Multi-objective optimization design for accelerated degradation test based on game theory by Jinyan Guo, Zhiwu Han, Shuangfeng Wu, Junqi Long, Chuanhai Chen, Zhifeng Liu

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DETERMINATION OF THE BEST OPTIMIZER FOR A NEURONETWORK IN THE DEVELOPMENT OF AUTOMATIC IMAGE TAGGING SYSTEMS by Andrian Kozynets

“…Comparing these optimizers allows us to determine the most suitable optimizer for solving specific machine learning problems. …”
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Machine Learning Application for Improving Customer and Postal Logistics Operator Satisfaction in Urban Areas – A Review by Adisa MEDIĆ, Amel KOSOVAC, Ermin MUHAREMOVIĆ, Muhamed BEGOVIĆ

“…The significance of this research is highlighted through the identification of shortcomings in existing literature, offering guidelines for future research in developing new machine learning model for optimal operator selection. …”
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An Enhanced Machine Learning Framework for Type 2 Diabetes Classification Using Imbalanced Data with Missing Values by Kumarmangal Roy, Muneer Ahmad, Kinza Waqar, Kirthanaah Priyaah, Jamel Nebhen, Sultan S Alshamrani, Muhammad Ahsan Raza, Ihsan Ali

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Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning by Muhamad Ilham Fauzan, Jaka Adi Baskara, Wahyuningdiah Trisari Harsanti Putri, Quintin Kurnia Dikara

“…This study aims to develop a machine learning model that can accurately classify FOSS-NIRS data to detect oil losses that are either above the standard (red category) or below the standard (green category). …”
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Applying Radom Forest and Support Vector Machine for Land-use Classification in Phu Giao District Vietnam by Trong Dieu Hien Le, Linh Truong Vinh

“…The results revealed that the accuracies of the SVM model were 0.87 (overall accuracy) and 0.89 (Cohen’s kappa), which are 2% lower than those of the optimal RF model. …”
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Integrated Neural Network for Ordering Optimization with Intertemporal-Dependent Demand and External Features by Minxia Chen, Ke Fu

“…Our customized neural network model integrates demand estimation with inventory optimization, circumventing the potential suboptimality of sequential estimation and optimization methods. …”
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Accelerated and precise skin cancer detection through an enhanced machine learning pipeline for improved diagnostic accuracy by SM Masfequier Rahman Swapno, S.M. Nuruzzaman Nobel, P.K. Meena, V.P. Meena, Jitendra Bahadur, Abhishek Appaji

“…The model achieved a training accuracy of 99.57% and a validation accuracy of 99.93%. …”
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SIMULATION OF THE CONTACT PROCESS INTERACTIONS OF LAND-MOVING AND EARTH-TRANSPORT MACHINES’ WORKING BODIES WITH FROZEN SOIL by V. N. Kuznetsova, I. S. Kuznetsov

“…The main aspects of the developed mathematical model and the original method for studying the processes of spatial interaction of the excavating machines’ working bodies with frozen soil are presented. …”
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Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data by Sajad Amouei Sheshkal, Morten Gundersen, Michael Alexander Riegler, Øygunn Aass Utheim, Kjell Gunnar Gundersen, Helge Rootwelt, Katja Benedikte Prestø Elgstøen, Hugo Lewi Hammer

“…The models were evaluated and optimized using nested k-fold cross-validation. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…Objective: The purpose of this study is to explore the epidemiological characteristics of acute myeloid leukemia (AML) and establish a more accurate model for predicting the prognosis of AML patients based on machine learning. …”
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Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning by Islam N. Fathy, Hany A. Dahish, Mohammed K. Alkharisi, Alaa A. Mahmoud, Hala Emad Elden Fouad

“…Hyperparameters in the RF and XGB models were optimized using grid search. K-fold cross-validation and several statistical measures, including R2MAPE, RMSE, and MAE, were utilized to validate and check the accuracy of the proposed models. …”
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Optimizing Solar Radiation Prediction with ANN and Explainable AI-Based Feature Selection by Ibrahim Al-Shourbaji, Abdalla Alameen

“…This paper presents an Artificial Neural Network (ANN) model optimized using feature selection techniques based on Explainable AI (XAI) methods to enhance SR prediction performance. …”
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Machine Learning for Precision Agriculture: Predicting Persimmon Peak Harvest Dates and Yield Using Meteorological Data by Atsushi Okayama, Atsushi Yamamoto, Yutaka Matsuno, Masaomi Kimura

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Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method by Yang CUI, Zhenghong CHEN, Peihua XU

“…Therefore, this paper proposes a method for short-term regional wind and PV power prediction based on feature clustering. Firstly, the machine learning-based Bisecting K-Means(BKM) clustering algorithm is used to reasonably divide the wind farms and PV stations in the region into clusters; Secondly, based on the correlation between of the historical power data of each power station and the total historical power data in the region, a representative power station is selected for each region; Thirdly, after optimizing and correcting the NWP(numerical weather prediction) model of each representative power station, a short-term power prediction framework model is established using BP neural network based on the cluster division of wind farms and PV power plants. …”
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Acoustic Log Prediction on the Basis of Kernel Extreme Learning Machine for Wells in GJH Survey, Erdos Basin by Jianhua Cao, Yancui Shi, Dan Wang, Xiankun Zhang

“…Finally the optimal model is set up as a predictor. A case study for wells in GJH survey from the Erdos Basin, about velocity inversion using the KELM-estimated DT values, is presented. …”
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Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning by Md Wadud Ahmed, Asher Sprigler, Jason Lee Emmert, Ryan N Dilger, Girish Chowdhary, Mohammed Kamruzzaman

“…This study developed a fast, accurate, and non-destructive method of pre-incubated chicken egg fertility detection using hyperspectral imaging (HSI) and machine learning. The Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF), and Support Vector Machine (SVM) calibration models were developed at full wavelengths (374–1015 nm), and the performance of the models was evaluated by 10-fold cross-validation and independent validation. …”
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Assessing environmental determinants of subjective well-being via machine learning approaches: a systematic review by Min Yang, Yuxuan Zou

“…Analysis of the importance of variables within these models enables policymakers to prioritize interventions that target the most influential factors. …”
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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

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Does Eplet Load and Electrostatic Mismatch Score Matter in Kidney Transplantation? A Machine Learning Approach by Desmond D. Offei, Dulat Bekbolsynov, Stanislaw Stepkowski, Robert C. Green II

“…The RSF model was the best-performing model in KT outcome prediction (C-index = 0.6945, Brier score = 0.1460). …”
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