Smart solutions for dissolved oxygen control in semi-batch fermenters: A machine learning approach by J. Sumathi, P. Aravind, G. Gandhimathi

“…This study has been carried out using Multi-Layered Feed Forward Back Propagation Artificial Neural Network (ML-FFBPANN) and the experimental results were optimized with fourteen different machine learning algorithms such as Polak-Ribiere Conjugate Gradient (CGP), Conjugate Gradient with Powell/Beale Restarts (CGB), Bayesian Regularization (BR), Gradient Descent (GD), BFGS Quasi-Newton (BFG), GD with Momentum (GDM), Gaussian Discriminate Analysis(GDA), Fletcher-Powell Conjugate Gradient (CGF), Resilient Backpropagation (RP), Variable Learning Rate Gradient Descent (GDX), One Step Secant (OSS), Regression (R), Levenberg-Marquardt (LM), Scaled Conjugate Gradient(SCG). …”
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Machine Learning (ML) Based Repair-Count and Periodic Maintenance Policy for Multipurpose CNC Machinery by Alifin Fakhri Ikhwanul, Winarno, Fasa Nadia, Darajatun Rizki Achmad, Kusnadi, Safariyani Eva

“…This study deals with developing a maintenance policy optimization framework using a machine learning approach for multipurpose CNC machinery in an automotive part manufacturer. …”
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Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction by Qi Wang, Yonggang Yao

“…Beyond methodology, we address key challenges and future directions, including benchmarking ML strategies, developing HEA-specific datasets, pretraining and fine-tuning, integrating chained ML models, advancing multi-objective optimization, and bridging ML predictions with experimental validation. …”
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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 rectangular hand claw is embedded with EVA flexible material (maximum clamping pressure of the clamping mechanism: rootstock 266.35 Pa and scion 158.92 Pa), to overcome the fragile seedling damage control problems; Optimization of rootstock dynamics modeling with the V-shaped gathering block opening angle of 90° to achieve adaptive positioning of heterogeneous stems; The dual cutting system of positive-cutting and rotary-cutting matches the different cutting requirements of rootstock and scion. …”
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Analyzing Student Graduation and Dropout Patterns Using Artificial Intelligence and Survival Strategies by Behrouz Alefy, Vahid Babazadeh

“…From the careful investigation of the convergence curves obtained from the improved models, the study showed that the Cat Boost Classifier modified with the Weevil Damage Optimization Algorithm (CAWD) model was the best among all with an amazing accuracy of 0.993 after 140 iterations. …”
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Prediksi Kesiapan Sekolah Menggunakan Machine Learning Berbasis Kombinasi Adam dan Nesterov Momentum by Indah Mustika Rahayu, Ahmad Yusuf, Mujib Ridwan

“…The research used Artificial Neural Network algorithms with a combination of Adam and Nesterov Momentum optimization method. Model testing used a 5-fold cross validation scenario. …”
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The Weight Minimization of a UAV Wing Component Through Structural Optimization by Andreas Psarros, Georgios Savaidis

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Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix by Suprunenko M. K., Zborshchyk O. P., Sokolov O.

“…The article considers the problem of machine learning of a wrist prosthesis control system with a non-invasive biosignal reading system. …”
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Optimization Design for Gear Pair Transmission with Considering Torsional Stiffness Constraint by Han Lin, Xu Lixin

“…Furthermore,the mathematical model for optimal design of transmission chain is constructed,a lightweight design of the transmission chain is carried out by considering the torsional stiffness constraint. …”
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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The considered drug is 5-aminosalicylic acid for colonic drug delivery, and its release was estimated using Raman data as inputs along with other categorical parameters. The models, including Kernel Ridge Regression (KRR), Kernel-based Extreme Learning Machine (K-ELM), and Quantile Regression (QR) incorporate sophisticated approaches like the Sailfish Optimizer (SFO) for hyperparameter optimization and K-fold cross-validation to enhance predictive accuracy. …”
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Non-Invasive Glucose Sensing on Fingertip Using a Mueller Matrix Polarimetry With Machine Learning by Chih-Yi Liu, Yu-Lung Lo, Wei-Chun Hung

“…Phantom models simulated the interference properties of biological tissue polarization measurements. …”
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Optimizing maize germination forecasts with random forest and data fusion techniques by Lili Wu, Yuqing Xing, Kaiwen Yang, Wenqiang Li, Guangyue Ren, Debang Zhang, Huiping Fan

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The S-matrix bootstrap with neural optimizers. Part I. Zero double discontinuity by Mehmet Asım Gümüş, Damien Leflot, Piotr Tourkine, Alexander Zhiboedov

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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

“…To address this issue, we developed machine learning models for fast prediction of GGNMOS performance and inverse design of its structure according to performance metrics. …”
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Estimation and trend analysis of grassland aboveground biomass on the Qinghai-Xizang Plateau based on machine learning by Ruoqi Zhang, Qisheng Feng, Yonghui Zhang, Jingjing Mai, Tiangang Liang

“…The optimal model was identified through an exhaustive comparison and hyperparameter optimization process, and then applied to estimate grassland AGB across the plateau and analyze its spatiotemporal dynamics. …”
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Kernel to computation: identifying optimal feature set for red rice classification by Suma D, Narendra V G, Darshan Holla M, Shreyas, Raviraja Holla M

“…Feature selection was performed using Recursive Feature Elimination and Backward Feature Elimination to enhance model efficiency. Hyperparameter tuning was applied to optimize classification performance, and k-fold cross-validation with statistical significance testing was used to assess generalization and validate model performance differences. …”
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Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis by Tianyi Wang, Ruiyuan Chen, Ning Fan, Lei Zang, Shuo Yuan, Peng Du, Qichao Wu, Aobo Wang, Jian Li, Xiaochuan Kong, Wenyi Zhu

“…Although increasing studies of traditional machine learning (TML) and deep learning (DL) were conducted in the field of diagnosing LSS and gained prominent results, the performance of these models has not been analyzed systematically. …”
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Object Re-Identification Based on Federated Incremental Subgradient Proximal Optimization by Li Kang, Chuanghong Zhao, Jianjun Huang

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Analysis of stress distribution of CFRP bonded joints: A study of numerical and machine learning approach by Shah Mohammad Azam Rishad, Md Ashraful Islam, Md Shahidul Islam

“…As this study also supports engineers and researchers in devising optimized machine learning models for addressing CFRP-bonded joint challenges, the accuracy of stress prediction is improved by applying machine learning techniques to the collected data more refinedly.…”
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Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters by Qingxiang Li, Zhanyou Luo, Man Huang, Jiangbo Pan, Guoshu Wang, Yunxin Cheng

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