Forthcoming Articles
International Journal of Data Science
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International Journal of Data Science (21 papers in press) Regular Issues
Abstract: With the prominent issue of population aging, home-based elderly care has become an important way of elderly care. The current home-based elderly care environment is unable to meet the practical needs of the elderly in terms of functionality and safety. This paper is based on the characteristics of the elderly, the needs and development status of indoor space design, and combined with network security, to study the design and renovation of indoor spaces for home-based elderly care. And from the perspectives of functionality, safety, and aesthetics, the results showed that the average score for the design and renovation of indoor spaces for home-based elderly care based on network security in terms of safety evaluation was 8.27 points, higher than the traditional method's 7.11 points. This indicates that the method proposed in this paper can effectively ensure the daily safety of elderly people and meet their housing safety needs. Keywords: interior space design; network security; home-based care for the aged; elderly group; functional improvement; aging population; needs of the elderly; aesthetic design. DOI: 10.1504/IJDS.2025.10070395
Abstract: This paper proposes an Intelligent Financial Risk Blockchain Management system under the Internet of Things (IFRBM-IoT) to address the challenges of financial risk management (FRM) in e-commerce. With the rise of IoT, e-commerce platforms have grown rapidly, but regulatory gaps and security concerns hinder FRM development. The IFRBM-IoT framework integrates Hyperledger blockchain technology with a Neuro-fuzzy decision method and control chart analysis to enhance data credibility, accountability, confidentiality, and integrity. It aims to improve service quality, standardise assurance processes, and optimise security and profitability. Empirical analysis demonstrates the model's high accuracy in financial risk evaluation and its effectiveness in ensuring data security within e-commerce operations. This approach provides a practical solution for managing financial risks and assessing data security measures. Keywords: IoT; Internet of Things; FRM; financial risk management; E-commerce; panel data regression analysis; hyper ledger blockchain. DOI: 10.1504/IJDS.2025.10071212
Abstract: This study proposes a multilevel grey evaluation method integrated with machine learning to assess tourism resource (TR) development potential. Addressing challenges like poor management, resource scarcity, and unsustainable practices, the approach optimises resource allocation through an evaluation system analysing variation and grey weight vectors. Results show steady increases in evaluation weights over time, with average variation weight at 1.87 (total increase: 1.80) and grey weight at 0.50 (increase: 0.21). Compared to traditional systems, the optimised model improved service quality (9.92%), management level (10.25%), and government support (9.07%). This method enhances resource utilisation efficiency and promotes sustainable tourism development by identifying optimal strategies for TR exploitation. Keywords: tourism resources; development potential; management level; government support; evaluation method; multilevel grey evaluation; machine learning; resource allocation; sustainable development. DOI: 10.1504/IJDS.2025.10071283
Abstract: A financial shared service centre is an emerging management model widely adopted by multinational enterprises. It improves financial efficiency, reduces resource consumption, and optimises allocation. Traditional models face issues like low efficiency and high costs. Assessing its operational benefits is crucial for long-term growth. This study uses a fuzzy comprehensive evaluation method and finds that 39% of internal processes achieve core process uniformity, indicating a mature establishment. Other companies can use this performance system as a reference when building their own financial shared service centres. Keywords: financial sharing; deep learning; blockchain technology; performance evaluation; resource optimisation; operational efficiency; fuzzy comprehensive method; multinational enterprises; shared service centre. DOI: 10.1504/IJDS.2025.10071763
Abstract: This paper explores green logistics network optimisation and carbon emission reduction through blockchain technology, IoT, and big data. A blockchain-based logistics model was developed, incorporating smart contracts for automated carbon management and IoT devices for real-time emission monitoring. Big data analysis enabled logistics path optimisation. Experimental results showed that using ant colony optimisation reduced transportation time by 20%, fuel consumption by 15%, and carbon emissions by 18%. The proposed method enhances logistics efficiency and reduces environmental impact, offering practical solutions and theoretical support for sustainable logistics networks. Keywords: blockchain technology; green logistics; carbon emissions; path optimisation; smart contracts; IoT; Internet of Things; ACO; ant colony optimisation. DOI: 10.1504/IJDS.2025.10072254
Abstract: This paper aims to solve the problem of emergency network public opinion (NPO) risk identification (RI) and early warning (EW). Firstly, the back propagation neural network (BPNN) optimised by Genetic Algorithm (GA) is used to process and model the data obtained on the network, identify the public opinion risk of emergencies, and realise the risk prediction and early warning. Secondly, through the analysis and mining of NPO data of emergencies, the factors affecting the risk of NPO, such as social media platforms, user characteristics, and text content, are explored. These factors are incorporated into the model to improve the predictive ability of the model. Finally, through the research, effective Risk Management (RM) and countermeasures of NPO in emergencies are proposed to provide feasible RM schemes for governments, enterprises, and the public to ensure social stability and security. Keywords: BPNN; back propagation neural network; data mining; emergencies; NPO; network public opinion; risk identification and early warning. DOI: 10.1504/IJDS.2025.10072258
Abstract: This work explores consumer demand and preferences for cultural and creative tourism products to enhance the effectiveness of visual design and market competitiveness. A comprehensive model integrating the deep convolutional neural network (DCNN) and deep belief network (DBN) is developed using deep learning technology. This model aims to extract both the underlying features of product images and the semantic features of consumers, thereby providing data support to optimise product design. The results indicate that the constructed model achieves a prediction accuracy of 98.5% and a recall rate of 98.2% in product image recognition, demonstrating its effectiveness in capturing consumer demand characteristics. Keywords: deep learning; image analysis; product visual design; cultural and creative products; DBN; deep belief network. DOI: 10.1504/IJDS.2025.10072738
Abstract: In recent years, the rapid development of Financial Technology (FinTech) has profoundly changed the operation of the financial market, where the extensive application of machine learning technology in risk assessment, credit approval, and asset pricing has significantly impacted the financing structure of enterprises. This paper breaks through the traditional research framework, constructing a 'technology-market' two-dimensional variable system from the perspective of the dynamic adjustment of enterprise financing structures, and quantitatively analyses the influence of FinTech driven by machine learning on the proportion of enterprise financing sources, financing costs, and term structure. It is found that the investment intensity in FinTech is positively correlated with the direct financing ratio of enterprises, with a more pronounced impact on information-sensitive industries. This paper not only enriches the research on the relationship between FinTech and corporate financing structures but also provides valuable policy suggestions and practical guidance for regulators, corporate decision-makers, and financial institutions. Keywords: machine learning; quantitative analysis; financial technology; enterprise financing structure; SMEs; small and medium-sized enterprises. DOI: 10.1504/IJDS.2025.10072736
Abstract: With the rapid growth of residents' disposable income, demand for tourism has surged, exposing critical challenges in the traditional tourism industry. A major issue is the inability of scenic spots to market tourism projects according to tourists' characteristics and preferences, resulting in resource waste and hindering sustainable development. To address these challenges, this study aimed to design a smart tourism information resource management (IRM) system based on geographical information system (GIS) and big data (BD) technology. The intelligent tourism IRM system was developed by integrating tourism information collection with GIS and BD technologies. To evaluate its effectiveness in solving industry pain points and improving scenic spot revenue, 10 well-known scenic spots with an annual passenger volume exceeding 10 million were selected for comparative analysis. The evaluation focused on three key metrics: annual tourist reception, service satisfaction, and total annual revenue. Keywords: smart tourism; IRM; information resource management system; GIS; geography information system; big data; visitor satisfaction. DOI: 10.1504/IJDS.2025.10072775
Abstract: This paper focuses on the construction of an intelligent outcome-based education (OBE) platform driven by deep learning and explores how to integrate deep learning technology with the OBE concept to meet students' personalised learning needs and improve teaching quality. Firstly, the research reviews the application status of deep learning in the field of education and the progress of OBE, elucidating the necessity and feasibility of combining the two. Based on this, a design scheme for an intelligent OBE platform, structured around a hierarchical architecture, is proposed, covering key modules such as user modelling, knowledge map construction, intelligent recommendation, and evaluation feedback. The experimental results demonstrate that the platform significantly improves students' grades, knowledge mastery, and the accuracy of personalised recommendations. The improvement in students' grades in the experimental group is 2.4 times greater than that of the control group, and the click-through rate of recommended resources reaches 78%. Keywords: OBE; outcome-based education; deep learning; intelligent; intelligent recommendation; OBE concept. DOI: 10.1504/IJDS.2025.10072737
Abstract: For the development of rural economy, accurately predicting the demand and price trend of agricultural products will help investors optimise their trading strategies and provide scientific reference for the government's macro-control. This paper focuses on the application of intelligent algorithm in the accurate assessment and effect prediction of rural economic development policies, and puts forward a deep learning (DL) model that integrates deep belief network (DBN) and long-term and short-term memory network (LSTM) for the joint prediction of agricultural product demand and price. The model integrates multi-source sales data from e-business platform, and combines historical transaction records, market supply and demand relationship and external environmental factors to build a learning framework with temporal and spatial characteristics. The results show that the proposed model is significantly superior to traditional statistical methods such as random forest (RF) in many forecasting indexes, and has higher forecasting accuracy and stability. Keywords: artificial intelligence; intelligent algorithm; rural economic development; accurate policy assessment; effect prediction. DOI: 10.1504/IJDS.2025.10072980
Abstract: With the rapid development of e-commerce and increasingly fierce market competition, customer satisfaction has become the key to enterprise competition. However, the current quality of e-commerce services is uneven, and there is room for improvement in customer satisfaction. This paper's method is not only targeted, but also reflects the real feelings and needs of customers more accurately. This paper not only calculates the mean, standard deviation and variance of related variables, but also further discusses the specific impact of various service quality indicators on customers' shopping experience. It is found that the six service quality indicators, practicality, safety, enthusiasm, reliability, feedback mechanism and compensation mechanism, have different degrees of influence on customer satisfaction and loyalty. This paper shows innovations in research methods, data analysis and interpretation of results, which provides strong theoretical support and practical guidance for e-commerce enterprises to improve service quality and enhance market competitiveness. Keywords: online shopping; E-commerce; service quality; customer satisfaction; big data. DOI: 10.1504/IJDS.2025.10072981
Abstract: The proposed intelligent automatic English translation system leverages advanced feature extraction algorithms and big data technologies to enhance translation accuracy and efficiency. Central to this system is an N-Gram-based scoring model, which evaluates translation quality by analysing word sequences. This model is further refined through the development of an English corpus scoring framework, enabling more precise assessments. Incorporating Latent Dirichlet Allocation (LDA), the system employs weighted LDA indices to assess the semantic depth of translations. When these indices are well-aligned, they indicate a translation that captures the nuances and depth of the original text. Conversely, scattered LDA indices suggest a loss of key semantic elements during translation. The integration of behavioural decompression algorithms facilitates the optimisation of translation processes, ensuring that the system delivers high-quality English-Chinese translations by effectively capturing and preserving semantic information. Keywords: feature extraction; big data information technology; English- Chinese translation; interactive. DOI: 10.1504/IJDS.2025.10072978
Abstract: With the rapid growth of tourism, traditional scenic area management struggles to meet increasing demands. This paper proposes an intelligent management system for tourist attractions based on Internet of Things (IoT) technology to optimise crowd control, enhance service quality, and improve visitor satisfaction. By integrating IoT with cloud and edge computing, the system enables real-time monitoring, efficient flow allocation, and personalised services. Experimental results show tourist satisfaction rose from 58% to 83% after implementation. The system also improves attraction capacity and operational efficiency, demonstrating the potential of IoT in smart tourism. The study highlights the synergy between information devices and mobile applications, offering a reference for future intelligent scenic area development. Keywords: tourism industry; intelligent management system; IoT; Internet of Things; scenic area management; service quality; tourist satisfaction; cloud computing; edge computing; mobile application. DOI: 10.1504/IJDS.2025.10072979
Abstract: Industry-education integration has become a pivotal national strategy in China, yet its complex interactions are difficult to capture with traditional models, which often rely on empirical methods lacking precision. To address this gap, this study introduces a BP neural network-based evaluation approach. By iteratively adjusting neuron weights to fit nonlinear functions, the method enables accurate assessment of industry-education integration. The research highlights the importance of vocational education, identifies key challenges, and explores its role in China's educational system. A neural network model is built using 10 secondary indicators, with simulation and validation performed in MATLAB on real-world data. Results show strong dynamic tracking and fitting accuracy, providing a scientific basis for precise evaluation. This study contributes an innovative data-driven method for optimising vocational training models and offers valuable insights for policy development and system improvement. Keywords: industry-education integration; BP neural network; vocational education; oriented training; system construction. DOI: 10.1504/IJDS.2025.10073096
Abstract: As the internet continues to advance, the volume of data generated daily has grown exponentially, posing challenges for traditional search engines to fully meet modern user needs. In response, recommendation systems have emerged as a transformative solution, evolving into a multidisciplinary field aimed at addressing the complexities of big data while enhancing user experiences. Over the years, recommendation systems have become indispensable in information filtering and retrieval, with widespread applications in social networks, e-commerce, and news delivery, yielding substantial economic and social benefits. The concept of 'slow living' offers a thoughtful counterbalance to the pressures of modern life, emphasising individual well-being amidst time constraints. This paper introduces a tourist attraction recommendation model, leveraging machine learning algorithms to cater to the 'slow living' preferences of tourists. The proposed model achieves an 18% performance improvement over traditional algorithms, showcasing its potential for extensive real-world applications. Keywords: slow living; machine learning algorithm; tourist attraction recommendation model. DOI: 10.1504/IJDS.2026.10074542
Abstract: Predicting athletic performance is highly complex because it depends on many interacting factors. Conventional approaches - largely statistical analyses and expert judgement - tend to offer limited accuracy. To overcome these shortcomings, this study adopts machine-learning techniques to deliver real-time performance forecasts. First, we compiled athletes' demographic information and training records, then normalised and preprocessed the data. Drawing on extensive event-level datasets, we constructed a dynamic long short-term memory (LSTM) model that continuously captures and predicts athletes' competitive states. The model accurately anticipated swimmers' breathing patterns, with the predicted traces closely matching the observed values. It also estimated fatigue levels during the 08:00-12:00 time block: blood lactate concentrations rose from 1.2 mmol L-1 to 5.0 mmol L-1, and subjective fatigue scores climbed from 2 to 8. Comprehensive experiments across multiple sports confirm the effectiveness of the proposed real-time performance-prediction framework. Keywords: machine learning; competitive sports; real time performance prediction; LSTM; long short-term memory network; motion data analysis. DOI: 10.1504/IJDS.2025.10074530 Survey Logistic Regression Analysis of HIV/AIDS Knowledge, Attitudes, and Testing Among Sudanese Women by Mohammed Omar Musa Mohammed, Ahmed Saied Rahama Abdallah Abstract: This study explores HIV/AIDS-related knowledge, attitudes, and testing behaviours among Sudanese women of reproductive age, based on data from the 2014 Sudan Multiple Indicator Cluster Survey (MICS), which involved 13,017 women nationwide. Survey Logistic regression analysis was used to examine how demographic factors relate to HIV/AIDS outcomes. Higher education (OR = 2.27, 95% CI: 1.822.83) and older age (4549 years: OR = 1.37, 95% CI: 1.121.68) were significantly linked to better HIV/AIDS knowledge. Only 44% of women showed adequate knowledge, while negative attitudes were common (78%). Women with higher education were more likely to have positive attitudes (OR = 0.33, 95% CI: 0.260.43 for higher education vs. none). HIV testing rates were very low (5.5%). Interestingly, rural residence (OR = 1.59, 95% CI: 1.212.10) and lower wealth were associated with increased odds of HIV testing. Disparities at the state level were noted across all outcomes. The results emphasise the urgent need for targeted, inclusive HIV education, stigma-reduction initiatives, and expanded testing services, aligned with Sudanese national strategies and international guidelines. Programs should focus on rural, less educated, and economically vulnerable populations to improve HIV-related outcomes among Sudanese women. Keywords: survey logistic; multiple indicator cluster survey; MICS; knowledge; attitude; HIV/AIDS. DOI: 10.1504/IJDS.2025.10074904 Dynamic Decision-Making and Optimisation Based on Artificial Intelligence and Network Big Data Integration by Xinjie Qian, Guixiang Hu Abstract: With the growth of network big data, AI-driven decision analysis is crucial for optimising operations. Existing methods face limitations, such as poor handling of sparse user-item interactions, inadequate modelling of temporal patterns, and insufficient adaptability in dynamic decision-making. This study proposes an integrated model combining neural collaborative filtering (NCF), long short-term memory (LSTM), and reinforcement learning (Q-learning). The model leverages NCF to capture user-item interactions, LSTM to model temporal dependencies, and Q-learning to optimise strategies. Experimental results on benchmark datasets show the model outperforms baselines in root mean square error (RMSE) and mean absolute error (MAE). These results validate the models accuracy in sparse data environments. This research offers a framework integrating predictive modelling with dynamic optimisation, demonstrating the potential of network big data to enhance decision-making. Keywords: AI-driven decision analysis; network big data; e-commerce; neural collaborative filtering; reinforcement learning; customer satisfaction. DOI: 10.1504/IJDS.2025.10074905 High-Precision Anomaly Detection Method Based on Variational Autoencoder and Multi-Source Data Fusion by Guo Li Abstract: Anomaly detection in multi-source environments (e.g., network security, industrial monitoring) faces challenges in handling heterogeneous data and complex patterns. To address this, this paper proposes a high-precision method combining variational autoencoder (VAE) with multi-source data fusion. First, a weighted average strategy integrates diverse sources into a unified feature representation. The VAE learns a latent distribution via its encoder-decoder structure, and anomalies are identified from reconstruction errors. The model employs dynamic thresholding, setting thresholds based on a percentile (e.g., 95%) of normal-sample error distribution to enhance adaptability. KL divergence regularisation stabilises latent space learning. Evaluated on KDD Cup 1999 and SMAP & MSL datasets, the method achieves precision of 96.75%, recall of 96.45%, and AUC of 94.51%, outperforming traditional techniques and demonstrating strong robustness and generalisation for complex, multi-source scenarios. Keywords: Multi-source Data Fusion; Variational Autoencoder; Anomaly Detection; Dynamic Threshold Setting; KL Divergence Regularization. DOI: 10.1504/IJDS.2025.10075037 Measuring Consumer Sentiment using Self-Evolving Data Collection through Analytics and Business Intelligence by Timothy J. Haase, Adam Moyer, William A. Young II Abstract: This study implements the Self-Evolving Data Collection Engine through Analytics and Business Intelligence (SEDCABI) to measure consumer sentiment. The SEDCABI engine leverages diverse data sources to collect unstructured, unsolicited input beyond traditional surveys. We apply the engine to collect and analyze lexicon-specific social media data. Our analysis demonstrates the engine's ability to predict macroeconomic trends, specifically real personal consumption expenditures. Traditionally, consumer sentiment is measured via surveys conducted through the University of Michigan. Its ability to predict future macroeconomic behavior has weakened over time. The director emeritus of the University of Michigan Surveys of Consumers has noted that the existing index may not be suitable for all predictive purposes. Our application of the SEDCABI engine allows us to construct simple measures of positive and negative sentiment. Increasing volume in negative activity precedes significant declines in durable consumption spending by one month. Keywords: Sentiment; Consumer Spending; Social Media; Data Collection. DOI: 10.1504/IJDS.2025.10075049 |
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