Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…The findings underscore the significant potential of optimized machine learning approaches in predicting extreme events, such as wildfires, and improving fire management strategies. …”
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Establishment and Test Effect of Artificial Intelligence Optimization Model Based on Convolutional Neural Network by Chunrong Zhou, Zhenghong Jiang

“…In this paper, the optimized model (AL-CNN) is tested for noise image recognition, and the AL-CNN model is established by using activation functions, matrix operations, and feature recognition methods, and the noisy images are processed after custom configuration. …”
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Integrated Analytical Modeling and Numerical Simulation Framework for Design Optimization of Electromagnetic Soft Actuators by Hussein Zolfaghari, Nafiseh Ebrahimi, Yuan Ji, Xaq Pitkow, Mohammadreza Davoodi

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An advanced CNN-attention model with IFTTA optimization for prediction air consumption of relay nozzles by Shen Min, Shao Ning, Cao Yongbo, Xiong Xiaoshuang, Yang Xuezheng, Wang Zhen, Yu Lianqing

“…Abstract The air jet loom is an energy-intensive machine, it is significantly reducing air consumption of relay nozzles for saving energy of air compressor. …”
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Explainable AI and optimized solar power generation forecasting model based on environmental conditions. by Rizk M Rizk-Allah, Lobna M Abouelmagd, Ashraf Darwish, Vaclav Snasel, Aboul Ella Hassanien

“…This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. …”
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Predicting algorithm of attC site based on combination optimization strategy by Zhendong Liu, Xi Chen, Dongyan Li, Xinrong Lv, Mengying Qin, Ke Bai, Zhiqiang He, Yurong Yang, Xiaofeng Li, Qionghai Dai

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Optimizing Pre-Trained Models for Medical Dataset Classification with a Fine-Tuning Approach by N. Kumar, T. Christopher

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Territorial Space Optimization Method Based on Multi-Objective Genetic Algorithm and FLUS Model by Lin Ge

“…For additional spatial layout design, the study suggests a multi-objective genetic algorithm based on spatial data prediction and coupled with land simulation modeling in the context of big data and machine learning development. …”
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Insights into Machining Techniques for Additively Manufactured Ti6Al4V Alloy: A Comprehensive Review by Abdulkadir Mohammed Sambo, Muhammad Younas, James Njuguna

“…Additionally, it covers the qualification process for machined components and the optimization of cutting parameters. …”
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Dementia Classification Based on Magnetic Resonance Scans Comparing Traditional and Modern Machine Learning Models’ Quintessence by Andreea POPOVICIU, Diogen BABUC, Todor IVAŞCU

“…This paper aimed to analyze and compare several machine learning models used for the classification of Magnetic Resonance Imaging (MRI) scans of patients with or without dementia. …”
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Machining Center Opportunistic Maintenance Strategy Using Improved Average Rank Method for Subsystem Reliability Modeling by Yingzhi Zhang, Minqiao Song, Wei Wu, Feng Han

“…Firstly, the reliability of the machining center subsystem was modeled, which serves as the basis for determining when to repair a subsystem. …”
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Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression by Haoshen Qin, Lal Hussain, Ziang Liu, Xu Yan, Fuad A. Awwad, Faisal Mehmood Butt, Umair Ahmad Salaria, Emad A.A. Ismail

“…Subsequently, hyperparameter optimization significantly enhanced the deep learning model's performance, achieving the highest prediction accuracy (RMSE: 4.511, R2: 0.718). …”
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Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry by Siqi Liu, Ruina Li, Jiayi Zhou, Chaoyuan Dai, Jingui Yu, Qiaoxin Zhang

“…However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. …”
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Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling by Javier Arduengo, Nicolas Hascoet, Francisco Chinesta, Jean-Yves Hascoet

“…Experimental results confirm the efficacy of this machine learning model and the open-loop control system in achieving optimal bioprinting outcomes.…”
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Improved genetic algorithm based on Shapley value for a virtual machine scheduling model in cloud computing by Lili Chen, Lili Chen, Yuxia Niu

“…As the number of evolutionary generations increases, the ability of SVM-GA to reach the optimal solution of the model increases. In the simulated light load case, the SVM-GA migration time and Q10 migration count optimal solutions are slightly inferior to those of the logistic regression algorithm (3.02 s > 2.38 s; 1,129 times >999 times), but the migration energy consumption and service level agreement violation rate optimal solutions are superior. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Evaluated the performance of a system by using the following classifiers as Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Model—GMM, Expectation Maximization—EM, Bayesian Linear Discriminant Analysis—BLDA, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). …”
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Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry by Hye Ju Yeo, Hye Ju Yeo, Dasom Noh, Eunjeong Son, Eunjeong Son, Sunyoung Kwon, Sunyoung Kwon, Sunyoung Kwon, Woo Hyun Cho, Woo Hyun Cho

“…The Gradient Boosting Machine (GBM) model achieved the highest performance (AUC: 0.958, accuracy: 0.949). …”
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The Downscaled GOME-2 SIF Based on Machine Learning Enhances the Correlation with Ecosystem Productivity by Chenyu Hu, Pinhua Xie, Zhaokun Hu, Ang Li, Haoxuan Feng

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Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics by Sefika Dincer, Muge Akmansu, Oya Akyol

“…Radiomics and dosiomics were extracted using PyRadiomics. Machine learning models were optimized using the Tree-based Pipeline Optimization Tool (TPOT), identifying the gradient-boosted classification as the best-performing algorithm. …”
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Machine learning model for early diagnosis of breast cancer based on PiRNA expression with CA153 by Limin Niu, Weicheng Zhou, Xiao Li, Jinming Zhao, Lei Li, Xingguo Song

“…Through systematic ML optimization, we developed a stratified diagnostic model where XGBoost algorithm showed optimal performance in both training and validation cohorts for early-stage BC identification. …”
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