Data Mining Models Research Articles

Effect of digital transformation on open innovation

ABSTRACT In the digital economy, digital transformation presents significant opportunities for companies in developing countries to enhance their open innovation capabilities. However, the effectiveness of this transformation is often undermined by managerial short-sightedness, which prevents a long-term strategic outlook. This study investigates the relationship between digital transformation and corporate open innovation, specifically examining how managerial myopia moderates this effect. Using a sample of A-share listed companies in Shenzhen and Shanghai from 2007 to 2020, we employ text data mining and panel data linear regression models to analyze our data. Our findings reveal that digital transformation positively influences the level of corporate open innovation. However, this relationship is negatively moderated by managerial myopia, which significantly inhibits the benefits of digital transformation. Interestingly, while digital transformation facilitates closed innovation more strongly than open innovation, managerial short-sightedness exerts a greater negative impact on this relationship. This research contributes to the literature on innovation and management by highlighting the critical role of managerial characteristics in hindering digital transformation and open innovation in developing countries. By addressing these issues, the study underscores the need for organisations to foster a long-term perspective among managers to fully leverage the benefits of digital transformation for open innovation.

The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior

This study establishes a deep learning model for personalized travel recommendations based on factors that affect tourists’ purchases to provide users with more accurate and personalized travel recommendations. Firstly, Natural Language Processing (NLP) technology is used to process and emotionally analyze tourism review information, dividing it into positive, negative, or neutral to understand tourists’ attitudes towards purchasing products and services. Secondly, a High-Performance Network (HPN) model is constructed based on factors that affect tourists’ purchases. The relationship among tourists, products, and word of mouth (WOM) is represented as a complex network to analyze and predict event occurrence patterns and influencing factors in tourism electronic word-of-mouth (EWOM) data. The construction of the model considers various factors, such as the spread of WOM, the impact of price, etc., to reveal the complex relationships among tourists, WOM, products, etc. Finally, the Recurrent Neural Network (RNN) model is combined with the Backpropagation (BP) model, the time series data is processed with the help of the gated recurrent unit, and the HPN model is trained and evaluated. The Yelp dataset is employed to verify the accuracy and feasibility of the model, which contains the score and review data of many tourist destinations. The results reveal that price, WOM, and destination are one of the main factors influencing tourists’ purchasing behavior, with WOM being the most significant. Positive WOM reviews remarkably increase product sales, while negative WOM has the opposite effect. The minimum expectation for age, occupation, education, personal monthly income, and tourists’ willingness to purchase is 0.00, and the minimum expectation for gender factors is 0.31. The RNN-BP hybrid model has higher accuracy and predictive ability, which is 1.73% and 2.30% more accurate than single models and traditional machine learning predictive models. In short, this study contributes to a better understanding travelers’ needs and preferences to optimize products and services and improve market competitiveness. In addition, the methods and models of this study can also be applied in EWOM data mining in other fields.

Harnessing network pharmacology in drug discovery: an integrated approach.

Traditional drug discovery approach is based on one drug-one target, that is associated with very lengthy timelines, high costs and very low success rates. Network pharmacology (NP) is a novel method of drug designing, that is based on a multiple-target approach. NP integrates systems such as biology, pharmacology and computational techniques to address the limitations of traditional methods of drug discovery. With help of mapping biological networks, it provides deep insights into biological molecules' interactions and enhances our understanding to the mechanism of drugs, polypharmacology and disease etiology. This review explores the theoretical framework of network pharmacology, discussing the principles and methodologies that enable the construction of drug-target and disease-gene networks. It highlights how data mining, bioinformatics tools and computational models are utilised to predict drug behaviour, repurpose existing drugs and identify novel therapeutic targets. Applications of network pharmacology in the treatment of complex diseases-such as cancer, neurodegenerative disorders, cardiovascular diseases and infectious diseases-are extensively covered, demonstrating its potential to identify multi-target drugs for multifaceted disease mechanisms. Despite the promising results, NP faces challenges due to incomplete and quality of biological data, computational complexities and biological system redundancy. It also faces regulatory challenges in drug approval, demanding revision in regulatory guidelines towards multi-target therapies. Advancements in AI and machine learning, dynamic network modelling and global collaboration can further enhance the efficacy of network pharmacology. This integrative approach has the potential to revolutionise drug discovery, offering new solutions for personalised medicine, drug repurposing and tackling the complexities of modern diseases.

Evaluating the accuracy of hyperspectral and multispectral images in wetland cover classification using data mining models (Case study: Shadegan wetland)

Evaluating the accuracy of hyperspectral and multispectral images in wetland cover classification using data mining models (Case study: Shadegan wetland)

High-Order Local Clustering on Hypergraphs

Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods.

Comparison and analysis of denoising method in TBM key tunnelling data

ABSTRACT Environmental vibrations and other factors cause significant random errors in raw data collected during TBM tunnelling. Such errors can reach up to 25% and will severely reduce the performance of data mining and machine learning model. To improve the data quality and find a suitable denoising method for TBM data, this study first proposed two assurance indicators to evaluate the effectiveness of data denoising, such as distortion degree (θ) and denoising magnitude (λ). Then, the effects of the data denoising schemes were compared. The results demonstrate that: (1) The Convlove19 scheme has better performance and is recommended for the denoising processing for TBM data. It can preserve the statistical characteristics of the raw data to the maximum extent possible and can improve the performance of the machine learning model by 15∼25%. (2) “Boring cycle segmentation after denoising strategy” (SAD) has better performance than “Boring cycle segmentation before denoising strategy” (SBD) and is recommended. The findings in this study can help with TBM data cleaning and quality improvement, as well as provide a reference for noise reduction of other data with similar features.

Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining

As the basic guarantee for people’s production and life, the safe operation of the power system has an important impact on the development and operation of society. To ensure the safe and stable operation of the power grid, predicting potential faults and taking reasonable preventive measures can effectively avoid the occurrence of power accidents. However, due to the difficulty in ensuring the prediction accuracy of traditional methods, there are issues of protection misoperation and rejection. Therefore, in order to achieve accurate prediction of power grid faults and avoid protection misoperation and rejection issues, a distribution network fault classification prediction model using a combination of three-layer data mining model (TLDM) and adaptive moment estimation (Adam) algorithm/random gradient descent algorithm improved backpropagation neural network (BPNN) is proposed. The implementation results showed that the classification accuracy of artificial fish school [Formula: see text] priori, [Formula: see text]-means clustering convolutional neural network model and TLDM for single-phase grounding faults was 93.2%, 91.5% and 96.6%, respectively. The classification accuracy for two-phase faults was 92.8%, 92.4% and 95.7%, respectively. The classification accuracy for two-phase grounding faults was 93.7%, 91.2% and 96.9%, respectively. The classification accuracy for three-phase faults was 93.3%, 92.1% and 97.1%, respectively. The TLDM had the highest classification accuracy. The average accuracy, average accuracy and average recall of the BPNN improved by the combination of the ADAM algorithm and random gradient descent algorithm were 94.1%, 90.9% and 88%, respectively, which were higher than the BPNN improved by the combination of ADAM algorithm and random gradient descent algorithm. The above results indicate that the proposed distribution network fault classification and prediction model has good performance and can achieve accurate prediction of distribution network faults.

Retraction Note: Towards developing hybrid educational data mining model (HEDM) for efficient and accurate student performance evaluation

Retraction Note: Towards developing hybrid educational data mining model (HEDM) for efficient and accurate student performance evaluation

Vigilancia Inteligente del Comercio Exterior: Detección de Anomalías en las Importaciones del Ecuador con Minería de Datos

This study employs advanced data mining techniques to comprehensively analyze Ecuador's import data spanning three decades, from 1990 to 2021. By leveraging the k-means clustering algorithm, we meticulously identified anomalies within tariff items. The study utilized a comprehensive dataset that encompassed tariff items, years, and critical variables such as import volume, CIF and FOB values, import cost, and trade attractiveness. The data mining model generated insightful reports that accurately pinpointed an omalous patterns in tariff items. These reports empower experts to delve into the underlying causes of these anomalies, enabling them to make well-informed decisions to optimize Ecuador's import strategies. Our research underscores the transformative potential of data mining in detecting import anomalies, providing valuable intelligence for the strategic management of Ecuador's foreign trade. The findings contribute significantly to the prevention of customs fraud and unfair trade practices, ultimately enhancing the competitiveness of the country's import sector.

Identifying influential individuals and predicting future demand of chronic kidney disease patients

ABSTRACTTo ensure high service quality, managers need to personalize treatment options and meet their customer demands. Our research is motivated by the need to better anticipate and prepare for that. We develop a generalizable framework that is the first to address two healthcare risk management goals: (1) identifying high risk and stable‐demand customers and (2) predicting the medium‐term demand for services of stable‐demand customers. We also design a model‐agnostic method for variable evaluation. It can rank predictors based on their global impact, and highlight their effect on a model's local accuracy. In this research, we leverage a large electronic medical records' data set, which comprised of 48,344 chronic kidney disease patients treated across geographically diverse Veterans Affairs regions. Our framework indicates that although only 1.3% of the examined individuals are high‐risk patients, it can correctly identify 35% of them and highlight an additional 8.9% as having important demand implications. Identifying high‐risk individuals can be used in (1) monitoring prioritization, (2) patients' motivation, and (3) patients' stabilization. Furthermore, our model accurately predicts the monthly need for care of stable‐demand individuals up to 3 years into the future and outperforms popular statistical and data mining models. This information is especially critical for hospital management in identifying future hiring needs.

Integrity management system of hydrogen pipeline based on big data mining model optimized by improved whale algorithm

ABSTRACT In order to improve the integrity management level of the hydrogen pipeline, the intelligent information management system of the hydrogen pipeline based on big data intelligent mining model is proposed. The improved whale optimization algorithm is proposed by applying the adaptive weight update formula and quantum revolving door operation. Finally, a hydrogen pipeline is selected to carry out abnormal data detection, and the running time with different nodes and different clustering algorithms optimized by different intelligent algorithms is obtained, and the results show that the proposed Gaussian clustering optimized by an improved whale optimization algorithm can effectively find out abnormal data of integrity management of hydrogen pipeline. Monitoring precision of the proposed model is improved effectively, and the running time of the proposed model is also shortened; therefore, the proposed model can be an effective means for integrity management of the hydrogen pipeline.

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

Measuring the impact of enterprise risk management on performance, value, and risk indicators of Borsa Istanbul XBANK companies with data mining prediction models

Risk management (RM) is viewed by organizations from a holistic perspective instead of a silo-based point of view and a change in basic assumptions has occurred in recent years. The comprehensive approach to RM that is developed based on a holistic perspective and is adopted by companies is called enterprise risk management (ERM). From the general point of view, it is expected that companies will have sustainable operations, improved performance and value, and controlled risks by using the ERM concept. In this article, the relations of ERM adaptation with firm performance, firm value, and risks were researched through ordinal variables such as the implementation level of ERM and the sophistication level of the organizational structure of ERM in regression models based on panel data (PD). Empirical evidence depending on a sample of ten banking companies listed on the Borsa Istanbul (BIST) Banks index (XBANK) for the period from 2019 to 2022 confirms the above basic argument for firm performance, firm value, and the insolvency risk of banks in general terms. In addition, the prediction accuracy of PD models is calculated for the performance, value, and risk indicators of banks, and the partial least squares regression model is proposed as the alternative prediction model of data mining.

Hybrid data mining and data-driven algorithms for a green logistics transportation network in the post-COVID era: A case study in the USA

This study examines the problem of item allocation in a post-COVID environment with various products and a large customer base. The number of customers has increased due to the rise of internet access and the growing willingness to shop online. Problems such as the timely delivery of goods or services, the selection and destination of orders in decentralized warehouses, and the allocation of warehouses to customers are difficult to overcome with a large variety of items and many customers. It has been proposed that mathematical modeling in combination with meta-heuristic solution techniques solve these problems. However, solving mathematical models is very time-consuming and labor-intensive because there are many different location situations. Due to computing power and memory capacity advances, researchers have been looking at data-driven solutions to these problems. This study aims to tackle the diversity of commodities and the number of consumers in the post-COVID era by proposing a hybrid data-driven approach that combines data mining and mathematical modeling to solve mathematical location models with high accuracy in less time. This paper was implemented based on data from real cases in the USA.

Career planning matters: Intelligence-based career path predictions using data mining models - A longitudinal study

It is essential for students to plan their careers because selecting the right career path shapes a person’s life. In such disciplines as computer science, information technology, and software engineering, assisting students toward appropriate employment is even more helpful. Thus, throughout the student’s education, they must evaluate their strengths to determine which professional sector corresponds to their abilities. It is for this reason that this research introduces an intelligencebased career recommendation system that will incorporate the analysis of such factors as the student’s academic performance, economic status, and demographic features to use data mining models in determining the best prospective career path to offer the student a more transparent and more informed vision and course to set on in the future. Three key aspects were addressed: first, one or another model and classifier for assessing the impact of pre-university education on the choice of a profession and, consequently, the selection of technologies were used. Second, these models accurately forecast students' careers using core courses, CGPA, and FYP data. Third, socioeconomic or demographic data was incorporated into the prediction to make it more accurate. Regarding the method of class distribution balancing, the Synthetic Minority Oversampling Technique (SMOTE) approach was used. The study reveals that variables specifying preuniversity education directly impact students’ career choices and that, employing data mining techniques, career choices could be forecasted considering academic performance and other related factors.

Usage of intellectual analysis of ecological-economic data in the pension insurance system and for forecasting expenditures on social protection and social security

The paper is devoted to an actual scientific and applied problem – the development of methodology for applying mathematical models and data mining methods for actuarial calculations in mandatory state pension insurance system. The paper describes methodology for modeling changes in the number of pension recipients taking into account the impact of environmental factors, in particular air pollution. The basis of the proposed method is a multi-model approach, characterized by combination of data mining and probabilistic models in the form of Bayesian network, which are appropriate in conditions of statistical, parametric and structural uncertainty.The proposed approach describes the change in number of pension recipients, in particular for disability and breadwinner loss, under influence of air pollution from organic and inorganic compounds. The scientific novelty of the paper is in the use of an ensemble of models including probabilistic and statistical models in the form of Bayesian network and regression models, in the system of actuarial calculations of mandatory state pension insurance.The paper considers several scenarios for the impact of pollutants on the growth of number of pension recipients. The indicator of the share of expenditures on social protection and social security of the population in the gross national product was chosen as the target variable of the process under study. Mathematical models were found to be adequate to the modeling process, and the Bayesian network classification error is about 20%. The model structure is built in Genie 2.0 modeling system. The principal component analysis, is used to reduce the data dimension. The proposed methodology can also be applied to other tasks of forecasting social protection and social security expenditures.

The correlative factors of enterprise culture and enterprise human resource based on data analysis and federated learning

Under the current big topic of enterprise management, how to cultivate, use, and maintain talents is the critical problem of modern human resource management. Based on big data mining, cloud computing, and other related model optimization technologies, this study explores the crucial significance of enterprise culture construction and enterprise human resource management in achieving the business objectives of the entire enterprise. The establishment of this model also means that the enterprise’s profit margin and business indicators have been improved and developed under a certain model. The case results demonstrate that the optimized competency model is significant to studying corporate culture management system behavior, policy analysis, and improving corporate human resource management policies.

DATA ANALYSIS AND MACHINE LEARNING APPLICATIONS IN ENVIRONMENTAL MANAGEMENT

The rapid expansion of data on air contaminants and climate change, particularly concerning public health, presents both opportunities and challenges for traditional epidemiological methods. This study aims to address these challenges by exploring advanced data collection, pattern identification, and predictive modeling techniques in the context of air pollution research. The focus is leveraging data mining and computational methods to enhance the understanding of air pollution's impact on public health, specifically ozone exposure. A comprehensive review of the scientific literature was conducted, utilizing databases such as Professor, Scholar, Embl, and Nih to identify relevant studies on air pollution epidemiology. The review highlights the integration of data mining, machine learning, and spatiotemporal modeling to improve the detection, analysis, and forecasting of air pollution-related health issues. The findings reveal a growing trend in applying data mining techniques within the field of air pollution epidemiology. Advanced methods, such as spatiotemporal analysis and geographic data mining, enable more precise tracking and forecasting of pollution-related health risks. Continuous advancements in artificial intelligence and the development of more sophisticated sensors and data storage technologies are enhancing the accuracy and reliability of air quality monitoring and public health predictions. This study highlights the transformative potential of integrating data mining and AI techniques into air pollution epidemiology. Exploring emerging technologies like spatiotemporal mining and next-generation sensors paves the way for more accurate, timely, and scalable solutions to monitor air quality and predict its impact on public health, opening new avenues for research and policy interventions.

Potential of multimodal large language models for data mining of medical images and free-text reports

Medical images and radiology reports are essential for physicians to diagnose medical conditions. However, the vast diversity and cross-source heterogeneity inherent in these data have posed significant challenges to the generalizability of current data-mining methods for clinical decision-making. Recently, multimodal large language models (MLLMs), especially Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models, have revolutionized numerous domains, significantly impacting the medical field. In this study, we conducted a detailed evaluation of the performance of the Gemini series models (including Gemini-1.0-Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash) and GPT series models (including GPT-4o, GPT-4-Turbo, and GPT-3.5-Turbo) across 14 medical datasets, covering 5 medical imaging categories (dermatology, radiology, dentistry, ophthalmology, and endoscopy) and 3 radiology report datasets. The investigated tasks encompass disease classification, lesion segmentation, anatomical localization, disease diagnosis, report generation, and lesion detection. Moreover, we also validated the performance of the Claude-3-Opus, Yi-Large, Yi-Large-Turbo, and LLaMA 3 models to gain a comprehensive understanding of the MLLM models in the medical field. Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization. Conversely, GPT-series models exhibited proficiency in lesion segmentation and anatomical localization but encountered difficulties in disease diagnosis and lesion detection. Additionally, both the Gemini series and GPT series contain models that have demonstrated commendable generation efficiency. While both models hold promise in reducing physician workload, alleviating pressure on limited healthcare resources, and fostering collaboration between clinical practitioners and artificial intelligence technologies, substantial enhancements and comprehensive validations remain imperative before clinical deployment.

Reinforcement learning for occupant behavior modeling in public buildings: Why, what and how?

Effective control of energy consumption in public buildings holds paramount significance for global sustainable development. However, uncertainty in occupant behavior during operational phase often leads to a substantial discrepancy between the designed and actual energy consumption. While there is existing research on occupant behavior in public buildings utilizing stochastic modeling, statistical modeling, data mining, and agent-based modeling methods, the connotations and formation mechanisms have not been systematically revealed, rendering these developed models weak in applicability and reliability in practical scenarios. Reinforcement learning, which can perceive aspects of external environment and learn from stochastic interactions spontaneously, has been distinguished as a promising method for occupant behavior modeling. This research aims to develop a paradigm for reinforcement learning application in occupant behavior modeling in public buildings. Semi-structured interviews and literature review were combined to collect relevant information, while reasons for using reinforcement learning and applicable algorithms were discerned through qualitative analysis. Finally, detailed frameworks were proposed and validated through focus groups, offering a comprehensive guide that will serve as a reference for future research. This includes steps for collecting occupant-related data, selecting appropriate algorithms, and training and deploying reinforcement learning agents effectively. This research innovatively proposed a “panorama” of reinforcement learning for occupant behavior modeling in public buildings, laying a solid foundation for indoor environment and energy efficiency improvement in public building operations.