Comparison of Off-the-Shelf Methods and a Hotelling Multidimensional Approximation for Data Drift Detection by J. Ramón Navarro-Cerdán, Vicent Ortiz Castelló, David Millán Escrivá

“…It allows the user to check if a particular model needs retraining using updated data to adapt to the evolving process dynamics. This study compares five different statistical tests, namely four unidimensional and a new multidimensional test (MSPC), to identify data drift in both mean and deviation. …”
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Data-Driven Selection of Decontamination Robot Locomotion Based on Terrain Compatibility Scoring Models by Prithvi Krishna Chittoor, A. Jayasurya, Sriniketh Konduri, Eduardo Sanchez Cruz, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

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Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach by Resty Ayu Ramadhani, Rina Fitriana, Anik Nur Habyba, Yun-Chia Liang

“… Six Sigma is of paramount importance to organizations as it provides a structured and data-driven approach, fostering continuous improvement, minimizing defects, and optimizing processes to meet and exceed customer expectations. …”
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Knowledge distillation with resampling for imbalanced data classification: Enhancing predictive performance and explainability stability by Kazuki Fujiwara

“…This study proposes a novel method combining random undersampling (RUS) with knowledge distillation (KD) to enhance both predictive performance and explainability stability for imbalanced data classification. Our approach employs a two-step learning process: (1) training a balanced teacher model using RUS and (2) training an imbalanced student model through response-based KD, utilizing both soft and hard targets. …”
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A framework for IoT and Blockchain Based on Marketing Systems with an Emphasis on Big Data Analysis by Alireza Aliahmadi, Hamed Nozari, Javid Ghahremani-Nahr

“…The importance of big data in business, like other key processes in the organization, has not hidden from experts. …”
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Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita

“…The results demonstrate that the VAE model consistently detects a broader range of anomalies, particularly in sectors with complex operational processes like NACE05 and NACE30. In contrast, the CAE model tends to focus on more specific, moderate anomalies. …”
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A Comparative Study of Unsupervised Deep Learning Methods for Anomaly Detection in Flight Data by Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri

“…The paper finds that LSTM, when integrated with a self-attention mechanism, offers notable benefits over other deep learning methods as it effectively handles lengthy time series like those present in flight data, establishes a generalized model applicable across various airports and facilitates the detection of trends across the entire fleet. …”
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Analyzing large text data for vocabulary profiling in corpus-based studies of academic discourse by Ismail Xodabande, Mahmood Reza Atai, Mohammad R. Hashemi

“…Given the complexity and volume of data typical in academic fields, this protocol integrates advanced corpus compilation techniques with lexical analysis tools to effectively identify and categorize vocabulary suitable for academic use. …”
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Soulbound Tokens: Enabler for Privacy-Aware and Decentralized Authentication Mechanism in Medical Data Storage by Biagio Boi, Franco Cirillo, Marco De Santis, Christian Esposito

“…Standardization efforts like HL7 have been implemented to enhance data interoperability. …”
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Business Open Big Data Analytics to Support Innovative Leadership and Management Decision in Canada by Nadia Delanoy, Karina Kasztelnik

“…Also, we discover the different existing support of big data analytics to make the rational business decision. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…USLE and its associates become widespread even in spatially extended studies in spite of its original plot-level concept, as well as with certain constraints on the supply of suitable input spatial data. At the same time, there is a continuously expanding opportunity and offer for the application of remote sensing (RS) imagery together with machine learning (ML) techniques to model and monitor various environmental processes utilizing their versatile benefits. …”
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LL37 complexed to double-stranded RNA induces RIG-I-like receptor signalling and Gasdermin E activation facilitating IL-36γ release from keratinocytes by Jennifer Keller, Judit Danis, Isabella Krehl, Eleftheria Girousi, Takashi K. Satoh, Barbara Meier-Schiesser, Lajos Kemény, Márta Széll, W. Wei-Lynn Wong, Steve Pascolo, Lars E. French, Thomas M. Kündig, Mark Mellett

“…These data highlight the distinct responses triggered by dsRNA sensors in keratinocytes. …”
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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

“…This study embarks on a comprehensive review and in-depth analysis of state-of-the-art algorithms across various facets of remanufacturing processes and operations. Additionally, it identifies key challenges to advancing remanufacturing practices through data-driven and ML methods and uncovers research opportunities in synergy with smart manufacturing techniques. …”
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Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems by Alireza Jahandoost, Razieh Dashti, Mahboobeh Houshmand, Seyyed Abed Hosseini

“…Abstract Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. …”
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Testing life-cycle assessment data quality with Benford’s law reveals geographic variation by Bogdan Šinik, Aleksandar Tošić

“…Moreover, the data acquisition process can be subjective, raising concerns about potential inconsistencies. …”
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Delivering data: A real-world dataset for last-mile delivery optimizationZenodo by Anna Vrani, Savvas D. Apostolidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos

“…These matrices were generated using an API that computes travel durations based on historical traffic patterns, real-time data, and predictive models.From the API, we derived four distinct matrices: one for distances and three for travel times under different traffic scenarios: optimistic, pessimistic, and most likely. …”
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Real-time analysis of Delphi panel data to facilitate iterative rating within meetings by Julien B. Weinstein, Cainnear K. Hogan, Taylor Whittington, Jennifer Cano, Sarah M. Seelye, Jeremy B. Sussman, Steven J. Bernstein, Hallie C. Prescott

“…Abstract Objective The Modified Delphi panel process involves iterative ratings, often with lengthy lags between rounds to allow time for data analysis. …”
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Prevalence and predictors of annual asthma reviews in Scottish primary care data: an observational study by Holly Tibble, Alexandria Ming Wai Chung

“…Those aged 18–35 years in the index year or those with missing addresses in the practice registration data were the least likely groups to have an asthma review in the following year. …”
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Hyperspectral and LiDAR space-borne data for assessing mountain forest volume and biomass by Rodolfo Ceriani, Sebastian Brocco, Monica Pepe, Silvio Oggioni, Giorgio Vacchiano, Renzo Motta, Roberta Berretti, Davide Ascoli, Matteo Garbarino, Donato Morresi, Francesco Bassi, Francesco Fava

“…We compared EMIT with Sentinel-2 (S2) multispectral data as model inputs, with and without GEDI data integration, using five Machine Learning (ML) algorithms: Partial Least Squares Regression (PLSR), Boosted Regression Trees (BRT), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR). …”
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Krüppel-like factor 4 modulates the miR-101/COL10A1 axis to inhibit renal fibrosis after AKI by regulating epithelial–mesenchymal transition by Jingying Zhao, Xiuli Wang, Yubin Wu, Chengguang Zhao

“…These hypoxic effects were significantly mitigated or abrogated by KLF4 over-expression in the HK-2 cells. Our data indicate that KLF4 up-regulates miR-101 expression, leading to the downregulation of COL10A1 expression, inhibition of EMT and renal fibrosis during the pathogenic process of I/R-related renal fibrosis.…”
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