorization management system based on blockchain. The trust in the blockchain environment is expressed in terms of Trust PDP.
We propose a novel nonparametric approach for estimating the production frontier based on a data-fitting technique. The proposed approach allows for stochastic noise and provides decision-makers with a general and flexible estimation procedure to support and facilitate the decision-making process in the stochastic context. A major feature of the proposed approach is that the estimation procedure is completely nonparametric and easy to implement. Similar to other existing nonparametric approaches, our proposed approach results in an estimate of the piece-wise linear production frontier. In contrast to the existing ones, the evaluation of each data point is performed within a unit-specific data range. We also propose a naive method for determining the data ranges of each data point. The performance of our proposed approach is examined using various simulated scenarios. For each scenario, we compare our proposed approach with the existing methods, including the data envelopment analysis (DEA), the stochastic nonparametric envelopment of data (StoNED), and the stochastic frontier analysis (SFA). The simulation results suggest that our approach performs better than the existing methods in the single input and single output case. Our proposed approach can also be easily extended to a multi-input setting. Moreover, the proposed naive method on data ranges also shows its flexibility and usefulness in the simulated examples.
Traffic psychology covers not only the act of driving but also includes inherent phenomena such as accidents, violations, and the generation of emotions. This article proposes the development of a driving simulator within the video game Grand Theft Auto V, taking advantage of the artificial intelligence of characters that interact with the player, as well as other drivers and pedestrians. The implementation includes the adaptation of steering wheel type controls and the creation of a mod in C# for data collection and export. This implementation seeks to obtain useful data for the analysis of the behavior of the drivers evaluated during the driving tests carried out in three virtual circuits within a city. By performing the tests with drivers of different levels of experience, it was possible to verify the effectiveness of the tool in its use and realism. According to the individuals evaluated, the simulator achieved an average realism of 4.6 on a Likert scale from 1 to 5. Likewise, the results obtained show that the data collected by the simulator is less biased than those obtained through a post-driving survey.
When a major public health incident breaks out, in order to prevent the explosion of multiple types of public opinion, relevant government departments need to guide the online public opinion according to the needs and characteristics of different audiences in order to achieve reasonable regulation and control. In this process, gender differences among the participating public in areas such as comprehension ability often affect the effectiveness of government guidance. A proper understanding of these differences will enable the government to allocate resources on the basis of needs to save resources and achieve the same goals with half the effort. This paper takes the outbreak of the COVID-19 as an example to analyze the gender differences among users in terms of the overall volume of participation and specific participation behaviors from the dimension of time and geographical locations. A total of 735,271 comments posted by users in responding to tweets published by 144 official government accounts on Weibo during the COVID-19 outbreak were collected and analyzed with a combination of the methods of natural language processing and propensity score analysis. The results show that in comparison to male users, female users participated more, and their responses were more emotionally expressive. Female users tended to respond faster than male users by 30 minutes to an hour, which allowed female users to play a more important role in the process of government guidance of public opinion during major public health incidents. Therefore, this study further provides policy recommendations for the government to provide reasonable guidance of public opinion and give future direction.
China has now entered a stage of high-quality development. Realizing the transformation and upgrading of the manufacturing industry in the new stage is conducive to promoting China’s high-quality development process. However, the current problem of insufficient capacity utilization in the manufacturing industry will affect its transformation and upgrading. From the perspective of micro-enterprises based on the data of 1,628 A-share listed companies in China’s manufacturing industry, this paper uses a random panel Tobit model to study the impact of technological innovation on enterprise capacity utilization. The research results show that technological innovation promotes the optimization and upgrading of enterprise products, improves the production efficiency of enterprises, and effectively promotes the improvement of enterprise capacity utilization.
The increasingly effective managing of risks in construction projects requires the stakeholders to collaborate, resulting in the need to integrate the use of Building Information Modelling (BIM) to mitigate the risks in project collaboration. Our understanding of strategic planning of BIM adoption amidst a pandemic is still limited, and it is widely accepted that COVID-19 is a long-term pandemic that require a constant and innovative range of mitigation approaches to protect public health. The significant construction advances emphasize remote work and digital tools that assist in the project’s on-time completion. A fully digitalized approach is necessary for service continuity and rapid processing, particularly during a pandemic. Therefore, this study develops an adaptive digital collaboration framework based on Cloud-Based BIM technology to reduce risks while increasing workplace productivity and mobility. It resulted in a new way of managing the project information, enhancing the design team collaboration, and transforming 2D plans into 3D models. It integrates information to take a building through a virtual construction process long before it is completed, and each team member has access to the most up-to-date and current project information.
The development of roads has been one of the nation’s most essential infrastructural initiatives. It is an essential mode of transportation that plays an important role in our everyday lives. Because of its importance, the government has allotted large budgets in making roads in different parts of the country. The quantity and complexity of road construction projects have substantially expanded in recent years. Numerous novel methods and technology have been developed to facilitate road construction budgeting, planning, and decision-making. Using Artificial Neural Network (ANN), this study constructed a forecasting model to accurately anticipate the future costs of road improvements. Between 2017 and 2020, fifty (50) completed road projects from the Department of Public Works and Highways (DPWH) Regional Office XI were utilized by the researcher. The DPWH RO XI is one of the country’s largest implementing offices for constructing public roads catering the entire Davao Region. This research used the project cost as the dependent variable while the independent variables are the construction activities’ revised duration and variation order in running the model. Multiple linear regression model performance was compared to the performance of the neural network prediction model. The data included the major construction activities for road projects with its corresponding revised duration, actual and planned cost, and the reason for variation order. It demonstrates that the neural network models outperform to the multiple linear regression (MLR) model in terms of prediction accuracy. This research offers a model to the government agencies and contractors implementing road construction in predicting road construction costs more accurately.
With the deepening of industrial automation, a large number of edge intelligent devices are deployed in industrial meter detection. In view of the limited computing and storage capacity of these embedded devices, we propose a lightweight meter detection method. Our proposed method is based on the widely used Yolov5, the depthwise separable convolution and squeeze and excitation channel attention module are used to simplify the backbone and head of the network, and further prune the filters of convolution layers via geometric median. Finally, model parameters and floating-point operations are reduced to 0.250M and 0.687G on the premise of ensuring the effect of the meter detection.
This research presents an immersive oral English teaching mode by combining optimized GMM-HMM, VR technology with immersive learning theory, which enables learners to learn English in a real context with .This model can cultivate learners’ cultural awareness and enhance their output ability in an effective way, making them achieve self-innovative development in the process of independent English learning. Informed by the current development of VR technology and English teaching through literature research, this study centers on the designs of immersive VR teaching models. It presents some practical experience by undertaking comparative research which was implemented based on immersive VR teaching and multimedia teaching. This study aimed to facilitate the trend of combining information technology and education.
The main function of Chinese medicine decision support system is to cluster Chinese medicines based on K-means (cluster Chinese medicines with similar properties into groups, so as to discover new properties of Chinese medicines, which can be referred to during drug use). The other is to classify Chinese medicines by Bayesian classification. The purpose of classification is to find the traditional Chinese medicine that can be used as nutritious food, or to find the traditional Chinese medicine with food and nutritional properties. Firstly, the traditional Chinese medicine with known food properties is used as the sample, and the Bayesian algorithm is used for learning and training. Then, the training model is used to analyze the new traditional Chinese medicine, so as to analyze whether a certain traditional Chinese medicine has the properties of nutritional food. Other functions include traditional Chinese medicine basic information management, traditional Chinese medicine data collection, traditional Chinese medicine data preprocessing, etc. After the operation of classification and clustering, the input data of traditional Chinese medicine will have more predictive values (mainly whether it has food nutrition or which traditional Chinese medicine can be divided into the same group), which can be used for reference by traditional Chinese medicine practitioners.
This paper aims to evaluate the impact of Islamic FinTech innovations on Malaysia’s banks’ performance by utilizing the eighteen regional commercial banks that started Islamic banking in Malaysia. The data is from 2005–2020 with four hundred and seventy observations. Multivariate regression has been used to evaluate the research questions empirically. This research is the first to statistically evaluate the Islamic fintech innovation impact on the bank’s performance in Malaysia. Furthermore, research on financial metrics is presented thoroughly for the years 2005–2020. We have contributed to the Islamic FinTech era with the following findings: (i) The Islamic FinTech has a positive effect on the bank’s performance in Malaysia; (ii) The Islamic FinTech has also positively affected the banks’ income; (iii) The impact of Islamic FinTech on economic performance was more substantial for the small-banks compared with large-banks; (iv) In terms of balance sheet debts, small banks’ funds of money market are positively influenced by the application of Islamic FinTech; (v) In terms of consumer loan repayments to the small banks have been positively impacted by the app of Islamic FinTech; (vi) The per capita GDP does have a good impact on ROE of banks; (vii) Penetration rates of Islamic banking has positively impacted bank’ return on assets and equity.
Aiming at the shortcomings of the current widely used contact and noncontact tube metal temperature measurement technology and the method of diagnosing the degree of coking of the furnace tube, a new tube metal temperature (TMT) measurement method and a method of diagnosing the coking degree of the furnace tube and predicting the coking trend were introduced in this study. Also, referring to the new generation of intelligent temperature-measuring devices developed for measuring TMT, an intelligent temperature-processing algorithm based on machine learning and neural network was proposed. This method not only improved the accuracy of measuring TMT and the accuracy of tube identification but also reduced the technical cost of TMT measurement to some extent.
Listed companies are the backbone of the regional economic growth. The enterprise innovation capability is the ability of the enterprises to integrate the internal and external resources through multi-dimensional innovation activities to improve the enterprise performance and obtain the economic benefits in a short period. This paper constructs the evaluation index system and evaluation model of enterprise innovation ability from three perspectives: technological innovation ability, institutional innovation ability and management innovation ability, and uses the improved AHP method to determine the weight of each index. Based on the data of 30 listed companies in Gansu Province from 2016 to 2020, this paper measures their innovation efforts and suggests the ways to improve their innovation ability.
The traditional collaborative filtering algorithm does not consider the influence of item popularity in similarity calculation, and the prediction score does not consider the influence of time on the change of user interest, resulting in inaccurate similarity calculation and single recommendation result. To solve these problems, this paper improved the traditional similarity calculation method by combining the item popularity penalty coefficient, improved the recommendation diversity of the algorithm, and integrated the time factor into the prediction method to solve the problem of interest attenuation. Experiments on the 100K and 1M data set of Movielens show that the improved algorithm effectively improves the accuracy and coverage of recommendations.
This research aims to know some examples of papers that relate the intellectual capital and information (the theory of) and the relationships established between both. Previous research, up to this date of 2022, has been insufficient and disjointed in a single direction. There is a lack of objective approaches (instead of subjective), and there is a lack of approaches that allow replications to reality and, above all, that make it possible to know the intellectual capital and information (the theory of) to make known an objective value, expressed in euros, as if it were other products. At the same time, there must be dynamics that make it vary over time, know its sources of change and know what makes it remain the same, raise or lower its value. Once this desideratum is reached, the real objective that materializes the intellectual capital and the information (the theory of) that underlies it is reached.
This paper has approached the investment method by alpha value as the excess return to compensate for risks other than the market risk with the data sample of filtered stocks from three major exchanges of the Vietnam stock market HOSE, HNX, and UPCOM from January 2016 to December 2020. Then, we compare the performance of the portfolio through 2021, the year Vietnam fell into the 4th wave of Covid and was the hardest hit. The results of the paper have shown that the portfolio selected by the alpha method has eliminated the beta market risk of the portfolio and has the actual portfolio return higher than the general rate of return of the stock market index, thereby reinforcing and proving the effectiveness of the alpha investment model.
Based on the theory of Technology Acceptance Model (TAM) and American Customer Satisfaction Scale (ACSL), this study selects 130 students majoring in preschool education, Chinese education and primary education in a higher vocational college in China as the research sample from the perspective of technology and consumers to carry out college English flipped classroom teaching based on SPOC (Small Private Online Course) mobile interactive learning platform. Through descriptive statistical analysis, factor regression analysis and the influence mechanism of common factors, this study aims to investigate and analyze students’ satisfaction of flipped classroom on 30 items. The results show that learners’ satisfaction cognition of flipped classroom is deeply influenced by three common factors: Learners’ expectation, cognitive quality and learning acceptance. Furthermore, the findings indicate that high quality activity design is the key factor for the success of flipped classroom, learners’ expectation of flipped activities is the internal driving force, and learners’ acceptance of flipped classroom teaching mode is the main factor.
Engineering cost has been a major issue in private vocational schools leading to problems like: lack of students learning initiative like a low self-learning ability, poor habit of preview before lesson, low attendance rate, short classroom listening time, low initiative to review after lesson, insufficient investment in practical training room, unreasonable teaching structure, high teacher mobility and inadequate teaching staff. The engineering cost practical training lesson adopts blending teaching construction and always penetrates the civic political elements. The teaching mode is online before the lesson, online and offline blending teaching mode during the lesson, and online teaching mode after the lesson. Blending teaching is great for improving the learning initiative and conscientiousness of private senior vocational students, alleviating the problem of insufficient investment in the training room of private senior vocational engineering costing major, alleviating the problem of unreasonable teacher structure, high teacher mobility and teacher shortage in private senior vocational engineering costing major.
to discuss about the self-directed learning readiness of backbone teachers in primary and middle schools, and its relationship with their learning effectiveness.
“scales for teachers’ self-directed learning readiness” and “questionnaires about teachers’ in-service training effects” were conducted on 592 primary and middle school backbone teachers who were receiving National Teacher Training Program.
The self-directed learning readiness of primary and middle school backbone teachers is commonly high; the readiness differs in different genders, teaching ages, and between rural and urban areas; there is significant positive correlation between the teachers’ self-directed learning readiness and their learning effectiveness.
Supportive system for self-directed learning should be created, to encourage and lead backbone teachers in primary and middle schools to achieve self-directed learning with high efficiency.
to investigate the application of formative evaluation to online teaching of “Doctor-Patient Communication Skills”.
taking three classes of students majored in Medical Imaging Technology (entered our college in the year 2018) as the research objects, who are divided into the experimental group and control group. The experimental group focuses on the teaching method of online formulative evaluation, while the control group centers on face-to-face classroom teaching. Contrastive analysis is conducted on the online learning of experimental group, including online independent learning, online interaction, module completion, chapter test, and final exam results. Then, the final scores of students in each group are compared. Through questionnaires in each class, students’ assessment about formative evaluation of online teaching and classroom teaching is compared.
by contrasting the satisfaction of teaching methods and the satisfaction of self-evaluation, the experimental group is better than contrast group (P<0.05); by contrasting theoretical score, the experimental group is higher than the contrast group (P<0.05); the teaching ability of members in experimental group is better than that of contrast group (P<0.05); the CTDI-CV score of students in experimental group is obviously higher than that of the contrast group (P<0.05).
Formed in the doctor-patient communication skills online teaching evaluation scheme applied value, students can very clearly know oneself in the course of the study situation, timely reflection, find out the deficiencies, lessons learned, at the same time making efforts in the direction of the next stage and target, at the same time, teachers can grasp the characteristics of formative assessment, according to the result of formative assessment, Grasp the analysis and improvement of students’ learning process in teaching practice and help formulate more effective teaching methods. It can improve students’ final score and teaching satisfaction.
Dormitory equally shared fee is the expense collected from all dormitory members voluntarily and used for dormitory group activities or communal purposes. Investigating the average level, sources, and the spending structure of the dormitory equally shared fee opens up a new pathway to understanding the dormitory life of college students and the status quo of collaboration between the school and dormitory in talent fostering. A questionnaire survey was administered to 242 dormitories at Xiangnan University. Some individuals were picked for case interviews. The prevalence, amount, source, spending structure and the influence factors of the dormitory equally shared fee were analyzed. The research findings provide clues for guiding college students’ dormitory life, dormitory management, and collaboration between the school and dormitory in talent fostering.
The purpose of this study is to discuss the re-engineering strategies on human resource management practices in China’s higher education system, particularly in Nursing institutes. The Lecturers in Chinese nursing higher education institutions have limited deliveries (sessions) on Human Resources Management (HRM) module, thus the nurses have a challenge while dealing with patients. Through this research, it has been highlighted with evidence to teach students practical HRM skills has numerous benefits and so that Chinese nursing students can match their academic understanding with their practical experience. By reviving this practice, it becomes a process of re-engineering in Chinese teaching systems. This applied research had 91 respondents collected through primary data. SPSS tool is used to analyse these datasets. ANOVA and Measurement model analysis were done based on the data collected.
Moral education in colleges and universities goes through the teaching of all the courses, which integrates into all the links of teaching and practice. In daily teaching, moral education is a kind of implicit education influencing in a imperceptible way. The teaching content of “International Law” includes profound moral education, which can effectively transfer from “ideological and political courses” to “ideological and political theories teaching in all courses”. Therefore, interactive case-based teaching method is a good way to achieve ideological and political teaching in a “silent and soft” way. Through interactive case-based teaching, moral education can be penetrated into the lecturing of professional course knowledge and a series of activities, which can form the course teaching with the integration of “intellectual education” and “moral education”, to integrate the values shaping, knowledge imparting and ability cultivation. In this way, the fundamental task of building morality and cultivating people will be fully implemented.
Through analyzing the characteristics of higher education, a teaching quality monitoring big data platform of local colleges and universities is constructed.
The data base of basic teaching state is applied to the teaching quality monitoring system, to improve the information gathering, processing, and feedback ability of quality monitoring, as well as to enhance the working efficiency and quality of monitoring.
The platform framework consists of business management information system, database, data analysis, data application and so on.
Based on database, the platform input the systematic data of all kinds of businesses in the data center into database, offering fundamental data support to systems like state database, academy evaluation, and application for the first-of-class majors. Then, these data can be obtained automatically, and processed to the fundamental basis required by all the systems, providing teaching quality monitoring and evaluation, and big data analysis for both universities and its affiliated institutes. On the other hand, the data generated by the system can be pushed to the data center of the university.