Use Of Data Mining Techniques Research Articles

Predicting game-induced emotions using EEG, data mining and machine learning

BackgroundEmotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal’s changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers’ performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions.ResultsThe RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases’ results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm’s capabilities in precisely determining the emotion label for each participant’s EEG features’ instance.ConclusionThe findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm’s performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result.

Smart Medical Health Prediction System Application Using Data Mining

Abstract: This project can benefit from the use of data mining techniques in a variety of fields, including science, medicine, and education. Because of laws and computer accessibility, a lot of data is now available in the medical and health care sectors. It is no longer necessary to handle such a large amount of data at once because of the significant advancements in computer technology made by modern technology. Evaluating data mining methods for clinical and healthcare applications is one of the paper's main goals, which aims to facilitate informed decision-making. In the world of computers, this well-known and potent technology is highly sought after. It is a branch of computer science that creates new research and findings from data that is already available in various databases. It extracts new patterns from massive data sets and the knowledge associated with these patterns by utilizing Artificial Intelligence, machine learning, and database management techniques. This method can be used to extract data automatically or semi-automatically. Clustering, forecasting, path analysis, and predictive analysis are some of the various parameters used in data mining.

A Study of Role and Applicability of Data Mining Techniques in Intrusion Detection Systems

Abstract: The swift development of computers technologies changed the way data and information were kept. The risk of this data being exposed to uninvited and unauthorized users arises with this new paradigm of data access. Numerous systems have been created that examine data to look for deviations from a user's or system's typical behavior or look for a known signature in the data. Intrusion Detection Systems (IDS) is the name given to these systems. These systems use a variety of approaches, including machine learning algorithms and statistical methodologies. With the massive rise in the use of network-based services and information sharing on networks, network security has emerged as the fundamental component. The integrity, confidentiality, and availability of computer and network resources are all seriously compromised by intrusion, which also poses a severe risk to network security. Network audit data classification by humans is a costly, time-consuming, and laborious task. An intrusion detection system (IDS) is one tool used to find anomalies and attacks on a network. The network intrusion detection system has made extensive use of data mining techniques to extract valuable information from vast amounts of network data. This work proposes a hybrid model that combines two distinct intrusion detection techniques: anomaly-based and signaturebased. The model is separated into two stages. Systems for detecting intrusions make use of audit data produced by network devices, operating systems, and application software. These sources generate enormous databases that contain tens of millions of records. Data mining, which is the process of extracting meaningful patterns from a sizable amount of information, is used to analyze this data. The presented paper deals with the role and the applicability of data mining techniques in designing and developing the IDS Systems.

A Survey on Association Rule Mining for Enterprise Architecture Model Discovery

AbstractAssociation Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predictive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suitable for designing organizational structures. It uses viewpoints derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applications, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it discusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.

Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5

The Covid-19 pandemic has had an impact on the economies of countries in the world, including Indonesia. The low trade balance, public consumption and the implementation of large-scale social restrictions (PSBB) have caused business actors to suffer losses and even go out of business, especially Micro, Small and Medium Enterprises (MSMEs). Assistance to MSMEs has been provided by the government to maintain the functioning of the economy in the micro environment. However, the provision of this assistance needs to be carried out further analysis because there are reports that the assistance is not on target, causing the budget spent to be ineffective. In this research, an analysis will be carried out on the provision of assistance from the government to MSMEs, especially in Pekalongan district, using data mining techniques, especially classification. The algorithms used are K-Nearest Neighbor (K-NN) and C.45 which are then compared to determine the highest level of effectiveness in recommendations for providing MSME assistance in the District. Pekalongan. The data used was 312 MSMEs and after going through the data preprocessing process we got accurate data, namely 279. The data was divided into 2, namely training data of 200 data and testing data of 79 data. The results of this research, the K-NN algorithm obtained an accuracy level of 94.94%, precision 94.73% and recall 94.73%, while the C.45 algorithm obtained an accuracy level of 86.08%, precision 87.21% and recall 88.3%. Based on the results of this research, it can be concluded that the use of data mining techniques with the K-NN and C4.5 algorithms has a high level of accuracy for recommendations for assistance to MSMEs, however for the best results you can use the K-NN algorithm which has an accuracy level of 94.94%.

Improving the analysis of the judiciary performance - the use of data mining techniques to assess the timeliness of civil trials

PurposeThe purpose of this paper is to show how data mining techniques can improve the performance management of the judiciary, helping judges in steering position with specific and timely measures. It explores different approaches to analyse the length of trials, based on the case of an Italian judicial office.Design/methodology/approachThe paper presents a temporal analysis to compare the timeliness of trials, using data and process mining approaches with the support of a specific software to represent graphically the results. Data were gathered directly from the office data base, improving precision and the opportunity to monitor specific phases of the trials.FindingsThe results highlight the progress that can be reached using data mining approaches to develop performance analyses helping courts to correct inefficiencies and to manage the personnel distribution, overcoming the critical comments arisen against traditional KPI (Raine, 2000). The work proposes a methodology to analyse cases deriving from different juridical matters useful to set up a performance monitoring system that could be diffused to different courts.Research limitations/implicationsThe limitations of the research regard the analysis of a selected, limited number of cases in terms of judicial matters.Practical implicationsData mining techniques can improve the performance management processes in providing more accurate feedback to the judicial offices leaders and increasing the organisational learning.Social implicationsThe performance of the judiciary is one of the relevant issues that emerged in the recent decade in the field of public sector reforms. Several reasons explain this interest, which has gone beyond the specific legal disciplines to involve public policy, management, economics and ICT studies.Originality/valueConsidering the literature on the judiciary (Visser et al., 2019; Di Martino et al., 2021; Troisi and Alfano, 2023) the contribution differs as both the methodological approach and the predictive analysis considers the intrinsic differences that define cases belonging to different juridical matters performing a cross-sectional analysis, with a specific focus of process variants.

Monetary policy document analysis for prediction of monetary policy board decision

In terms of market capitalization, the bond market is larger than the stock market, and the bond market is affected by macroeconomic indicators. Despite this, there has been relatively little research, making it a good candidate for the use of data mining techniques. In this paper, a novel approach designed to predict the vote results of the Korean Monetary Policy Committee regarding the base interest rate was proposed. To predict sentence sentiment, prior monetary policy decision text was used as input for classification models. The sentence sentiment prediction model showed 83.7% performance when using a support vector machine. In addition, it was observed that the bigrams extracted from documents provided important descriptions of the Korean economy at the time. Finally, the document sentiment of monetary policy decision was calculated using aggregating sentence sentiment, and the vote results were predicted using this sentiment. As a result, when using the support vector machine to predict the Monetary Policy Committee vote results, the performance improved by 29.5% over the baseline model. Statistical tests confirmed whether there is a difference in document sentiments between unanimous and non-unanimous, and the null hypothesis was rejected at a significance level of 5%.

INSIGHTQUEST FROM DATA

Data mining is the process of discovering useful patterns and insights from large datasets, using statistical and machine learning techniques. It involves extracting knowledge from data and transforming it into an understandable structure for further use. Data mining algorithms can be used to analyze various types of data such as text, images, and videos, and can be applied to various domains such as finance, healthcare, and marketing. Data mining has many practical applications, such as customer segmentation, fraud detection, predictive modeling, and recommendation systems. It has become an important tool for businesses and organizations to gain insights from their data and make data-driven decisions. However, it also raises concerns about privacy, data protection, and ethics, as it involves handling large amounts of sensitive data. Therefore, ethical and responsible use of data mining techniques is crucial to ensure the protection of individual rights and the preservation of social values.

Evaluation of Artificial Intelligence and Efficacy of Audit Practice in Nigeria

Artificial Intelligence (AI) has become increasingly popular globally as a crucial tool for auditing financial statements, but in Nigeria, the adoption and use of AI tools by auditors is still in its early stages. Attention has been primarily focused on the Big 4 accounting firms, with little attention given to small-scale audit practitioners in Nigeria. This study seeks to examine the impact of AI on audit practice in Nigeria by employing a survey research design. The population of this study comprises 89 accounting firms operating in the Ikeja Local Government area of Lagos State, with a sample size of 62 firms selected using purposive sampling. Data was collected through a well-structured questionnaire, and the reliability of the research instrument was confirmed with a Cronbach Alpha test result of an average of 70%. Descriptive analysis and regression analysis were used to analyze the data, and the results indicated that data mining, machine learning, and image recognition exhibited a significant positive relationship with audit practice in Nigeria. The study concluded that the use of AI will enable auditors to predict future trends and make more informed decisions that focus on improving audit practice. The study recommended constant training of accountants and audit personnel on the use of data mining techniques to improve audit practice, investment in machine learning tools by audit firms in Nigeria, and increased use of image recognition to assist in object classification.

DATA AND WEB MINING

Data and web mining involves the use of data mining techniques to extract information from the web in order to gain insights into online behavior, customer preferences, and trends. The process requires data preprocessing to clean and prepare the data for analysis, and pattern recognition to identify relationships and patterns within the data. The benefits of data and web mining include improved decision-making and business growth, while challenges include the large volume of data and ensuring data quality. Web mining can be divided into three main categories: web content mining, web structure mining, and web usage mining. Web content mining involves extracting information from the content of web pages, such as text, images, and videos. Web structure mining involves analyzing the links between web pages to identify patterns and relationships. Web usage mining involves analyzing user behavior on the web, such as clickstream data and session logs, to extract useful information. KEYWORDS: data mining, web mining, information extraction, data preprocessing, pattern recognition, online behavior, customer preferences,

Recovering Microscopic Images in Material Science Documents by Image Inpainting

Microscopic images in material science documents have increased in number due to the growth and common use of electron microscopy instruments. Through the use of data mining techniques, they are easily accessible and can be obtained from documents published online. As data-driven approaches are becoming increasingly common in the material science field, massively acquired experimental images through microscopy play important roles in terms of developing an artificial intelligence (AI) model for the purposes of automatically diagnosing crucial material structures. However, irrelevant objects (e.g., letters, scale bars, and arrows) that are often present inside original microscopic photos should be removed for the purposes of improving the AI models. To avoid the issue above, we applied four image inpainting algorithms (i.e., shift-net, global and local, contextual attention, and gated convolution) to a learning approach, with the aim of recovering microscopic images in journal papers. We estimated the structural similarity index measure (SSIM) and ℓ1/ℓ2 errors, which are often used as measures of image quality. Lastly, we observed that gated convolution possessed the best performance for inpainting the microscopic images.

Data Mining in Auditing: Challenges and Opportunities

Audit of financial statements is an activity that can usually be carried out to evaluate the fairness of a company's financial statements carried out by an independent party. Assessment of the fairness of financial statements is very important because of the conflict of interest between the preparers of financial reports and stakeholders. The audit usually uses sampling to obtain data related to the fairness assessment. The auditor uses sampling because of time and cost constraints. Sampling also has a risk that can lead to errors in assessing the fairness of financial statements. Therefore we need a method that can improve accuracy in data collection and processing, for example data mining. Data mining is a method that can be used to collect and process data more quickly and accurately. The use of data mining techniques that may develop in the future can have an impact on the audit process. Data mining in audits will be both an opportunity and a challenge for auditors in the future.

Integrating expert system in managing basic education: A survey in Ghana

Management of basic education in developing countries like Ghana is a major challenge as resources are not adequately available for effective teaching and learning in schools. Careful planning and prediction using available data is also a major challenge as there are inaccuracies and inconsistencies in the available data. An investigation into the use of an Expert System for easy management of the resources is carried out in this research to know the level of readiness to accept an ES to assist in management. Stakeholders of education are contacted to solicit their views. With 216 districts for managing education in the country, a minimum of 3 participants were selected from each district to constitute a sample for the survey. In all 648 participants data were analyzed. The unstructured interview was also conducted using 9 members of an executive position in management. A thematic analysis was done on the responses and presented in diagrammatic form. The Acceptance Model for Educational Expert System (AMEES) is also presented. The results showed the majority of respondents agree with the use of an Expert System (ES) to assist in managing basic education. The use of data mining techniques to filter the data in an ES and help in easy prediction for decision-making accuracy is a necessity.

Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review

Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast capabilities. In this context, this paper sought to review the modeling procedure of GESM using ML models, including the required datasets and model development and validation. The results showed that elevation, slope, plan curvature, rainfall and land use/cover were the most important factors for GESM. It is also concluded that although ML models predict the locations of zones prone to gullying reasonably well, performance ranking of such methods is difficult because they yield different results based on the quality of the training dataset, the structure of the models, and the performance indicators. Among the ML techniques, random forest (RF) and support vector machine (SVM) are the most widely used models for GESM, which show promising results. Overall, to improve the prediction performance of ML models, the use of data-mining techniques to improve the quality of the dataset and of an ensemble estimation approach is recommended. Furthermore, evaluation of ML models for the prediction of other types of gully erosion, such as rill–interill and ephemeral gully should be the subject of more studies in the future. The employment of a combination of topographic indices and ML models is recommended for the accurate extraction of gully trajectories that are the main input of some process-based models.

Data Mining Pengelompokan Pembayaran Listrik Menggunakan Metode Clustering


 Electricity is a basic need that greatly influences all community and government activities. Almost all people from the upper to the lower classes, both in the fields of education, government, economics and social, really need a source of electrical energy as a means of supporting activities and productivity. PLN (Persero) is a State-Owned Enterprise (BUMN) that provides services to the community in the provision of electricity. The K-Means algorithm is one of the clustering technique algorithms that starts with random selection, which is the number of clusters that you want to form from the data to be clustered, namely electricity payments. the system made from k-means displays the results of the clustering of electricity payments, namely the pattern of electricity payments whose clusters are up, fixed and down. By utilizing a number of data owned by the agency, it can be grouped using data mining technology. The use of data mining techniques in classifying electricity payments is expected to be useful to simplify the process of searching for system data, which previously was still manual
 Keywords: Data Mining, K-Means, Electricity

A Course Level Analysis of Academic Performance on Adult Learners


 
 
 Thanks, in part, to the rapid development and widespread adoption of the Internet and other online technologies, academic institutions are increasingly using analytics to enhance learning and teaching. Through the use of data mining techniques, this study examines some of the determinants at a course level that affect the academic performance of adult learners (which we will refer to as students in this paper) in the Singapore University of Social Sciences (SUSS). Formerly known as SIM University, SUSS is an institution that caters mainly to the learning needs of working adults although it offers a number of full-time undergraduate degree programmes to fresh school leavers. The data analysis found that students taking introductory blended courses performed better than those who took face-to-face courses of the same level. Furthermore, students of similar age taking level-2 courses outperformed students taking similar courses where the age difference was more significant. The findings indicate that no single optimal course design will lead to improved academic performance across all courses. Instead, educators should be ready to consider the nature, level, discipline and coursework component of each course to cater to the various students’ needs.
 
 

Penerapan Algoritma C4.5 Untuk Memprediksi Penjualan Lele Pada Kolam Pancing Galatama

Advances in technology and information today can produce a smart innovation in business, which we can call business intelligence. In this technological advancement is the use of previous data which will later be used to obtain a data update to increase sales of a product in this case the technique is called a data mining technique which can extract useful information from the sales data warehouse. The use of data mining techniques is expected to help speed up the decision-making process,enabling decision-makers to manage the information contained in transaction data into new knowledge. Therefore the seller must be more careful in providing the type of product, in this case the researcher conducts research on the sale of catfish which will be in great demand, so that the seller will be able to provide recommendations for the type of catfish that has the most demand, so that the seller can classify types of catfish from the type, price, and weight of the catfish using aclassification method carried out using the C4.5 data mining algorithm. The C4.5 algorithm is a tree data type decision classification algorithm. The decision tree C4.5 algorithm is built in several stages which include selecting the attribute as the root, creating a branch for each value and dividing the case on the branch. This step will be repeated for each branch until all cases in the branch have the same class. From the results of tree testing of decision search results from sales datausing the RapidMiner Studio application, the results that have the highest gain in predicting catfish sales are weight and type so that it will produce the status of selling catfish in demand and those that are not selling by giving a value of >= 25 in katakana in demand. and <25 are said to be not selling well, so the Galatama pond manager can make a reference to pay attention to these two variables in selling catfish to get the best sales.

Brand position in the eyes of customers: assessment of selected airlines by the passengers' online reviews

Motivation: The motivation to write an article on airlines was the desire to rank them based on customer reviews and see how these reviews reflect the actual brand image. The opinions that companies collect about themselves have a very strong power when it comes to building its reputation. Aim: The aim of the study was to use digital transformation and transform raw data into specific information that expressed customer emotions to create a profile of selected airlines. A secondary goal of the article was also to check how the analyzed airlines perform in similar areas. Materials and methods: The data used for the analysis was collected from the eSky.com website and covers the 2019-2020 period. The airlines concerned by the customer reviews were LOT, Ryanair, Wizzair, Czarter, EasyJet, Lufthansa and Laudamotion. Their selection was dictated by the number of opinions necessary to conduct the analysis. The research based on the use of data mining techniques, but it should be noted that most of it uses text mining tools. Topic modelling was used to prepare the data properly and assign each word to groups with similar themes. In order to obtain information whether a given opinion has a positive, negative or neutral tenor, sentiment analysis was used. The final part of the analysis was based on the net sentiment score indicator. The entire analysis was carried out in the R-Studio. Results: The most common subjects of opinions written by customers were "delay", "service", "boarding" and "airline". It was confirmed that the opinions of each airline concern different topics, although some common topics were noticeable. Two topics were repeated among the 7 analyzed airlines: "service" and "delay". Based on the sentiment analysis, for the Ryanair airline the percentage of negative opinions was highest and equal to 35%, almost 40%, of neutral opinions fell on the WizzAir airline and the largest percentage of positive feedback, as much as 46%, was attributed to EasyJet. EasyJet line looks the best in the eyes of customers. The line that evoked uniformly positive, negative and neutral emotions in the opinions was Ryanair.

Promotion Strategy of Ideological and Political Education Management in Colleges and Universities Using Clustering Techniques

Computer-based technologies have changed our daily lives, such as the way we live, interact, learn, and play. Data collected through these technologies are now being used to support the second round of revolution in all of these areas. Data mining (DM) research has progressed rapidly in recent years, and the information design of educational management systems has also progressed significantly. Using big data technologies, several educational management systems have collected a great deal of information. However, the DM implementation in educational management systems is still in its initial stages. The use of DM techniques in educational management systems is theoretically important and practically useful. With the advancement of information technology (IT) in the field of college education, the present ideological and political theory courses in colleges and universities have gradually developed into information-based teaching, which has increased teaching efficiency and quality. This paper investigates the “work assessment quantitative table” of ideological and political education (IAPE) administration in universities using data mining and clustering technology techniques. In addition, this study utilizes the k-means clustering algorithms to analyze the data from the “work assessment quantitative table” of IAPE of university administration, which successfully overcomes the limitations and inadequacies of standard analysis approaches.

Use of data mining techniques to explain the primary factors influencing water sensitivity of asphalt mixtures

The water sensitivity of asphalt mixtures affects the durability of the pavements, and it depends on several parameters related to its composition (aggregates and binder) and the production and application processes. One of the main parameters used in the European Standards to measure the water sensitivity of asphalt mixtures is the indirect tensile strength ratio (ITSR). Therefore, this work aims to obtain a predictive model of ITSR of asphalt mixtures using several parameters that affect water sensitivity and assess their relative importance. The database used to develop the model comprises thirteen parameters collected from one hundred sixty different asphalt mixtures. Data Mining techniques were applied to process the data using Multiple Regression, Artificial Neural Networks, and Support Vector Machines (SVM). The different metrics analysed showed that SVM is the best predictive model of the ITSR (mean absolute deviation of 0.116, root mean square error of 0.150 and Pearson correlation coefficient of 0.667). The application of a sensitivity analysis indicates that the binder content is the parameter that most influences the water sensitivity of asphalt mixtures (26%). However, this property depends simultaneously on other factors such as the characteristics of the coarse and fine aggregates (24.9%), asphalt binder characteristics (19.3%) and the use of additives (10%).