Condition-based maintenance for OEM’s by application of data mining and prediction techniques

This study aimed to identify patients with the highest risk of periodontal disease (PD), and to provide recommendations for the effective use and application of data mining (DM) techniques when establishing evidence-based dental-care policies for vulnerable groups at a high risk of PD. This study used the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology to construct DM models based on data acquired from the fifth and sixth Korea National Health and Nutrition Examination Surveys (2000-2015). We analyzed the sociodemographic and comorbidity variables that influence PD by applying the popular DM techniques of decision-tree, neural-network, and regression models, and also attempted to improve the predictive power and reliability by comparing the results obtained by these three models. Our comparisons of the three DM algorithms confirmed that the average squared error, misclassification rate, receiver operating characteristic index, Gini coefficient, and Kolmogorov-Smirnov test results were the most appropriate for the decision-tree model. The analysis of the decision-tree model revealed that age and smoking status exert major effects on the risk of PD, and that stress and education level exert effects in rural areas, whereas education level, sex, hyperlipidemia, and alcohol intake exert effects in urban areas. We demonstrated that the decision-tree model is an effective DM technique for identifying the complex risk factors for PD. These results are expected to be helpful in improving the equality and efficacy of dental-care policies for vulnerable groups at a high risk of PD.

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

The online version contains supplementary material available at 10.1007/s40200-021-00884-2.

Energy supply is characterized by its diversity, including traditional energy, such as fossil fuels, nuclear power, as well as renewable energy, such as solar, hydroelectric, geothermal, biomass, and wind energy. It involves a complex network system composed of energy generation, energy transformation, energy transportation, and energy consumption. The network does provide the great flexibility for energy transformation and transportation; meanwhile, it presents a complex task for conducting agile energy dispatching when extreme events have caused local energy shortages that need to be restored timely. One of the useful methodologies to solve such a problem is data mining and analysis. Their main objective is to take advantage of inherent tolerance of the imprecision and uncertainty to obtain tractability, robustness, and low solution-cost. The applications and developments of data mining and analysis have amazingly evolved in the last two decades. Many of these applications can be found in the field of renewable energy and energy efficiency where data mining and analysis techniques are showing a great potential to solve the problems that arise in this area. In this chapter, data mining and analysis techniques are briefly introduced. Then the implementation procedures are presented to demonstrate the application of curve fitting for renewable energy network design and optimization, which has the capability to handle the restoration during extreme and emergency situations with uncertain parameters.

With the requirement to find methods to analysis the rapidly increasing data, as a branch of computer science, data mining technique has been widely apply in different domain .One of the most enticing application areas of data mining in these emerging technologies is in finance. To complete the financial tasks, data mining technique is used refer to Stock market forecasting, Bankruptcy Prediction, Credit Risk Estimation, Corporate Performance Prediction and Management Fraud. This paper focuses on new data mining applications in finance. The fast development of Compute technology (such as Cloud Computing) and database together gives data mining a new challenge and chance in data mining of finance. How to apply the new computer and database technique to the data mining, and integrate the previous framework and the new technology together to build a more efficiency and safer framework is a long way to go in the future . Meanwhile, after accumulate enough empirical regularity and experiences, deep field-specific theories will be set up. This means, in the future, it will have more standardized and accurate model and method to apply in different domain of finance by generating more empirical regularities and combining them with domain knowledge.

Data mining techniques in social media: A survey

Shortage of water is gradually accelerated because a high standard of living is required and water resources are rapidly run dry. Therefore, effective water treatment is necessary to retain the required quality and amount of water. The general treatment includes coagulation, flocculation, filtering, and disinfections. Coagulation, flocculation, and disinfections are major parts of the water treatment processes. In this paper, new automatic algorithm is proposed for coagulation that is one of the water treatment processes. The proposed method is showing how to determine the coagulant soil and amounts using data mining techniques.

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Objectives: The nonlinearity of the stock market is widely accepted all over the world and to reveal such non-linearity the most effective technique has proved to be constructed through application of either data mining or neural network. Pharmaceutical sector is a rapidly growing in Bangladeshi stock market. The objective of this paper is to investigate whether the hybridization of data mining and neural network technique can be applied in predicting the stock price for Pharmaceutical sector of Dhaka Stock Exchange (DSE). Methods/Analysis: This study uses daily trade data for Pharmaceutical sector of DSE. We have analysed the behaviour of daily average price for Pharmaceutical sector of DSE. For this study, 6 top listed pharmaceutical companies have been selected to perform the analysis and selected time frame for the research is 15 years (2000-2015). The analysis is performed in two stages where first stage performs the K-means clustering of data mining method to discover the stock with most useful pattern and second stage applies the nonlinear autoregressive with Exogenous Input neural network method to predict the closing price for the selected stock. Findings: The prediction performance through the hybridization of data mining and neural network technique is evaluated and positive performance improvement of prediction is observed which is very encouraging for investors. The research also depicts that hybridization of data mining and neural network technique can be applied in determining the stock investment decision for Pharmaceutical sector of DSE though the impact of many different information has greater influence in determining the stock price. Novelty/Improvement: We intend to apply the data mining and optimized neural network in predicting stock market. We would like to work with the parameter and learning of the neural network to achieve better result. We will further investigate the effect of various factors viz. dollar price, gold price, FDI, bank interest rate etc. on stock price and index movement.

The application of Data Mining (DM) techniques for DQ, often called Data Quality Mining (DQM), offers a wide range of possibilities for DQ assessment. The goal of this work is to propose a mechanism for data currency assessment using statistics and DM techniques. The proposed approach consists on estimating the validity period for the entities using a training set and then evaluating the probability of currency of the last known data value for each entity. The proposed scheme helps in two ways to lead to an always up-to-date database: it can warn if a certain data value is becoming obsolete, and it can inform the data manager about the best frequency for updating data.

A cosmetic cheerful shop located in the Marelan area of Medan city is a shop that sells women's cosmetic equipment. The owner of a cheerful cosmetics shop is very disciplined in recording sales and purchases of goods. Every transaction is always recorded and stored in the Microsoft Excel application. Meanwhile, the Ceria Cosmetics store has been running since 2015 and has recorded lots of transactions, both sales and purchases. From 2015 until now the data has been increasing, and so far this data has only been a record and stored in a folder on the store's computer. From the piles of transaction data at the cheerful cosmetics store, the authors took the idea of research to utilize this pile of data. One of the applications of information technology that can be used to utilize the pile of data is the data mining technique. By using data mining, a pile of data from a cheerful cosmetic store can be used to find association patterns. And the application of data mining with association rules can use the FP-Growth algorithm. The FP-Growth algorithm can be used as an alternative to determine the most frequently occurring data set (frequent itemset) in a data set. Later, the combination patterns obtained in this study can be used to arrange the layout of goods (cosmetics) at the Ceria Cosmetics store in Medan Marelan.

Data mining techniques provide people with new power to research and manipulate the existing large volume of data. A data mining process discovers interesting information from the hidden data that can either be used for future prediction and/or intelligently summarising the details of the data. There are many achievements of applying data mining techniques to various areas such as marketing, medical, and financial, although few of them can be currently seen in software engineering domain. In this paper, a proposed data mining application in software engineering domain is explained and experimented. The empirical results demonstrate the capability of data mining techniques in software engineering domain and the potential benefits in applying data mining to this area.

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

The objective of data mining is to find the useful information from the huge amounts of data. Many researchers have been proposed the different algorithms to find the useful patterns but one of the most important drawbacks they have found that data mining techniques works for single data table. This technique is known as traditional data mining technique. In this era almost all data available in the form of relational database which have multiple tables and their relationships. The new data mining technique has emerged as an alternative for describing structured data such as relational data base, since they allow applying data mining in multiple tables directly, which is known as Multi Relational data mining. To avoid the more number joining operations as well as the semantic losses the researchers bound to use Multi Relational Data Mining approaches. In this paper MRDM focuses multi relational association rule, Multi relational decision tree construction, Inductive logic program (ILP) as well three statistical approaches. We emphasize each MR-Classification approach as well as their characteristics, comparisons as per the statistical values and finally found the most research challenging problems in MRDM.

Breast cancer is the most common invasive cancer in females worldwide. Breast cancer diagnosis and breast cancer prognosis are the two important challenges for the researchers in the medical field and also for the practitioners. If the cells in the breast start to grow without any control, it leads to cancer. Normally, the growth of the lump can be seen using x-ray. The benign and malignant breast lumps are distinguished during breast cancer diagnosis. The prognosis process predicts the period at which the breast cancer is likely to reappear in patients who have had their cancers removed. Data mining techniques and machine learning algorithms are mostly used in the whole process of breast cancer diagnosis and treatment. They utilize the large volume of breast cancer data for extracting knowledge. The application of data mining and machine learning methods in biomedical research is presently vital and crucial in efforts to transform intelligently all available data into valuable knowledge.