Data Mining Research Articles

The Use and Development of Artificial Intelligence in Architectural Design Processes

Artificial intelligence is widely used as an interactive technology in various professional disciplines. The widespread use of these technologies, which we benefit from in most areas of our lives, in the education sector will provide important developments in the field of education. The main purpose of this study is based on analysing the existing studies in which the use of artificial intelligence helps in architectural design processes. In the study, identification, screening, eligibility, inclusion and data analysis processes were carried out in three search engines such as Web of Science, ScienceDirect and ULAKBIM. While reporting the research, ‘Systematic Literature Review’ and ‘Preferred Reporting Items for Meta-Analysis’ protocols were followed and a total of 35 relevant articles were identified. In the research, three popular Artificial Intelligence applications used in architectural design processes were identified as Generative Adversarial Networks (GAN), Machine Learning and Data Mining. In addition, Systematic Literature Review (SLR) outputs show that most researchers are supported by artificial intelligence applications in architectural design processes. As a result of the research, it was determined that artificial intelligence is widely used in architectural design processes, however, it has positive effects in 3D and animation parts.

Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis.

Linking past insights with contemporary understanding: an ontological and knowledge graph approach to the transmission of ancient Chinese classics

Ancient Chinese classics embody and transmit the intellectual heritage of China across generations. These texts, rich in enduring ideas, narratives, and insights, have been passed down through a lengthy historical process. While digital modeling of cultural heritage has advanced, research that integrates both the unique characteristics of these classics and their transmission processes remains limited. This study introduces ClaOnto, an ontology specifically designed for ancient Chinese classics. It captures the various versions of these texts and their interrelations, contents, related individuals, locations, periods, and languages. It also emphasizes the processes and evaluations involved in their transmission. This contributes to a more nuanced understanding and increased awareness of preserving ancient classics. ClaOnto comprises three modules: Basic Information, Transmission Information, and Evaluation Information. Its development involved adapting and enhancing existing mature ontologies, as well as designing new concepts and relationships to articulate the knowledge and intangible values transmitted through these classics across generations. Additionally, by employing methods such as data mining, text analysis, and data visualization, this study outlines a workflow for implementing this ontology-based knowledge graph, demonstrated using the Tao Te Ching as a case study. The methodological enhancement enriches the analysis of how the intangible value in classics manifests through transmission.

Construction of Student Performance Prediction Model Based on Data Mining and Optimized RBF neural Network

Construction of Student Performance Prediction Model Based on Data Mining and Optimized RBF neural Network

Strategic Transformation of E-Commerce Big Data Classification and Mining Algorithms Based on Artificial Intelligence Era

Strategic Transformation of E-Commerce Big Data Classification and Mining Algorithms Based on Artificial Intelligence Era

Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy.

To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in lung cancer through a systematic review and meta-analysis. Relevant studies were identified through a systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from October 2003 to December 2023. Additional studies were located by reviewing bibliographies and relevant websites. Two independent researchers screened titles, abstracts, and full-text articles according to predefined inclusion and exclusion criteria. Data extraction was performed using standardized forms, and study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes, including combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC), were calculated using STATA MP-64 software(Stata Corporation LLC, College Station, USA) with a random-effects model. Meta-analysis was conducted to synthesize diagnostic accuracy measures, and analyses of heterogeneity and publication bias were performed. A total of 1,406 patients with primary lung cancer were included in this systematic review, drawing data from 9 studies. The pooled analysis revealed a sensitivity of 0.74 [0.58-0.85] and a specificity of 0.91 [0.87-0.95] for machine learning models in diagnosing radiation pneumonitis. The positive likelihood ratio (PLR) was 8.69 [5.21-14.50], the negative likelihood ratio (NLR) was 0.28 [0.16-0.49], and the diagnostic odds ratio (DOR) was 30.73 [11.96-78.97]. The area under the curve (AUC) was 0.93 [0.90-0.95], indicating excellent diagnostic performance. Meta-regression analysis identified that the number of machine learning models, year of publication, and study design contributed to heterogeneity among studies. No evidence of publication bias was found. Overall, machine learning models incorporating multimodal characteristics demonstrated 75% accuracy in predicting moderate to severe radiation pneumonitis. In conclusion, by integrating the current machine learning (ML) algorithm's ability in big data mining, a predictive model can be constructed by combining multi-modal features such as genetics, imaging, and cell factors. By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved. PROSPERO (CRD42024497599).

Social Media Data Mining in Public Relations Research

Social Media Data Mining in Public Relations Research

GCM and gcType in 2024: comprehensive resources for microbial strains and genomic data.

Microbial culture collections play a crucial role in the collection, maintenance, and distribution of quality-assured living microbial strains, along with their associated phenotypic and omics data. To enhance the find-able, accessible, interoperable, and re-usable (FAIR) data utilization of microbial resources, the World Data Center for Microorganisms (WDCM) has developed the Global Catalogue of Microorganisms (GCM) and the Global Catalogue of Type Strains (gcType). These platforms provide interactive interfaces for cataloging the holdings of collections, along with detailed annotations of type strain genomes and curated metadata, including ecosystems, growth conditions, and collection locations. The system maximizes the scientific impact of microbial resources and culture collections through an integrated data mining tool that links strain- and species-related information from various public resources. Currently, the GCM and gcType include 574422 strains from 154 culture collections across 51 countries and regions, along with 25980 genomes from type species. Additionally, 2702655 articles and 103337 patents are integrated with these microbial resources.The system supports microbial taxonomic research and provides evidence for implementing the Nagoya Protocol in the field of microbial resources and their digital sequence information (DSI). Access is freely available at gcm.wdcm.org and gctype.wdcm.org.

Structure Prediction and Mechanical Properties of Tantalum Carbide (TaC) on ab initio Level

AbstractTantalum carbide (TaC) is an extremely hard, brittle, refractory ceramic material with excellent physical properties, which makes it a desirable material in e. g. aerospace industries. In order to explore the range of feasible modifications of TaC, we have executed a crystal structure prediction study of the TaC chemical system using a multi‐methodological approach, via enthalpy landscape explorations of pristine TaC at different pressures, supplemented by data mining searches in the ICSD database. Local structure relaxations have been accomplished by using Density Functional Theory (DFT). The global minimum is found to correspond to the equilibrium rock salt (NaCl) type modification. Additionally, eight new phases of tantalum carbide are predicted to be feasible: the WC‐type, the NiAs‐type, the 5‐5‐type, the ZnS‐type, the RingTaC‐type, the CsCl‐type, the OrthoTaC‐type, and the TetraTaC‐type. Furthermore, the elastic and mechanical properties of the predicted TaC modifications were explored on the DFT level of computation. The promising values of some of the mechanical properties of the proposed tantalum carbide modifications suggest that various scientific, industrial, and technological applications of TaC should be possible.

Predicting nodal response to neoadjuvant treatment in breast cancer with core biopsy biomarkers of tumor microenvironment using data mining.

To generate a model for predicting nodal response to neoadjuvant systemic treatment (NAST) in biopsy-proven node-positive breast cancer patients (cN+) that incorporates tumor microenvironment (TME) characteristics and could be used for planning the axillary surgical staging procedure. Clinical and pathologic features were retrospectively collected for 437 patients. Core biopsy (CB) samples were reviewed for stromal content and tumor-infiltrating lymphocytes (TIL). Orange Datamining Toolbox was used for model generation and assessment. 151/437 (34.6%) patients achieved nodal pCR (ypN0). The following 5 variables were included in the prediction model: ER, Her-2, grade, stroma content and TILs. After stratified tenfold cross-validation, the logistic regression algorithm achieved and area under the ROC curve (AUC) of 0.86 and F1 score of 0.72. Nomogram was used for visualization. We developed a clinical tool to predict nodal pCR for cN+ patients after NAST that includes biomarkers of TME and achieves an AUC of 0.86 after tenfold cross-validation.

Molecular Evolution and Adaptation Strategies in Marine Ciliates: An Inspiration for Cold-Adapted Enzyme Engineering and Drug Binding Analysis

In the present review, we summarize genome mining of genomic data obtained from the psychrophilic Antarctic marine ciliate Euplotes focardii and its evolutionary-close mesophilic cosmopolitan counterpart E. crassus. This analysis highlights adaptation strategies that are unique to the Antarctic ciliate, including antioxidant gene duplication and distinctive substitutions that may play roles in increased drug binding affinity and enzyme reaction rate in cold environments. Enzymes from psychrophiles are usually characterized by high activities and reaction rates at low temperatures compared with their counterparts from mesophiles and thermophiles. As a rule, catalyst cold activity derives from an increased structural flexibility that may lead to protein denaturation in response to temperature fluctuation. Molecular thermolability has been a major drawback of using macromolecules from psychrophiles in industrial applications. Here, we report a case study in which the role of peculiar amino acid substitution in cold adaptation is demonstrated by site-directed mutagenesis. Combined with a rational design approach, these substitutions can be used for site-directed mutagenesis to obtain cold-active catalysts that are structurally stable. Furthermore, molecular docking analysis of β-tubulin isotypes extrapolated from E. focardii and E. crassus genomes allowed us to obtain additional insight on the taxol binding site and drug affinity. E. focardii genome mining and the comparison with the mesophilic sibling counterpart can be used as an inspiration for molecular engineering for medical and industrial applications.

Integrated pan-cancer analysis and experimental verification of the roles of ZBED3 in kidney renal clear cell carcinoma

The present study investigated the Zinc finger BED-type containing 3 (ZBED3) mRNA expression levels, diagnostic and prognostic values, co-expressed and differentially expressed genes, immune infiltration, and functional enrichment analysis in patients with kidney renal clear cell carcinoma (KIRC) using various public databases such as The Cancer Genome Atlas, Genotype-Tissue Expression Project, LinkedOmics, Gene Set Enrichment Analysis databases, and the Xiantao academic tool. Through extensive data mining, our study revealed differential expression of ZBED3 in various tumor and paired normal tissues. Specifically, ZBED3 expression was found to be significantly decreased in KIRC, particularly in advanced pathologic stages and histologic grades, when compared to corresponding control tissues. Our analysis revealed that the overexpression of ZBED3 is associated with a favorable overall survival outcome in KIRC patients. Moreover, functional enrichment analysis identified key pathways related to inflammation response and DNA methylation of ZBED3 in KIRC. Additionally, our findings suggest that the upregulation of ZBED3 leads to the inhibition of cell proliferation by modulating cell-cycle progression in KIRC cell lines. Taken together, these results suggest that ZBED3 may serve as a promising biomarker and prognostic indicator for individuals diagnosed with KIRC.

Comprehensive Analysis and Verification of the Prognostic Significance of Cuproptosis-Related Genes in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We investigated alterations in DNA and chromatin of cuproptosis-related genes (CRGs) in COAD. In order to identify predictive differentially expressed genes (DEGs) and various molecular subtypes, we used consensus cluster analysis. Through univariate, multivariate, and Lasso Cox regression analyses, four CRGs were identified. A risk prognostic model for cuproptosis characteristics was constructed based on four CRGs. This study also examined the association between the risk score and the tumor microenvironment (TME), the immune landscape, and drug sensitivity. We distinguished two unique molecular subtypes using consensus clustering analysis. We discovered that the clinical characteristics, prognosis, and TME cell infiltration characteristics of patients with multilayer CRG subtypes were all connected. The internal and external evaluations of the predicted accuracy of the prognostic model built using data derived from a cuproptosis risk score were completed at the same time. A nomogram and a clinical pathological analysis make it more useful in the field of medicine. A significant rise in immunosuppressive cells was observed in the high cuproptosis risk score group, with a correlation identified between the cuproptosis risk score and immune cell infiltration. Despite generally poor prognoses, the patients with a high cuproptosis risk but low tumor mutation burden (TMB), cancer stem cell (CSC) index, or microsatellite instability (MSI) may still benefit from immunotherapy. Furthermore, the cuproptosis risk score positively correlated with immune checkpoint gene expression. Analyzing the potential sensitivity to medications could aid in the development of clinical chemotherapy regimens and decision-making. CRGs are the subject of our in-depth study, which exposed an array of regulatory mechanisms impacting TME. In addition, we performed additional data mining into clinical features, prognosis effectiveness, and possible treatment medications. COAD’s molecular pathways will be better understood, leading to more precise treatment options.

New Directions and Development Models for College Student Education based on Machine Learning Classification Algorithms

For the contemporary student teaching system, the information-based education management system has played a positive role in the rapid promotion of teaching. In order to get the teaching exploration data of modern information management from the data increment on the Internet, this paper updates the technology of data mining. This paper constructs a quantitative table for teacher positions and conducts ideological and political job evaluations in the database. This paper mainly focuses on the management and construction of ideological informatization based on the [Formula: see text]-means algorithm. By using the [Formula: see text]-means algorithm to analyze the daily behavior data of students, we can discover their behavior patterns and trends, providing decision support for student management. For example, by analyzing data such as students’ internet time, social activities, and library borrowing records, we can understand their interests, hobbies, and learning habits, providing a foundation for personalized education. Cluster analysis of students’ ideological and political performance using the [Formula: see text]-means algorithm can identify student groups with different ideological tendencies. This helps schools to carry out targeted ideological and political education work, and improve the pertinence and effectiveness of ideological and political education. Through quality tracking analysis of educational evaluation, it has carried out educational informatization tracking management using an information model. In the process of judging and analyzing different subjects, it utilized the hot topics of universities for a more comprehensive information intelligent tracking management.

Exploring Core Knowledge in Interdisciplinary Research: Insights from Topic Modeling Analysis

Although interdisciplinary research has garnered extensive attention in academia, its core knowledge structure has yet to be systematically explored. To address this gap, this study aims to uncover the underlying core knowledge topics within interdisciplinary research, enabling researchers to gain a deeper understanding of the knowledge framework, improve research efficiency, and offer insights for future inquiries. Based on the Web of Science (WoS) database, this study collected 153 highly cited papers and employed the LDA topic model to identify latent topics and extract the knowledge structure within interdisciplinary research. The findings indicate that the core knowledge topics of interdisciplinary research can be categorized into four major areas: the knowledge framework and social impact of interdisciplinary research, multidisciplinary approaches in cancer treatment and patient care, Covid-19 multidisciplinary care and rehabilitation, and multidisciplinary AI and optimization in industrial applications. Moreover, the study reveals that AI-related interdisciplinary research topics are rapidly emerging. Through an in-depth analysis of these topics, the study discusses potential future directions for interdisciplinary research, including the cultivation and development of interdisciplinary talent, evaluation systems and policy support for interdisciplinary research, international cooperation and interdisciplinary globalization, and AI and interdisciplinary research optimization. This study not only uncovers the core knowledge structure of interdisciplinary research but also demonstrates the effectiveness of the LDA topic model as a data mining tool for revealing key topics and trends, providing practical tools for future research. However, this study has two main limitations: the time lag of highly cited papers and the dynamic evolution of interdisciplinary research. Future research should address these limitations to further enhance the understanding of interdisciplinary research.

Optimization of frequent item set mining parallelization algorithm based on spark platform

In this paper, we propose a new method that combines the parallelism of the Spark-based platform with fast frequent mining, called STB_Apriori. Previous research has shown that traditional frequent itemset mining algorithms have high overhead when faced with large datasets and high-dimensional data computation, and generate a large number of candidate itemsets; at the same time, when faced with diverse user requirements, they often generate very sparse and diverse data. In order to solve the problem of fast mining of massive data, our idea originates from the capability of Spark distributed computing and the common optimisation ideas in Apriori mining, by using the efficient operator BitSet to achieve transaction compression, bit storage and data manipulation by Boolean matrices, and at the same time by parallelising the processing and optimising the algorithmic logic to achieve fast and frequent mining. In experiments on real-world datasets, our model consistently outperforms five widely used methods by a significant margin on very large data and maintains its excellence in the remaining cases, proving its effectiveness on real-world tasks, while further analysis shows that increasing the number of distributed nodes also incrementally and continuously improves performance.

Research on BP neural network model-based data mining technique in KOL identification

Abstract. Accompanied by the development of artificial intelligence industry, to play the data value of web crawler technology has been the focus of research in the field of computer network and data science, and the data mining technology based on artificial neural network intelligent algorithm is widely used. In view of this, this paper takes the BP neural network data mining technology, which has excellent nonlinear mapping capability, parallel processing capability and fault tolerance and is widely used, as the basis, and integrates the methods and ideas of data mining into the mining of data laws in the field of KOL identification of key opinion leaders, with a view to finding valuable intrinsic laws and relationships between the mining of web crawler technology and the identification of KOL features. The research content of this paper mainly includes two aspects of research work, the design of high-performance data mining technology and the actual work in the field of KOL recognition. On the one hand, this paper comprehensively describes the basic theory and methods of data mining, and focuses on the in-depth analysis and elaboration of BP neural network-based data mining technology on the basis of understanding and analyzing a variety of data mining technologies. On the other hand, this paper aims to solve the problem of bias in the prediction of traditional KOL model and design the experimental method of KOL recognition by BP neural network algorithm.

An Approach Based on Data Mining and Genetic Algorithm to Optimizing Time Series Clustering for Efficient Segmentation of Customer Behavior

An Approach Based on Data Mining and Genetic Algorithm to Optimizing Time Series Clustering for Efficient Segmentation of Customer Behavior

Research and performance analysis of random forest-based feature selection algorithm in sports effectiveness evaluation

The rapid progress in fields such as data mining and machine learning, as well as the explosive growth of sports big data, have posed new challenges to the research of sports big data. Most of the available sports data mining techniques concentrates on extracting and constructing effective features for basic sports data, which cannot be achieved simply by using data statistics. Especially in the targeted mining of sports data, traditional mining techniques still have shortcomings such as low classification accuracy and insufficient refinement. In order to solve the problem of low accuracy in traditional mining methods, the study combines the random forest algorithm with the artificial raindrop algorithm, and adopts a sports data mining method based on feature selection to achieve effective analysis of sports big data. This study is based on the evaluation method of motion effects using random forests, and uses feature extraction algorithms to study the motion effect impacts. It uses the information gain index to rank the importance of features and accurately gain the degree of influence of exercise on various indicators of the human body. Through simulation verification, the algorithm proposed by the research institute performs the best in accuracy and FI scores on the training and testing sets, with accuracies of 0.849 ± 0.021 and 0.819 ± 0.022, respectively, and F1 scores of 0.837 ± 0.020 and 0.864 ± 0.021, respectively. This indicates that the algorithm proposed by the research institute has high classification accuracy and performance proves that the Random Forest-based feature selection algorithm established in this study is superior to the existing traditional feature extraction and extraction methods in terms of both performance and accuracy. The proposal of this data analysis method has achieved accurate and efficient utilization of sports big data, which is of great significance for the development of the sports education industry.

Riboflavin transporter deficiency, the search for the undiagnosed: a retrospective data mining study

BackgroundRiboflavin transporter deficiency (RTD) is an inborn error of riboflavin transport causing progressive neurological symptoms if left untreated. While infants with symptomatic RTD rapidly deteriorate, presentation later in childhood or in adulthood is more gradual. Symptoms overlap with more common diseases, carrying a risk of misdiagnosis, and given the relatively recent discovery of the genetic basis of RTD in 2010 it is likely that older patients have not been tested. Treatment with oral riboflavin (vitamin B2) halts disease progression and can be lifesaving. We hypothesized that patients may have been left unrecognized at the time of presentation and therefore we performed a datamining study to detect undiagnosed RTD patients in a tertiary referral hospital.MethodsA systematic search in Electronic Health Records (EHR) of all patients visiting the Amsterdam University Medical Centers between January 2004 and July 2021 was performed by a medical data text-mining tool. Pseudonymized patient records, matching pre-defined search terms (hearing loss or auditory neuropathy spectrum disorders combined with key clinical symptoms or riboflavin) were screened and included if no definitive alternative diagnosis for symptoms indicating possible RTD was found. Included patients were offered genetic testing. We documented total number of patients with possible RTD, number of patients that underwent genetic testing for RTD and results of genetic testing.ResultsEHR of 2.288.901 patients were automatically screened. Thirteen patients with possible RTD were identified and offered genetic testing. Seven patients chose not to participate. Genetic testing was performed in 6 patients and was negative. The datamining did detect all previously known RTD patients in the hospital.ConclusionsBy screening a large cohort of patients of all ages in a tertiary referral hospital in a period spanning 17 years, no new RTD patients were found. Although not all suspected patients underwent genetic testing, our findings suggest that the prevalence of RTD is low and the chance of having missed this diagnosis in a tertiary referral hospital is limited.