A Fundamental Statistics Self-Learning Method with Python Programming for Data Science Implementations by Prismahardi Aji Riyantoko, Nobuo Funabiki, Komang Candra Brata, Mustika Mentari, Aviolla Terza Damaliana, Dwi Arman Prasetya

“…At its core is a solid understanding of statistics, which is necessary for conducting a thorough analysis of data and deriving valuable insights. Unfortunately, conventional statistics learning often lacks practice in real-world applications using computer programs, causing a separation between conceptual knowledge of statistics equations and their hands-on skills. …”
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Hybrid machine learning for flood prediction: comparing CHIRPS satellite and ground station data by Agustina Rachmawardani, Budhy Kurniawan, Sastra Kusuma Wijaya, Ardhasena Sopaheluwakan, Marzuki Sinambela

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Incremental Pyraformer–Deep Canonical Correlation Analysis: A Novel Framework for Effective Fault Detection in Dynamic Nonlinear Processes by Yucheng Ding, Yingfeng Zhang, Jianfeng Huang, Shitong Peng

“…However, capturing nonlinear and temporal dependencies in dynamic nonlinear industrial processes poses significant challenges for traditional data-driven fault detection methods. To address these limitations, this study presents an Incremental Pyraformer–Deep Canonical Correlation Analysis (DCCA) framework that integrates the Pyramidal Attention Mechanism of the Pyraformer with the Broad Learning System for incremental learning in a DCCA basis. …”
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Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey by Yong Han Kim, Wei Ye, Ritbik Kumar, Finn Bail, Julia Dvorak, Yanchao Tan, Marvin Carl May, Qing Chang, Ragu Athinarayanan, Gisela Lanza, John W. Sutherland, Xingyu Li, Chandra Nath

“…While remanufacturing holds immense promise, its full potential can only be realized through concerted efforts towards resolving the inherent complexities and obstacles that impede its operations. Machine learning (ML) and data-driven models emerge as transformative tools to mitigate numerous challenges encountered by manufacturing industry. …”
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Efficiency enhancement of energy supply chains using a machine learning-driven network evaluation framework for blockchain adoption by Ardavan Babaei, Erfan Babaee Tirkolaee, Shahryar Sorooshian, Sadia Samar Ali, Gongming Wang

“…The application of the Super-Efficiency Network Data Envelopment Analysis (SENDEA) model represents its robustness in evaluating blockchain adoption within the industry. …”
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Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics by Snehal Satish, Hari Gonaygunta, Akhila Reddy Yadulla, Deepak Kumar, Mohan Harish Maturi, Karthik Meduri, Elyson De La Cruz, Geeta Sandeep Nadella, Guna Sekhar Sajja

“…This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. …”
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Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

“…Background: Oral cancer is the most common head and neck malignancy and may develop from oral leukoplakia (OL) and oral lichenoid disease (OLD). Machine learning classifiers using structured (tabular) data have been employed to predict malignant transformation in OL and OLD. …”
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Industrial data-driven machine learning soft sensing for optimal operation of etching tools by Feiyang Ou, Henrik Wang, Chao Zhang, Matthew Tom, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. …”
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Brain tumor segmentation using deep learning: high performance with minimized MRI data by Jacky Huang, Banu Yagmurlu, Powell Molleti, Richard Lee, Abigail VanderPloeg, Humaira Noor, Rohan Bareja, Yiheng Li, Michael Iv, Haruka Itakura

“…PurposeBrain tumor segmentation with MRI is a challenging task, traditionally relying on manual delineation of regions-of-interest across multiple imaging sequences. However, this data-intensive approach is time-consuming. We aimed to optimize the process by using a deep learning (DL) based model while minimizing the number of MRI sequences required to segment gliomas.MethodsWe trained a 3D U-Net DL model using the annotated 2018 MICCAI BraTS dataset (training dataset, n = 285), focusing on sub-segmenting enhancing tumor (ET) and tumor core (TC). …”
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Research Development Trends in Project Based Learning (PjBL) Learning Models in Science Learning in Elementary Schools: Bibliometric Analysis by Muhamad Suhardi, Mar'atus Sholihah, Rohmani

“…The data analysis method uses the publication frequency analysis method to see annual trends, keyword analysis to identify main topics, and data visualization in the form of simple graphs or maps to illustrate trends and relationships between topics. …”
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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…Use of multimodal data, meaning data coming from multiple sources, might contribute to machine-learning stress severity detection. …”
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Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models by Mehmet Tahir Ucar, Asim Kaygusuz

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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…The analysis leverages stated preference (SP) data and employs Bayesian optimization in conjunction with a stratified 10-fold cross-validation scheme to ensure model robustness. …”
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Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data by Guihua Zhang, Jiayue Liu, Yuxin Wu, Guangxi Yue

“…To construct a conditional <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>β</mi></mrow></semantics></math></inline-formula> PDF with better performance, a systematic PDF modeling and analysis framework coupled with machine learning methods based on the sparse experimental data was proposed. …”
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A Crime Data Analysis of Prediction Based on Classification Approaches by Fatima Shaker Hussain, Abbas Fadhil Aljuboori

“…The aim is focused on comparative study between three supervised learning algorithms. Where learning used data sets to train and test it to get desired results on them. …”
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Landslide mapping with deep learning: the role of pre-/post-event SAR features and multi-sensor data fusion by Aiym Orynbaikyzy, Frauke Albrecht, Wei Yao, Mahdi Motagh, Wandi Wang, Sandro Martinis, Simon Plank

“…In the context of increasing demands for scalable and automated solutions, Earth Observation (EO) data coupled with deep learning offer great potential to enhance the speed and accuracy of emergency mapping. …”
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Logistics efficiency in Brazilian cities applying data envelopment analysis by Reinaldo Fioravanti, Giset Natalia Montoya, Janaina Antonino Pinto, Orlando Fontes Lima

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Enhancing Temperature Data Quality for Agricultural Decision-Making with Emphasis to Evapotranspiration Calculation: A Robust Framework Integrating Dynamic Time Warping, Fuzzy Logi... by Christos Koliopanos, Alexandra Gemitzi, Petros Kofakis, Nikolaos Malamos, Ioannis Tsirogiannis

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Transfer learning and the early estimation of single-photon source quality using machine learning methods by David Jacob Kedziora, Anna Musiał, Wojciech Rudno-Rudziński, Bogdan Gabrys

“…Validation metrics quickly reveal that even a linear regressor can outperform standard fitting when it is tested on the same contexts it was trained on, but the success of transfer learning is less assured, even though statistical analysis, made possible by data augmentation, suggests its superiority as an early estimator. …”
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Electromyography Signal Acquisition, Filtering, and Data Analysis for Exoskeleton Development by Jung-Hoon Sul, Lasitha Piyathilaka, Diluka Moratuwage, Sanura Dunu Arachchige, Amal Jayawardena, Gayan Kahandawa, D. M. G. Preethichandra

“…This review presents a comprehensive analysis of the EMG signal processing pipeline tailored to exoskeleton applications, spanning signal acquisition, noise mitigation, data preprocessing, feature extraction, and control strategies. …”
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