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Journal articles on the topic 'Operations Research. Data mining. Machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Zelinska, Snizhana. "Machine learning: technologies and potential application at mining companies." E3S Web of Conferences 166 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202016603007.

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Implementation of machine learning systems is currently one of the most sought-after spheres of human activities at the interface of information technologies, mathematical analysis and statistics. Machine learning technologies are penetrating into our life through applied software created with the help of artificial intelligence algorithms. It is obvious that machine learning technologies will be developing fast and becoming part of the human information space both in our everyday life and in professional activities. However, building of machine learning systems requires great labour contribut

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Percha, Bethany. "Modern Clinical Text Mining: A Guide and Review." Annual Review of Biomedical Data Science 4, no. 1 (2021): 165–87. http://dx.doi.org/10.1146/annurev-biodatasci-030421-030931.

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Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.

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Fedushko, Solomia, Taras Ustyianovych, and Michal Gregus. "Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies." Electronics 9, no. 4 (2020): 668. http://dx.doi.org/10.3390/electronics9040668.

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An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure

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Troncoso Espinosa, Fredy Humberto, Yamil Gerard Avello Betancur, and Luis Andres Martinez Flores. "Prediction of cellulose sheet cutting using Machine Learning." Universidad Ciencia y Tecnología 25, no. 110 (2021): 109–18. http://dx.doi.org/10.47460/uct.v25i110.481.

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Cellulose is the main raw material for the production of paper. Companies that produce it present in their production line the cutting of the cellulose sheet. This failure is sporadic and has a high economic impact since it paralyzes the production line for several hours, incurring unproductive hours and a large deployment of human and financial resources. In this research, the use of Data Mining is proposed to define a machine learning algorithm that allows predicting the cutting of the cellulose sheet in a production line of a cellulose plant in Chile. The results show that by applying this

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Qiu, Yongtao, Weixi Ji, and Chaoyang Zhang. "A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products." Applied Sciences 9, no. 24 (2019): 5286. http://dx.doi.org/10.3390/app9245286.

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Nowadays, the production model of many enterprises is multi-variety customized production, and the makespan and total tardiness are the main metrics for enterprises to make production plans. This requires us to develop a more effective production plan promptly with limited resources. Previous research focuses on dispatching rules and algorithms, but the application of the knowledge mining method for multi-variety products is limited. In this paper, a hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness for multi-variety products is proposed. F

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Esmaeilzadeh, Ehsan, and Seyedmirsajad Mokhtarimousavi. "Machine Learning Approach for Flight Departure Delay Prediction and Analysis." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (2020): 145–59. http://dx.doi.org/10.1177/0361198120930014.

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The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine

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Lucero, Robert, and Ragnhildur Bjarnadottir. "ADVANCING AN INTERDISCIPLINARY SCIENCE OF AGING THROUGH A PRACTICE-BASED DATA SCIENCE APPROACH." Innovation in Aging 3, Supplement_1 (2019): S480. http://dx.doi.org/10.1093/geroni/igz038.1786.

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Abstract Two hundred and fifty thousand older adults die annually in United States hospitals because of iatrogenic conditions (ICs). Clinicians, aging experts, patient advocates and federal policy makers agree that there is a need to enhance the safety of hospitalized older adults through improved identification and prevention of ICs. To this end, we are building a research program with the goal of enhancing the safety of hospitalized older adults by reducing ICs through an effective learning health system. Leveraging unique electronic data and healthcare system and human resources at the Univ

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Witulska, Justyna, Paweł Stefaniak, Bartosz Jachnik, Artur Skoczylas, Paweł Śliwiński, and Marek Dudzik. "Recognition of LHD Position and Maneuvers in Underground Mining Excavations—Identification and Parametrization of Turns." Applied Sciences 11, no. 13 (2021): 6075. http://dx.doi.org/10.3390/app11136075.

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The Inertial Measurement Unit (IMU) is widely used in the monitoring of mining assets. A good example is the Polish underground copper ore mines of KGHM, where research work with the use of the IMU has been carried out for several years. The potential of inertial sensors was ensured by the development of advanced analytics using machine learning methods to support the maintenance management of an extensive machine park and machine manufacturer in adapting various construction elements to mining conditions. The key algorithms developed in the field of inertial data concern: identification of cy

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Kopeć, Anna, Paweł Trybała, Dariusz Głąbicki, et al. "Application of Remote Sensing, GIS and Machine Learning with Geographically Weighted Regression in Assessing the Impact of Hard Coal Mining on the Natural Environment." Sustainability 12, no. 22 (2020): 9338. http://dx.doi.org/10.3390/su12229338.

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Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the stati

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Lammatha, Kranthi K. "Data Mining on 5G Technology IOT." International Journal of Engineering and Computer Science 8, no. 05 (2019): 24655–60. http://dx.doi.org/10.18535/ijecs/v8i05.4291.

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Data Mining on 5G Technology IOT Currently, data mining is regarded as one of the essential factors for the next generation of mobile networks. Through research and data analysis, there are expectations that complexity of these networks will be overcome and it will be possible to carry out dynamic management and operation activities. In order to full comprehend the particulars of 5G network, there are certain kind of information that should be gathered by network components in order to be analyzed by a data mining scheme. The recent years have seen a tremendous effort put in the course of desi

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Wu, Zongxiao, Cong Zang, Chia-Huei Wu, Zilin Deng, Xuefeng Shao, and Wei Liu. "Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms." Journal of Global Information Management 30, no. 3 (2022): 1–23. http://dx.doi.org/10.4018/jgim.20220701.oa1.

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Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model

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Habibi, Reza. "Application of Predictive Methods to Financial Data Sets." Financial Internet Quarterly 17, no. 1 (2021): 50–61. http://dx.doi.org/10.2478/fiqf-2021-0006.

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Abstract Financial data sets are growing too fast and need to be analyzed. Data science has many different techniques to store and summarize, mining, running simulations and finally analyzing them. Among data science methods, predictive methods play a critical role in analyzing financial data sets. In the current paper, applications of 22 methods classified in four categories namely data mining and machine learning, numerical analysis, operation research techniques and meta-heuristic techniques, in financial data sets are studied. To this end, first, literature reviews on these methods are giv

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Wang, Qun, Ruixin Zhang, Yangting Wang, and Shuaikang Lv. "Machine Learning-Based Driving Style Identification of Truck Drivers in Open-Pit Mines." Electronics 9, no. 1 (2019): 19. http://dx.doi.org/10.3390/electronics9010019.

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The significance in constructing a driving style identification model for open-pit mine truck drivers is to reduce diesel consumption and improve training. First, we developed a driving behavior and mining truck condition monitoring system for an open-pit mine. Under heavy-load and no-load conditions of a mining truck, based on the same experimental truck and haulage road, the data of driving behavior and truck status of different drivers were collected. The driving style characteristic parameters of mining trucks under heavy-load and no-load conditions were constructed through Pearson correla

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Kim, Jiyun, and Han-joon Kim. "Multidimensional Text Warehousing for Automated Text Classification." Journal of Information Technology Research 11, no. 2 (2018): 168–83. http://dx.doi.org/10.4018/jitr.2018040110.

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This article describes how, in the era of big data, a data warehouse is an integrated multidimensional database that provides the basis for the decision making required to establish crucial business strategies. Efficient, effective analysis requires a data organization system that integrates and manages data of various dimensions. However, conventional data warehousing techniques do not consider the various data manipulation operations required for data-mining activities. With the current explosion of text data, much research has examined text (or document) repositories to support text mining

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Li, Chenghao, Yiding Wang, Changwei Miao, and Cheng Huang. "Cross-Site Scripting Guardian: A Static XSS Detector Based on Data Stream Input-Output Association Mining." Applied Sciences 10, no. 14 (2020): 4740. http://dx.doi.org/10.3390/app10144740.

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The largest number of cybersecurity attacks is on web applications, in which Cross-Site Scripting (XSS) is the most popular way. The code audit is the main method to avoid the damage of XSS at the source code level. However, there are numerous limits implementing manual audits and rule-based audit tools. In the age of big data, it is a new research field to assist the manual auditing through machine learning. In this paper, we propose a new way to audit the XSS vulnerability in PHP source code snippets based on a PHP code parsing tool and the machine learning algorithm. We analyzed the operati

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Shevtsova, I. V. "The Training Method for Digital Data Operation." Open Education 24, no. 4 (2020): 32–40. http://dx.doi.org/10.21686/1818-4243-2020-4-32-40.

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The purpose of study is to develop the training method for operation with digital data. The article discusses the issues of training for mining and analyzing digital data on the example of social networks for higher education programs in the areas of “Management”, “Public administration”, “Human resources” and “Political Science”. The relevance of the study is justified by factors: digital transformation of economy; development of digital data sources; increasing the importance of digital data in management. Universities have a new task - to prepare students for working with digital data in th

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Khan, Bilal, Rashid Naseem, Muhammad Arif Shah, et al. "Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques." Journal of Healthcare Engineering 2021 (March 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/8899263.

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Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning

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Huang, Jun, Haibo Wang, and Gary Kochenberger. "Distressed Chinese firm prediction with discretized data." Management Decision 55, no. 5 (2017): 786–807. http://dx.doi.org/10.1108/md-08-2016-0546.

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Purpose The authors develop a framework to build an early warning mechanism in detecting financial deterioration of Chinese companies. Many studies in the financial distress and bankruptcy prediction literature rarely do they examine the impact of pre-processing financial indicators on the prediction performance. The purpose of this paper is to address this shortcoming. Design/methodology/approach The proposed framework is evaluated by using both original and discretized data, and a least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset o

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Chen, Wei-Jen, Mao-Jhen Jhou, Tian-Shyug Lee, and Chi-Jie Lu. "Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association." Entropy 23, no. 4 (2021): 477. http://dx.doi.org/10.3390/e23040477.

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The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed feature

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Sathiyamoorthi V. "An Intelligent System for Predicting a User Access to a Web Based E-Learning System Using Web Mining." International Journal of Information Technology and Web Engineering 15, no. 1 (2020): 75–94. http://dx.doi.org/10.4018/ijitwe.2020010106.

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In this Internet era, with ever-increasing interactions among participants, the size of the data is increasing so rapidly such that the information available to us in the near future is going to be unpredictable. Modeling and visualizing such data are one of the challenging tasks in the data analytics field. Therefore, business intelligence is the way in which a company can use data to improve business and operational efficiency whereas data analytics involves improving ways of making intelligence out of that data before acting on it. Thus, the proposed work focuses on prevailing challenges in

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V.Phani Krishna, K., M. Mani Kumar, and P. S.G.Aruna Sri. "Student Information System and Performance Retrieval Through Dashboard." International Journal of Engineering & Technology 7, no. 2.7 (2018): 682. http://dx.doi.org/10.14419/ijet.v7i2.7.10922.

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The principle focal point of information mining is to gather diverse information from databases or information ware houses and the data gathered that had never been known, it is legitimate and operational. Instructive establishments can utilize this to keep up all the data of understudy scholastics effectively which is basically imperative. The execution of understudies in their scholastics is a defining moment for their brightest vocation. Foreseeing understudy scholarly execution has been a basic research point in Educational Data Mining (EDM) which utilizes machine learning and information

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Lau, Henry, C. K. M. Lee, Dilupa Nakandala, and Paul Shum. "An outcome-based process optimization model using fuzzy-based association rules." Industrial Management & Data Systems 118, no. 6 (2018): 1138–52. http://dx.doi.org/10.1108/imds-08-2017-0347.

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Purpose The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment. Design/methodology/approach This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing

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Vostokin, Sergei, Yuriy Artamonov, and Daniil Tsarev. "Templet Web: the use of volunteer computing approach in PaaS-style cloud." Open Engineering 8, no. 1 (2018): 50–56. http://dx.doi.org/10.1515/eng-2018-0007.

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Abstract This article presents the Templet Web cloud service. The service is designed for high-performance scientific computing automation. The use of high-performance technology is specifically required by new fields of computational science such as data mining, artificial intelligence, machine learning, and others. Cloud technologies provide a significant cost reduction for high-performance scientific applications. The main objectives to achieve this cost reduction in the Templet Web service design are: (a) the implementation of “on-demand” access; (b) source code deployment management; (c)

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Frantz, David. "FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond." Remote Sensing 11, no. 9 (2019): 1124. http://dx.doi.org/10.3390/rs11091124.

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Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is ca

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Druzhinina, Olga V., Irina A. Karpacheva, Olga N. Masina, and Alexey A. Petrov. "DEVELOPMENT OF INSTRUMENTAL AND METHODOLOGICAL SUPPORT FOR THE ASSESSMENT OF STUDENTS ' KNOWLEDGE IN MATHEMATICS IN THE FRAMEWORK OF HYBRID INTELLIGENT LEARNING ENVIRONMENT." Educational Psychology in Polycultural Space 54, no. 2 (2021): 48–65. http://dx.doi.org/10.24888/2073-8439-2021-54-2-48-65.

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The article is devoted to the development of an integrated complex of knowledge base and artifi-cial intelligence tools for assessing students' knowledge in mathematics within the framework of a hybrid intellectual learning environment. In accordance with the structural diagrams of mod-els of intellectual assessment of knowledge and creative potential of schoolchildren in mathe-matics, the structure of the corresponding knowledge base is formalized. The knowledge base is developed within the framework of data mining methods to create a module for monitoring and assessing knowledge in mathemati

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Teng, Xiuyi, and Yuxia Gong. "Research on Application of Machine Learning in Data Mining." IOP Conference Series: Materials Science and Engineering 392, no. 6 (2018): 062202. http://dx.doi.org/10.1088/1757-899x/392/6/062202.

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Kavakiotis, Ioannis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, and Ioanna Chouvarda. "Machine Learning and Data Mining Methods in Diabetes Research." Computational and Structural Biotechnology Journal 15 (2017): 104–16. http://dx.doi.org/10.1016/j.csbj.2016.12.005.

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Di Mauro, Nicola, Paolo Frasconi, Fabrizio Angiulli, et al. "Italian Machine Learning and Data Mining research: The last years." Intelligenza Artificiale 7, no. 2 (2013): 77–89. http://dx.doi.org/10.3233/ia-130050.

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Li, Shangran. "Research on Data Mining Technology Based on Machine Learning Algorithm." Journal of Physics: Conference Series 1168 (February 2019): 032132. http://dx.doi.org/10.1088/1742-6596/1168/3/032132.

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Reis, Rita, Hugo Peixoto, José Machado, and António Abelha. "Machine Learning in Nutritional Follow-up Research." Open Computer Science 7, no. 1 (2017): 41–45. http://dx.doi.org/10.1515/comp-2017-0008.

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Abstract Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able

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Wei, Xian Min. "Analysis of Machine Learning Research and Application." Advanced Materials Research 171-172 (December 2010): 740–43. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.740.

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The artificial intelligence is an important branch of computer science, in recent years with the development of computer technology, artificial intelligence has also been in good development. Machine learning is a core part of artificial intelligence, machine learning background, research status, and applications in network intrusion detection, text categorization and data mining were studied in this paper.

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Wen, Gege, Pin Wen, and Zukai Tang. "Research on Data Mining Method of TCM Prescription Based on Machine Learning." Journal of Physics: Conference Series 1952, no. 2 (2021): 022033. http://dx.doi.org/10.1088/1742-6596/1952/2/022033.

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Chubukova, Ponomarenko, and Nedbailo. "Using data mining to process business data." Problems of Innovation and Investment Development, no. 23 (April 10, 2020): 71–77. http://dx.doi.org/10.33813/2224-1213.23.2020.8.

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The subject of the research is the approach to the possibility of applying data mining methods in the framework of business analytics in order to optimize the adoption of management decisions by the company.The purpose of writing this article is to study of data mining methods features use of primary data, which act as a valuable resource of the company, which can be used to ensure competitive- ness in a particular market. Methodology. The research methodology is system- structural and comparative analyzes (to study the approaches of data mining data for the complex analysis of first data); mo

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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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Aghili, Maryamossadat, and Ruogu Fang. "Mining Big Neuron Morphological Data." Computational Intelligence and Neuroscience 2018 (June 24, 2018): 1–13. http://dx.doi.org/10.1155/2018/8234734.

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The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive res

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Zhu, Jian Xin. "Arithmetic Research on Data Mining Technology and Associative Rules Mining." Applied Mechanics and Materials 556-562 (May 2014): 3949–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3949.

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Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of

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Chaitanya, K. Krishna. "Crop Production Analysis using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 572–75. http://dx.doi.org/10.22214/ijraset.2021.36332.

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As we all know, in the agricultural industry, farmers and agribusinesses must make countless decisions every day, and the different elements influencing them are complex. The proper yield calculation for the different crops involved in the planning is a critical issue for agricultural planning. Data mining techniques are a critical component of achieving practical and successful solutions to this issue. Agriculture has always been a natural fit for big data. Environmental conditions, soil variability, input amounts, combinations, and commodity pricing have all made it more important for farmer

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Hernández-Blanco, Antonio, Boris Herrera-Flores, David Tomás, and Borja Navarro-Colorado. "A Systematic Review of Deep Learning Approaches to Educational Data Mining." Complexity 2019 (May 12, 2019): 1–22. http://dx.doi.org/10.1155/2019/1306039.

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Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image re

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Yedla, Anurag, Fatemeh Davoudi Kakhki, and Ali Jannesari. "Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations." International Journal of Environmental Research and Public Health 17, no. 19 (2020): 7054. http://dx.doi.org/10.3390/ijerph17197054.

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Mining is known to be one of the most hazardous occupations in the world. Many serious accidents have occurred worldwide over the years in mining. Although there have been efforts to create a safer work environment for miners, the number of accidents occurring at the mining sites is still significant. Machine learning techniques and predictive analytics are becoming one of the leading resources to create safer work environments in the manufacturing and construction industries. These techniques are leveraged to generate actionable insights to improve decision-making. A large amount of mining sa

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Avalos, Sebastian, Willy Kracht, and Julian M. Ortiz. "Machine Learning and Deep Learning Methods in Mining Operations: a Data-Driven SAG Mill Energy Consumption Prediction Application." Mining, Metallurgy & Exploration 37, no. 4 (2020): 1197–212. http://dx.doi.org/10.1007/s42461-020-00238-1.

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Anami, Basavaraj S., Ramesh S. Wadawadagi, and Veerappa B. Pagi. "Machine Learning Techniques in Web Content Mining: A Comparative Analysis." Journal of Information & Knowledge Management 13, no. 01 (2014): 1450005. http://dx.doi.org/10.1142/s0219649214500051.

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With incessantly growing amount of information published over Web pages, the World Wide Web (WWW) has become prolific in the field of data mining research. The heterogeneous and semi-structured nature of Web data has made the process of automated discovery a challenging issue. Web Content Mining (WCM) essentially uses data mining techniques to effectively discover knowledge from Web page contents. The intent of this study is to provide a comparative analysis of Machine Learning (ML) techniques available in the literature for WCM. For analysis, the article focuses on issues such as representati

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Abraham, Cerene Mariam, Mannathazhathu Sudheep Elayidom, and Thankappan Santhanakrishnan. "Big Data Analysis for Trend Recognition Using Machine Learning Techniques." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (2020): 540–50. http://dx.doi.org/10.2174/2210327910666200304141238.

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Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets. Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data. Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work in

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YANG, QIANG, and XINDONG WU. "10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH." International Journal of Information Technology & Decision Making 05, no. 04 (2006): 597–604. http://dx.doi.org/10.1142/s0219622006002258.

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In October 2005, we took an initiative to identify 10 challenging problems in data mining research, by consulting some of the most active researchers in data mining and machine learning for their opinions on what are considered important and worthy topics for future research in data mining. We hope their insights will inspire new research efforts, and give young researchers (including PhD students) a high-level guideline as to where the hot problems are located in data mining. Due to the limited amount of time, we were only able to send out our survey requests to the organizers of the IEEE ICD

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Orooji, Azam, and Farzaneh Kermani. "Machine Learning Based Methods for Handling Imbalanced Data in Hepatitis Diagnosis." Frontiers in Health Informatics 10, no. 1 (2021): 57. http://dx.doi.org/10.30699/fhi.v10i1.259.

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Introduction: Hepatitis C virus is the leading cause of mortality from liver disease. Also, diagnosis systems are usable tools for better disease control and management. The aim of this study was to design an HCV disease prediction system and classify its severity based on data mining methods. Method: This is an applied research that uses the hepatitis C dataset in the UCI library. The study was conducted in four steps including data preprocessing, data mining, evaluation and system design. In data pre-processing, data balancing techniques were performed. Then, three data mining algorithms (Mu

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Dr. T. Senthil Kumar. "Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm." September 2020 2, no. 3 (2020): 185–93. http://dx.doi.org/10.36548//jaicn.2020.3.006.

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Data mining is widely used in engineering and science, On the contrary, it is used in finance and marketing applications to resolve the challenges in the respective fields. Data mining based decision support system enhances the organization performance by analysing the ground reality. Turbulent economy is common for every organization due to the competition, cost, tax pressures, etc., Privatization, Globalization and liberalization drags the organization more into a competitive environment. In order to balance the competition and withstand to achieve desired gain proper marketing strategies ar

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Dr. T. Senthil Kumar. "Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm." September 2020 2, no. 3 (2020): 185–93. http://dx.doi.org/10.36548/jaicn.2020.3.007.

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Data mining is widely used in engineering and science, On the contrary, it is used in finance and marketing applications to resolve the challenges in the respective fields. Data mining based decision support system enhances the organization performance by analysing the ground reality. Turbulent economy is common for every organization due to the competition, cost, tax pressures, etc., Privatization, Globalization and liberalization drags the organization more into a competitive environment. In order to balance the competition and withstand to achieve desired gain proper marketing strategies ar

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Zhou, Qian. "Research on the Guidance Methods of Sports Training Based on Data Mining Technology." Applied Mechanics and Materials 155-156 (February 2012): 590–95. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.590.

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Data mining is use of machine learning, statistical learning from the data mining technology found in. In view of the current sports training problems, and discusses the data mining technology in the application of sports training theory, and through the key neural network method to forecast the athlete's performance in the application. The experimental data show that using neural network to predict athletic performance has a good approximation ability, but has good extension, which indicates that the use of the relevant data mining technology to guide the scientific nature of the sports train

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Lu, Guo Sheng, Chen Sheng Wang, Hai Lu Yang, and Li Chang Zhao. "Research and Analysis of Data Mining Based on Clustering Algorithm." Applied Mechanics and Materials 602-605 (August 2014): 3321–24. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3321.

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Data mining is the frontier research topic in the field of information processing and database technology, is recognized as one of the most promising key technologies. Data mining is a collection of statistics, machine learning, database, pattern recognition, artificial intelligence and other disciplines, which is a new inter-discipline. Data mining put more emphasis on discovering implicit knowledge in huge amounts of data and the scalability of the algorithm, and is a technology very close to the actual use, with high technical content, bigger implementation difficulty.

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Rufai, Dr Aliyu Y., Dr Hassan U. Suru, and James Afrifa. "The Role of Machine Learning and Data Mining Techniques in Predicting Students’ Academic Performance." International Journal of Computer Applications Technology and Research 10, no. 8 (2021): 198–207. http://dx.doi.org/10.7753/ijcatr1008.1001.

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The advancement in Information Technology makes it easier and cheaper to collect large amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. These large amounts of data set have motivated research and development in various fields to extract meaningful information with a view of analyzing it to solve complex problem. With new methods and techniques, data can be analyze and be of great advantage. Data mining and machine learning are two computing disciplines that enable analysis of large data sets using different techniques. This paper gave an overview

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Gupta, Meenu, Vijender Kumar Solanki, Vijay Kumar Singh, and Vicente García-Díaz. "Data Mining Approach of Accident Occurrences Identification with Effective Methodology and Implementation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 4033. http://dx.doi.org/10.11591/ijece.v8i5.pp4033-4041.

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Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we h

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