Template-Type: ReDIF-Article 1.0 Author-Name: Athir Alghamdi Author-X-Name-First: Athir Author-X-Name-Last: Alghamdi Author-Name: Raghad Alharbi Author-X-Name-First: Raghad Author-X-Name-Last: Alharbi Author-Name: Elaaf Aljohani Author-X-Name-First: Elaaf Author-X-Name-Last: Aljohani Author-Name: Manal Abdullah Author-X-Name-First: Manal Author-X-Name-Last: Abdullah Title: The impact of group decision support systems on decision-making Abstract: The decision-making process became more challenging due to the huge amount of available data, the increase in solutions' dimensions, and context complexity. That encouraged many organisations to rely on group decision-making. As a consequence of the rapid development of technologies, the concept of group decision support systems (GDSS) has been enabled and frequently used. GDSS is a class of decision-support systems where the participants are a group of people instead of one person. As time passed, some established technologies like cloud computing and web services contributed to evolving the use of GDSS. That led to the adoption of new concepts such as gaming and social networks. In this paper, the evolution of GDSS and its impact on the decision-making process have been discussed, and the used technologies have been determined. Also, GDSS applications in different fields are highlighted. Moreover, the GDSS applications explored three fields: health, education, and crisis management. Journal: Int. J. of Data Science Pages: 64-78 Issue: 1 Volume: 9 Year: 2024 Keywords: DSS; decision support system; GDSS; group decision support system; web-GDSS; web-based group decision support systems; cloud-based GDSS; gamification; social networks. File-URL: http://www.inderscience.com/link.php?id=135942 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:1:p:64-78 Template-Type: ReDIF-Article 1.0 Author-Name: Natarajan Meghanathan Author-X-Name-First: Natarajan Author-X-Name-Last: Meghanathan Title: Assortativity analysis of complex real-world networks using the principal components of the centrality metrics Abstract: Principal component analysis (PCA) captures the variations in the data spread over <i>m</i> features into <i>n</i> dominating principal components (typically, <i>n</i> < <i>m</i>, if the features are correlated) that are orthogonal to each other. Assortativity analysis for complex networks has been so far conducted just with the degree centrality metric or some node-level metric of interest, but not with respect to more than one metric. In this paper, we show that PCA can be used to determine the assortativity index (AI) of complex real-world networks with respect to the four major centrality metrics (degree, eigenvector, betweenness, and closeness) considered together (as features representing the data) for the nodes. Assortativity index of the network is computed as the weighted average (<i>AI<SUB align="right"><SMALL>PC</SMALL></SUB><SUP align="right"><SMALL>w.avg</SMALL></SUP></i>) of the assortativity indices of the network with respect to each principal component, and the weights are the eigenvalues of the eigenvectors corresponding to the principal components. Journal: Int. J. of Data Science Pages: 79-97 Issue: 1 Volume: 9 Year: 2024 Keywords: PCA; principal component analysis; assortativity index; centrality metrics; degree centrality; real-world networks. File-URL: http://www.inderscience.com/link.php?id=135945 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:1:p:79-97 Template-Type: ReDIF-Article 1.0 Author-Name: Jinhang Jiang Author-X-Name-First: Jinhang Author-X-Name-Last: Jiang Author-Name: Karthik Srinivasan Author-X-Name-First: Karthik Author-X-Name-Last: Srinivasan Title: A discrete Bayesian network for analysing hospital discharge data Abstract: Exploratory research requires models that can explain the underlying phenomena of interest in new research areas. We present the design and application of discrete Bayesian networks (DBN) for knowledge discovery in a hospital discharge dataset. For the learning phase of the network, the automated learning methods are preceded by customising the initial network. Structural learning is done using three state-of-the-art algorithms and is inter-validated. A new method is suggested for drawing selective inferences from the posterior conditional probability tables (CPT). As an illustration, functional inferences are drawn on length of stay and treatment charges for three disease groups using the developed method. Our analysis shows that for longer hospital stays, hospital visits involving mental disorders cost less than visits with other types of health conditions. This study contributes to data science research by demonstrating the application of Bayesian networks, evaluating different structure learning methods for given contexts, and developing a measure for selective inference using the CPT of the network. Journal: Int. J. of Data Science Pages: 1-18 Issue: 1 Volume: 9 Year: 2024 Keywords: Bayes-net; Bayesian belief networks; exploratory analysis; structural learning; hospital discharge data; selective inference. File-URL: http://www.inderscience.com/link.php?id=135946 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:1:p:1-18 Template-Type: ReDIF-Article 1.0 Author-Name: G.S. David Sam Jayakumar Author-X-Name-First: G.S. David Sam Author-X-Name-Last: Jayakumar Author-Name: A. Sulthan Author-X-Name-First: A. Author-X-Name-Last: Sulthan Author-Name: W. Samuel Author-X-Name-First: W. Author-X-Name-Last: Samuel Title: Testing the model selection in regression analysis with the Del distribution Abstract: This paper proposes a new Del distribution and discusses its applications in the model selection. The authors attempted to propose the exact distribution Del, which is defined as the difference between two generalized information criteria (GIC) of the <i>i</i>th and <i>j</i>th fitted models, and identified the relationship of Del with Correlated F-Ratio. Moreover, the density function and moments of the Del distribution were derived, and the critical points were also computed for different sample sizes at 5% and 1% significance levels. Finally, the application of the proposed distribution was used to scrutinise the equality between the pair of fitted candidate models. If the Del is said to be statistically significant, then the information loss between the models is not equal, and the predictive power of the models is not the same. This was numerically illustrated by fitting four different types of stepwise regression models based on 30 independent variables belonging to macroeconomic factors. Journal: Int. J. of Data Science Pages: 35-63 Issue: 1 Volume: 9 Year: 2024 Keywords: model selection; Del distribution; GIC; generalised information criterion; correlated F-ratio; macroeconomic variables. File-URL: http://www.inderscience.com/link.php?id=135947 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:1:p:35-63 Template-Type: ReDIF-Article 1.0 Author-Name: Yifu Shu Author-X-Name-First: Yifu Author-X-Name-Last: Shu Author-Name: Wenda Wang Author-X-Name-First: Wenda Author-X-Name-Last: Wang Title: Innovative research on encryption and protection of e-commerce with big data analysis Abstract: In the e-commerce sector, protecting data privacy is crucial. This study introduces the symmetric balanced funnel Pˆ5 model as a method for addressing data protection challenges. The Pˆ5 model organises data protection into five levels, each tailored to the specific security needs of different types of e-commerce data. It employs encryption algorithms like DES, AES, and RSA, with increasing encryption strength across the levels to ensure adequate protection. This approach not only safeguards user confidentiality and commercial interests but also provides balanced protection for data exchanged between parties. By offering focused protection for various categories of sensitive data, the Pˆ5 model enhances overall data security in e-commerce. This study offers a new and comprehensive strategy for ensuring data privacy in e-commerce environments. Journal: Int. J. of Data Science Pages: 123-142 Issue: 2 Volume: 9 Year: 2024 Keywords: big data security; e-commerce data; symmetric balanced funnel Pˆ5; multilevel encryption; data privacy; cybersecurity. File-URL: http://www.inderscience.com/link.php?id=139805 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:2:p:123-142 Template-Type: ReDIF-Article 1.0 Author-Name: Danhui Dong Author-X-Name-First: Danhui Author-X-Name-Last: Dong Title: Research into a risk assessment model for online public opinions based on big data: random forest and logistic model Abstract: The current risk assessment index system for online public opinions has some deficiencies; therefore, the risk assessment method for online public opinions has some disadvantages. In order to overcome these disadvantages, this research attempts to propose a risk assessment model for online public opinions based on a random forest and logistic model, and then the risks of online public opinions can be evaluated effectively. With the incident of "patient relatives purposefully hurting doctors at Beijing Civil Aviation General Hospital" as the research object, a systematic analysis was conducted in this research on the model indexes. The critical risk indexes of online public opinions are confirmed; sensitivity, reporting speed, reporting frequency, emotional tendency, satisfaction, and timeliness of the processing process are the main factors affecting the risk of online public opinions. The results can provide practical reference for the development trend and risk assessment of online public opinions. Journal: Int. J. of Data Science Pages: 19-34 Issue: 1 Volume: 9 Year: 2024 Keywords: online public opinion; risk assessment; random forest; logistic model; big data. File-URL: http://www.inderscience.com/link.php?id=135966 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:1:p:19-34 Template-Type: ReDIF-Article 1.0 Author-Name: Lu Xiong Author-X-Name-First: Lu Author-X-Name-Last: Xiong Author-Name: Xi Chen Author-X-Name-First: Xi Author-X-Name-Last: Chen Author-Name: Jingsai Liang Author-X-Name-First: Jingsai Author-X-Name-Last: Liang Author-Name: Xingtong Cao Author-X-Name-First: Xingtong Author-X-Name-Last: Cao Author-Name: Pengyu Zhu Author-X-Name-First: Pengyu Author-X-Name-Last: Zhu Author-Name: Mingyuan Zhao Author-X-Name-First: Mingyuan Author-X-Name-Last: Zhao Title: Tree-based methods for analytics of online shoppers' purchasing intentions Abstract: The recent speedy growth of e-commerce and big data has accumulated vast amounts of data about online shopping behaviour. Analysing this data can help online retailers gain competitive advantages. We propose four tree-based methods for analytics of online shoppers' purchasing intentions. After exploring data through various visualisation techniques, we conduct feature engineering to improve the model's accuracy. AUC is the primary measurement used to evaluate models. To make the conclusion more statistically robust, k-fold cross-validation is applied to obtain the statistics of AUCs, such as the average and standard deviation. By analysing the global and local feature importance of each model, the most critical predictor, PageValues is found. Furthermore, we do sensitivity analysis for PageValues concerning the target variable Revenue to examine the relationship. Our findings support the decision on how to improve sales. The interpretation of the models and the explanation of their business implications make this paper unique. Journal: Int. J. of Data Science Pages: 99-122 Issue: 2 Volume: 9 Year: 2024 Keywords: online shopping data analytics; feature engineering; decision tree; random forest; SGB; stochastic gradient boosting; XGBoost; feature importance; sensitivity analysis. File-URL: http://www.inderscience.com/link.php?id=139680 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:2:p:99-122 Template-Type: ReDIF-Article 1.0 Author-Name: K. Shao Author-X-Name-First: K. Author-X-Name-Last: Shao Author-Name: Q. Shao Author-X-Name-First: Q. Author-X-Name-Last: Shao Title: Comparing the impact of COVID-19 on three states: a data-driven approach Abstract: The states of Florida, Michigan, and Ohio implemented rather different public health emergency policies to flatten the curve and save lives after the COVID-19 outbreak. This study aims to provide insight into one of the most important and fundamental topics for making public health policy: how to effectively handle life-threatening infectious diseases while minimising overall disruption of society. To compare these three states objectively, three severity risk metrics are proposed, and their log odds data are analysed. Both linear and multivariate models are applied to the log odds of the three severity rates. Contrary to visual inspection of the count data, only the result of one hypothesis test is statistically significant from the linear model, and none are significant from the multivariate model, at the significance level of 0.05. For a significant result, the estimates of the model parameters are in favor of Florida and Ohio. Journal: Int. J. of Data Science Pages: 162-172 Issue: 2 Volume: 9 Year: 2024 Keywords: COVID-19; population infection rate; case fatality rate; senior fatality rate; log odds; statistical models; statistical hypothesis testing; state of Florida; State of Michigan; State of Ohio. File-URL: http://www.inderscience.com/link.php?id=139707 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:2:p:162-172 Template-Type: ReDIF-Article 1.0 Author-Name: Naiqi Chen Author-X-Name-First: Naiqi Author-X-Name-Last: Chen Author-Name: Yumei Wu Author-X-Name-First: Yumei Author-X-Name-Last: Wu Title: The evaluation of college students' entrepreneurship education performance using the t-test method Abstract: This paper briefly introduced evaluation methods for entrepreneurship education performance, conducted a questionnaire survey with college students at Zhejiang Normal University, and compared the differences between the entrepreneurship education performance of students who participated in entrepreneurship education courses and those who did not use the T-test method. The results showed that students who participated in the entrepreneurship education course had higher overall entrepreneurship levels, but the entrepreneurship education course did not play a significant role in the dimension of "entrepreneurial behaviour", which involves entrepreneurial practice, but only improved performance in "determining the direction of entrepreneurship quickly and developing a plan"; after the entrepreneurship course, there was a need to focus more on the teaching of entrepreneurship practice. Journal: Int. J. of Data Science Pages: 173-182 Issue: 2 Volume: 9 Year: 2024 Keywords: entrepreneurship education; T-test method; analytical hierarchy process; college students; education performance. File-URL: http://www.inderscience.com/link.php?id=139709 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:2:p:173-182 Template-Type: ReDIF-Article 1.0 Author-Name: Xu Jing Author-X-Name-First: Xu Author-X-Name-Last: Jing Author-Name: Li Wei Author-X-Name-First: Li Author-X-Name-Last: Wei Author-Name: Wang Jun Author-X-Name-First: Wang Author-X-Name-Last: Jun Title: Research on the reconstruction algorithm of a finite element deformation model based on digital twins Abstract: To ensure the integrity of the digital twins in the virtual-reality symbiosis stage, the problem that the finite element deformation and failure models can only be displayed in the software and cannot be exported needs to be solved. In this paper, a finite element reconstruction technique is developed through the biomimetic study of natural objects. First, the deformation data in the finite element is obtained by the 'hexagonal method' and 'slice method' through the study of mathematical principles and development based on Visual Studio, then the model format that can be recognised by the 3D printing equipment is reconstructed, smoothed, and optimised, and finally the modified model in the finite element is presented by using prototyping 3D printing technology rapidly. The innovation of the method and the development of the reconstruction algorithm solved the problem that digital twins can not accurately perceive the transient deformation of virtual realilty, which has strong application value and practical significance. Journal: Int. J. of Data Science Pages: 143-161 Issue: 2 Volume: 9 Year: 2024 Keywords: digital twins; finite element analysis; model reconstruction; full-life cycle; virtual-reality symbiosis. File-URL: http://www.inderscience.com/link.php?id=139744 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:2:p:143-161 Template-Type: ReDIF-Article 1.0 Author-Name: Ming Tang Author-X-Name-First: Ming Author-X-Name-Last: Tang Author-Name: Lincheng Qi Author-X-Name-First: Lincheng Author-X-Name-Last: Qi Author-Name: Sibo Bi Author-X-Name-First: Sibo Author-X-Name-Last: Bi Author-Name: Xinyun Cheng Author-X-Name-First: Xinyun Author-X-Name-Last: Cheng Author-Name: Shijie Zhang Author-X-Name-First: Shijie Author-X-Name-Last: Zhang Title: Comparison and database performance optimisation strategies based on NSGA-II genetic algorithm: MySQL and OpenGauss Abstract: With the widespread application of databases in real-time environments, higher requirements are placed on their performance optimisation strategies. In response to the lack of dynamic adjustment and optimisation capabilities for real-time environmental changes in database performance optimisation strategies, as well as poor query throughput and response time performance, this paper adopted Non-dominated Sorting Genetic Algorithm II (NSGA-II) to study performance optimisation of My Structured Query Language (MySQL) and OpenGauss databases. Firstly, it defined three objective functions and the corresponding constraints for the response time of the database query, the performance of the query, and the utilisation of the query resource, and calculated the fitness of each individual and the distance between the layers. Then, the tournament rotation method can be used to output parents with high fitness, and the crossover and mutation probabilities can be set. Finally, the optimal parameter configuration of the database can be output. The experiment was based on the TPC-DS dataset (transaction processing performance council decision support benchmark) and compared the performance of MySQL and OpenGauss databases under different parameter configurations. The experimental results show that after optimisation by the NSGA-II genetic algorithm, MySQL and OpenGauss databases have certain improvements in query throughput, query response time, and query resource utilisation. Moreover, the optimisation effect on the MySQL database was as high as 90.30%, which is more significant than that on the OpenGauss database. Journal: Int. J. of Data Science Pages: 222-238 Issue: 3/4 Volume: 9 Year: 2024 Keywords: database performance optimisation; MySQL and OpenGauss; NSGA-II; Non-dominated Sorting Genetic Algorithm II; query response time; dynamic adjustment capability; resource utilisation. File-URL: http://www.inderscience.com/link.php?id=142817 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:222-238 Template-Type: ReDIF-Article 1.0 Author-Name: Feng Chen Author-X-Name-First: Feng Author-X-Name-Last: Chen Author-Name: Bin Chen Author-X-Name-First: Bin Author-X-Name-Last: Chen Author-Name: Huan Xu Author-X-Name-First: Huan Author-X-Name-Last: Xu Author-Name: Qiuyong Yang Author-X-Name-First: Qiuyong Author-X-Name-Last: Yang Author-Name: Xiaowen Zeng Author-X-Name-First: Xiaowen Author-X-Name-Last: Zeng Title: Application of weaving based on log files in database systems Abstract: Aspect-oriented database (AODB) systems can effectively integrate and manage various data, improve data processing efficiency, and provide powerful data support for complex business scenarios. In order to improve the weaving efficiency of aspect oriented programming (AOP), this paper focuses on the weaving of log files in AODB. This paper introduces AOP technology in AODB and compares it with object-oriented programming (OOP) technology. This paper proposes a fast repair method for the normal operation and abnormal restart of the AODB system, and verifies the effectiveness of this fast repair mechanism through simulation experiments. The research results indicate that compared with OOP technology, AOP technology can be better applied to the study of log weaving. When notification modifications and connection point changes occur, incremental weaving has shorter weaving time and higher weaving efficiency. The weaving method based on log files can effectively improve the weaving efficiency of AODB and has certain application value. Journal: Int. J. of Data Science Pages: 183-202 Issue: 3/4 Volume: 9 Year: 2024 Keywords: log weaving; AODB; aspect-oriented database; AOP; aspect-oriented programming; incremental weaving; weaving state recovery; intelligent decision-making technology. File-URL: http://www.inderscience.com/link.php?id=142818 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:183-202 Template-Type: ReDIF-Article 1.0 Author-Name: Wenjuan Wang Author-X-Name-First: Wenjuan Author-X-Name-Last: Wang Author-Name: Donghui Shen Author-X-Name-First: Donghui Author-X-Name-Last: Shen Author-Name: Anyin Bao Author-X-Name-First: Anyin Author-X-Name-Last: Bao Author-Name: Jianming Shao Author-X-Name-First: Jianming Author-X-Name-Last: Shao Author-Name: Shunkai Sun Author-X-Name-First: Shunkai Author-X-Name-Last: Sun Title: Intelligent factory perception ability using distributed knowledge graph Abstract: Traditional research often faces the problem of information segregation, resulting in a lack of access to comprehensive, cross-domain data during the decision-making process, limiting a comprehensive understanding of the entire smart factory ecosystem. In this paper, we introduce the proximal policy optimisation (PPO) algorithm, combined with the inference capability of knowledge graph, to support complex decision-making problems in smart factories. In this paper, we collected smart factory data from different departments and constructed a distributed knowledge graph, defined semantic labels for entities and relationships, and mapped data from different data sources into the semantic model of the knowledge graph, built a decision network using multilayer perceptron, and updated the parameters of the policy network through PPO. The experimental results show that the average fault prediction accuracy of PPO combined with distributed knowledge graph reaches 96.1%, and the fluctuation of fault prediction accuracy within 12 months is only 0.1%. Journal: Int. J. of Data Science Pages: 203-221 Issue: 3/4 Volume: 9 Year: 2024 Keywords: intelligent factory; perception ability; distributed knowledge graph; fault prediction; PPO; proximal policy optimisation. File-URL: http://www.inderscience.com/link.php?id=142819 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:203-221 Template-Type: ReDIF-Article 1.0 Author-Name: Han Zhou Author-X-Name-First: Han Author-X-Name-Last: Zhou Author-Name: Danping Chen Author-X-Name-First: Danping Author-X-Name-Last: Chen Author-Name: Gengxin Chen Author-X-Name-First: Gengxin Author-X-Name-Last: Chen Author-Name: Xiaoli Lin Author-X-Name-First: Xiaoli Author-X-Name-Last: Lin Title: Privacy protection and anomaly detection in intelligent sorting based on convolutional neural networks in IoT environment Abstract: At present, the Internet of Things (IoT) has improved people's lives. IoT provides users with various intelligent sorting, networked devices, and applications across different fields. Therefore, detecting anomalies in IoT devices with intelligent sorting is crucial to minimise threats and improve safety. The convolutional neural network-assisted anomaly detection (CNN-AD) method has been developed to enhance security by detecting anomalies in the IoT environment with intelligent sorting. The Anomaly detection method uses a focused event system to increase its efficiency in intelligent sorting with event grouping tasks and improve detection accuracy. The event privacy is obtained by utilising the feature selection, mapping, and normalisation to enhance security. CNN automatically extracts characteristics from data and identifies and classifies the different types of events and attacks in intelligent sorting. The performance analysis and assessments of CNN are based on detecting different classes of attacks and computation times that are significantly shorter. Journal: Int. J. of Data Science Pages: 256-275 Issue: 3/4 Volume: 9 Year: 2024 Keywords: anomaly detection; CNN; convolutional neural network; classification; different attacks; privacy; security; intelligent sorting. File-URL: http://www.inderscience.com/link.php?id=142820 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:256-275 Template-Type: ReDIF-Article 1.0 Author-Name: Zhifeng Liang Author-X-Name-First: Zhifeng Author-X-Name-Last: Liang Author-Name: Weixi Ji Author-X-Name-First: Weixi Author-X-Name-Last: Ji Author-Name: Jie Yu Author-X-Name-First: Jie Author-X-Name-Last: Yu Author-Name: Li Chang Author-X-Name-First: Li Author-X-Name-Last: Chang Author-Name: Lili Li Author-X-Name-First: Lili Author-X-Name-Last: Li Title: Inter-provincial demand-side resource trade mechanism for enhancing new energy consumption Abstract: With the acceleration of the construction of the new power system, the proportion of new energy continues to increase, and the randomness and volatility of the system will be further aggravated. It may appear in a certain period due to the large amount of new energy and the situation that the region cannot be consumed, which is necessary to carry out trans-regional or trans-provincial surplus new energy trading to ensure the consumption of new energy and realise the mutual benefit of resource surplus and shortage. Because of this, based on the existing trading mechanism, this paper proposes a framework, design principle, and market positioning which is the interprovincial demand-side resource mutual trading market with the participation of demand-side resources and the consideration of electricity energy as the trading variety - and constructing the mechanism of the mutual trading market based on this. Then, the trading strategies of both buyers and sellers are studied. Finally, the validity of the proposed market mechanism and model is verified by an example analysis. Journal: Int. J. of Data Science Pages: 276-296 Issue: 3/4 Volume: 9 Year: 2024 Keywords: demand-side resources; virtual power plant; new energy; inter-provincial transaction; auction theory; mutual aid transactions. File-URL: http://www.inderscience.com/link.php?id=142821 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:276-296 Template-Type: ReDIF-Article 1.0 Author-Name: Bo Wang Author-X-Name-First: Bo Author-X-Name-Last: Wang Title: Construction of stock price fluctuation prediction model based on ABC-SVR artificial bee colony algorithm Abstract: Traditional research on stock price volatility prediction faces problems such as complex models, difficulty in parameter optimisation, and insufficient model generalisation ability. In this paper, the artificial bee colony support vector regression (ABC-SVR) algorithm is applied to optimise the parameter combination of the SVR model. Firstly, the paper collects historical stock price data and related factor data, and extracts technical indicators such as closing price, trading volume, moving average, as well as company financial data features from them. Then, the ABC-SVR algorithm is applied to select the kernel function, adjust the penalty parameters, and construct a stock price volatility prediction model. Finally, the dataset is divided into training and testing sets through cross validation, and the MAE and RMSE of the models on the testing set are determined. Research shows that the model has high prediction accuracy and small errors on various test sets. Journal: Int. J. of Data Science Pages: 297-311 Issue: 3/4 Volume: 9 Year: 2024 Keywords: stock price prediction; ABC; artificial bee colony; SVR; support vector regression; model optimisation; generalisation ability; prediction accuracy. File-URL: http://www.inderscience.com/link.php?id=142822 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:297-311 Template-Type: ReDIF-Article 1.0 Author-Name: Fanyu Meng Author-X-Name-First: Fanyu Author-X-Name-Last: Meng Title: Image analysis of a museum intelligent digital navigation system based on a virtual 3D deep neural network Abstract: The aim of this study is to develop an intelligent digital tour guide system that utilises virtual 3D deep neural network (DNN) technology to improve the visiting experience and cultural dissemination of museums, providing visitors with more information and interactive experiences. This study conducted a questionnaire survey on 20 tourists using an intelligent digital tour guide system based on virtual 3D DNN technology, and compared the performance of the system designed in the work with traditional systems 1 and 2. The research results indicate that the designed system outperforms traditional systems 1 and 2 in terms of information entropy, average gradient, signal-to-noise ratio (SNR), and equivalent coefficient. For example, in terms of information entropy, the system designed in this paper has a value of 6.974 compared to 5.127 and 5.368 in conventional systems 1 and 2, respectively. Journal: Int. J. of Data Science Pages: 239-255 Issue: 3/4 Volume: 9 Year: 2024 Keywords: virtual 3D technology; DNN; deep neural network; image analysis; smart museum; digital tour guide system. File-URL: http://www.inderscience.com/link.php?id=142823 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:239-255 Template-Type: ReDIF-Article 1.0 Author-Name: Danping Chen Author-X-Name-First: Danping Author-X-Name-Last: Chen Author-Name: Han Zhou Author-X-Name-First: Han Author-X-Name-Last: Zhou Author-Name: Xiaoli Lin Author-X-Name-First: Xiaoli Author-X-Name-Last: Lin Author-Name: Yanpei Song Author-X-Name-First: Yanpei Author-X-Name-Last: Song Title: Machine learning model training method and device based on artificial intelligence Abstract: As an important research direction in artificial intelligence (AI), machine learning (ML) has been widely used in many complex systems. This paper aimed to study how to improve and train graph based semi-supervised learning algorithm (GBSSLA) based on ML. This paper chooses decision trees (DT) and backpropagation neural networks (BPNN) as classifiers to train ML models. Experimental analysis shows that when the labelled data accounts for 20%, 50%, and 80% of the training set, the average error improvement rate of the improved graph based semi supervised learning algorithm (IGBSSLA) is always higher than that of the self training algorithm (STA) and cooperative training algorithm (CTA). From the experimental results, it could be seen that under the same experimental conditions, the same experimental data and the same classifier method, the final error of IGBSSLA and the percentage of error increase were better than STA and CTA. Journal: Int. J. of Data Science Pages: 333-352 Issue: 3/4 Volume: 9 Year: 2024 Keywords: machine learning model; artificial intelligence; training method; GBSSLA; graph based semi supervised learning algorithm. File-URL: http://www.inderscience.com/link.php?id=142824 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:333-352 Template-Type: ReDIF-Article 1.0 Author-Name: Yi Wang Author-X-Name-First: Yi Author-X-Name-Last: Wang Author-Name: Jiaru Lao Author-X-Name-First: Jiaru Author-X-Name-Last: Lao Author-Name: Xiaowei Niu Author-X-Name-First: Xiaowei Author-X-Name-Last: Niu Author-Name: Eun-Young Nam Author-X-Name-First: Eun-Young Author-X-Name-Last: Nam Title: Construction of a credit risk measurement system for small and micro firms in the context of internet financing Abstract: Small and micro firms (SMFs) are indispensable elements of the socioeconomy. However, they face significant and costly financing challenges due to their inherent characteristics and market factors. The advent of internet financing offers a potential solution to these financing difficulties for SMFs. Nevertheless, given the current lack of sophisticated regulations over internet financing in China, balancing the provision of financial support to SMFs while maintaining the safety and stability of the financial market and institutions has become a critical area of interest. This paper aims to construct a credit risk measurement indicator system for SMFs in the context of internet financing. The proposed system consists of a goal layer, a criterion layer, an index layer, and a secondary criteria layer, and the analytic hierarchy process (AHP) method is used to develop a corresponding weight system. This paper offers a reference model to promote the development of financial services such as credit financing for SMFs while ensuring financial security. Journal: Int. J. of Data Science Pages: 312-332 Issue: 3/4 Volume: 9 Year: 2024 Keywords: internet financing in China; SMFs; small and micro firms; credit risk measurement; AHP; analytic hierarchy process method. File-URL: http://www.inderscience.com/link.php?id=142825 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijdsci:v:9:y:2024:i:3/4:p:312-332