Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms by Vedant Jaiswal, Narendiranath Babu T, Pandiyan Murugan, Rama Prabha D

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Interpretable Machine Learning Predictions of Bruch’s Membrane Opening-Minimum Rim Width Using Retinal Nerve Fiber Layer Values and Visual Field Global Indexes by Sat Byul Seo, Hyun-kyung Cho

“…We developed an interpretable machine learning model that integrates structural and functional parameters to predict BMO-MRW. …”
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Research on the rock cutting performance and feasibility verification of small-scale rotary cutting test for disc cutter by Zilong Yang, Yong Hu, Mingxu Xu, Hao Pang, Youpeng Gu, Baicheng Zheng

“…Abstract Small-scale rock cutting tests serve as a simple approach to evaluate the performance of tunnel boring machines (TBMs), but the feasibility of this method requires further investigation. …”
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Optimizing oil production forecasts in Iranian oil fields: a comprehensive analysis using ensemble learning techniques by Mohammad Ghodsi, Pouya Vaziri, Mahdi Kanaani, Behnam Sedaee

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A Comparative Study of Mathematical Methods for Determining Colding’s Constants for Milling of Steels and Experimental Validation by Sujan Khadka, Rizwan Abdul Rahman Rashid, John H. Navarro-Devia, Angelo Papageorgiou, Guy Stephens, Sören Hägglund, Suresh Palanisamy

“…The optimization of cutting parameters is critical for improving machining efficiency and extending tool life. …”
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The hybrid algorithms in constrained optimization of the permanent magnet motors by Łukasz Knypiński, Ramesh Devarapalli, Frederic Gillon

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Brock's approaching zero method improved as approaching fixed point frequency to solve optimum damping ratio of dynamic vibration absorber in machine tools and experimental confirm... by Hongliang Tian, Xixiao Liang, Xuan Du

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Enhanced Hyperspectral Forest Soil Organic Matter Prediction Using a Black-Winged Kite Algorithm-Optimized Convolutional Neural Network and Support Vector Machine by Yun Deng, Lifan Xiao, Yuanyuan Shi

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Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Next, to tackle the challenge of parameter selection for DHKELM, an optimal point set strategy, the Gompertz growth model, and a Levy flight strategy are employed to optimize the parameters of DHKELM using IBKA before model training. …”
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Impact of nanoparticles (B4C-Al2O3) on mechanical, wear, fracture behavior and machining properties of formwork grade Al7075 composites by T. B. Prakash, M. Gangadharappa, Santhosh Somashekar, M. Ravikumar

“…These findings offer valuable insights into the ease of machining of composite metal alloys, emphasizing the importance of parameter selection and optimization for desired machining outcomes…”
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A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof... by José Pablo Martínez Barbero, Francisco Javier Pérez García, David López Cornejo, Marta García Cerezo, Paula María Jiménez Gutiérrez, Luis Balderas, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez, Antonio Jesús Láinez Ramos-Bossini

“…This study aimed to develop and evaluate a machine learning model that integrates radiomics features and T2*-weighted Dynamic Susceptibility Contrast MRI perfusion (DSC MRI) parameters to improve diagnostic accuracy in distinguishing these entities. …”
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Designing Ship Hull Forms Using Generative Adversarial Networks by Kazuo Yonekura, Kotaro Omori, Xinran Qi, Katsuyuki Suzuki

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Portable XRF and Vis-NIR spectrometry for predicting chemical properties of forest soils in the Amazon: Insights into sensor data dimensionality reduction by Quésia Sá Pavão, Paula Godinho Ribeiro, Gutierre Pereira Maciel, Sérgio Henrique Godinho Silva, Suzana Romeiro Araújo, Antonio Rodrigues Fernandes, José Alexandre Melo Demattê, Pedro Walfir Martins e Souza Filho, Silvio Junio Ramos

“…Proximal sensor data integrated with machine learning algorithms have been successful in assessing soil chemical parameters. …”
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Soybean development as a function of traffic of tractor with radial tires by Renan R. Espessato, Fabrício Leite, Julio C. Guerreiro, Erci M. Del Quiqui, Ana P. de Azevedo, Edward V. Aleixo

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Implementation framework for AI deployment at scale in healthcare systems by Hassan Sami Adnan, Amitis Shidani, Lei Clifton, Clare R. Bankhead, Rafael Perera-Salazar

“…This framework targets health systems that integrate multiple machine learning (ML) models with various modalities. …”
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Analysis of drug crystallization by evaluation of pharmaceutical solubility in various solvents by optimization of artificial intelligence models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…Abstract For analysis of crystallization, the solubility of drug in solvents should be correlated to input parameters. In this investigation, the solubility of salicylic acid as drug model in a variety of solvents is predicted through the utilization of multiple machine learning techniques. …”
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Synthetic GPR datasets to evaluate hybridization inverse approach for pavement tack coat characterization—Geometrical and physical parametric studyDataverse by Grégory Andreoli, Amine Ihamouten, Xavier Dérobert

“…Variations in geometrical and physical parameters were applied to the wearing course, tack coat, and binder course. …”
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Vertically Recurrent Neural Networks for Sub‐Grid Parameterization by P. Ukkonen, M. Chantry

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Radiomics approach for identifying radiation-induced normal tissue toxicity in the lung by Olivia G. G. Drayson, Pierre Montay-Gruel, Charles L. Limoli

“…Additionally, a radiation difference was not observed in a secondary comparison cohort, but there was no impact of imaging machine parameters on the radiomic signature of unirradiated mice. …”
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Development and Validation of a Clinical Risk Model for Predicting Malignancy in Patients with Thyroid Nodules by Shiva Borzouei, Ali Safdari, Erfan Ayubi

“…The diagnostic performance of the GLM was compared with five machine learning (ML) algorithms, including linear discriminant analysis (LDA), random forest, neural network, support vector machine, and k-nearest neighbor.  …”
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