The assessment of applicability of single-stage piston long-stroke low-speed compressors in low-temperature refrigeration machines by V. L. Yusha, S. S. Busarov, A. V. Nedovenchany

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CO2 adsorption on NaOH and acid modified montmorillonite: Response surface methodology and machine learning modeling by Pardis Mehrmohammadi, Amir Ahmadvand, Ahad Ghaemi

“…This research underscores the critical role of machine learning in optimizing CO₂ adsorption models, emphasizing its potential to address the complex interactions between operational and modification parameters that traditional methods struggle to capture. …”
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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

“…Our study aimed to employ supervised machine learning to accurately identify blood gas samples as arterial or venous using PDMS data. …”
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Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm by Fatemeh Hataminia, Anahita Azinfar

“…Abstract In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnetic Resonance Imaging (MRI) using the physical properties of the nanoparticles and the MRI machine. The parameters considered include the size of the magnetic core of the nanoparticles, their magnetic saturation (Ms), the concentration of the nanoparticles (C), and the magnetic field (MF) strength of the MRI device. …”
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Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme by Xiaohui Yu, Meng Wang, Chaolie Ning, Kun Ji

“…The AutoML model integrates essential parameters derived from the mainshock, including its Sa, and rupture parameters (moment magnitude, source-to-site distance), and site information (average shear-wave velocity in the top 30 m). …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Five ML algorithms were developed to identify OME using demographic, clinical, laboratory, and acoustic immittance parameters. The dataset underwent 7:3 stratified partitioning for training and testing cohorts. …”
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A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things. by Umhara Rasool Khan, Javaid A Sheikh, Aqib Junaid, Shazia Ashraf, Altaf A Balkhi

“…The HSPA designed resonates at 2.45 GHz in the frequency band of 1.75-2.98 GHz with SAR of 1.89 W/kg for an input power of 16.98 dBm, peak gain of 1.91 dBi and radiation efficiency of 62.07% when mounted on the human body. 1080 samples of data comprising of three EM parameters have been generated using a conventional EM tool by varying the physical and electrical parameters of the design. …”
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Relative Humidity Prediction using XGBoost Machine Learning Model, Case Study: Bajgah Climatological Station, Iran by Reza Piraei, Ali Mohammadi, Seied Hosein Afzali

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Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition by Shaikh Yassir Yousouf Jewan, Deepak Gautam, Debbie Sparkes, Ajit Singh, Lawal Billa, Alessia Cogato, Erik Murchie, Vinay Pagay

“…The use of multimodal data and machine learning (ML) algorithms could overcome these challenges. …”
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Synergistic effects of titanium dioxide and graphene nanofillers on delamination and thrust forces in machining glass fiber reinforced nanocomposites by Machabhaktuni Sri Harsha, Vennelakanti Sai Hemanth Kumar, Mandapati Bhavani Prasad, Chandrasekar Muthukumar, Senthil Muthu Kumar Thiagamani, Nadir Ayrilmis, Senthilkumar Krishnasamy, Lin Feng Ng, Naveen Jesuarockiam

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Mathematical Modeling as a Aspect for Designing Agricultural Machines and Units (Development History Of Southern Urals Scientific School) by Yu. S. Tsench, E. V. Godlevskaya

“…(Conclusions) The study concludes that to enhance the performance of tillage machines and units and achieve the desired quality parameters, further research should focus on machine computer-aided design, including the design of vibratory working bodies, utilization of compressed air and electromagnetic fields, creation of highly automated soil-cultivating machines capable of adjusting to specified working conditions, and development of remote control systems for managing tillage and sowing machine operations.…”
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Thermofluidic transport of Williamson flow in stratified medium with radiative energy and heat source aspects by machine learning paradigm by S. Bilal, Asadullah, Muhammad Bilal Riaz

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Countermeasuring Anti-Ship Missiles for Surface Naval Platforms: A Machine Learning Approach With Explainable Artificial Intelligence by Murat Ertop, Ali Oter, Ali Kara

“…This model has been used to predict target parameters. The simulator includes parameters for the ship, missile, and chaff/flare. …”
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Predicting macular hole surgery outcomes: Integrating preoperative OCT features with supervised machine learning statistical models by Ramesh Venkatesh, Priyanka Gandhi, Ayushi Choudhary, Gaurang Sehgal, Kanika Godani, Shubham Darade, Rupal Kathare, Prathiba Hande, Vishma Prabhu, Jay Chhablani

“…Purpose: To evaluate various supervised machine learning (ML) statistical models to predict anatomical outcomes after macular hole (MH) surgery using preoperative optical coherence tomography (OCT) features. …”
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In Vitro/In Vivo Evaluation of a Portable Anesthesia Machine with an Oxygen Concentrator for Dogs Under General Anesthesia with Isoflurane by Jungha Lee, Donghwi Shin, Taehoon Sung, Minha Kim, Changhoon Nam, Wongyun Son, Inhyung Lee

“…This prospective, non-blinded study assessed the performance of a portable anesthesia machine with an oxygen concentrator (PAM_OC) across various oxygen flow rates and vaporizer settings, incorporating both in vitro and in vivo experiments. …”
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Improving Dynamic Performance of a Small Rhizome Chinese Herbs Harvesting Machine via Analysis, Testing, and Experimentation by Lixun Dai, Wei Sun, Petru Aurelian Simionescu, Bugong Sun, Zongpeng Huang, Xiaolong Liu

“…The parameters studied included the rotational speed of the tractor’s power take-off (PTO) shaft, the machine’s overall mass and stiffness, the transmission ratio, and the excitation force generated by the machine’s reciprocating parts. …”
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Machine Learning-Driven Parametric Analysis of Eco-Friendly Ultrasonic Welding for AL6061-CU Alloy Joints by A. Karan, S. Arungalai Vendan, M. R. Nagaraj, M. Chaturvedi, S. Sivadharmaraj

“…Electrical resistance at the joints is measured to understand the electrical parametric variations if any due to process parameters. A machine learning tool is employed to forecast the weld strength and joint resistance for differing ranges of process parametric values and accordingly optimize it.…”
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Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques by Atefeh Rostami, Mostafa Robatjazi, Amir Dareyni, Ali Ramezan Ghorbani, Omid Ganji, Mahdiye Siyami, Amir Reza Raoofi

“…Models’ performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score. …”
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Prediction of weld quality in laser welding of hardmetal and steel using high-speed imaging and machine learning methods by Mohammadhossein Norouzian, Mahan Khakpour, Marko Orosnjak, Atal Anil Kumar, Slawomir Kedziora

“…Improper laser welding parameters can result in unstable joints, ultimately leading to reduced mechanical strength of the weld. …”
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Fusing Machine Learning and AI to Create a Framework for Employee Well-Being in the Era of Industry 5.0 by Cosmina-Mihaela Rosca, Adrian Stancu

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