Optimizing data collection requirements for machine learning models in wild blueberry automation through the application of DALL-E 2 by Connor C. Mullins, Travis J. Esau, Qamar U. Zaman, Patrick J. Hennessy

“…This research developed a workflow to assess the viability of AI-generated imagery in training machine learning models for detecting ripe wild blueberries (Vaccinium angustifolium Ait.), hair fescue weeds (Festuca filiformis Pourr.), and red leaf disease (Exobasidium vaccinii). …”
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Establishment and Solution Test of Wear Prediction Model Based on Particle Swarm Optimization Least Squares Support Vector Machine by Xiao Huang, Yongguo Wang, Yuhui Mao

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Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

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Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study. by Jae-Geum Shim, Kyoung-Ho Ryu, Sung Hyun Lee, Eun-Ah Cho, Sungho Lee, Jin Hee Ahn

“…<h4>Objective</h4>To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning.…”
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An assessment of optimizing biofuel yield percentage using K-fold integrated machine learning models for a sustainable future by Krishnamoorthy Ramalingam, Mohd. Zulkifly Abdullah, Perumal Venkatesan Elumalai, Allam Sangeetha, Xu Yong, Nasim Hasan, Wei Shangzhi

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Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies by Seyyed Ali Hosseini, Ghasem Hajianfar, Brandon Hall, Stijn Servaes, Pedro Rosa-Neto, Pardis Ghafarian, Habib Zaidi, Mohammad Reza Ay

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Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning by Mohamed Kamel Elshaarawy, Martina Zeleňáková, Asaad M. Armanuos

“…Abstract This study investigates the effectiveness of inclined double cutoff walls installed beneath hydraulic structures by employing five machine learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). …”
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Modeling of Systematic Errors and Precision Optimization Methods for Workpiece Clamping and Alignment System in Aeroengine Gearbox Automated Line Machining by DU Xueming, XIANG Yang, LIU Shun, JIN Sun

“…However, it has also brought about an increase in systematic errors during multi-process machining. To mitigate the adverse impact of these new systematic errors on machining accuracy, this paper investigates error modeling and precision optimization methods for the alignment system under uncertainty. …”
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A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine by S. Chitra, Tariq Mohammed Alqahtani, Abdulrahman Alduraywish, Mohamed Yacin Sikkandar

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Machine learning models for reinjury risk prediction using cardiopulmonary exercise testing (CPET) data: optimizing athlete recovery by Arezoo Abasi, Ahmad Nazari, Azar Moezy, Seyed Ali Fatemi Aghda

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Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures by Mohammed Maree, Wala’a Shehada

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Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning by Yusuf Alaca

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Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study by Una Kjällquist, Nikos Tsiknakis, Balazs Acs, Sara Margolin, Luisa Edman Kessler, Scarlett Levy, Maria Ekholm, Christine Lundgren, Erik Olsson, Henrik Lindman, Antonios Valachis, Johan Hartman, Theodoros Foukakis, Alexios Matikas

“…We developed a machine learning model using simple prognostic factors (size, progesterone receptor expression, grade, and Ki67) to predict ROR/Prosigna output and compared the performance regarding over- and undertreatment with commonly employed risk stratification schemes. …”
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Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups by Abayomi B. David, Oladimeji B. Olalusi, Paul O. Awoyera, Lenganji Simwanda

“…This paper assesses the effectiveness of mechanics-based and optimized machine learning (ML) models for predicting shear strength in stirrup-less, slender beams using a dataset of 784 tests. …”
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A Review on Machine Learning Models in Injection Molding Machines by Senthil Kumaran Selvaraj, Aditya Raj, R. Rishikesh Mahadevan, Utkarsh Chadha, Velmurugan Paramasivam

“…Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. …”
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A comprehensive approach to portfolio optimization based on modern mathematical modeling methods by Shkanov Bulat

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Multiclass weed and crop detection using optimized YOLO models on edge devices by Arjun Upadhyay, Sunil G C, Samriddha Das, Joseph Mettler, Kirk Howatt, Xin Sun

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Double machine learning and design in batch adaptive experiments by Li Harrison H., Owen Art B.

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Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada

“…Using structural, traffic, and climatic data taken from the Long-Term Pavement Performance (LTPP) database, this study presents a machine learning system based on a gradient boosting machine (GBM) optimized using particle swarm optimization (PSO) to forecast longitudinal cracking. …”
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Enhancing predictive modeling across industries with automated machine learning: applications in insurance and agriculture by K. P. Swain, S. K. Mohapatra, Santosh Kumar Sahoo

“…Abstract This study explores the efficacy of Automated Machine Learning (AutoML) tools in enhancing linear regression models across industries, focusing on insurance and agriculture. …”
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