Training of Production Managers for Machine-building Enterprises by I. A. Ivanov, G. E. Persiyanova

“…The result is an acute shortage of qualified top managers and middle managers, whereas university graduates can’t find specialty related occupation.To assess the needs of machine-building industry in production managers and various professionals a study was conducted at Novocherkassk Electric Locomotive Plant, which is the largest machine-building enterprise in Russia for production of mainline freight and passenger electric locomotives. …”
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ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

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Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions by Lwamaza, Eddy, Nuwasiima, Happy

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Aplicación de una herramienta avanzada de análisis de datos en el entorno de la construcción = Application of advanced data analysis tool in building environment. by Arrate Hernández-Arizaga, Ana Picallo-Pérez, José María Sala-Lizarraga

“…The aim of this work is to promote FDD with machine learning applications in building environment. …”
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Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms by R. Kabilan, V. Chandran, J. Yogapriya, Alagar Karthick, Priyesh P. Gandhi, V. Mohanavel, Robbi Rahim, S. Manoharan

“…One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. …”
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TEICHI and the Tools Paradox by Sebastian Pape, Christof Schöch, Lutz Wegner

“…In fact, we suggest that tool developers need to find ways of turning these conflicting aims into concurrent aims if they want to build successful tools and broaden their user base.…”
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Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions by Lingyun Feng, Danyang Ma, Min Xie, Mengzhu Xi

“…Commonly applied methods for estimating anthropogenic heat include the inventory method, the energy balance equation method, and the building model simulation method. In recent years, the rapid development of computer technology and the availability of massive data have made machine learning a powerful tool for estimating anthropogenic heat fluxes and assessing its effects. …”
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Predicting Energy and Emissions in Residential Building Stocks: National UBEM with Energy Performance Certificates and Artificial Intelligence by Carlos Beltrán-Velamazán, Marta Monzón-Chavarrías, Belinda López-Mesa

“…The nUBEM is a powerful tool for analyzing residential building stock and supporting data-driven decarbonization strategies. …”
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Analyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble by James A. Casaletto, Ryan T. Scott, Makenna Myrick, Graham Mackintosh, Hamed Chok, Amanda Saravia-Butler, Adrienne Hoarfrost, Jonathan M. Galazka, Lauren M. Sanders, Sylvain V. Costes

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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…<h4>Conclusion</h4>The machine learning model developed in this study, serving as an effective tool for predicting NPM recurrence, aids doctors in making more individualized treatment decisions, thereby enhancing therapeutic efficacy and reducing the risk of recurrence.…”
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Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit by Mohammed G. Albayati, Jalal Faraj, Amy Thompson, Prathamesh Patil, Ravi Gorthala, Sanguthevar Rajasekaran

“…This is mainly because the building owners do not previously have good tools to detect and diagnose these faults, determine their impact, and act on findings. …”
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Effect of a Rapid Tooling Technique in a 3D Printed Part for Developing an EDM Electrode by Turki Alamro, Mohammed Yunus, Rami Alfattani, Ibrahim A. Alnaser

“…The copper-coated FDM (CCF) and solid copper (SC) electrodes are used to conduct experiments on a die-sinking EDM machine using tool alloy steel as a workpiece. The CCF polymer electrode can be efficiently used in EDM operations as the build time of any complex shape was substantially reduced. …”
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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. …”
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A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change by Julia Forster, Stefan Bindreiter, Birthe Uhlhorn, Verena Radinger-Peer, Alexandra Jiricka-Pürrer

“…Ex-ante evaluation of planning measures is insufficient owing to a lack of data and linear models capable of simulating the impacts of complex systemic relationships. Integrating machine learning (ML) into systemic planning increases awareness of impacts by providing decision-makers with predictive analysis and risk mitigation tools. …”
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Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance by Sulaiman O. Atiku, Ibidun C. Obagbuwa

“…In this study, eight different machine learning algorithms were employed to build performance models to predict the prospective performance of commercial banks in Nigeria based on human resources outcomes (employee skills, attitude, and behavior) through the Python software tool with machine learning libraries and packages. …”
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An investigative study on the parameters optimization of the electric discharge machining of Ti6Al4V by Muhammad Mansoor Uz Zaman Siddiqui, Syed Amir Iqbal, Ali Zulqarnain, Adeel Tabassum

“…This investigative study explored the field of electrical discharge machining (EDM), with a particular focus on the machining of Ti6Al4V, a titanium alloy that finds widespread application in aerospace, airframes, engine components, and non-aerospace applications such as power generation and marine and offshore environments. …”
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Small- to Large-Scale Electron Beam Powder Bed Fusion of Functionally Graded Steels by Carlos Botero, William Sjöström, Emilio Jimenez-Pique, Andrey Koptyug, Lars-Erik Rännar

“…In this way, two pre-alloyed powders—a stainless steel (SS) powder and a highly alloyed cold work tool steel (TS) powder—were combined during processing in an S20 Arcam machine. …”
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Risk prediction tools for pressure injury occurrence: an umbrella review of systematic reviews reporting model development and validation methods by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica Velickovic, Jacqueline Dinnes

“…All included reviews contributed to building a comprehensive list of risk prediction tools. …”
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Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning by Elin Panca Saputra, Siti Nurajizah, Mawadatul Maulidah, Nadiyah Hidayati, Taufik Rahman

“…Support Vector Machine (SVM) with PSO. While the attributes that have an influence to determine the algorithm on the level of accuracy are Practice Questions, Quizzes, Mid-Semester Exams, and Final Exams. it is evident from our previous studies that the PSO-based neural network algorithm does have a very good advantage. based on ANN is a method that has calculations that build several units of interconnected connectivity, the ANN method with predictive accuracy can be an efficient and good tool for forecasting and classification research in the field of education. …”
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Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. by Alemu Birara Zemariam, Biruk Beletew Abate, Addis Wondmagegn Alamaw, Eyob Shitie Lake, Gizachew Yilak, Mulat Ayele, Befkad Derese Tilahun, Habtamu Setegn Ngusie

“…The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. …”
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