Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading by Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, Mohammed El-Meligy, Muhammad Faisal Javed, Rizwan Ullah

“…A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. …”
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Development and validation of a machine learning model for predicting pulmonary metastasis in hepatocellular carcinoma patients by Gangfeng Zhu, Qiang Yi, Rui Xu, Yi Xie, Siying Chen, Yipeng Song, Yi Xiang, Xiangcai Wang, Li Huang

“…Feature selection was conducted using the Boruta algorithm and multivariate logistic regression. Eight machine learning models were then developed and evaluated using validation cohorts for predictive performance. …”
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Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration by M.D. Mukelabai, E.R. Barbour, R.E. Blanchard

“…Previous studies have included many aspects into their optimizations, including technical parameters and different costs/socio-economic objective functions, however there is no clear best-practice framework for model development. …”
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Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…To address this, we employed five ML models, Decision Tree, Ranger, Random Forest, Support Vector Machine, and k-nearest Neighbors, and optimized their parameters using the random search technique. …”
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The Design and Testing of a Combined Operation Machine for Corn Straw Crushing and Residual Film Recycling by Jiuxin Wang, Wuyun Zhao, Xiaolong Liu, Fei Dai, Ruijie Shi, Keping Zhang, Xiaoyang Wang, Wenhui Zhang, Jiadong Liang

“…The optimal combination of operating parameters was devised based on theoretical calculations and single- and multifactor simulation tests. …”
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Machine Learning-Enabled Fast Prediction of GGNMOS Performance and Inverse Design for Electrostatic Discharge Applications by Zihan Wang, Ruichen Chen, Shengyao Lu, Ian Then, Di Niu, Xihua Wang

“…Our work represents an advancement in design of electronic devices and circuits using machine learning.…”
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Enhancing Healthcare With WBAN and Digital Twins: A Machine Learning Approach for Predictive Health Monitoring by Rishit Mahapatra, Deepak Sethi, Kaushik Mishra

“…The collected data undergoes processing and is then sent to a remote medical server over the Internet. Machine learning (ML) has reinvented many paradigms, especially in healthcare, where it is a key resource in WBAN. …”
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Low-cost single foot operated mechanical suction machine for rural health centers and hospitals by Ahmed Ali Dawud, Ahmed Mohammed Abagaro

“…It includes user and maintenance training, displayed parameters, corrosion-resistant components, and pump pedal spring loading. …”
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A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys by Lokesh Kumar, Ashish Goyal, Vimal Kumar Pathak, Abhijit Bhowmik

“…Parameters in electrical discharge machining (EDM) and wire electrical discharge machining (WEDM) are coupled with sensors for real-time monitoring of the machining zone and powder concentration. …”
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Optimization of Flavor Quality of Lactic Acid Bacteria Fermented Pomegranate Juice Based on Machine Learning by Wenhui ZOU, Fei PAN, Junjie YI, Linyan ZHOU

“…Furthermore, key volatile compounds that influenced sensory preferences were predicted in 2 FPJs by using headspace solid phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) combined with machine learning (ML). It was found that HWPS exhibited a higher viable bacterial count, indicating that consumers preferred FPJ with higher viable bacteria count, while there was no significant differences in color parameters and antioxidant substances between 2 FPJs. …”
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Prognostic machine learning models for thermophysical characteristics of nanodiamond-based nanolubricants for heat pump systems by Ammar M. Bahman, Emil Pradeep, Zafar Said, Prabhakar Sharma

“…This study compares prognostic machine learning (ML) models designed to predict the thermal conductivity and viscosity of nanolubricants used in HP compressors. …”
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Machine Learning-Based Detection of Icebergs in Sea Ice and Open Water Using SAR Imagery by Zahra Jafari, Pradeep Bobby, Ebrahim Karami, Rocky Taylor

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Human identification via digital palatal scans: a machine learning validation pilot study by Ákos Mikolicz, Botond Simon, Aida Roudgari, Arvin Shahbazi, János Vág

“…Abstract Background This study aims to validate a machine learning algorithm previously developed in a training population on a different randomly chosen population (i.e., test set). …”
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Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas by Xiang Wang, Mi Tian, Qiang Qin, Jingwei Liang

“…., the maximum runout distance) is a necessary prerequisite for the debris-flow risk assessment and countermeasures design. Recently, machine-learning models have been proved to be an effective tool in predicting debris-flow parameters. …”
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Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model by D. V. Gavrilov, T. Yu. Kuznetsova, M. A. Druzhilov, I. N. Korsakov, A. V. Gusev

“…To develop a model for predicting the subclinical carotid atherosclerosis (SCA) in order to refine cardiovascular risk (CVR) using machine learning methods in overweight and obese patients without hypertension, diabetes and/or cardiovascular disease (CVD).Material and methods. …”
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Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach by Raneem Mansour, Seifelden Osama, Hazem Ahmed, Mohamed Nasser, Norhan Mahmoud, Amira Elkodama, Amr Ismaiel

“…This work presents a data-based machine learning (ML) approach towards the design of a micro-scale wind turbine. …”
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IoT-driven real-time weather measurement and forecasting mobile application with machine learning integration by Jul Jalal Al-Mamur Sayor, Nishat Tasnim Shishir, Bitta Boibhov Barmon, Sumon Ahemed, Md. Moshiur Rahman

“…Existing weather forecasting systems often lack the precision required for localized conditions, relying on data from distant weather stations and limited environmental parameters. This paper introduces a real-time weather forecasting mobile application that integrates machine learning and IoT technology to address these challenges effectively. …”
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Nano-Tailored Triple Gas Sensor for Real-Time Monitoring of Dough Preparation in Kitchen Machines by Dario Genzardi, Immacolata Caruso, Elisabetta Poeta, Veronica Sberveglieri, Estefanía Núñez Carmona

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Comparative Study on Total Organic Carbon Content Logging Prediction Method Based on Machine Learning by TANG Shengshou, YANG Bin, JIN Jiulong, LIU Hongrui, DAI Xingyu, PU Jincheng

“…In this paper, the sensitive parameters for the prediction of total organic carbon content are selected based on the Pearson correlation coefficient matrix, and three machine learning methods are used to model the total organic carbon content. …”
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Replacing Gauges with Algorithms: Predicting Bottomhole Pressure in Hydraulic Fracturing Using Advanced Machine Learning by Samuel Nashed, Rouzbeh Moghanloo

“…Using a large body of work, including 42 vertical wells, an extensive dataset was constructed and meticulously packed using processes such as feature selection and data manipulation. Eleven machine learning models were then developed using parameters typically available during hydraulic fracturing operations as input variables, including surface pressure, slurry flow rate, surface proppant concentration, tubing inside diameter, pressure gauge depth, gel load, proppant size, and specific gravity. …”
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