Data Mining Methods Research Articles

How the crisis of trust in experts occurs on social media in China? Multiple-case analysis based on data mining

The importance of experts in the communication of scientific knowledge cannot be ignored, however, the crisis of trust in experts persists and is widening on social media in China. In this study, nine cases related to experts’ communication of scientific knowledge on Chinese social media Weibo during the period of 11 November 2022 to 22 January 2023 were selected based on the principles of high relevance, high discussion, and high visibility, and public comments on key post under the case were clustered and compared through data mining methods to understand what aspects of the public questioned the experts. The study found that the crisis of trust in experts on social media was the result of the combined effect of the experts, the media, and the public. Conflicting knowledge in expert systems, the perceived common-sense character of communicated knowledge, the lack of rigor and data-supported knowledge, the blurring of the boundaries between experts and administration and between experts and capital, and the dominance of instrumental rationality for experts could all affect public trust in experts. The media’s conscious or unconscious artistic processing of knowledge communicated by experts might mislead the public. The irrational emotion and populist tendencies of the public can amplify feelings of distrust towards experts. This study provides a theoretical and factual basis for understanding the mechanisms of the public’s behavior after receiving knowledge communicated by experts and for enhancing the effectiveness of science communication on social media.

Hybrid Machine Learning Approach using Real Time Data Processing for Educational Data Mining

Educational data produced by various systems, such as e-learning, student admission process, online student exam, and automated significance management solutions, may be successfully analyzed using educational data mining (EDM) methods to provide beneficial for the academic student’s progress. A highly desired use of EDM is the prediction of standardized testing from previous academic data. In this intelligence, it is critical to establish an automated approach for predicting student success. The majority of existing research on academic prediction problem use traditional feature extraction and illustration approaches, in which retrieved characteristics are feed to machine learning classifier. Supervised learning has recently allowed academics to extract high-level characteristics from raw data automatically. The proposed enhanced feature representation approaches allow for better performance on real time heterogenous data. In this paper, we demonstrate the Long Short-Term Memory (LSTM) with Recurrent Neural Network (RNN) called as RNN-LSTM. By examining current research difficulties based on evolving feature classification and prediction, we applied the most sophisticated LSTM paired with an attentiveness process approach in this work. Academicians, institutions, and governmental organizations benefit from this study since it allows them to anticipate achievement early. In extensive experimental analysis proposed work compared with numerous state-of-the-art, the better recurrent neural network functionalities of LSTM matching with the hybrid mechanism give higher performance. Predictive performance of 90.50% has achieved using the proposed algorithm.

Adaptive Data Sampling and Feature Optimization with LSH-IS and Pearson Correlation in Big Data Contexts

A huge amount of data is produced with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). Storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. Instance and Feature selection has been a key research area in data mining, which chooses a subset of relevant features for use in model building. This paper aims to provide an overview of instance and feature selection methods for big data mining. It first discusses the current challenges and difficulties faced when mining valuable information from big data.Instance and feature selection has become an effective approach due to enormous data which is continuously being produced in the field of research. It is difficult to process such large datasets by many systems. Though the traditional techniques are useful for large datasets, the numbers when in hundreds, thousands or millions face scaling problems. The proposed work focuses on, scalable instance and feature selection in big data environment. Locality-sensitive hashing instance selection (LSH-IS) is a two pass method used to find similar instances along with Pearson correlation coefficient for feature selection. Hash function family is used which is a general method of reducing the size of a set; this is achieved by re- indexing the elements into buckets. This process find similar instance in same bucket, hence instance can be reduced. The work aims at improving the performance of locality sensitive hashing by storing additional information of the instances and features assigned of each class in the bucket and also to improve accuracy of instance and feature selection algorithm.

Can computed tomography findings and radiomics analysis of mediastinal lymph nodes differentiate between sarcoidosis and lymphoma?

To assess the ability of computed tomography (CT) findings and radiomics analysis to differentiate mediastinal lymphadenopathies as sarcoidosis versus lymphoma. 94 patients with lymphoma and 97 patients with sarcoidosis, who had > 1cm mediastinal lymph node were included. Size, location of lymph nodes, and distribution of the largest lymph nodes in two groups were compared. A total of 636 lymphadenopathies in four different regions were segmented for radiomics. Lesion segmentation was semiautomatically performed with a dedicated commercial software package on chest CT images. 149 patients were grouped as a training cohort, while 42 patients who underwent CT in the oncology hospital were used for external validation. The least absolute shrinkage and selection operator (LASSO) analysis was used to perform feature selection. Using selected features, the classification performance of various data mining methods in separating groups of sarcoidosis and lymphoma was investigated. Distribution and size of lymphadenopathies were significantly different in sarcoidosis and lymphoma groups (<0.05). Radiomics and data mining methods showed excellent performance in differentiating lymph nodes of sarcoidosis and lymphoma according to both the largest lymphadenopathy and lymphadenopathies in four different mediastinal regions (AUC >0,95). Distribution and size of lymphadenopathies can help differential diagnosis in patients with sarcoidosis and lymphoma. CT radiomics analysis can discriminate the lymph nodes of sarcoidosis and lymphoma with great performance regardless of lymph node size and location and it can be used safely in the diagnosis of these diseases, which can sometimes be challenging to distinguish from each other.

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.

Identifikasi Teknik Data Mining Metode Asosiasi: Systematic Literature Review

Data mining is a process of collecting important information from large data. Data mining has many functions, one of which is the description function. In this case, the description function is that later data mining can find certain patterns hidden in data. This research aims to identify which association methods in data mining are most often used, identify the most dominant dataset models in association methods, identify the most relevant journal years in association methods, identify what fields most often use association methods, and find out the objectives of researchers using association methods such as FP-Growth and Apriori in various related journals. Using a Systematic Literature Review (SLR) approach, to answer the research questions that have been prepared from the analysis with a total of 33 related journals. So as a result, this study shows that the application of the SLR method can provide answers to the research questions that have been prepared.

Development of Mental Health and Social Rehabilitation Service Models for Psychosocial Disabilities

This study aims to determine the extent to which policies related to persons with disabilities and mental health can accommodate the rights of people with psychosocial disabilities, then the extent to which the model of mental health services and social rehabilitation for psychosocial disabilities contributes to mental health treatment in DIY. YAKKUM Rehabilitation Center through capital development of mental health services and post-recovery social rehabilitation that is accessible and community-resourced. This model is oriented towards improving the quality of life of people with psychosocial disabilities and the existence of communities that contribute proactively to community-based mental health efforts in DIY. The data mining method in this study uses a mixed method, namely discriptive studies, descriptive qualitative, participant as observer and participant non observer. Subjects in this study included people with psychosocial disabilities, caregivers, mental health cadres, village governments, puskesmas and related local organizations. The results of this study show that the service model developed by the YAKKUM rehabilitation center is able to encourage social recovery in people with psychosocial disabilities, strengthen their role in the community in community-based mental health, local mental health policies that accommodate the rights of people with psychosocial disabilities, the existence of a mental health service system that focuses on fulfilling the rights of psychosocial disabilities and social rehabilitation.

Assessment of hypertensive patients' lipid profile using data mining methods

Assessment of hypertensive patients' lipid profile using data mining methods

Identifying Learning Preferences and Strategies in Health Data Science Courses: Systematic Review.

Learning and teaching interdisciplinary health data science (HDS) is highly challenging, and despite the growing interest in HDS education, little is known about the learning experiences and preferences of HDS students. We conducted a systematic review to identify learning preferences and strategies in the HDS discipline. We searched 10 bibliographic databases (PubMed, ACM Digital Library, Web of Science, Cochrane Library, Wiley Online Library, ScienceDirect, SpringerLink, EBSCOhost, ERIC, and IEEE Xplore) from the date of inception until June 2023. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and included primary studies written in English that investigated the learning preferences or strategies of students in HDS-related disciplines, such as bioinformatics, at any academic level. Risk of bias was independently assessed by 2 screeners using the Mixed Methods Appraisal Tool, and we used narrative data synthesis to present the study results. After abstract screening and full-text reviewing of the 849 papers retrieved from the databases, 8 (0.9%) studies, published between 2009 and 2021, were selected for narrative synthesis. The majority of these papers (7/8, 88%) investigated learning preferences, while only 1 (12%) paper studied learning strategies in HDS courses. The systematic review revealed that most HDS learners prefer visual presentations as their primary learning input. In terms of learning process and organization, they mostly tend to follow logical, linear, and sequential steps. Moreover, they focus more on abstract information, rather than detailed and concrete information. Regarding collaboration, HDS students sometimes prefer teamwork, and sometimes they prefer to work alone. The studies' quality, assessed using the Mixed Methods Appraisal Tool, ranged between 73% and 100%, indicating excellent quality overall. However, the number of studies in this area is small, and the results of all studies are based on self-reported data. Therefore, more research needs to be conducted to provide insight into HDS education. We provide some suggestions, such as using learning analytics and educational data mining methods, for conducting future research to address gaps in the literature. We also discuss implications for HDS educators, and we make recommendations for HDS course design; for example, we recommend including visual materials, such as diagrams and videos, and offering step-by-step instructions for students.

K-Nearest Neighbor Method for Early Detection of Diabetes Patients Based on Symptoms and Clinical Data

Diabetes is a chronic disease rarely detected and develops quickly. Diabetes can trigger other chronic diseases such as kidney failure and heart disease. Early detection is necessary to help patients treat diabetes before the disease becomes more severe. Various health examination methods to detect diabetes, but these examinations require medical expert action and cannot be carried out by anyone. In addition, examination costs are often unaffordable. This research aims to apply data mining methods, especially k-Nearest Neighbor (KNN), for early detection of diabetes patients based on disease symptoms and patient clinical data. KNN is used to classify patient symptoms and clinical data into two classes, diabetes and non-diabetes, calculating the distance between test data and training data using Euclidean Distance. The research results show that a lower k-value provides a higher accuracy value. However, accuracy at low k-values ​​is insufficient to conclude the performance of KNN for early diabetes detection. High accuracy at low k-values ​​has the potential for overfitting, and the model is not generalizing well. Apart from that, if you use a low k-value, the model only sees patterns from 1 or a few neighbors, which results in the pattern of the data not being captured by the KNN model using a k-value that is too high also risks the model becoming underfitting. The model is too general, which makes the model unreliable. This research made use of the k-fold cross-validation technique to circumvent these issues. It is possible to avoid overfitting in the constructed KNN model by employing this method. The researchers are employing k-fold=10 and k-fold=20 in their investigation. KNN This research carried out this analysis by looking at the accuracy of each iteration of the k and k-fold values. The higher the k-fold value, the more accuracy the KNN produces. Inversely proportional to the k-fold cross-validation value, the higher the k-value in KNN, the decreases the accuracy. The KNN method applied in this research provides an accuracy of 98.2692% with higher precision than recall. These findings suggest that KNN can be an effective and efficient tool for early diabetes detection.

Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach

Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.

Prediction of heart disease using a novel slap swarm optimized multi-objective random forest

Heart disease is a leading cause of death worldwide, becoming a major health concern for many people. Among the most important tasks and regular diagnostic research entails the identified cardiac issues, like heart diseases, valve conditions, etc. Early detection of heart disease may save many lives. The application of machine learning techniques in the medical sector has advanced significantly. A novel Slap Swarm Optimized Multi-Objective Random Forest (SSO-MORF) approach was presented in the proposed work for predicting cardiac disease. For this suggested investigation, heart disease information using individual customer identification at the University of California Irvine (UCI) was utilized, and data mining methods like categorization were applied. Min–max normalization and principal component analysis (PCA) were used for data preparation and feature extraction. As a result, a rather basic supervised machine learning technique may be used to predict heart disease quite accurately and with excellent potential value. According to this study, a classification based on the RF technique produced results with 99.45% accuracy, 98.3% recall, 98.6% precision, and a 98.9% f1 score. This finding demonstrates that our system is more efficient at predicting cardiac disease than other cutting-edge techniques.

Data Mining Clustering Analysis of Child Growth and Development Using the K-Means Method

This research aims to group children based on their growth and development characteristics. This method helps identify groups of children with normal growth and development, early signs of growth and development problems, and serious growth and development problems. The stages used in this research follow the Knowledge Discovery in Database (KDD) process, which consists of data selection, data pre-processing, data transformation, data mining, and evaluation or interpretation of results. By applying the K-Means method, this research aims to provide a clearer and more detailed picture of the distribution of children's growth and development problems and assist in decision making for more appropriate interventions. The K-Means method in data mining was used to group 102 sample data into three clusters based on children's growth and development characteristics. The results of this analysis show that 38 samples fall into Cluster 1 (C1), 36 samples into Cluster 2 (C2), and 28 samples into Cluster 3 (C3). Evaluation of clustering results is carried out using Box Plot and Scatter Plot. Box Plot shows a clear distribution of data for each cluster, ensuring that the data grouping corresponds to statistical evaluation. Cluster C1 is toddlers with normal growth and development. Cluster C2 shows early signs of growth and development problems. Cluster C3 indicates serious growth and development problems

Using data mining to understand how technology is expressed in curriculums to mediate learning outcomes

The role of technology in education increases with the increasing need for students to master technology in their practice. However, mastering technology in modern practice has become increasingly complex and requires a deeper understanding of the technologies—technologies hold premises in terms of how they limit and enable actions. While correct use can improve practice, wrong use can harm and lay waste to resources and job satisfaction. Ensuring that students can manage this complexity requires that technology is actively reflected in realistic settings. However, how do the curriculums that steer education reflect the role of technology, and do the curriculums steer the learning of technology in an advantageous direction? In this study we investigate curriculums across educations to explore how technology is expressed. This exploration will be used to discuss the role of technology in education, how it is expressed in curriculums documents, and its potential advantages and problems. The exploration is done using text mining to identify and extract specific features of the natural language of the curriculums. The features extracted will be used to highlight specific patterns related to the use of technology in education. The contribution of this paper is twofold. Firstly, it provides a method for data mining to identify patterns of technological use in education, manifested in curriculums. Secondly, it shows how technology is viewed in education.

Применение машинного обучения и интеллектуального анализа данных в автоматизированной системе неразрушающего вихретокового контроля поверхностного слоя деталей подшипников

Changing the properties of the material during physical and mechanical processing can significantly reduce the working life of the manufactured product, therefore it is important to control the quality of the surface layer of the parts. To solve this problem, non-destructive testing techniques such as etching, visual, capillary, magnetic powder, ultrasonic, vibration, eddy current methods are used at bearing enterprises. The article discusses the physical foundations of the presented techniques and provides their comparative analysis. Machine vision and digital signal processing approaches can be used to automate the processing of the results of non-destructive testing of the surface layer of bearing parts within the framework of the Industry 4.0 concept. From the point of view of productivity and the possibility of integration into the production system, the eddy current method is the most promising, the result of surface control in this way is an array of digital values. The development of modern methods of information analysis makes it possible to efficiently process a large amount of data, and machine learning makes it possible to solve problems of classification, regression, etc. This article provides methodological support for the development and application of an automated eddy current control system using machine learning and data mining methods. The works of scientists devoted to the processing of the results of the shock control of various objects, including bearing parts, are considered, it is noted that previously attention had not been paid to the issue of a reasonable choice of a machine learning model for recognizing defects on the surface of parts. The possibility of using the median polishing method to transform the eddy current signal is shown. The development and implementation of a bearing defect recognition system based on the methodological support presented in this paper can significantly improve the efficiency of product quality control and optimize the technological process.

Association Relationship Analysis in Finding Sales of Goods With Apriori Algorithm

Technology can be designed to help human life from all aspects ranging from agriculture, health science, industry and daily life. Toko Intan, a business engaged in the sale of basic and daily necessities. Every day, Toko Intan records every sales transaction in an archive stored in Microsoft Excel, containing data on goods sold every day. The purpose of this research is to find out what items are bought simultaneously by consumers to manage inventory, with the data mining method used in this research is the Association Rules method. Association Rules is one of the data mining techniques from the a priori algorithm which functions to find a combination pattern of an item. Tests carried out to process data in this study using the RapidMiner application, from tests carried out with the specified parameters, namely minimum support 30% and minimum confidence 65%, resulted in a lift ratio validation rule of 1.206. Personal Care Biscuits with 30.8% support and 90.9% confidence with a validation lift ratio of 1.206. Sales transaction data analysis can be applied well, and is able to generate a new association rule from the sales transaction dataset. With this research, it is hoped that it can provide information to the owner of the Intan store in providing the stock of goods needed by consumers and to find out the combination of item sets from the sales transaction dataset.

Implementation of the C4.5 and Naive Bayes Algorithms to Predict Student Graduation

This research aims to determine student graduation using two data mining methods, namely the Naive Bayes Classifier and the C4.5 Algorithm. Research stages include data analysis, data pre-processing, model design in data mining, classification results, method evaluation, and evaluation results. This research uses student data consisting of training data and testing data to evaluate the performance of the two methods in predicting student graduation based on attributes such as attendance scores, behavior scores, Final Semester Examination (UAS) scores, and report card scores. The classification results show significant differences between the two methods. The Naive Bayes Classifier produces predictions that 37 students pass and 17 students do not pass, while the C4.5 Algorithm predicts that 30 students pass and 24 students do not pass. This difference in results indicates that there are differences in the approaches of the two methods to student graduation data, with the Naive Bayes Classifier tending to provide more positive predictions than the C4.5 Algorithm. Evaluation of the performance of the method shows that the Naive Bayes Classifier has an accuracy rate of 100%, which is a perfect result, while the C4.5 Algorithm has an accuracy rate of 89%. This significant difference in evaluation results confirms that the Naive Bayes Classifier is superior in classifying student graduation compared to the C4.5 Algorithm in the context of this research. These findings can help in making decisions regarding student graduation evaluations in the future.

Exploration of Data Mining Techniques in Business Decision-Making

This research examines the use of data mining techniques in business decision-making. By analyzing various data mining methods such as classification, clustering, and association, the study demonstrates how data mining can enhance operational efficiency and marketing strategies. The literature review provides insights into the practical applications and benefits of data mining across different industries. The research highlights the potential of data mining to uncover hidden patterns and trends in large datasets, which can be used to make more informed and timely business decisions. Additionally, the study identifies key challenges in implementing data mining, such as data integration, selecting appropriate algorithms, and interpreting results. The findings are expected to offer practical guidance for companies aiming to leverage data mining in their operations. By understanding the advantages and applications of data mining, businesses can improve their decision-making processes, optimize resource allocation, and develop more effective strategies. This research serves as a valuable resource for organizations looking to harness the power of data mining to gain a competitive edge in the market.

Algorithms for Route Planning and Navigation of Unmanned Aerial Vehicles

A brief overview of the results of recent research published in open sources in the field of route planning and navigation algorithms for unmanned aerial vehicles (UAV) is presented. Works devoted to global and local planning of trajectories taking into account known and detected obstacles in flight, as well as issues of navigation of groups of drones, are considered. Various approaches are analyzed, including graph algorithms (A*, Dijkstra, Rapidly-exploring Random Trees), methods of data mining in real time, potential fields. Special attention is paid to work on the use of neural networks and machine learning, SLAM and multi-agent technologies for planning UAV routes. The advantages and disadvantages of the main groups of algorithms are considered. A conclusion is drawn about the prospects for using hybrid methods, as well as machine learning technologies, to build intelligent UAV traffic control systems.

Probability Prediction of Curing Process-Induced Deformation for V-Shape Composite Structures Based on FEM Method and Data Mining.

Continuous fiber-reinforced composites are increasingly used in industry for their superior specific modulus and strength. The curing process-induced deformation (PID) has been a critical problem during manufacturing, which always exhibits dispersed values even if the curing process curve and structural parameters remain consistent. This work conducted probability prediction of PID for V-shape composite structures based on the FEM method and data mining. A sequential coupling thermal-chemical-mechanical coupling FE model is established in ABAQUS. The prediction accuracy of the included angle between two sides is verified by the experimental results. Material parameter uncertainties are considered for V-shape structures with different radii and thicknesses. Based on the dataset from the FE model, a decision tree is established and trained to analyze the sensitivity and to predict the probability distribution of PID. The results show that PID increases with the coefficients of thermal expansion in the in-plane perpendicular fiber direction and out-of-plane normal direction. The data-mining method is accurate enough for the PID prediction, and its efficiency provides an additional calculation option in engineering applications.