An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques by Ireneusz Czarnowski, Piotr Jędrzejowicz

“…In the paper, several data reduction techniques for machine learning from big datasets are discussed and evaluated. …”
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Process mining in mHealth data analysis by Michael Winter, Berthold Langguth, Winfried Schlee, Rüdiger Pryss

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The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release by Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu

“…Our analysis employs data primarily sourced from the Stellar Abundances and Galactic Evolution Survey (SAGES), which aims to observe much of the Northern Hemisphere. …”
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Social Intelligence Mining: Transforming Land Management with Data and Deep Learning by Mohammad Reza Yeganegi, Hossein Hassani, Nadejda Komendantova

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THE ROLE OF DATA VISUALIZATION IN ENHANCING TEXTUAL ANALYSIS by P. Milev

“…METHODS: It investigates the evolution of data visualization methodologies and their integration with textual analysis. …”
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To be(t) or not to be(t): A Bayesian approach to statistical data analysis by Pisano Silvia

“…The process of learning from observation is the founding step of Science. …”
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Federated Learning Approach for Breast Cancer Detection Based on DCNN by Hussain AlSalman, Mabrook S. Al-Rakhami, Taha Alfakih, Mohammad Mehedi Hassan

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Enhancing Competency-Based Learning with Neutrosophic Regression and the Deming Cycle by Dr Hortencio Flores Flores, Dr. Irma Reyes Blácido, Dr. Magda Patricia Ramos Cevallos, Dr. Nemesia Hidalgo Penadillo, Mg. Luz Magali Villarroel Nuñez

Subjects: “…competency-based learning…”
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Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources by Hongwei Guo, Chao Zhang, Hongyuan Fang, Timon Rabczuk, Xiaoying Zhuang

“…Various datasets, including shear wave velocity-based, CPT-based, SPT-based, and real cases, are collected and utilized to verify the effectiveness and accuracy of the proposed model. Because different data sources in soil liquefaction generally share several geotechnical and mechanical parameters, we work to combine model prior information, feature mapping and data reconstruction in transfer learning models to tackle multi-source domain adaption, which can be further applied to other predictive analysis and facilitate online learning models in geotechnical engineering. …”
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Conceptualization and scale development for big data-based learning organization capability by Nesrin Alkan, Deniz Ersan Yilmaz, Bilal Baris Alkan

“…While big data significantly influences organizational learning, a comprehensive tool to measure this capability has been lacking in the literature. …”
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Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data by Sadullah Çelik, Bilge Doğanlı, Mahmut Ünsal Şaşmaz, Ulas Akkucuk

“…In conclusion, this study offers valuable information for more effective classification and analysis of World Happiness Index data by comparing the performance of various machine learning algorithms.…”
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Data-driven insights into groundwater quality: machine and deep learning approaches by Gift Mbuzi, Abdur Rashid Sangi, Baha Ihnaini, Anil Carie, Sruthi Sivarajan, Satish Anamalamudi

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Predictive athlete performance modeling with machine learning and biometric data integration by Qin Jianjun, Haytham F. Isleem, Walaa J. K. Almoghayer, Mohammad Khishe

“…Abstract The Purpose of this study is to propose a new integrative framework for athletic performance prediction based on state-of-the-art machine learning analysis and biometric data biometric scanning. …”
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Efficient Observation Time Window Segmentation for Administrative Data Machine Learning by Musa Taib, Geoffrey G. Messier

“…Machine learning models benefit when allowed to learn from temporal trends in time-stamped administrative data. …”
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Big Data, Machine Learning, Artificial Intelligence and Blockchain in Corporate Governance by Meiryani Meiryani, Dezie Leonarda Warganegara, Vidhiya Andini

“… The paper analyses the dynamics of scientific research in, and practical application of key Industry 4.0 technologies in corporate governance, namely big data, artificial intelligence, machine learning, and blockchain. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

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Forecasting the Sugarcane Yields Based on Meteorological Data Through Ensemble Learning by Sumit Kumar, Millie Pant, Atulya Nagar

“…This research aims to enhance the precision of sugarcane yield prediction in India by developing a stacking ensemble learning model. The developed model incorporates the least absolute shrink and selection operator (LASSO), artificial neural network (ANN), and random forest (RF) as base models alongside random forest regression (RFR) and Ridge regression (RR) as meta-models and utilizes principal component analysis (PCA) and SHAPLEY values to reduce dimensions and explore feature correlations within the dataset. …”
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Predicting cancer risk using machine learning on lifestyle and genetic data by Mohamed Abdelmoaty Ahmed, Ahmed AbdelMoety, Asmaa Mohamed Ahmed Soliman

“…This study investigates the application of Machine Learning (ML) techniques to predict cancer risk based on a combination of genetic and lifestyle factors. …”
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The impacts of training data spatial resolution on deep learning in remote sensing by Christopher Ardohain, Songlin Fei

“…Deep learning (DL) is ubiquitous in remote sensing analysis with continued evolution in model architectures and advancement of model types. …”
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Modern Deep Learning Techniques for Big Medical Data Processing in Cloud by Mohammed Y. Shakor, Mustafa Ibrahim Khaleel

“…The recent advancements in Machine Learning (ML) and Deep Learning (DL) provide a new dimension in biomedical big data analysis, while the cloud computing technologies present the breakthroughs of handling massive data from hardware, software, and storage. …”
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