Data Mining Methods Research Articles

ASSESSMENT OF HYPERTENSIVE PATIENTS’ COMPLEX METABOLIC STATUS IN THE NORTHEASTERN REGION OF HUNGARY USING DATA MINING METHODS

Objective: Previously, the metabolic status of Central and Eastern European hypertensive adult patients has not been thoroughly explored. Therefore, we aimed to identify the prevalence of hypertension and its associated metabolic abnormalities in a relatively large adult population using data mining methods. Design and method: We analyzed the data of adults who visited the University of Debrecen Clinical Center’s hospital (n = 937,249) by evaluating a period of 20 years (2001-2021). Results: We identified a total of 292,561 adults with hypertension (male/female: 45.4/54.6%), with a 31.2% calculated prevalence of hypertension in the whole study population and a higher prevalence in males (33%) compared to females (29.9%). We found an increasing prevalence in consecutive age groups both in males and females up to the age of 80. The ratio of males with hypertension was higher from 18 to 65 years of age. Markedly higher body mass index (BMI) values were found in hypertensive patients as compared to non-hypertensives (29.5±6.3 vs. 26.57±5.6 kg/m2, p<0.001). A significantly higher triglyceride level was found in hypertensive adults compared to non-hypertensives (1.46 (1.06–2.07) vs. 1.19 (0.82–1.73 mmol/L, p<0.001). We could not find significant differences in total cholesterol and LDL-C levels in the entire patient population (5.1±1.2 vs. 5.0±1.2 mmol/L and 3.1±0.99 vs. 3.1±1.0 mmol/L, respectively). Significantly higher serum glucose levels (5.8 (5.2–6.9) vs. 4.7 (4.6–5.8) mmol/L, p<0.001) were found in subjects with hypertension. Significantly higher urea and creatinine levels were found in hypertensive patients compared to normotensives (5.78 (4.6–7.5) vs. 4.6 (3.7–5.7) mmol/L, p<0.001 and 76 (64–92) vs. 69 (59–81) μmol/L, p<0.001, respectively). Furthermore, we also assessed the rate of significant concomitant diseases in our hypertensive population. Conclusions: The results proved that data mining is an excellent yet not-too-widespread method used to define prevalence in large patient cohorts. This novel way of statistical analysis could be beneficial not only for regional clinical practice but nationwide strategies as well.

Penerapan Metode K-Means Dalam Pengelompokkan Buku Untuk Menentukan Minat Baca Pada Perpustakaan Daerah Kota Medan

The library is a facility that functions as an information center, a source of various knowledge, research, recreation and cultural preservation. The Medan City Regional Library is a library organized by the regional government using general funds which aims to serve the public in obtaining comprehensive information without distinguishing gender, religion, race, age, occupation and position. In 2021 the Medan city regional library has 28 thousands of titles books in several categories. Of the many books contained in the library, a system must be needed where the system is useful for both the library and the reader in maximizing grouping of books and searching books easily by the reader, therefore the K-Means Clustering method is used, where the method This is a method in data mining that processes clustering data that is grouped into one or more clusters. In this study, 100 samples of book category data were used in the Medan City library. This study groups the data categories into 3 clusters, namely the most desirable, desirable and least desirable. The results of this method process will find out the most popular book category data so that in the future it will be a consideration for the librarian to increase the collection of books at the Medan City regional library. The process of calculating the K-Means method in grouping books is only carried out until the 2nd iteration because iteration -3 gets the same value. In Cluster 1 (Most Interested) choose 6 categories of books including Category 020-Library and Information, 070-Mass Media, Journalism and Publication, 050-Psychology, 420-Indonesian, 600-Technology, 650-Management. In Cluster 2 (Desired) choose 16 categories of books including 000-General Publications and General Information, 030-Encyclopedias and Books, 040-Biography, 050-Magazines and Journals, 090-Manuscripts and Rare Books, 210-Islamic Religion, 300-Science Social, 320-Political Science, 330-Economics, 410-Indonesian, 510-Mathematics, 620-Technical Sciences, 770-Photography and Photos, 780-Music, 910-General Travel Geography, 930-Old World History. And in cluster 3 (Less Interested) 78 categories of books were selected. 10 of them 010-Bibliography, 060-Association of Organizations and Museums, 080-Quotes, 100-Philosophy and Psychology, 110-Mathematics, 120-Epistimology, 030-Parapsychology and Occultism, 040-Philosophical Thought, 060 Logical Philosophy, 070-Ethics.

Does digital transformation benefit innovation in manufacturing firms? Empirical evidence based on data mining and textual analysis methods

With the increasing spread of the digital economy and the application of digital technologies, more and more manufacturing enterprises have begun to improve the efficiency of digital transformation with the help of big data and other digital technologies to promote the high-quality development of enterprises. Through the open big data platform, manufacturing enterprises can collect data and information reflecting the needs of users in a timely manner to achieve real-time optimization of the production process and timely detection of problems to a certain extent, thereby improving the ability to research and innovation. Through the establishment of econometric models and empirical tests, we also find that the education level of managers affects the process of digital transformation and influences the impact of digital transformation on innovation.

Advancements in Neural Machine Translation: Methodological Innovations and Empirical Insights for Cross-Linguistic Discourse Preservation

Abstract: Natural Language Processing (NLP), in the form recognizable today, really began to take hold in the 1980s, when machine learning helped propel it to soaring heights. However, due to a lack of processing power, machine learning, and to an extent, NLP, started slowing down in innovation and ideas and had almost ground to a relative halt, until the last decade, when a sudden increase in both productivity and interest in the machine learning helped increase the amount of knowledge in the space itself. This review provides several different case studies using different methodologies. The first paper was a deep analysis on how researchers were able to use Tesseract and Google Vision in tandem with automatic data mining methods to enrich the Cherokee language database in order to preserve it from extinction. The second paper takes a query translation-based approach toward translating English to Indian languages and utilizes a Multilingual Cross-Language Information Retrieval (MLCIR) system with tools such as Part of Speech Tagger (POST), Stop-Word, and Porter Stemmer. The third paper presents CoVe, which transfers knowledge from machine translation to improve performance on NLP tasks like sentiment analysis and question answering by using contextualized word vectors along with word embeddings, achieving new state-of-the-art results on some datasets. The fourth paper aims to translate English to Pakistan Sign Language (PSL) and also uses POST and goes through dependency analysis, sentence classification, and PSL using PLS trees. The fifth paper uses a Multilingual Neural Machine Translation (NMT) system for LowResource languages and incorporates two main models: a recurrent NMT and a transformer NMT. The sixth paper analyzes how a fine-tuned transformer model seems to work better than transformer models trained from scratch on high-resource languages, while vice-versa seems to occur for low-resource languages. The seventh paper adds to this by talking about how multilingual translation seems to work better than a back-translation model. Given the diverse array of approaches that could be used, we aim to identify the most efficient and correct methodology for future researchers to use in their work, based on the papers in this literature review.

Using data mining methods for risk assessment and intervention planning in diabetic patients-An exploratory study

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Predicting Diabetes Disease Using Data Mining Classification Algorithms and Comparison of Algorithm Performances

Data is always produced everywhere in the universe. In our globalizing world, where the internet is rapidly spreading, countless data set examples can be given. We produce data when we shop at a bookstore, make a transfer from our bank account, travel with an airline, or get tested in a hospital. Test results of hospital patients, production reports of a manufacturing factory, and exam results of students are examples of data sets. This data pile grows daily, and the data has no meaning. Data gains meaning by being processed by various tools and transformed into information. Today, information has become the most incredible power. In this study, a prediction study of diabetic patients was made with ten different algorithms from classification methods, one of the data mining methods. At the end of the prediction study, the algorithms' performances were compared. The unique aspect of this study is that the performance of the algorithms is compared by making predictions with ten different classification methods instead of just one or a few classification methods.

Audit Risks and Prevention Strategies of Corporate Accounting Based on Computer Technology

Business owners use professional auditors to do audits on their companies because auditors are essential partners in the growth of enterprises. Auditors may improve their ability to organize their audit work sensibly and provide accurate audit opinions if they effectively identify the risks associated with the audit. In this day and age of big data and the World Wide Web, businesses produce a significant quantity of data through the normal course of their activities. Using data mining techniques, deep learning, neural networks, and other developing technologies to extract excellent auditing data from the massive amounts of data produced by audited businesses is a significant challenge for auditors. As a result, the purpose of this research is to use computer data mining methods in order to develop an audit risk model. This model will give an example for auditors to use when doing big data analysis and will mine important data, which will ultimately result in an improvement in the audit process's efficiency as well as its correctness.

Application of Recurrence Plot Analysis to Examine Dynamics of Biological Molecules on the Example of Aggregation of Seed Mucilage Components.

The goal of the research is to describe the aggregation process inside the mucilage produced by plant seeds using molecular dynamics (MD) combined with time series algorithmic analysis based on the recurrence plots. The studied biological molecules model is seed mucilage composed of three main polysaccharides, i.e. pectins, hemicellulose, and cellulose. The modeling of biological molecules is based on the assumption that a classical-quantum passage underlies the aggregation process in the mucilage, resulting from non-covalent interactions, as they affect the macroscopic properties of the system. The applied recurrence plot approach is an important tool for time series analysis and data mining dedicated to analyzing time series data originating from complex, chaotic systems. In the current research, we demonstrated that advanced algorithmic analysis of seed mucilage data can reveal some features of the dynamics of the system, namely temperature-dependent regions with different dynamics of increments of a number of hydrogen bonds and regions of stable oscillation of increments of a number of hydrophobic-polar interactions. Henceforth, we pave the path for automatic data-mining methods for the analysis of biological molecules with the intermediate step of the application of recurrence plot analysis, as the generalization of recurrence plot applications to other (biological molecules) datasets is straightforward.

Implementation of Data Mining to Determine Public Interest in Automatic Motorcycles

Research on public interest in automatic motorbikes was carried out with the aim of understanding the factors that influence the decision to purchase an automatic motorbike. Using data mining methods, this research applies the K-Nearest Neighbor (KNN) and Neural Network techniques to identify and analyze people's interest patterns. The data used amounted to 139 samples, of which 127 showed interest in automatic motorbikes, while 12 others showed no interest. The research process begins with data analysis, the next stage is preprocessing, which includes data cleaning, in the model design stage in data mining, two models are built: one using KNN and the other using Neural Network. These two models are designed to classify sample data based on interest in automatic motorbikes. The next stage is model testing. Test results show that both models can classify interests accurately, with most of the sample data being classified correctly. Model evaluation was carried out to measure the effectiveness and accuracy of the two methods. The evaluation results show that both models provide very good performance, with results that almost reach a perfect score. This shows that both methods, KNN and Neural Network, are very effective in classifying and predicting people's interest in automatic motorbikes based on available data. In conclusion, this research not only shows the effectiveness of KNN and Neural Network in data mining for analyzing people's interests, but also provides valuable insights for automatic motorbike manufacturers and sellers about consumer preferences.

PENERAPAN ALGORITMA METODE NAÏVE BAYES UNTUK PENENTUAN PENERIMAAN BANTUAN PROGRAM INDONESIA PINTAR (PIP)

The Smart Indonesia Program (PIP) through the Smart Indonesia Card (KIP) is a government program offered in the form of direct education financing to students (6-21 years). KIP is an improvement part of the Poor Student Assistance (BSM) program since the end of 2014. The target of PIP at SMP PGRI 1 Cilacap is still not well targeted, due to the lack of criteria for KKS recipients. Therefore, the author added criteria for KKS recipients in the research. This research was created based on previously existing data, namely 100 training data and 9 test data using the Naïve Bayes data mining method and with 6 attributes, namely parents' occupation, number of dependents, parents' income, KIP recipients, KPS recipients, KKS recipients. The accuracy test results obtained were 88.89% and the Recall calculation was 85.71%.

Formulation of stress intensity factor under pure bending condition in multilayer pavements using numerical study and model tree approach

AbstractThe tensile deformation of top‐down cracks significantly influences pavement delamination. Accurately formulating the stress intensity factor (SIF) is crucial for assessing the stress/strain near crack tips. In the present study, a robust data mining method called M5´ is employed to formulate the SIF induced by pure bending in a multilayer pavement. A comprehensive database is generated through two‐dimensional finite element analysis to train the M5’ model. Several key parameters pertaining to asphalt and base layers are incorporated into the formulation. A comparative analysis is conducted to evaluate the performance of the developed model against traditional regression methods. A comparison of the developed model with other existing methods confirms its superior performance. Additionally, a parametric study validates the robustness of the developed model in capturing fundamental concepts of fracture mechanics. Finally, a sensitivity analysis is performed to determine the most critical parameters in the development of a predictive model for SIF.

APPLICATION OF ARTIFICIAL INTELLIGENCE FOR SOLUTION OF ENGINEERING PROBLEMS. ADVANTAGES AND CHALLENGES

The intensive development of information technology leads to an increase in data volumes, which requires the application of effective methods for data processing and analysis for managing organizations and strategic planning. Data mining methods (DM) are widely used in various fields, including education, where they can help improve the quality of learning and the efficiency of teachers. This work demonstrates the use of the Orange platform, a framework for data visualization and analytics, which allows integrating visual programming with Python to solve complex analytical tasks. The application of Data Mining methods and the use of Orange allow for a deep analysis of educational data, contributing to the development of strategies to improve the efficiency of the learning process.

Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.

Real And Fake Job Post Detection Using Machine Learning

The transition to online platforms has streamlined processes, reducing manual efforts. Job postings, now predominantly online, offer companies a broader reach for talent acquisition. However, amidst legitimate postings, fraudulent ones exist. This study aims to distinguish between real and fake job postings using machine learning techniques with optimal accuracy. Employing various data mining methods and classification algorithms—Logistic Regression, KNN, Decision Tree, XGBoost, Support Vector, Random Forest, and Multilayer Perceptron—we predict job authenticity. Supervised machine learning techniques guide dataset analysis, including variable identification and univariate, bivariate, and multivariate analyses, alongside handling missing values. Comprehensive data validation, cleaning, preparation, and visualization are conducted. Our experimentation, utilizing the Employment Scam Aegean Dataset (EMSCAD) with 17,881 samples, informs our approach. Ultimately, this research aims to enhance job seekers' security by effectively identifying fraudulent job postings. remove the writing issues. Key Words: Logistic Regression, KNN, Decision Tree, XGBoost, Support Vector, Random Forest, and Multilayer Perceptron.

Analysis of Student Excellence Classes in Data Mining Using the KNN Method

Excellent classes are programs designed to maximize the academic and non-academic potential of students and girls, with the aim of improving their overall achievement. This program aims to provide more intensive learning and a curriculum tailored to students' needs and abilities, so that they can develop their talents and competencies optimally. In order to evaluate the effectiveness of the superior class program and to identify students who are most suitable for the program, this research was conducted using the K-Nearest Neighbors (KNN) method in data mining. The research process includes several critical stages, namely determining relevant data, designing a machine learning model, testing the model to ensure its effectiveness, and evaluating the model to assess the accuracy and reliability of the results. This research used sample data consisting of 92 male and female students, where the results of the analysis showed that 42 of them met the criteria to enter the superior class, while 50 other students did not. These criteria are determined based on various factors, including academic achievement, participation in extracurricular activities, and other individual characteristics assessed through the KNN method. The accuracy results obtained from the model evaluation show excellent performance, confirming that the approach used is effective in classifying students based on their potential to excel in superior class programs. The conclusion of this research shows that the use of the KNN method in data mining can accurately identify students who will benefit most from superior class programs. Thus, this approach offers a valuable tool for educational institutions to optimize student potential and raise overall standards of achievement.

Study on dynamic response and long-term settlement of silty soil around Shanghai metro tunnel

At present, the method for calculating long-term tunnel settlement predictions under metro loading considers only one working condition of passenger loading, which is inconsistent with actual working conditions. To establish a tunnel settlement model that accounts for variations in passenger flow, this study uses data mining methods to categorize metro operation into three working conditions: "peak period, secondary period, and low period." The impact of these passenger flow conditions on the dynamic response of the soil around the tunnel is analyzed. Then, based on the principles of calculus, a calculus-based prediction model is established to consider the changing patterns of metro passenger flow. The model is applied to analyze the long-term settlement characteristics of Shanghai Metro Line 10. The results indicate that, under identical conditions, soil displacement and dynamic deviatoric stress around the tunnel increase with passenger capacity. The calculus prediction model aligns more closely with actual working conditions than the conventional model. The predicted tunnel settlement of Shanghai Metro Line 10 after 20 years of operation is approximately 37.07 mm, with most settlement occurring in the early stages, primarily due to cumulative plastic deformation of the soil.

Physicochemical reaction mechanism study and model optimization based on big data analysis

The huge amount of data generated by information technology, intelligence and automation has serious redundancy and duplication, which becomes a hidden cost of consuming resources. In the field of physicochemical analysis, for the explanation of mechanism, the analysis of big data can provide reference and integration. The purpose of big data-based analysis is to remove redundancies and inconsistencies in the given raw characterization data to better improve its completeness and accuracy. The paper takes the density matrix of quantum bits, which is different from the classical real number representation, etc. as the representation of feature data, and investigates the cleaning of feature data based on quantum representation from the perspective of the intersection of theoretical physics and computer science. This paper comprehensively describes the basic theories and methods of data mining. On the basis of understanding and analyzing a variety of data mining techniques, it focuses on the BP neural network-based data mining techniques to provide in-depth analysis and elaboration. Further, this paper proposes an improved algorithm with multi-algorithm advantage integration and joint optimization for the deficiencies in the BP neural network algorithm, which is fully demonstrated in the nonlinear function simulation. In addition, based on the HA-BP algorithm, this paper has designed anomaly data detection model and variable factor analysis model, and their reliability and practicality are also fully demonstrated in the nonlinear function simulation in the analytical study of physicochemical reaction mechanism.

Patient Journey of Generalized Pustular Psoriasis: A Real-world Study Using Data-mining Methods and Japanese Claims Data.

Patient Journey of Generalized Pustular Psoriasis: A Real-world Study Using Data-mining Methods and Japanese Claims Data.

Identifying disputants’ attitudinal variations in family mediations: A data mining approach

This article combines linguistic analysis and data mining methods to explore variations in speakers’ evaluative meaning-making in conflict talks. It focuses on conflict style construction through evaluative language, specifically how disputants advance attitudes. The corpus consists of 230 minutes of family mediation talks involving 12 divorcing spouses. The research draws from the Appraisal framework to analyse evaluative meaning-making at a discourse semantics level, capturing both explicit and implicit attitudes, as well as the scaling and dialogic framing of attitudes. Data exploration uses clustering algorithms via RStudio to identify variations in disputants’ discursive behaviour. The findings uncover three conflict styles based on disputants’ preference for attitude advancement formulations, with varying degrees of assertiveness and forcefulness. This study’s contributions include a holistic treatment of evaluative meaning-making, the marriage of digital tools to nuanced linguistic annotation, and a novel interpretation for conflict style. The findings offer fresh insights into disputants’ discursive self-presentation in confrontational exchanges.

Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques

The COVID-19 pandemic has profoundly reshaped human life. The development of COVID-19 vaccines has offered a semblance of normalcy. However, obstacles to vaccination have led to substantial loss of life and economic burdens. In this study, we analyze data from a prominent health insurance provider in the United States to uncover the underlying reasons behind the inability, refusal, or hesitancy to receive vaccinations. Our research proposes a methodology for pinpointing affected population groups and suggests strategies to mitigate vaccination barriers and hesitations. Furthermore, we estimate potential cost savings resulting from the implementation of these strategies. To achieve our objectives, we employed Bayesian data mining methods to streamline data dimensions and identify significant variables (features) influencing vaccination decisions. Comparative analysis reveals that the Bayesian method outperforms cutting-edge alternatives, demonstrating superior performance.