Methodological Basis and Experience of Using Data Mining Methods in Trade by D. T. Kaiyp, M. G. Zhartybayeva, Zh. O. Oralbekova

“…However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. …”
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Automatic Scan-to-BIM—The Impact of Semantic Segmentation Accuracy by Jidnyasa Patil, Mohsen Kalantari

“…Given the rapid advancement of deep learning algorithms in recent years, it is crucial to analyze how their accuracy impacts reconstruction quality. …”
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Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. by M Isabel Ramos, Juan J Cubillas, Ruth M Córdoba, Lidia M Ortega

“…The methodology uses Machine Learning algorithms together with an exhaustive analysis of predictor variables. …”
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A Novel Toolbox for Generating Realistic Biological Cell Geometries for Electromagnetic Microdosimetry by Mehrdad Saviz, A.H. Buchali Safiee, Elham Sharifi

“…Researchers in bioelectromagnetics often require realistic tissue, cellular and sub-cellular geometry models for their simulations. …”
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Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies by Mohamed Ahwiadi, Wilson Wang

“…Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. …”
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The future of 3D printing in instrumented implantable polymer meta-stents by C. Brosseau, G. Nocchiero, J. Ville

“…This new predictive analysis in healthcare systems relies largely on the use of deep learning algorithms, appropriate for managing massive data integration. …”
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Machine Learning Prediction of Left Ventricular Assist Device Thrombosis from Acoustic Harmonic Power by Kent D. Carlson, Dan Dragomir-Daescu, Barry A. Boilson

“…This study explored the use of machine learning algorithms to predict thrombosis from harmonic power values determined from the acoustic signatures of a cohort of HVAD patients (<i>n</i> = 11). …”
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Machine learning-driven discovery of anoikis-related biomarkers in Adult T-Cell Leukemia/Lymphoma subtypes by Mohadeseh Zarei Ghobadi, Elaheh Afsaneh

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Quantum Computation of the Cobb–Douglas Utility Function via the 2D Clairaut Differential Equation by Isabel Cristina Betancur-Hinestroza, Éver Alberto Velásquez-Sierra, Francisco J. Caro-Lopera, Álvaro Hernán Bedoya-Calle

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Responsible AI in structural engineering: a framework for ethical use by Vagelis Plevris, Haidar Hosamo

“…The framework offers actionable guidance for engineers, policymakers, and researchers seeking to align AI adoption with ethical principles and regulatory standards. …”
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Towards Reliable ECG Analysis: Addressing Validation Gaps in the Electrocardiographic R-Peak Detection by Syed Talha Abid Ali, Sebin Kim, Young-Joon Kim

“…Overall, this review underscores the importance of complete transparency in reporting testing procedures, which prevents misleading assessments of R-peak detection algorithms and enables fair methodological comparison.…”
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A New Approach for Brain Tumor Detection Using Machine Learning by Elsadek Hussien Ibrahim, Shaaban Ebrahim Abo-Youssef, Khaled El-Bahnasy, Khaled Ahmed Mohamed Fathy

“…As patient numbers and data volumes rise, traditional methods have become costly and inefficient. Methods: Researchers have developed algorithms for detecting and classifying brain tumors and prioritizing accuracy and efficiency. …”
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Recent Tools of Software-Defined Networking Traffic Generation and Data Collection by Tabarak Khudhair, Omar Athab

“…Many obstacles and mistakes are also explored and listed to be prevented when researchers try to generate such datasets in their next scientific efforts. …”
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Symmetric Grayscale Image Encryption Based on Quantum Operators with Dynamic Matrices by Luis Olvera-Martinez, Manuel Cedillo-Hernandez, Carlos Adolfo Diaz-Rodriguez, Leonardo Faustinos-Morales, Antonio Cedillo-Hernandez, Francisco Javier Garcia-Ugalde

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Comprehensive benchmarking of genome editing quantification methods for plant applications by Zheng Gong, Yan Zhang, Di Xia, Sohye Yoon, Peter Alexander Crisp, José Ramón Botella

“…This study will be useful to experienced and new researchers in the field, providing suggestions and guidelines to standardize data output and advice about the most suitable technique/s to quantify genome edits for different plant applications.…”
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Review of machine learning applications for predicting the quality of biomass briquettes for sustainable and low-carbon energy solutions by Constance Nakato Nakimuli, Fred Kaggwa, Johan De Greef, David Kilama Okot, Julien Blondeau, Simon Kawuma

“…The findings are intended to aid researchers, industry professionals, and policymakers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.…”
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Using Neural Networks to Automate the Identification of Brightest Cluster Galaxies in Large Surveys by Patrick Janulewicz, Tracy M. A. Webb, Laurence Perreault-Levasseur

“…This can make studying BCGs in such surveys difficult, forcing researchers to either adopt oversimplified algorithms or perform cumbersome visual identification. …”
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Steganographic model to conceal the secret data in audio files utilizing a fourfold paradigm: Interpolation, multi-layering, optimized sample space, and smoothing by Daffa Tristan Firdaus, Ntivuguruzwa Jean De La Croix, Tohari Ahmad, Didacienne Mukanyiligira, Louis Sibomana

“…To address these limitations, this study offers valuable insights to guide researchers in developing high-performing audio steganography models. …”
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Comprehensive evaluation of phosphoproteomic-based kinase activity inference by Sophia Müller-Dott, Eric J. Jaehnig, Khoi Pham Munchic, Wen Jiang, Tomer M. Yaron-Barir, Sara R. Savage, Martin Garrido-Rodriguez, Jared L. Johnson, Alessandro Lussana, Evangelia Petsalaki, Jonathan T. Lei, Aurelien Dugourd, Karsten Krug, Lewis C. Cantley, D. R. Mani, Bing Zhang, Julio Saez-Rodriguez

“…Overall, benchmarKIN helps researchers to select reliable methods for identifying deregulated kinases.…”
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Cuproptosis genes in predicting the occurrence of allergic rhinitis and pharmacological treatment. by Ting Yi

“…Cuproptosis is a novel form of programmed cell death, and its role in allergic rhinitis has not yet been explored. Researching the interaction between cuproptosis and allergic rhinitis will likely pave the way for future treatment of this disease.…”
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