Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations by Ahmad J. Obaidullah, Wael A. Mahdi

“…Abstract This study introduces a sophisticated predictive framework for determining drug solubility and activity values in formulations via machine learning. The framework utilizes a comprehensive dataset consisting of more than 12,000 data rows and 24 input features containing a wide range of parameters to estimate drug solubility in formulation. …”
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Magnetic Source Detection Using an Array of Planar Hall Effect Sensors and Machine Learning Algorithms by Miki Vizel, Roger Alimi, Daniel Lahav, Moty Schultz, Asaf Grosz, Lior Klein

“…To address this, we employed the Levenberg–Marquardt Algorithm (LMA) as a deterministic optimization method to estimate the magnetic source’s position and parameters, as well as machine earning (ML) algorithms, which consist of a Fully Connected Neural Network (FCNN). …”
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Modeling and Real-time Visual Simulation of Multi-axis CNC Machine Tools for Digital Twins by MENG Boyang, SUN Wenxing, YUE Caixu, ZHU Tongjie, FU Zizhen, LIU XianLi

“…As the core engine of intelligent manufacturing, digital twins hold immense potential in the fields of virtual simulation and machining visualization. However, traditional offline digital twin modeling methods rely on the accuracy of preset parameters for fidelity, resulting in poor synchronization between simulation results and physical entities. …”
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Strategies for Automated Identification of Food Waste in University Cafeterias: A Machine Vision Recognition Approach by Yongxin Li, Chaolong Zhang, Hui Xu, Yuantong Yang, Han Lu, Lei Deng

“…Our findings indicate that machine vision technology is suitable for rapid identification and location of clean plates. …”
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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. …”
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Thermodynamic and Heat Transfer Evaluation of Pocket Drying Section in the Multi-Cylinder Dryers of Paper Machine by Sh. Ghodbanan, R. Alizadeh, S. Shafiei

“…The applied model can be used to compute the drying parameters and analyze the pocket drying conditions. …”
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Advanced hybrid computational analysis of febuxostat solubility using machine learning in supercritical processing method by Turki Al Hagbani, Rami M. Alzhrani, Majed Ahmed Algarni

“…This research was done with the aim of modeling the solubility of febuxostat (FBX) drug with the help of machine learning methods. Temperature and pressure are the input values on which the modeling is done. …”
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Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques by Ahmed I. Saleh, Nabil S. Mahmoud, Fikry A. Salem, Mohamed Ghannam

“…Abstract This study investigates the use of machine learning (ML) models to predict the ultimate load capacity of demountable shear connectors in steel–concrete composite structures. …”
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Machine learning-based predictive analysis of energy efficiency factors necessary for the HIFU treatment of adenomyosis by Ziyan Liu, Ziyi Liu, Yuan Wang, Xiyao Wan, Xiaohua Huang

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Design and experiment of key components of a insertion vegetable grafting machine with six plants synchronous by Yongtao Yu, Yanjun Li, Fuxiang Xie, Jian Song, Yang Bai, Yu Fan

“…The optimal combination of parameters after optimization was the punching needle diameter of 1.8 mm, the punching speed of 40 mm s−1 and the docking speed of 60 mm s−1. …”
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Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning by Jitao Zhao, Miaomiao Zhu, Lidan Xu, Ming Chen, Mingfang Shi

“…To address the existing challenges of lacking a unified and reliable shear capacity prediction model for fiber-reinforced polymer (FRP)-strengthened reinforced concrete beams (FRP-SRCB) and the excessive experimental workload, this study establishes a shear capacity prediction model for FRP-SRCB based on machine learning (ML). First, the correlation between input and output parameters was analyzed by the Pearson correlation coefficient method. …”
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Analysis of the occurrence of damage to concrete surfaces through the cavitation phenomenon – cavitation machine type-ventury by Aline Saupe Abreu, Guilherme Santanna Castiglio, Pedro Guido Mottes Bassegio, Edgar Fernando Trierweiler Neto, Eder Daniel Teixeira, Marcelo Giulian Marques, Mauricio Dai Prá

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The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia by Ilija Doknić, Mirjana Mitrović, Zoran Bukumirić, Marijana Virijević, Nikola Pantić, Nikica Sabljić, Darko Antić, Živko Bojović

“…In order to ascertain which patients are at risk, statistical and machine-learning (ML) algorithms were employed to predict which patients with leukemia will develop thrombosis. …”
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Transient power stabiliser for a virtual synchronous machine control in doubly-fed induction generators by Jesús Castro Martínez, Francisco Gonzalez-Longatt, Santiago Arnaltes Gómez, José Luis Rodríguez Amenedo

“…In this research paper, a novel PSS (Power System Stabiliser)-like strategy is proposed to address this issue for Virtual Synchronous Machine (VSM) grid-forming (GFM) controls in type 3 wind turbines. …”
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Optimization of Mechanical Properties and Manufacturing Time through Experimental and Statistical Analysis of Process Parameters in Selective Laser Sintering by Adrian Korycki, Christian Garnier, Valerie Nassiet, Charles Tarek Sultan, France Chabert

“…The parametric study was carried out by varying five parameters on the SLS machine and by looking at their influence on five groups of responses relating to the physical, mechanical, and thermal properties as well as to the printing duration. …”
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POLISHING TECHNOLOGICAL PARAMETERS INFLUENCE LAW STUDY ON TC4 MATERIAL CUTTING DEPTH FOR ABRASIVE CLOTH WHEEL by HUAI WenBo, WANG XuHui, NIU QinHua, LIN XiaoJun

“…In order to master the influence rule of the flexible polishing technological parameters for abrasive cloth wheel on materials cutting depth,the single factor polishing experiment of TC4 specimen was carried out based on the polishing technological equipment of “NC machine + flexible grinding head + elastic grinding tool（ abrasive cloth wheel） ”,the process parameters influence on materials cutting depth was analyzed,and the materials cutting depth prediction model was established by orthogonal test,the results show that the cutting depth is the most sensitive to the amount of compression,the more sensitive to the rotational speed,the less sensitive to the feed speed and the less sensitive to abrasive size. …”
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Machine Learning and Metaheuristic Algorithms for Voice-Based Authentication: A Mobile Banking Case Study by Leili Nosrati, Amir Massoud Bidgoli, Hamid Haj Seyyed Javadi

“…Then, to crack the code, an artificial neural network is employed along with retrieved statistics and speech parameters such as energy and Mel frequency cepstral coefficient. …”
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Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects by Tong Wu, Jiawei Zhang, Qinghao Yan, Jingxiang Wang, Hao Yang

“…This review highlights the pivotal role of machine learning (ML) in overcoming these limitations by integrating multi-source data, constructing quantitative structure–property relationships, and enabling the cross-scale optimization of OFMs. …”
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Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units by Tong Wu, Ruo-Yu Zhuang, Yun-Zhe Wu, Xiao-Li Wang, Hong-ping Qu, Dan-Feng Dong, Yi-De Lu, Jing-yi Wu

“…Abstract This study aims to establish and validate prediction models based on novel machine learning (ML) algorithms for augmented renal clearance (ARC) in critically ill patients with sepsis. …”
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Instability and Stabilization Filter of df/dt-Type Virtual Inertia Control Comparing to Synchronous Machines by Takumi Ueda, Kenichiro Sano, Daisuke Terazono, Kaho Nada

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