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Journal articles on the topic 'Training data analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Ogiki Lawoko, Charles Richard. "Discriminant analysis with correlated training data." Bulletin of the Australian Mathematical Society 37, no. 2 (1988): 313–15. http://dx.doi.org/10.1017/s0004972700026873.

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Wei, Nai-Chieh, Tzu-Jou Liao, Chiung-Fen Chang, and Shui-Cheung Cham. "Using Data Envelopment Analysis to Evaluate Technical Training Program for Maintenance Units." International Journal of Engineering Research 4, no. 5 (2015): 264–67. http://dx.doi.org/10.17950/ijer/v4s5/511.

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Kabat, Dr Subash Ranjan. "Federated Learning for Privacy-Preserving AI: A Comparative Analysis of Decentralized Data Training." International Journal of Machine Learning, AI & Data Science Evolution 1, no. 01 (2025): 9–21. https://doi.org/10.63665/ijmlaidse.v1i1.02.

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The rapid adoption of Artificial Intelligence (AI) across industries, particularly in healthcare, finance, and smart devices, has introduced significant concerns regarding data privacy, security, and compliance with regulations such as GDPR, HIPAA, and CCPA. Traditional centralized machine learning (ML) models require large-scale data aggregation, increasing risks of data breaches, misuse, and unauthorized access. Federated Learning (FL) has emerged as a transformative solution, allowing multiple edge devices or organizations to collaboratively train machine learning models without sharing raw

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Liebal, U. W., L. M. Blank, J. Fensterle, et al. "Biotechnology data analysis training with Jupyter Notebooks." Chemie Ingenieur Technik 94, no. 9 (2022): 1378. http://dx.doi.org/10.1002/cite.202255355.

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Yang, Hui, Yanfei Sun, Xiangfeng Xue, Zhibin Hou, and Shuojun Yang. "Data Analysis for Field Orienteering Heat Training." Journal of Physics: Conference Series 1646 (September 2020): 012062. http://dx.doi.org/10.1088/1742-6596/1646/1/012062.

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Wang, Yihan, Shuang Liu, and Xiao-Shan Gao. "Data-dependent stability analysis of adversarial training." Neural Networks 183 (March 2025): 106983. https://doi.org/10.1016/j.neunet.2024.106983.

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Van Ness, John W., and Jim J. Yang. "Robust discriminant analysis: Training data breakdown point." Journal of Statistical Planning and Inference 67, no. 1 (1998): 67–83. http://dx.doi.org/10.1016/s0378-3758(97)00106-7.

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Batut, Bérénice, Saskia Hiltemann, Andrea Bagnacani, et al. "Community-Driven Data Analysis Training for Biology." Cell Systems 6, no. 6 (2018): 752–58. http://dx.doi.org/10.1016/j.cels.2018.05.012.

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Siregar, Bakti, Clara Della Evania, and Yosia. "Data Analysis Training Using Python at JNE." Jurnal Pengabdian Masyarakat Bestari 2, no. 2 (2023): 161–70. http://dx.doi.org/10.55927/jpmb.v2i2.3056.

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The need for the Data Analyst profession is very high, triggered by the development of digital technology. Thus, Matana University through the statistics study program feels it is important to carry out training activities in the field of data analysis. The aim is to provide benefits to companies that are partners in the implementation of this Community Service (PKM) is JNE. Participants who take part in the training are limited to twenty people, so that the quality of learning is more optimal with the practicum learning method. The training instructor also involves at least two i-students as

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Sirakov, Nikolay Metodiev, Tahsin Shahnewaz, and Arie Nakhmani. "Training Data Augmentation with Data Distilled by Principal Component Analysis." Electronics 13, no. 2 (2024): 282. http://dx.doi.org/10.3390/electronics13020282.

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This work develops a new method for vector data augmentation. The proposed method applies principal component analysis (PCA), determines the eigenvectors of a set of training vectors for a machine learning (ML) method and uses them to generate the distilled vectors. The training and PCA-distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors, the extended set trains an ML classifie

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Singh, Chandrashekhar. "Improving Cataract Surgery Procedure using Machine Learning and Thick Data Analysis." Journal of Robotics and Automation Research 5, no. 2 (2024): 01–08. https://doi.org/10.33140/jrar.05.02.03.

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Cataract surgery is one of the most frequent and safe Surgical operations are done globally, with approximately 16 million surgeries conducted each year. The entire operation is carried out under microscopical supervision. Even though ophthalmic surgeries are similar in some ways to endoscopic surgeries, the way they are set up is very different. Endoscopic surgery operations were shown on a big screen so that a trainee surgeon could see them. Cataract surgery, on the other hand, was done under a microscope so that only the operating surgeon and one more trainee could see them through addition

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王志全, 王志全, та 廖偉成廖偉成. "羽球競賽資料文獻分析與實務訓練應用". 運動表現期刊 11, № 2 (2024): 129–46. http://dx.doi.org/10.53106/240996512024091102003.

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<p>目的：運動競賽資訊的數據分析是評估選手運動表現和訓練處方的重要參考依據。羽球運動在2006年實施21分新賽制後，雖有相關研究針對新賽制競賽資料進行分析，但少有文章有系統的進行統整與歸納，使得相關研究結果資料的解釋與應用受到限制，而降低了資料的可用性。因此，本研究目的係以系統性文獻回顧，分析羽球五個單項的競賽資料結果，並依此結果進行討論和提出具體實務訓練建議，作為科學化訓練和提升運動表現與降低運動傷害之重要參考依據。方法：本文以Google學術搜尋、PubMed和華藝線上資料庫進行文獻檢索，經由排除非收錄在SSCI、SCI及TSSCI資料庫之論文，以及研究對象為非頂尖運動員和非真實競賽分析資料後，共計有8篇外文文獻，1篇中文文獻。結果：回顧相關文獻後發現，羽球五個單項的競賽結構會有差異，而造成差異主因是選手性別的不同，其中男子選手的競賽會有較快節奏和高強度的比賽，而女子選手的競賽則是會有較多的來回拍數，且羽球五個單項競賽結構內容在平均每回合擊球時間和休息時間皆有增加的趨勢。結論：羽球實務訓練應依五個單項和選手性別不同，安排差異和特殊性訓練安排，同時必須定期關注分析羽球競賽資料的發展與知識更新，以利訓練更符合實際競賽需求和提升訓練成效。</p> <p>&nbsp;</p><p>Purpose: Data anal

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Минязова, Е. Р. ""Big Data" and personalized training." Higher education today, no. 5-6 (July 18, 2022): 41–45. http://dx.doi.org/10.18137/rnu.het.22.05-06.p.041.

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Рассмотрена значимость анализа «больших данных» для современного образования, возможности работы с ними на примере функционирования образовательной платформы. Показаны перспективы применения технологий big data в персонализированном онлайн-обучении, а также риски применения анализа «больших данных». The article describes the importance of big data analysis for modern education. The possibilities of working with big data on the example of educational platform have been considered. Prospects of big data technologies in personalized online learning, as well as risks of big data analysis are consi

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Piroonsup, N., and S. Sinthupinyo. "Analysis of training data using clustering to improve semi-supervised self-training." Knowledge-Based Systems 143 (March 2018): 65–80. http://dx.doi.org/10.1016/j.knosys.2017.12.006.

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Jobson, Simon A., Louis Passfield, Greg Atkinson, Gabor Barton, and Philip Scarf. "The Analysis and Utilization of Cycling Training Data." Sports Medicine 39, no. 10 (2009): 833–44. http://dx.doi.org/10.2165/11317840-000000000-00000.

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Han, BongKyu, Woo-Seop Yun, and Jaeoh Kim. "Analysis of mobilization training data using beta regression." Journal of the Korean Data And Information Science Society 31, no. 3 (2020): 611–20. http://dx.doi.org/10.7465/jkdi.2020.31.3.611.

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Chun, Il Yong, David Hong, Ben Adcock, and Jeffrey A. Fessler. "Convolutional Analysis Operator Learning: Dependence on Training Data." IEEE Signal Processing Letters 26, no. 8 (2019): 1137–41. http://dx.doi.org/10.1109/lsp.2019.2921446.

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Romadlona, Nohan Arum, Tika Dwi Tama, Ema Novita Deniati, Aditya Yudha Pratama, Meirina Nur Asih Susanti, and Evada Mutiara Ramadhani. "Training Data Analysis To Increase The Capacity Alumni." Health Frontiers 2, no. 1 (2024): 14–18. https://doi.org/10.62255/mjhp.v2i1.115.

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Aftersales is a routine program carried out to increase the capacity of alumni, especially alumni of Public Health Sciences, Universitas Negeri Malang. This training took the theme of data analysis which was carried out six times both online and offline. Training results were measured through pre- and post-test questionnaires for 15 participants. The results show that there is a significant increase in knowledge p value (<0.005). Statistical data analysis is a topic that is expected to be able to increase knowledge, skills and support alumni according to their field of work.

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Yu, Bei, and Xiao Hu. "Toward Training and Assessing Reproducible Data Analysis in Data Science Education." Data Intelligence 1, no. 4 (2019): 381–92. http://dx.doi.org/10.1162/dint_a_00053.

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Reproducibility is a cornerstone of scientific research. Data science is not an exception. In recent years scientists were concerned about a large number of irreproducible studies. Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy. Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training. In this study, we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis. The results sh

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Wang, Diliang, and Guoyang Huang. "Analysis of the Influence of Outward Bound Training Based on Data Analysis in College Physical Training." Computational Intelligence and Neuroscience 2022 (September 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/6488562.

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The competition for talents in the modern society is constantly intensifying. College students not only have good physical and psychological quality but also bear hardships and stand hard work and adapt to the fast-paced working environment in order to adapt to the development of the times. With the advent of the era of big data, advanced technology has been applied to physical exercise and development, providing opportunities and challenges for the development of sports. Therefore, this paper focuses on the impact of expanding training on college sports training through extensive surveys on c

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Jinhui, Zheng, Wang Sheng, Zheng Jinhong, Cai Guoliang, Cai Zhiqiang, and Du Yuntao. "Analysis on Survey Data of Special Physical Training for Skiers in Summer Training Based on Big Data." Mobile Information Systems 2021 (December 28, 2021): 1–6. http://dx.doi.org/10.1155/2021/3024089.

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Due to the geographical and natural conditions, the development of skiing events is more resistant in China, and the training venues, methods, and concepts are insufficient, making it difficult for Chinese skiers to make some progress and aspire to the highest peak in this field. The purpose of this study is to explore and analyze the survey data of the professional physical training of skiers in summer training based on big data. Big data is employed to investigate and analyze the special physical training of skiers in summer training. Based on the data of professional physical training of sk

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Abdullateef Omitogun, Abdullateef Omitogun, and Khalid Al-Adeem Abdullateef Omitogun. "Auditors’ Perceptions of and Competencies in Big Data and Data Analytics: An Empirical Investigation." International Journal of Computer Auditing 1, no. 1 (2019): 092–113. http://dx.doi.org/10.53106/256299802019120101005.

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<p>This study presents evidence on practicing auditors&rsquo; perceptions of and competencies in applying big data and data analytics to audit engagements. An electronic questionnaire distributed to accountants shows that auditors have good information technology skills and are well-acquainted with big data and data analytics. However, they lack relevant technical skills and are unfamiliar with related data analysis tools, excluding Excel. The results reveal 64.71% of accountants have not attended any training on big data and data analytics, while 31.37% plan to enhance related knowl

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Sudirman, Sudirman. "Pelatihan Pengolahan Data Penelitian Pendidikan IPA Menggunakan STATA Data Analysis." Jurnal Pengabdian UNDIKMA 4, no. 3 (2023): 621. http://dx.doi.org/10.33394/jpu.v4i3.8481.

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The community service aims to increase the capacity of Universitas Qamarul Huda Badaruddin (UNIQHBA) lecturers in processing and analyzing science education research data using STATA Software. The method of community service was used for interactive training for two weeks with the Zoom application for 37 lecturers from UNIQHBA. To determine the participants' ability to use the STATA software, practice processing and data analysis were carried out using a science education case study guide provided by the facilitator. The evaluation was carried out at the end of the training to get participants

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Choi, Jihun, Jong Hoon Ahn, and Hyeon Deok Kim. "Utilization of Flight Data Analysis for EBT(Evidence Based Training) Program." Journal of the Korean Society for Aviation and Aeronautics 31, no. 4 (2023): 1–6. http://dx.doi.org/10.12985/ksaa.2023.31.4.001.

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Stawicki, Piotr, and Ivan Volosyak. "cVEP Training Data Validation—Towards Optimal Training Set Composition from Multi-Day Data." Brain Sciences 12, no. 2 (2022): 234. http://dx.doi.org/10.3390/brainsci12020234.

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This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain–computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user’s own pre-recorded EEG responses to the same stimulus target), which is

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Huang, Yahui. "Intelligent assistive robot design based on big data analysis and biomechanical analysis." Molecular & Cellular Biomechanics 22, no. 5 (2025): 1381. https://doi.org/10.62617/mcb1381.

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To improve the training effectiveness of rehabilitation training for patients with lower limb injuries, the research optimized the long short-term memory network algorithm using convolutional neural network algorithm, and conducted big data analysis on the biomechanics of the human lower limb based on the optimized algorithm. Through the results of big data analysis, the mechanical response mechanism of the human lower limb during movement was studied, and a rehabilitation training intelligent assistive robot that aligns more closely with the biomechanical properties of the human body was desi

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Bennett, Deborah E., and Stephanie Miller. "Electronic Educational Data Security: System Analysis and Teacher Training." Journal of Educational Technology Systems 29, no. 1 (2000): 3–20. http://dx.doi.org/10.2190/7m9u-3648-4jjb-qg64.

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Bergeron, Bryan P., Richard S. Shiffman, and Ronald L. Rouse. "Data qualification: Logic analysis applied toward neural network training." Computers in Biology and Medicine 24, no. 2 (1994): 157–64. http://dx.doi.org/10.1016/0010-4825(94)90073-6.

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Simmons, H. E., B. Matos, and S. A. Simpson. "Analysis of injury data to improve safety and training." Journal of Chemical Health and Safety 24, no. 1 (2017): 21–28. http://dx.doi.org/10.1016/j.jchas.2016.03.004.

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Palacios Zumba, Efrén Mesías, Vicente Anderson Aguinda Cajape, Jorge Luis Serrano Aguilar, et al. "Artificial Intelligence in Sports: Data Analysis to Enhance Training." Interdisciplinary Rehabilitation / Rehabilitacion Interdisciplinaria 4 (April 14, 2024): 85. http://dx.doi.org/10.56294/ri202485.

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The objective of this article was to explore the role of artificial intelligence in the field of sports, focusing on data analysis to enhance training. A literature review was conducted to examine different artificial intelligence algorithms to identify patterns and trends that enable the application of effective and personalized training strategies. Additionally, practical and ethical implications of using artificial intelligence in sports were discussed, along with potential future directions for interdisciplinary research and sports development. In conclusion, the transformative potential o

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Ek-Chacón, Edgar, Erik Molino-Minero-Re, Paul Erick Méndez-Monroy, Antonio Neme, and Hector Ángeles-Hernández. "Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis." Applied Sciences 14, no. 10 (2024): 4175. http://dx.doi.org/10.3390/app14104175.

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A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been

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Shcherbakov, V. S., and I. A. Karpov. "Regional Inflation Analysis Using Social Network Data." Economy of Regions 20, no. 3 (2024): 930–46. http://dx.doi.org/10.17059/ekon.reg.2024-3-21.

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Inflation is one of the most important macroeconomic indicators that have a great impact on the population of any country and region. Inflation is influenced by a range of factors, including inflation expectations. Many central banks take this factor into consideration while implementing monetary policy within the inflation targeting regime. Nowadays, a lot of people are active users of the Internet, especially social networks. It is hypothesised that people search, read, and discuss mainly only those issues that are of particular interest to them. It is logical to assume that the dynamics of

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Shan, Rui, Xianfei Xiao, Junwen Che, Jingfang Du, and Yuwu Li. "Data Mining Optimization Software and Its Application in Financial Audit Data Analysis." Mobile Information Systems 2022 (July 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/6851616.

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The scope of finance is very wide, data also plays a very important role in the financial industry, a small data change, and it may have a great impact on the economy. Therefore, the author proposes data mining optimization software and its application in financial audit data analysis. First, discuss the decision tree method, the main function module design of the system software, the financial analysis software method of weighted multiple random decision trees is described. To conduct verification experiments, the decision-making effect of constructing 10 random decision trees is the best. So

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Doonan, Ashley, Dharma Akmon, and Evan Cosby. "An Exploratory Analysis of Social Science Graduate Education in Data Management and Data Sharing." International Journal of Digital Curation 15, no. 1 (2020): 18. http://dx.doi.org/10.2218/ijdc.v15i1.671.

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Effective data management and data sharing are crucial components of the research lifecycle, yet evidence suggests that many social science graduate programs are not providing training in these areas. The current exploratory study assesses how U.S. masters and doctoral programs in the social sciences include formal, non-formal, and informal training in data management and sharing. We conducted a survey of 150 graduate programs across six social science disciplines, and used a mix of closed and open-ended questions focused on the extent to which programs provide such training and exposure. Resu

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Demšar, Janez, and Blaž Zupan. "Hands-on training about data clustering with orange data mining toolbox." PLOS Computational Biology 20, no. 12 (2024): e1012574. https://doi.org/10.1371/journal.pcbi.1012574.

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Data clustering is a core data science approach widely used and referenced in the scientific literature. Its algorithms are often intuitive and can lead to exciting, insightful results that are easy to interpret. For these reasons, data clustering techniques could be the first method encountered in data science training. This paper proposes a hands-on approach to data clustering training suitable for introductory courses. The education approach features problem-based training that starts with the data and gradually introduces various data processing and analysis methods, illustrating them thro

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Adamenko, O. V., and L. F. Panchenko. "Cloud data analysis technologies." CTE Workshop Proceedings 1 (March 21, 2013): 143–44. http://dx.doi.org/10.55056/cte.175.

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According to Gartner, big data processing and cloud computing are among the most significant trends in 2013. Consequently, an important task of higher education is to train a competitive specialist knowledgeable in the relevant technologies.The course on computer data analysis developed by the authors for the training of specialists of different specialties combines the performance of data analysis tasks in the Excel and SPSS environments. Seeking to expand the use of statistical data analysis software, we turned to SAS's Academic Support Program. This application (SAS OnDemand for Academics)

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Wang, Yiting, and Le Yu. "Multisource Analysis of Big Data Technology: Accessing Data Sources for Teacher Management of Sports Training Institutions." Mobile Information Systems 2022 (August 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/5115184.

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In the information age, “mobile Internet,” “cloud computing,” “Internet of Things,” and “data mining” concepts are emerging at the same time, as well as other fields of related data-based applications. The mobile application will be born as a result. Therefore, in the information age, big data, which involves information in a specific key or specialized field, has gradually begun to receive a lot of attention in recent years. In 2011, the US consulting firm McKinsey and Company first proposed the arrival of the “era of big data” and in August 2015 in China’s State Council issued a notice of ac

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Chen, Yuanyuan, Boyang Li, Han Yu, Pengcheng Wu, and Chunyan Miao. "HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7081–89. http://dx.doi.org/10.1609/aaai.v35i8.16871.

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The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HyDRA, which interprets the predictions made by DNNs as effects of their training data. Existing approaches generally estimate data contributions around the final model parameters and ignore how the training data shape the optimization trajectory. By unrolling the hypergradient of test loss w.r.t. the weights of training data, HyDRA assesses the contribution of training data toward test data points throughout the training trajector

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Downey, James, Zachary Ellis, Ethan Nguyen, Charlotte Spencer, and Paul Evangelista. "Data Analytics Development from Military Operational Data." Industrial and Systems Engineering Review 9, no. 2 (2022): 76–82. http://dx.doi.org/10.37266/iser.2021v9i2.pp76-82.

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Each year, the National Training Center (NTC) located at Fort Irwin, California, hosts multiple Brigade-level rotational units to conduct training exercises. NTC’s Instrumentation Systems (NTC-IS) digitally capture and store characteristics of movement and maneuver, use of fires, and other tactical operations in a vast database. The Army’s Engineer Research and Development Center (ERDC) recently partnered with Training and Doctrine Command (TRADOC) to make some of the data available for introductory analysis within a relational database. While this data has the potential to expose capability g

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Yu, Ziqin. "Application of Statistical Analysis to the Competition and Training Data." Advances in Engineering Technology Research 13, no. 1 (2025): 1488. https://doi.org/10.56028/aetr.13.1.1488.2025.

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This paper focuses on statistical analysis methods for sports events and athlete training data, including multiple regression models, regression trees, and XGBOOST models. For the big data situation of the training and competition of athletes, this paper explains and expands the principal component analysis methods for denoising and dimensionality reduction. For the above models and techniques, this paper reviews the relevant cutting-edge results of several excellent research papers, compares them horizontally, and analyses them. In addition, based on the strengths and weaknesses of the existi

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Izonin, Ivan, Roman Muzyka, Roman Tkachenko, Michal Gregus, Roman Korzh, and Kyrylo Yemets. "An enhanced cascade ensemble method for big data analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 963. https://doi.org/10.11591/ijai.v14.i2.pp963-974.

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In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their perf

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Ivan, Izonin, Muzyka Roman, Tkachenko Roman, Gregus Michal, Korzh Roman, and Yemets Kyrylo. "An enhanced cascade ensemble method for big data analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 963–74. https://doi.org/10.11591/ijai.v14.i2.pp963-974.

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In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their perf

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Manalastas, Maria Yna Diane, Maria Excelsis Orden, and Ana Maria Lourdes Latonio. "Enhancing Capacity on Data Analysis among Gender and Development Focal Persons Through Training." CLSU International Journal of Education and Development Studies 1, no. 1 (2020): 25–35. http://dx.doi.org/10.22137/ijeds.2020.v1n1.03.

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Gender and development (GAD)-related issues are important topics in nation building. GAD focal persons in government agencies are identified to mainstream implementation of GAD-related activities to include research and development. However, one of the limiting factors in gender-related research is the lack of technical knowledge on data analytics which is fundamental for decision-making. The Socio-Economics Research and Data Analytics Center in Luzon (SERDAC–Luzon) was established as a government’s response to this limitation. The Center aims to enhance the capacity of researchers in basic an

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Park, Jiho, Jihun Choi, Soon Jee Seol, Joongmoo Byun, and Young Kim. "A method for adequate selection of training data sets to reconstruct seismic data using a convolutional U-Net." GEOPHYSICS 86, no. 5 (2021): V375—V388. http://dx.doi.org/10.1190/geo2019-0708.1.

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Deep-learning (DL) methods have recently been introduced for seismic signal processing. Using DL methods, many researchers have adopted these novel techniques in an attempt to construct a DL model for seismic data reconstruction. The performance of DL-based methods depends heavily on what is learned from the training data. We focus on constructing the DL model that well reflect the features of target data sets. The main goal is to integrate DL with an intuitive data analysis approach that compares similar patterns prior to the DL training stage. We have developed a two-sequential method consis

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Liang, Chaohui, and Jiling Shang. "Optimization of Computer-aided English Pronunciation Training Data Analysis System." Computer-Aided Design and Applications 18, S4 (2021): 37–48. http://dx.doi.org/10.14733/cadaps.2021.s4.37-48.

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Kim, You-Jin, and Kyu-Hoon Kwak. "Keyword Analysis of Home Training by Period Using Big Data." Korean Journal of Sports Science 30, no. 1 (2021): 103–15. http://dx.doi.org/10.35159/kjss.2021.2.30.1.103.

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Xue, Yuan, Erjuan Du, and Zhihong Hou. "Sports training injuries and prevention measures using big data analysis." Molecular & Cellular Biomechanics 22, no. 2 (2025): 539. https://doi.org/10.62617/mcb539.

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This work explores the application of big data technology in monitoring sports training injuries, emphasizing the biomechanical principles underlying injury mechanisms to enhance the accuracy of injury prediction and provide scientific prevention measures. It collects training data from professional sports teams using big data technology and constructs a Bi-directional Long Short-Term Memory (BiLSTM)—Residual Network (ResNet) model through deep learning techniques. In this model, the BiLSTM module captures the temporal sequence features of sports data, while the ResNet module improves the mode

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Riddle, Dawn L., Michael D. Coovert, Linda R. Elliott, and Samuel G. Schiflett. "Potential Contributions of Rough Sets Data Analysis to Training Evaluations." Military Psychology 15, no. 1 (2003): 41–58. http://dx.doi.org/10.1207/s15327876mp1501_04.

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Wang, Defeng, and Lin Shi. "Selecting valuable training samples for SVMs via data structure analysis." Neurocomputing 71, no. 13-15 (2008): 2772–81. http://dx.doi.org/10.1016/j.neucom.2007.09.008.

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Kwon, Hyun, Yonggi Kim, and Jun Lee. "Analysis of methods for the model extraction without training data." Jouranl of Information and Security 23, no. 5 (2023): 57–64. http://dx.doi.org/10.33778/kcsa.2023.23.5.057.

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