Use Of Data Mining Methods Research Articles

Implementasi Data Mining Klasifikasi Fuel Surcharge Menggunakan Algoritma Naive Bayes Studi Kasus PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok

In the application of fuel surcharge in PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok, it is necessary to display data that can help become the basis for the imposition of fuel surcharge from data on the movement of pilot vessels / tugboats which is the basis for the imposition of fuel surcharge in PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok, this research was conducted to help provide information to workers, Kesyahbandaran Otoritas Pelabuhan Utama and ship service users or shipping agents in more detail related to the imposition of fuel surcharge in the PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok environment, using data mining methods in fuel surcharge peneran as a consideration to make it easier to understand, easier to see and easier to operate. With this data mining method, it will help the company to become an important data and facilitate workers in the data collection section related to fuel surcharge in which there is a recap of the number of ship movements, the number of notes, the strength of groos tons (GT), house power (HP) of the ship and payment status. The use of data mining methods is expected to be a solution for workers in data collection and become a history of fuel surcharge collection within PT Pelabuhan Indonesia (Persero) Regional 2 Tanjung Priok.

Application of Intelligent Data Analysis to Predict the Employment Success of Socially Vulnerable Groups

In modern society, where social vulnerability is defined through the existence of population groups with limited material resources and a corresponding need for social support, complex challenges arise for researchers and practitioners in the field of socio-economic policy. The article discusses the use of data mining methods to predict the effectiveness of employment of socially vulnerable categories of the population. A multidimensional analysis of this issue was carried out, covering its various facets.

Examining Seasonal Changes in Light-Vehicle Traffic Volume on Freeways Under Extreme Weather Conditions: A Combination of Temporal Statistical and Data Mining Non-Parametric Techniques

Today, with the increasing changes in weather patterns and the huge amount of data related to weather and traffic in different parts of the freeway network, the use of data mining methods to quantify the impact of weather on traffic flow is inevitable. The main objective of this study is to present a geostatistical method for computing and analyzing the effects of base and extreme cases of weather variables on light-vehicle traffic volumes on freeways, with an emphasis on temporal changes on different days of the week and between daytime and nighttime. In the proposed method for statistical analysis, the parametric test of two-way analysis of variance was used. In the following, with the development of a nonparametric method based on the classification and regression tree (CART) decision tree algorithm, the weather-related parameters with the greatest effect on traffic volumes were investigated separately for different seasons. For this purpose, nine years of statistics covering traffic and weather data for the studied freeway were analyzed. The computational results show that a fall in temperature of more than 35% on weekdays during the cold season and the parameters of horizontal visibility and rainfall during the day and temperature during the night in the spring cause a reduction of traffic volume of more than 45%. This study has shown that the combination of data-driven parametric and nonparametric data mining techniques is effective for traffic managers in planning and traffic control under extreme weather conditions. In this regard, an adverse weather conditions dynamic message sign (AWCDMS) framework was proposed as a practical way to warn drivers of adverse weather.

Methodological Basis and Experience of Using Data Mining Methods in Trade

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.

Cancer detection in breast cells using a hybrid method based on deep complex neural network and data mining.

Diagnosis of cancer in breast cells is an important and vital issue in the field of medicine. In this context, the use of advanced methods such as deep complex neural networks and data mining can significantly improve the accuracy and speed of diagnosis. A hybrid approach that can be effective in breast cancer diagnosis is the use of deep complex neural networks and data mining. Due to their powerful nonlinear capabilities in extracting complex features from data, deep neural networks have a very good ability to detect patterns related to cancer. By analyzing millions of data related to breast cells and recognizing common and unusual patterns in them, these networks are able to diagnose cancer with high accuracy. Also, the use of data mining method plays an important role in this process. Using data mining algorithms and techniques, useful information can be extracted from the available data and the characteristics of healthy and cancerous cells can be separated. This information can be given as input to the deep neural network to achieve more accurate diagnosis. Another method to diagnose breast cancer is the use of thermography, which we use in this research along with data mining and deep learning. Thermography uses an infrared camera to record the temperature of the target area. This method of breast cancer imaging is less expensive and completely safe compared to other methods. A total of 187 volunteers including 152 healthy people and 35 cancer patients were evaluated. Each person had ten thermographic images, resulting in a total of 1870 thermographic images. Four alternative deep complex neural network models, namely ResNet18, ResNet50, VGG19, and Xception, were used to identify thermal images, including benign and malignant images. The evaluation results showed that the use of a combined method based on deep complex neural network and data mining in the diagnosis of cancer in breast cells can bring a significant improvement in the accuracy and speed of diagnosis of this important disease.

Prediksi Ketebalan Powder Coating Menggunakan Algoritma SVM Dan Naïve Bayes

Data Mining is a method that has been widely used to make scientific discoveries from a collection of datasets which so far have only been stored without further management. In the industrial world the use of data mining methods has helped with problems that are often found in the industrial field. Data mining helps in making predictions regarding thickness quality problems in a panel box product. Data mining is very useful for finding patterns in complex manufacturing data processing processes. Especially when we talk about consumers or service users of our product panels who want the panel to have good powder coating quality. This made the researchers conduct research to find the accuracy value which would later be used as a definite reference regarding the thickness of the powder coating. The results of this test the svm algorithm is better than naïve Bayes because the data in general can be categorized as a good result which has an accuracy of 97.60%, precision 99.56% and 96.03% recall. This res ult is an illustration for consumers to ensure that the panels to be purchased are of the best quality. By showing the data that has been processed, the consumer is sure that the purchase is really valid

A feature selection for video quality of experience modeling: A systematic literature review

AbstractQuality of Experience (QoE) multidimensional concept is the key for successful delivery of multimedia services. Higher user requirements for new experiences such as augmented reality, virtual reality, and future 6G services set higher requirements for QoE. A more complex QoE space requires the use of data mining methods in order to process the data for better QoE prediction. The increased dimensionality of the QoE space becomes a limiting factor for achieving the desired QoE prediction accuracy. Existing studies considering the QoE multidimensional concept with approaches that overcome the challenge of increased QoE space dimensionality are of great importance for future research. Accordingly, this article aims to review the applications of Feature Selection (FS) methods in video QoE modeling. It provides a comprehensive overview of the existing studies with the categorization and review of applied FS methods with reference to the data collection and data modeling steps. The analysis included 71 studies which provides overview of the FS methods applications in video QoE modeling depending on the input Influence Factor (IF) dimension sizes, type of IFs, QoE prediction methods used and QoE evaluation type. Our review revealed the advantages of using FS methods in video QoE modeling, frequency of application of FS methods with potential of applying more FS methods in a series or a parallel, gives an overview of the achieved dimensionality reduction degree for different methods, and provides insights in opportunities for researchers for applying FS methods on complex multidimensional QoE space.This article is categorized under: Technologies > Data Preprocessing Algorithmic Development > Multimedia

On the Vital Aspects and Characteristics of Cryptocurrency—A Survey

Cryptocurrencies acquire user confidence by making the whole creation and transaction history transparent to the public. In exchange, the transaction history accurately captures the complete range of user activities related to cryptocurrencies. It is thought to be one of the safest and simplest payment methods that may be employed in the future. The trend of banks and other financial institutions investing in cryptocurrencies has increased rapidly in recent years. Therefore, it is necessary to synthesize the findings of previous studies on cryptocurrencies. In this paper, the use of data mining methods in Bitcoin transactions is analyzed and summarized. Cryptocurrencies, similar to the well-known Bitcoin, were targeted to ensure transaction security and privacy and overcome the drawbacks of traditional banking systems as well as other centralized systems. In addition, a comprehensive analysis of the literature on the challenges and applications of electronic currencies is conducted. The evolution of digital currency from electronic cash to cryptocurrencies is summarized and the methods used to increase user privacy are highlighted. The security threats in existing cryptocurrency systems (that compromise the privacy of Bitcoin users) are also highlighted. Finally, several research gaps and trends are identified that need to be further explored.

The use of data mining methods to identify risks of fetal pathology

Reducing child mortality is reflected in several United Nations Sustainable Development Goals and is a key indicator of human progress. The use of information technology makes it possible to process a large number of generated statistical data, or to form new ones over time based on the results of analyses and examinations of a huge number of patients, use the results to determine deviations in the early stages and identify the influence of certain indicators on the likelihood of pathologies. Based on the analysis, the functional requirements for the system are formulated and the task of developing a new information and analytical system is set. The article is devoted to the actual problem of determining the risks of fetal pathology during pregnancy. A variant of the classification of the presence of fetal abnormalities using multinomial logistic regression and a method for clustering patient risk groups using the k -means method was proposed, an optimization technique based on the Broyden - Fletcher - Goldfarb - Shanno method was applied, where memory (L-BFGS) was used as a limitation, the classification of fetal health occurs according to the specified three classes. An information system has been developed to visualize the results of analytical methods. When creating the software package, the client - server architecture was used, which allows the server device to support multi-user mode and provide simultaneous work with several clients. The system includes: an application for Windows, an application for Android. Server interaction module, database interaction module, data analysis module, patient data processing module, doctor data processing module, module, statistical sampling module, personal data protection module, rights differentiation module, visualization module. The modular architecture allows you to upgrade the system, add new modules as needed and make changes to the modules while maintaining the integrity of the system. The developed system has passed the stages of testing and performance evaluation.

PRINCIPAL COMPONENT ANALYSIS OF PHOTON COUNTING HISTOGRAMS IN FLUORESCENCE FLUCTUATION SPECTROSCOPY EXPERIMENTS

An integrated approach based on the use of data mining methods has been proposed to improve the efficiency of the analysis of photon counting histograms in the study of the molecular composition of a substance by the method of fluorescence fluctuation spectroscopy. The method of principal components is used to test the hypothesis about the cluster separability of multidimensional experimental data. The reason for the compression of a point cloud into a characteristic nonlinearity, or so-called arc-shaped cloud, in the space of first two principal components is investigated. Examples of simulated data sets on some selected molecular systems of various brightness and concentration are considered. Nonlinear effects complicate interpretation and subsequent quantitative analysis of data. It has been established that the arching of the data cloud is a consequence of the presence of a significant variation in one or more physical parameters. In particular, it is the result of a significant increase in the variation in the parameters of the brightness or concentration of molecules. These parameters can be as additional measure in assessing the quality of the experiments if only one type of molecule is studied, and also can be used for characterizing the system under study in the case of a mixture of molecules of different types. It is proposed to apply the locally weighted scatterplot smoothing normalization to eliminate the nonlinear effects in the space of principal components.

Preparation of high school students for solving tasks of the data mining olympiads

The article describes the preparation of high school students for the Olympiads on the basics of artifcial intelligence. An analysis of existing Russian and foreign competitions in artifcial intelligence for schoolchildren is presented. The main technologies of artificial intelligence are described. The place of data mining is determined as one of the basic sections of data science and an important fundamental skill for the implementation of tasks in the feld of artifcial intelligence. The features of the implementation of such Olympiads are highlighted, the difculties that participants may encounter when performing Olympiad tasks are identifed. As a solution to the problem, it is proposed to create educational modules for preparing for Olympiads in artifcial intelligence.The structure and content of the educational module for preparing high school students for the Olympiad "The Way to Success" (section "Fundamentals of Artifcial Intelligence"), held on the basis of Nosov Magnitogorsk State Technical University. An analysis of the creative task for preparing for the fnal round of the Olympiad is presented. The task is associated with the use of data mining methods, in the course of its implementation, the students examined a data set — comments of Internet users about a foreign series in order to determine their attitude towards the main characters of the series, to identify countries where the series turned out to be the most popular and discussed.The results of the qualifying round of the Olympiad showed the following: the schoolchildren who completed the training within the framework of the educational module were able to get into the fnal of the Olympiad, the rest of the schoolchildren could not overcome the threshold value of the qualifying testing. The article notes that preparation for modern Olympiads in any subject area is an important factor for passing to the fnal. However, teachers of informatics and mathematics are not yet ready to help schoolchildren solve Olympiad problems in artifcial intelligence, it is important that such educational tracks are provided for the participants by the organizers of the Olympiads.

Exploration of Social Attitudes and Summarization of Film Reviews

The goal of this study was to identify the specific characteristics of internet reviews that made them useful. An online movie review site, Rotten Tomatoes, was used to gather online movie reviews. The online impact factor review methodology developed in this research makes use of data mining methods. A study found that article quality influences users' views of the effectiveness of interventions. When a reviewer has a happy mood, a strong emotional predisposition, and a high degree of review reliability, their material is more effective. Negative emotions, on the other hand, are less beneficial and weaker. Emotionally charged assessments are shown to have a greater number of reviews and tend to be more effective. The results of this study, which looked at how people evaluate online reviews, could help come up with new ways to predict economic success.

Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity

Usually, optimizing productivity and optimizing worker well-being are separate tasks performed by engineers with different roles and goals using different tools. This results in a silo effect which can lead to a slow development process and suboptimal solutions, with one of the objectives, either productivity or worker well-being, being given precedence. Moreover, studies often focus on finding the best solutions for a particular use case, and once solutions have been identified and one has been implemented, the engineers move on to analyzing the next use case. However, the knowledge obtained from previous use cases could be used to find rules of thumb for similar use cases without needing to perform new optimizations. In this study, we employed the use of data mining methods to obtain knowledge from a real-world optimization dataset of multi-objective optimizations of worker well-being and productivity with the aim to identify actionable insights for the current and future optimization cases. Using different analysis and data mining methods on the database revealed rules, as well as the relative importance of the design variables of a workstation. The generated rules have been used to identify measures to improve the welding gun workstation design.

COMPARISON OF MACHINE LEARNING METHODS FOR A DIABETES PREDICTION INFORMATION SYSTEM

Diabetesisadiseaseforwhichthereisnopermanentcure;therefore,methodsandinformationsystemsarerequired for its early detection. This paper proposes an information system for predicting diabetes based on the use of data mining methods and machine learning algorithms. The paper discusses a number of machine learning methods such as random forest, AdaBoost algorithm, multilayer perceptron, neuro–like structure of Consecutive Geometric Transformations Models (CGTM), linear regression based on the stochastic gradient descent, generalized regression neural network and regression based on the support vector machine. The Pima Indian Diabetes dataset collected from the UCI machine learning repository was used in the research. The dataset contains information about 768 patients and their corresponding nine unique attributes: the number of times of pregnancy; plasma glucose concentration for two hours in an oral glucose tolerance test; diastolic blood pressure; the thickness of the folds of the skin of the triceps; the concentration of serum insulin for two hours; body mass index; a function of diabetes heredity; the age of a person; the result of a variable class (0 – no diabetes, 1 – a sick person). The research has been carried out to improve the prediction index based on the Recursive Feature Elimination method. It was found that the logistic regression model performed well in predicting diabetes. It has been shown that in order to use the created model to predict the likelihood of diabetes mellitus with an accuracy of 78%, it is necessary and sufficient to use such indicators of the patient's health status as the number of times of pregnancy, the concentration of glucose in the blood plasma during the oral glucose tolerance test, the BMI index and the result of the calculation of the heredity functions "Diabetes Pedigree Function".

Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement.

Preface

Digital technologies transform all sectors of the economy, and the forest sector is no exception. Science is a knowledge source for all technology sectors. Throughout history, technological development could start only after initial accumulation of scientific knowledge. Now the situation has changed: there is no time to wait, and both – science and digital technologies – must develop simultaneously. That is why scientific conferences focusing on digital technologies are timely – they attract much attention from both academia and industry.In February 2021, Saint Petersburg State Forest Technical University organized a scientific and technical conference Digital Technologies in the Forest Sector.This conference is an annual event; it takes place in February – March. In 2021, due to Covid-19 restrictions, the conference was changed to a virtual format. The conference could not be postponed because of a busy event schedule of the conference organizer - Saint Petersburg State Forest Technical University in 2021. If the conference could not take place in a virtual format, the research community would have lost an opportunity to share ideas, information and research results in 2021. Finally it took place on 18–19 February 2021 and was successfully delivered in ZOOM.Over 120 professionals from nearly all subjects of the Russian Federation and Finland participated in the conference.The conference model was divided into plenary session and thematic sessions. The plenary session was organized on February 18, 2021, from 10:00 to 12:30. Each of the five plenary speakers were given 20–30 min for a presentation followed by Q&A.Thematic sessions were organized on February 18, 2021, from 13:30 to 17:00, and February 19, 2021, from 11:00 to 14:00. The topics of the thematic sessions were: Geoinformation technologies and remote sensing methods in forestry and forest inventory; Economic development based on the use of modern digital technologies; Digital forestry pedagogy; and Communication and web-technologies: opportunities and prospects of application in the forest sector. Each thematic session was followed by a Q&A session.The papers presenting the most interesting research results were assembled in a special issue of the journal IOP Conference Series Earth and Environmental Science. They cover a wide range of topics within the research areas including: rent theory; linear programming models; GIS technologies in forestry; robotic systems and application of ensemble machine learning methods; application of digital technologies in the economy and business process; use of data mining methods in planning and management at wood enterprises; prediction of occurrence of forest fires; application of laser technologies in remote sensing of forest areas; blockchain technology in prevention of illegal wood trade; analysis of wood structure; application of digital technologies in quantification and design of park, garden and urban tree structure. The organizes strived to cover all applications of digital technologies in the forest sector in one conference.The research results published will be of great interest to scientists, university lecturers and specialists of the forest sector, as well as to students studying forest sector related disciplines.List of Organizing committee, Program committee are availble in this pdf.

Use of data mining methods in planning and management at Russian wood enterprises

The purpose of this article is to disclose utilization capacity of the Data Mining method for data mining (DM) at Russian wood enterprises. The authors provide a rationale for use cases of the data mining technology – a combination of mathematical tools and recent developments in the technology industry –for the purpose of efficient system planning and management. On the basis of researches previously conducted by the authors, a list of problems and tasks is provided by the areas of activities of wood enterprises, resolved through the use of Data Mining methods.DM significance and relevance have been emphasized with regard to adoption of efficient managerial decisions in the area of production at Russian wood enterprises.

Predicting patterns of persistence at a South African university: a decision tree approach

PurposeThe emerging field of educational data mining provides an opportunity to process large-scale data emerging from higher education institutions (HEIs) into reliable knowledge. The purpose of this paper is to examine factors influencing persistence amongst students enrolled in a Chemistry major at a South African university using enrolment data.Design/methodology/approachThe sample consisted of 511 students registered for a Chemistry major beginning in 2012, 2013 and 2014. Descriptive statistics in counts and percentages and classification (decision) tree methods were used in the analysis.FindingsGraduation from the Chemistry major is likely to occur after 4 years, which is regulation time plus 1 year, whilst departure mainly occurs in the first year of study. Classification tree modelling demonstrated that first year accumulated credits (FYAC), gender, financial aid status and school quintile were the factors associated with persistence. FYAC was the most critical factor.Research limitations/implicationsAlthough this study has many strengths, significantly the use of data mining methods to classify students, some limitations might affect how the results are interpreted. First, the analysis focused on a one-degree major in one institution, which leads to the suspicion that the observed results are discipline or institution-specific. Thus, the findings cannot be generalised to other contexts or disciplines. Second, with so many potential factors influencing student persistence, the analysis presented in this paper, which was limited to the covariates obtained in the institutional dataset the authors used, is by no means exhaustive. There is the possibility that some factors, which are not included in the present analysis, might have more predictive power.Originality/valueGlobally, university administrators are interested in predicting student outcomes and understanding the intricate balance between enrolment and throughput. Thus, whilst the findings from this study have an institutional focus, they resonate with other HEIs and present an alternative and highly visual way of identifying specific combinations of factors associated with persistence. The results from a classification tree model can also classify students at risk and inform the development of interventions that will support them.

Data Mining Techniques Based Diabetes Prediction

Data mining plays an important part in the healthcare sector disease prediction. Techniques of data mining are commonly used in early disease detection. Diabetes is one of the world’s greatest health challenges. A widespread chronic condition is a diabetes. Diabetes prediction is a science that is increasingly growing. Diabetes prediction at an early stage will lead to better therapy. It is necessary to avoid, monitor and increase diabetes consciousness because it causes other health issues. Diabetes of type 1 or type 2 can lead to heart disorders, kidney diseases or complications with the eye. This survey paper reflects on numerous approaches and data mining strategies used to forecast multiple diabetes disorders at an early stage. Become a chronic disease because of diabetes. The patient lives will be spared by an early prediction of this disease. By the use of data mining tools and processes, diabetes is avoided and treatment rates are reduced. The association rule mining, classification, clustering, Random Forest, Prediction as well as the Artificial Neural Network (ANN) are among the most common and important data mining technology. Different data mining methods are available to avoid diseases such as cardiac disease, cancer including kidney etc. This study examines the use of data mining methods to predict multiple disease types.

Data Mining Techniques Based Diabetes Prediction

Data mining plays an important part in the healthcare sector disease prediction. Techniques of data mining are commonly used in early disease detection. Diabetes is one of the world's greatest health challenges. A widespread chronic condition is a diabetes. Diabetes prediction is a science that is increasingly growing. Diabetes prediction at an early stage will lead to better therapy. It is necessary to avoid, monitor and increase diabetes consciousness because it causes other health issues. Diabetes of type 1 or type 2 can lead to heart disorders, kidney diseases or complications with the eye. This survey paper reflects on numerous approaches and data mining strategies used to forecast multiple diabetes disorders at an early stage. Become a chronic disease because of diabetes. The patient lives will be spared by an early prediction of this disease. By the use of data mining tools and processes, diabetes is avoided and treatment rates are reduced. The association rule mining, classification, clustering, Random Forest, Prediction as well as the Artificial Neural Network (ANN) are among the most common and important data mining technology. Different data mining methods are available to avoid diseases such as cardiac disease, cancer including kidney etc. This study examines the use of data mining methods to predict multiple disease types.