Facilitate Sustainable Architecture Through Computational Integration: A Three-Phase Framework by Passaint Massoud

“…The first phase, the Smart Learning Design Studio, Custom programmed algorithms are used to enhance the learning process in architectural education. …”
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Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers by Arpit Pandey, Sridharan Naveen Venkatesh, Prabhakaranpillai Sreelatha Anoop, B. R. Manju, Vaithiyanathan Sugumaran

“…Empirically, the research achieved 97.92% accuracy using the local weighted learning (LWL) algorithm, demonstrating the effectiveness of combined statistical, histogram, and ARMA features in enhancing TPMS predictive capabilities.…”
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Expert methods use in undergraduate political science programs by D. Yu. Znamenskiy

“…The key factors of successful expert methods application in educational process include the approach to the experts selection, their informational and organizational-methodological preparation for work, and strict adherence to the prescribed algorithms in the course of expertise.…”
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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…This dataset was used to train both ML and DL algorithms. ML algorithms included Bayesian regression (BR), K-nearest neighbors (KNN), Random Forest (RF), Decision Tree (DT), and DL algorithm convolutional neural network (CNN). …”
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Budget impact analysis of using procalcitonin to optimize antimicrobial treatment for patients with suspected sepsis in the intensive care unit and hospitalized lower respiratory t... by Osvaldo Ulises Garay, Gonzalo Guiñazú, Wanda Cornistein, Javier Farina, Ricardo Valentini, Gabriel Levy Hara

“…<h4>Results</h4>The model predicted that using procalcitonin-guided algorithms would result in 734.5 [95% confidence interval (CI): 1,105.2;438.8] thousand fewer antibiotic treatment days, 7.9 [95% CI: 18.5;8.5] thousand antibiotic-resistant cases avoided, and 5.1 [95% CI: 6.7;4.2] thousand fewer Clostridioides difficile cases. …”
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Analyzing key users’ behavior trends in volunteer-based networks by Nofar Piterman, Tamar Makov, Michael Fire

“…These algorithms allowed us to analyze the factors that significantly influence behavior predictions. …”
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Semisupervised Community Preserving Network Embedding with Pairwise Constraints by Dong Liu, Yan Ru, Qinpeng Li, Shibin Wang, Jianwei Niu

“…These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. …”
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Machine Learning Classification of First-Onset Drug-Naive MDD Using Structural MRI by Donghwa Kim, Pilsung Kang, Junhong Kim, Czang Yeob Kim, Jong-Ha Lee, Sangil Suh, Moon-Soo Lee

“…We aimed to determine whether it is possible to reliably train a highly accurate predictive classification algorithm, even with the first onset of drug-naive adolescent MDD, solely using structural magnetic resonance imaging and without using any other clinical data from the patients. …”
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Machine Learning in Public Governance: A Systematic Review of Applications, Trends and Challenges by Y. Nuruly, G. N. Sansyzbayeva, L. Z. Ashirbekova, S. K. Tazhiyeva

“…Despite significant progress in the models’ technical implementation and predictive accuracy, in many cases, mechanisms for equity, transparency, and citizen participation have been poorly implemented. …”
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Potential distribution of endemic lizards from Brazilian restingas: The present announcing the end by Hugo Andrade, Luisa Maria Diele‐Viegas Costa Silva, Carlos Frederico Duarte Rocha, Antônio Jorge Suzart Argôlo, Eduardo José dos Reis Dias

“…Here, we used an ensemble of three modeling algorithms (Bioclim, GLM, and SVM). In predicting the effects of climate change on their future distributions, we used intermediate and pessimistic socio‐economic pathway scenarios (SSP3 70 and SSP5 85, respectively) considering projections for 2081–2100. …”
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Adaptively Receding Galerkin Optimal Control for a Nonlinear Boiler-Turbine Unit by Gang Zhao, Zhi-gang Su, Jun Zhan, Hongxia Zhu, Ming Zhao

“…With the help of a mathematical predictive model, optimal control law is then obtained based on a Galerkin optimization algorithm. …”
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Responsible AI in structural engineering: a framework for ethical use by Vagelis Plevris, Haidar Hosamo

“…This study addresses these issues through eight fictional but realistic case studies that illustrate plausible ethical dilemmas, such as algorithmic bias in predictive models and tensions between AI-generated recommendations and human engineering judgment. …”
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AI-Driven Accounting and Sensing Applications for Investment Management by Foziljonov Ibrohimjon, Yuldashev Jahongir, Turgunov Jasurbek, Khujamurodov Askarjon, Rozhkova Elena

“…Al-driven accounting and sensing applications have enabled the formulation of multiple investment decision-support models with considerable predictive accuracy, real-time responsiveness, and cost-efficiency benefits. …”
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Internet of medical things and trending converged technologies: A comprehensive review on real-time applications by Shiraz Ali Wagan, Jahwan Koo, Isma Farah Siddiqui, Muhammad Attique, Dong Ryeol Shin, Nawab Muhammad Faseeh Qureshi

“…It also discusses various applied machine learning algorithms available in the healthcare industry. It compares a variety of disease predictions and the accuracy of the equipment and decision recommendations. …”
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Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation by Sambadi Majumder, Chase M. Mason

“…Methods Recursive feature elimination was applied to functional trait data from the HeliantHOME database, followed by the application of the Boruta algorithm to detect the traits that are most predictive of ecoregion. …”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…To identify patterns in the data, additive time series decomposition, spectral analysis based on the fast Fourier transform, and autocorrelation analysis were applied. The predictive model was implemented using a two-stage approach: the classical ARIMA algorithm was used for baseline forecasting, while an LSTM architecture with two recurrent layers and regularization was trained on 24-hour sequences. …”
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ROBUST NUMERICAL METHODS FOR SOLVING THE poorly determined taskS OF ELECTRODYNAMICS AND NONLINEAR DYNAMICS by A. A. Kuraev, T. L. Popkova, V. V. Matveenko

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Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model by Muhammad Adnan, Duaa H. AlSaeed, Heyam H. Al-Baity, Abdur Rehman

“…Particularly, in educational institutions, ML and DL algorithms can help instructors in predicting the learning performance of learners. …”
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Automatic screening for posttraumatic stress disorder in early adolescents following the Ya’an earthquake using text mining techniques by Yuzhuo Yuan, Yuzhuo Yuan, Zhiyuan Liu, Wei Miao, Xuetao Tian

“…Text classification models were constructed using three supervised learning algorithms (BERT, SVM, and KNN) to identify PTSD symptoms and their corresponding behavioral indicators in each sentence of the self-narratives.ResultsThe prediction accuracy for symptom-level classification reached 73.2%, and 67.2% for behavioral indicator classification, with the BERT performing the best.ConclusionsThese findings demonstrate that self-narratives combined with text mining techniques provide a promising approach for automated, rapid, and accurate PTSD screening. …”
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Hidden diversity in anthropophilic sand flies of the Monticola Series (Diptera, Psychodidae) by Bruno Leite Rodrigues, Alessandra Gutierrez de Oliveira, Leonardo Estevam Honorato da Silva, Thiago Vasconcelos dos Santos, Lidiane de Nazaré Carvalho de Oliveira, Felipe Dutra Rêgo, Andrey José de Andrade, Glédson Bandeira Maia, Israel de Souza Pinto, José Dilermando Andrade Filho, Eunice Aparecida Bianchi Galati

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