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Journal articles on the topic 'Predictive Data Mining'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Gothane, Suwarna. "Predictive Analysis In Data Mining Using Weighted Associative Classifier." Indian Journal of Applied Research 1, no. 6 (2011): 115–19. http://dx.doi.org/10.15373/2249555x/mar2012/40.

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Rani, Kumari, and Saini Kavita. "Data Mining and Predictive Analytics for Injection Molding: An Analysis." Indian Journal of Science and Technology 16, no. 45 (2023): 4131–40. https://doi.org/10.17485/IJST/v16i45.2567.

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Abstract <strong>Objectives:</strong> The objective of the research is to focus on the quality product of injection molding for the automobile industry. The root cause of the defects in the product needs to be understood in order to improve the product quality. <strong>Method:</strong> The research represents an industry Standard Process for Data Mining (CRISP-DM) framework for molding quality improvement. The Logistic Regression, AI ML algorithm has been used to develop the model. Because Logistic Regression is a classification supervised algorithm and our dependent variable al

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B, Mohamed Nowfal. "Smart Health Prediction Using Data Mining." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1219–25. https://doi.org/10.22214/ijraset.2025.68454.

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This project focuses on developing a smart health prediction system using data mining techniques to enhance early detection and prevention of heart disease. By integrating electronic health records, medical databases, and wearable device data, the system leverages classification, clustering, and predictive modeling to identify key risk factors and estimate disease likelihood. The proposed approach enables healthcare providers to make informed decisions, personalize treatment plans, and implement proactive interventions, ultimately improving patient outcomes and reducing healthcare costs. This

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Duong, Xuan-Lam, and Shu-Yi Liaw. "Comparative Analysis of Data Mining Classification Techniques for Prediction of Problematic Internet Shopping." International Journal of Applied Sciences & Development 3 (June 14, 2024): 82–88. http://dx.doi.org/10.37394/232029.2024.3.7.

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As online shopping has surged, so do disorders on internet purchasing. This study aims to develop and compare predictive models that use data mining methods to predict problematic internet shopping. We used the Artificial Neural Network (ANN), CHAID with bagging, and C5.0 and compared them with traditional logistic regression to construct predictive models on a training cohort of 858 shoppers. Another cohort of 368 buyers was utilized to confirm the accuracy of the predictive model. The accuracy, sensitivity, specificity, and the ROC-AUC were used to assess the predictive performance. The C5.0

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Kumari, Rani, and Kavita Saini. "Data Mining and Predictive Analytics for Injection Molding: An Analysis." Indian Journal Of Science And Technology 16, no. 45 (2023): 4131–40. http://dx.doi.org/10.17485/ijst/v16i45.2567.

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Agrawal, Ankit, Sanchit Misra, Ramanathan Narayanan, Lalith Polepeddi, and Alok Choudhary. "Lung Cancer Survival Prediction using Ensemble Data Mining on Seer Data." Scientific Programming 20, no. 1 (2012): 29–42. http://dx.doi.org/10.1155/2012/920245.

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We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer. Carefully designed preprocessing steps resulted in removal/modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several supervised classification methods were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best

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Tang, Linqiang, and Chen Sian. "Educational Data Mining for Student Performance Prediction." Scalable Computing: Practice and Experience 26, no. 3 (2025): 1551–58. https://doi.org/10.12694/scpe.v26i3.4336.

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The topic of Educational Data Mining (EDM) has gained significant traction in improving the quality of education by identifying patterns and insights through the analysis of data gathered from diverse educational settings. In order to discover important elements that affect educational achievement and to give educators and policymakers with useful insights, this study investigates the use of machine learning techniques in predicting student performance. We use a variety of machine learning methods, such as decision trees, support vector machines, and neural networks, to create predictive model

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Rungta, Sakshi, Vanita Jain, and Akanksha Utreja. "Data Mining Engine using Predictive Analytics." International Journal of Computer Applications 121, no. 5 (2015): 22–26. http://dx.doi.org/10.5120/21537-4545.

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Sreejit Ramakrishnan. "The Importance of Data Mining & Predictive Analysis." international journal of engineering technology and management sciences 7, no. 4 (2023): 593–98. http://dx.doi.org/10.46647/ijetms.2023.v07i04.081.

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Data mining is the process of analyzing enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements. Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. Data mining is a wide-ranging and varied process t

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Егорова, Е. С., and Н. А. Попова. "Data Mining in education: predicting student performance." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 11, no. 2(41) (2023): 3–4. http://dx.doi.org/10.26102/2310-6018/2023.41.2.003.

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Способность прогнозировать академические результаты учащихся имеет ценность для любого учебного заведения, стремящегося улучшить успеваемость и мотивацию студентов. Основываясь на сгенерированных прогнозах, учащимся, выявленным как подверженным риску отчисления или неуспеваемости, может быть оказана поддержка более своевременным образом. В статье рассмотрены различные классификационные модели для прогнозирования успеваемости студентов, используя данные, собранные в университетах г. Пензы. Данные включают сведения о зачислении студентов, а также данные о деятельности, полученные из университетс

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Bozorg-Haddad, Omid, Mohammad Delpasand, and Hugo A. Loáiciga. "Self-optimizer data-mining method for aquifer level prediction." Water Supply 20, no. 2 (2019): 724–36. http://dx.doi.org/10.2166/ws.2019.204.

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Abstract Groundwater management requires accurate methods for simulating and predicting groundwater processes. Data-based methods can be applied to serve this purpose. Support vector regression (SVR) is a novel and powerful data-based method for predicting time series. This study proposes the genetic algorithm (GA)–SVR hybrid algorithm that combines the GA for parameter calibration and the SVR method for the simulation and prediction of groundwater levels. The GA–SVR algorithm is applied to three observation wells in the Karaj plain aquifer, a strategic water source for municipal water supply

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FAN, CHEN MENG, AMIYA BHAUMIK Dr., and URMISHA DAS Dr. "PREDICTIVE DIAGNOSIS THROUGH DATA MINING FOR CARDIOVASCULAR DISEASES." Xi'an Shiyou Daxue Xuebao (Ziran Kexue Ban)/ Journal of Xi'an Shiyou University, Natural Sciences Edition 66, no. 09 (2023): 99–110. https://doi.org/10.5281/zenodo.8379632.

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<strong>Abstract</strong> Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and early detection and accurate diagnosis are critical for effective treatment and prevention. Data mining techniques have emerged as powerful tools for analyzing large datasets to extract meaningful patterns and make predictions. This research paper aims to explore the application of data mining in predictive diagnosis for cardiovascular diseases. The study will start by collecting a comprehensive dataset comprising patient information, including demographics, medical history, lifestyle facto

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Yaacob, Wan Fairos Wan, Syerina Azlin Md Nasir, Wan Faizah Wan Yaacob, and Norafefah Mohd Sobri. "Supervised data mining approach for predicting student performance." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (2019): 1584. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1584-1592.

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<span>Data mining approach has been successfully implemented in higher education and emerge as an interesting area in educational data mining research. The approach is intended for identification and extraction of new and potentially valuable knowledge from the data. Predictive model developed using supervised data mining approach can derive conclusion on students' academic success. The ability to predict student’s performance can be beneficial for innovation in modern educational systems. The main objective of this paper is to develop predictive models using classification algorithm to

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Anatoli, Nachev. "Data Mining Techniques for Analysing Employment Data." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 555–66. https://doi.org/10.35940/ijeat.B3311.129219.

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This paper proposes a methodology that uses a large-scale employment dataset in order to explore which factors affect employment and how. The proposed methodology is a combination of predictive modelling, variable significance analysis, and VEC analysis. Modelling is based on logistic regression, linear discriminant analysis, neural network, classification tree, and support vector machine. Following the CRISP-DM standard process model, we train binary classifiers optimising their hyper-parameters and measure their performance by prediction accuracy, ROC analysis, and AUC. Using sensitivity ana

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Mamaril, Julius Cesar O., and Melvin A. Ballera. "Multiple educational data mining approaches to discover patterns in university admissions for program prediction." International Journal of Informatics and Communication Technology (IJ-ICT) 11, no. 1 (2022): 45. http://dx.doi.org/10.11591/ijict.v11i1.pp45-56.

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<span>This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the

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Julius, Cesar O. Mamaril, and A. Ballera Melvin. "Multiple educational data mining approaches to discover patterns in university admissions for program prediction." International Journal of Informatics and Communication Technology 11, no. 1 (2022): 45–56. https://doi.org/10.11591/ijict.v11i1.pp45-56.

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This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission d

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NOOR, Saba, Waseem AKRAM, Touseef AHMED, and Qurat-ul Ain. "Predicting COVID19 Incidence Using Data Mining Techniques: A case study of Pakistan." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 11, no. 4 (2020): 168–84. https://doi.org/10.18662/brain/11.4/147.

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 The Outbreak of Coronavirus (COVID-19) came to theworld in early December 2019. The early cases of coronavirus werereported in Wuhan City, Hubei Province, China. Till May 18, 2020,198 countries have been affected by this life-threatening disease. The mostcommon and known traits of COVID-19 are tiredness, fever, and drycough. In this paper, we have discussed the Predictive data miningapproach for COVID-19 predictions. In Predictive data mining, amodel is developed and trained using supervised learning and then itpredicts the behavior of provided data. Predictive data mining is arenowned t

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Blanco Prieto, Jorge, Marina Ferreras González, Steven Van Vaerenbergh, and Oscar Jesús Cosido Cobos. "A Data Mining Approach for Health Transport Demand." Machine Learning and Knowledge Extraction 6, no. 1 (2024): 78–97. http://dx.doi.org/10.3390/make6010005.

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Efficient planning and management of health transport services are crucial for improving accessibility and enhancing the quality of healthcare. This study focuses on the choice of determinant variables in the prediction of health transport demand using data mining and analysis techniques. Specifically, health transport services data from Asturias, spanning a seven-year period, are analyzed with the aim of developing accurate predictive models. The problem at hand requires the handling of large volumes of data and multiple predictor variables, leading to challenges in computational cost and int

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Freitas, Elisabete, Joaquim Tinoco, Francisco Soares, Jocilene Costa, Paulo Cortez, and Paulo Pereira. "Modelling Tyre-Road Noise with Data Mining Techniques." Archives of Acoustics 40, no. 4 (2015): 547–60. http://dx.doi.org/10.1515/aoa-2015-0054.

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Abstract The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic va

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Salmam, Fatima Zahra, Mohamed Fakir, and Rahhal Errattahi. "Prediction in OLAP Data Cubes." Journal of Information & Knowledge Management 15, no. 02 (2016): 1650022. http://dx.doi.org/10.1142/s0219649216500222.

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Online analytical processing (OLAP) provides tools to explore data cubes in order to extract the interesting information, it refers to techniques used to query, visualise and synthesise the multidimensional data. Nevertheless OLAP is limited on visualisation, structuring and exploring manually the data cubes. On the other side, data mining allows algorithms that offer automatic knowledge extraction, such as classification, explanation and prediction algorithms. However, OLAP is not capable of explaining and predicting events from existing data; therefore, it is possible to make a more efficien

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Mebarkia, Mohamed, Asma Abdelmalek, Zoubir Aoulmi, Messaoud Louafi, Abdelhak Tabet, and Aissa Benselhoub. "Synergistic prediction of penetration rate in Boukhadhra mining using regression, design of experiments, fuzzy logic, and artificial neural networks." Technology audit and production reserves 4, no. 1(78) (2024): 32–42. http://dx.doi.org/10.15587/2706-5448.2024.309965.

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The comparative analysis of predictive methodologies highlights the original contribution of this study in optimizing the prediction of Rate of Penetration (ROP) in mining drilling operations. The emphasis on employing advanced Artificial Neural Networks (ANN), fuzzy logic, and linear regression models provides new insights into enhancing predictive accuracy and operational efficiency in mining practices. This study aims to quantify the effects of three pivotal drilling parameters: compressive strength, rotational pressure, and thrust pressure on the rate of penetration, a critical performance

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Singh, Arjun. "A survey on Predictive data mining techniques for disaster prediction." International Journal of Engineering Trends and Technology 30, no. 5 (2015): 223–27. http://dx.doi.org/10.14445/22315381/ijett-v30p242.

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Khafaga, Doaa Sami, Abdelhameed Ibrahim, S. K. Towfek, and Nima Khodadadi. "Data Mining Techniques in Predictive Medicine: An Application in hemodynamic prediction for abdominal aortic aneurysm disease." Journal of Artificial Intelligence and Metaheuristics 5, no. 1 (2023): 29–37. http://dx.doi.org/10.54216/jaim.050103.

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Due to its potential to enhance patient outcomes and ease individualized therapy, predictive medicine has received considerable interest in recent years. In this article we examine the use of data mining in predictive medicine, with a particular emphasis on hemodynamic prediction for abdominal aortic aneurysm (AAA) disease. In AAA, the abdominal aortic wall becomes weakened and may rupture, putting the patient's life in danger. Clinical decision making and treatment planning for AAA rely heavily on accurate hemodynamic prediction. For developing these predictive models for hemodynamic assessme

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B, Selvalakshmi, Vijayalakshmi P, Subha N, and Balamani T. "PREDICTIVE MAINTENANCE IN INDUSTRIAL SYSTEMS USING DATA MINING WITH FUZZY LOGIC SYSTEMS." ICTACT Journal on Soft Computing 14, no. 4 (2024): 3361–67. http://dx.doi.org/10.21917/ijsc.2024.0472.

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In industrial systems, predictive maintenance has emerged as a crucial strategy to minimize downtime and optimize operational efficiency. This study explores the utilization of data mining techniques, specifically fuzzy logic systems, for predictive maintenance. The background section examines the importance of predictive maintenance in industrial contexts and highlights the limitations of traditional approaches. The methodology section outlines the process of employing fuzzy logic systems for predictive maintenance, including data preprocessing, feature selection, fuzzy rule generation, and m

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Journal, IJSREM. "DATA SCIENCE: Data Visualization and Data Analytics in the Process of Data Mining." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28332.

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In the rapidly evolving landscape of data mining, the effective extraction of valuable insights from large datasets is paramount. This survey paper investigates the pivotal roles of data visualization and analytics in the intricate process of data mining abstraction. We delve into the symbiotic relationship between these two components, examining how they synergistically contribute to the extraction, representation, and interpretation of meaningful patterns and trends within complex datasets. The survey begins by elucidating the fundamental concepts of data mining abstraction and the significa

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Pandya, Vishal Kishorchandra, and Rajnikant A. Pandya. "CLASSIFICATION OF DATA MINING TECHNIQUES FOR PREDICTION OF STUDENTS EDUCATIONAL PERFORMANCE." Vidyabharati International Interdisciplinary Research Journal 6, no. 1 (2021): 2369–72. https://doi.org/10.5281/zenodo.6521716.

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In education, the projection of student educational achievement has received a lot of attention. Yet, the learning results are considered to enhance learning and teaching, predicting the achievement of student outcomes continues underexplored. The amount of data stored in educational databases is rapidly increasing these days. These databases hold information that can help students improve their grades. For all students, India’s higher education performance is a watershed moment in their academic careers. Because many factors influence academic performance, it is critical to develop a pr

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Saltos, Ginger, and Mihaela Cocea. "An Exploration of Crime Prediction Using Data Mining on Open Data." International Journal of Information Technology & Decision Making 16, no. 05 (2017): 1155–81. http://dx.doi.org/10.1142/s0219622017500250.

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The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the f

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Ganesh, C., and E. Kesavulu Reddy. "Overview of the Predictive Data Mining Techniques." International Journal of Computer Sciences and Engineering 10, no. 1 (2022): 28–36. http://dx.doi.org/10.26438/ijcse/v10i1.2836.

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Hong, Se June, and Sholom M. Weiss. "Advances in predictive models for data mining." Pattern Recognition Letters 22, no. 1 (2001): 55–61. http://dx.doi.org/10.1016/s0167-8655(00)00099-4.

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Markovikj, Dejan, Sonja Gievska, Michal Kosinski, and David Stillwell. "Mining Facebook Data for Predictive Personality Modeling." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 2 (2021): 23–26. http://dx.doi.org/10.1609/icwsm.v7i2.14466.

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Beyond being facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modeling user’s behavior. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modeling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be p

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Obodoekwe, Ekene, Xianwen Fang, and Ke Lu. "Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy." Electronics 11, no. 14 (2022): 2128. http://dx.doi.org/10.3390/electronics11142128.

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For the reliable prediction and analysis of large amounts of data, big data analytics may be applied in many disciplines. They facilitate the discovery of important information in large amounts of data that would otherwise be obscured. Almost all organizations stored their data in the cloud as event logs over the last few decades. These data can be utilized to extract useful information, which can be used to boost an organization’s productivity and effectiveness by identifying, monitoring, and optimizing its processes. Supporting operations, recognizing faults in running processes, predicting

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Kavya.V1 and Arumugam.S2. "A REVIEW ON PREDICTIVE ANALYTICS IN DATA MINING." International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) 05, no. 1/2/3 (2023): 08. https://doi.org/10.5281/zenodo.7845730.

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The data mining its main process is to collect, extract and store the valuable information and now-a-days it’s done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about future. The two main objectives of predictive analytics are Regression and Classification. It is comp

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Arumugam.S. "A REVIEW ON PREDICTIVE ANALYTICS IN DATA MINING." International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) 5, no. 3 (2016): 01–08. https://doi.org/10.5281/zenodo.1212341.

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The data mining its main process is to collect, extract and store the valuable information and now-a-days it’s done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about future. The two main objectives of predictive analytics are Regression and Classification. It is comp

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Kavya.V and Arumugam.S. "A REVIEW ON PREDICTIVE ANALYTICS IN DATA MINING." International Journal of Chaos, Control, Modelling and Simulation 5, no. 1/2/3 (2016): 01–08. https://doi.org/10.5281/zenodo.6423829.

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The data mining its main process is to collect, extract and store the valuable information and now-a-days it’s done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about future. The two main objectives of predictive analytics are Regression and Classification. It is comp

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Radhakrishnan, Nita, Mehul Awasthi, and P. Mahalakshmi. "A survey on Predictive Analysis in Employment Trends." International Journal of Engineering & Technology 7, no. 2.24 (2018): 358. http://dx.doi.org/10.14419/ijet.v7i2.24.12082.

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This paper addresses the theories of using predictive analysis and Data Mining in arriving at suitable patterns and predicting paths and trends in the current Employment Scenario more specifically to the Engineering sector. India produces 1.5 million engineers every year, and yet there is a significant gap between their skills and the jobs and corresponding salaries they are offered. Recognizing the factors that inﬂuence this gap can help us bridge it. The survey shows that the ideal route to doing so, is by employing various Predictive analysis and Data Mining techniques on appropriate data s

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Soto Valero, C. "Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods." International Journal of Computer Science in Sport 15, no. 2 (2016): 91–112. http://dx.doi.org/10.1515/ijcss-2016-0007.

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Abstract Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper,

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Sushil, Shrestha, and Pokharel Manish. "Educational data mining in moodle data." International Journal of Informatics and Communication Technology (IJ-ICT) 10, no. 1 (2021): 9–18. https://doi.org/10.11591/ijict.v10i1.pp9-18.

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The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies a wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by the Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses Moodle as an e-learning platform. The dataset contained eight features where Assignment.C

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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (2024): 45–77. http://dx.doi.org/10.69610/j.ases.20240515.

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<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of s

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Zhu, Ye. "Data Mining Technology-Based English Listening Prediction Strategy and Its Training Approach." Security and Communication Networks 2022 (March 7, 2022): 1–8. http://dx.doi.org/10.1155/2022/4852792.

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The big data era of “data-driven schools, analysis, and change education” has arrived, and the technology of data mining was born in the education industry. Based on data mining technology, this study explores English listening prediction strategies and training approaches. Prediction is an effective learning strategy in listening comprehension. Cultivating students to use predictive strategies is helpful to improve their listening comprehension, mining and analyzing the listening data generated by English skill training system, selecting the data related to students’ listening as features, ai

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Akter, Sumi, and K. M. Yaqub Ali. "Unveiling Patterns: Advanced Data Mining Techniques for Accurate Predictive Analytics." International Journal of Computer Science and Information Technology 16, no. 6 (2024): 81–98. https://doi.org/10.5121/ijcsit.2024.16607.

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With the rapid increase in the volume, variety and availability of data across organizations, managers are confronted with the task of analyzing data for meaningful information. As today’s undeniable evidence shows, data mining imperatives and its close cousin predictive analytics have become fundamental recognized approaches of operationalizing raw data into strategic decision supports. Data warehousing and data mining are two distinct ideas. Predictive analysis is the process of applying the extracted data model to anticipate possible future events, while data mining is the process of uncove

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Anouze, Abdel Latef M., and Imad Bou-Hamad. "Data envelopment analysis and data mining to efficiency estimation and evaluation." International Journal of Islamic and Middle Eastern Finance and Management 12, no. 2 (2019): 169–90. http://dx.doi.org/10.1108/imefm-11-2017-0302.

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PurposeThis paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.Design/methodology/approachDifferent statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their st

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Shrestha, Sushil, and Manish Pokharel. "Educational data mining in moodle data." International Journal of Informatics and Communication Technology (IJ-ICT) 10, no. 1 (2021): 9. http://dx.doi.org/10.11591/ijict.v10i1.pp9-18.

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<p>The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies a wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by the Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses Moodle as an e-learning platform. The dataset contained eight features where Ass

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Kavya.V1 and Arumugam.S2. "A REVIEW ON PREDICTIVE ANALYTICS IN DATA MINING." International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) 05, no. 1/2/3 (2023): 08. https://doi.org/10.5281/zenodo.7801686.

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The data mining its main process is to collect, extract and store the valuable information and now-a-days it’s done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about future. The two main objectives of predictive analytics are Regression and Classification. It is comp

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Balamurugan, S., and Dr M. Selvalakshmi. "Customer Relationship Management Using Data Mining Model." Restaurant Business 118, no. 7 (2019): 95–100. http://dx.doi.org/10.26643/rb.v118i7.7668.

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The paper describes marketing insights from Data Mining about new promotions to create, focus on profitability and emphasis on the most profitable promotion that could be sent. The paper shows about the development of predictive modeling, from data mining which provides insights into future customer behavior and customer profitability. Data Mining provides a blueprint and how to define and use customer profile. It shows how to acquire new customers in the most profitable way possible and retain profitable customers. Data mining is an effective method to target at risk-customers with the right

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Afzal, Hossain, Nawal Bithi Nawshin, and Ullah Ahsan. "Analysis, Design and Development of a Predictive Model to Predict Coronary Attorney of Human Body using Data Mining Algorithm." Journal of Network Security and Data Mining 2, no. 2 (2019): 1–21. https://doi.org/10.5281/zenodo.3378683.

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<em>Data mining have used worldwide for mining information from different types of database and data mining techniques are quite popular in medical sectors. Nowadays coronary attorney is the most leading cause of death according to many surveys all over the world which also can be predicted by data mining. The main goal of this research is to predict and classify coronary attorney by designing predictive and classification model. Authors have developed a system which contains both predictive and classification model. Predictive model have designed for predicting the coronary attorney. Authors

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Al Shibli, Kdhaiya Sulaiman, Amal Sulaiman Sayed Al Abri, Linitha Sunny, Nandakishore Ishwar, and Sherimon Puliprathu Cherian. "Model for Prediction of Student Grades using Data Mining Algorithms." European Journal of Information Technologies and Computer Science 2, no. 2 (2022): 1–6. http://dx.doi.org/10.24018/compute.2022.2.2.47.

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There has been a rapid growth in the educational domain since education has become an important need. Data is collected in this domain which can be put to meaningful use to derive a lot of benefits to the students. Predicting student performance can help students and their teachers keep track of student progress. Mining Educational data helps to uncover invisible patterns, relationships, or trends in the unstructured data and helps in delivering logical and meaningful recommendations. Several kinds of research are being conducted across the world to analyze the data regarding student learning

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Rohan Lohia and Vibhor Sharma. "Health Prediction by Data Mining." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 390–93. http://dx.doi.org/10.46501/ijmtst061273.

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The paper presents an overview of the Clinical Predictions and Medical Predictions with data mining and its techniques. In health care areas, due to regulations and due to availability of computers, such large amount of data cannot be processed by humans to schedules and diagnosis in short time of duration. It is a new technology which is of high interest in computer world. The computer world make an data in different databases to transfer into new researches and results. The database management extract a new patterns from large datasets. The different parameters included in data mining are: c

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Deepak, Nanuru Yagamurthy, and Azmeera Rajesh. "Data Mining and Machine Learning Role in Predictive Maintenance for Industrial Equipment." European Journal of Advances in Engineering and Technology 9, no. 1 (2022): 37–44. https://doi.org/10.5281/zenodo.12789630.

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Predictive maintenance has become a critical aspect of industrial operations, aiming to optimize equipment performance, minimize downtime, and reduce maintenance costs. This paper delves into the significant role played by data mining and machine learning techniques in predictive maintenance for industrial equipment. By leveraging historical data, real-time sensor readings, and advanced algorithms, predictive maintenance strategies enable proactive identification of potential equipment failures, thus facilitating timely maintenance interventions. Through a comprehensive analysis of data mining

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Wu, Yudong, Dandan Zhao, Jingyuan Peng, Xingyu Xiang, and Haibo Huang. "Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining." Electronics 13, no. 6 (2024): 1117. http://dx.doi.org/10.3390/electronics13061117.

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The meticulously engineered powertrain mounting system of hybrid electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybr

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Han, Jong-Gyu, Kwang Yeon, Kwang-Hoon Chi, and Keun-Ho Ryu. "Prediction of Forest Fire Hazardous Area Using Predictive Spatial Data Mining." KIPS Transactions:PartD 9D, no. 6 (2002): 1119–26. http://dx.doi.org/10.3745/kipstd.2002.9d.6.1119.

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