A Seasonal Fresh Tea Yield Estimation Method with Machine Learning Algorithms at Field Scale Integrating UAV RGB and Sentinel-2 Imagery by Huimei Liu, Yun Liu, Weiheng Xu, Mei Wu, Leiguang Wang, Ning Lu, Guanglong Ou

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Prediction of induction motor faults using machine learning by Ademola Abdulkareem, Tochukwu Anyim, Olawale Popoola, John Abubakar, Agbetuyi Ayoade

“…This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. …”
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Applying Machine Learning to Preselective Weighing of Moving Vehicles by Paweł Kowaleczko, Tomasz Kamiński, Mariusz Rychlicki, Zbigniew Kasprzyk, Marek Stawowy, Jacek Trzeszkowski

“…The results indicate the model’s potential in optimizing preselection systems, allowing for the effective identification of overloaded vehicles. …”
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Initial exploration into sarcasm and irony through machine translation by Zheng Lin Chia, Michal Ptaszynski, Marzena Karpinska, Juuso Eronen, Fumito Masui

“…Optimal translation settings and the best-finetuned model for irony are explored, with the most effective model being finetuned on both ironic and non-ironic data. …”
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Gaussian barebone mechanism and wormhole strategy enhanced moth flame optimization for global optimization and medical diagnostics. by Jingjing Ma, Zhifang Zhao, Lin Zhang

“…Employing BWEMFO, we optimize the kernel parameters of the kernel-limit learning machine, thereby crafting the BWEMFO-KELM methodology for medical diagnosis and prediction. …”
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Accelerating multi-objective optimization of concrete thin shell structures using graph-constrained GANs and NSGA-II by Zhichun Fang, Xiuhong Wang, Yuyong Sun, M. A. Adibhashimi

“…This work illustrates the potential of sophisticated machine learning and evolutionary algorithms to produce innovative, high-performance architectural solutions, thereby providing a new methodology for structural optimization.…”
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Machine learning for improved density functional theory thermodynamics by Sergei I. Simak, Erna K. Delczeg-Czirjak, Olle Eriksson

“…By applying supervised learning and rigorous data curation we ensure a robust and physically meaningful correction. The model is implemented as a multi-layer perceptron (MLP) regressor with three hidden layers, optimized through leave-one-out cross-validation (LOOCV) and k-fold cross-validation to prevent overfitting. …”
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Enhancing hydraulic fracturing efficiency through machine learning by Ali Karami, Ali Akbari, Yousef Kazemzadeh, Hamed Nikravesh

“…This study evaluated the performance of ANN, RF, and KNN models, achieving accuracies of 0.978, 0.979, and 0.893, respectively, which underscores their strong predictive modeling capabilities. …”
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Uncertain Single-Machine Scheduling with Deterioration and Learning Effect by Jiayu Shen

“…These models can be converted into equivalent models based on the inverse distribution method. …”
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Mechanisms of cage noise generation in machine tool bearings by Kazuho Takeshima, Keisuke Mutoh, Kenji Imanishi, Shunichi Oshima

“…To facilitate the optimal design of the cage to stabilize these behaviors, we developed a dynamic analysis model focusing on the friction between the cage and the outer ring under grease lubrication, considering fluid pressure effects. …”
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Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N₂O Emissions in China by Yuan Zeng, Sujuan Chen, Yunpeng Li, Li Xiong, Cheng Liu, Muhammad Azeem, Xiaoting Jie, Mei Chen, Longjiang Zhang, Jianfei Sun

“…The artificial neural network (ANN) model outperformed random forest (RF) and support vector machine (SVM) in predicting N₂O emissions (R²: 0.99; EF: 0.99), while all models showed high accuracy for crop yields (R², EF: 0.98–0.99). …”
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Predicting Employee Turnover Using Machine Learning Techniques by Adil Benabou, Fatima Touhami, My Abdelouahed Sabri

“…Model performance is optimized through hyperparameter tuning, using grid search with cross-validation.Results: Logistic regression achieves the highest accuracy and precision, making it the top-performing model overall. …”
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Fatigue damage reduction in hydropower startups with machine learning by Till Muser, Ekaterina Krymova, Alessandro Morabito, Martin Seydoux, Elena Vagnoni

“…In this study, we introduce a data-driven approach to identify transient start-up trajectories that minimize fatigue damage. We optimize the trajectory by leveraging a machine learning model, trained on experimental stress data of reduced-scale model turbines. …”
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A hybrid bio-inspired augmented with hyper-parameter deep learning model for brain tumor classification by Morolake Oladayo Lawrence

“…The CNN model is adjusted for different convolutional layers and fully connected layers to identify patterns and features in brain tumor pictures using an enhanced salp swarm algorithm (SSA) with kernel extreme learning machine (KELM). …”
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Machine Learning Based Engagement Prediction for Online Courses by Wang Wanning

“…This study investigates the performance of three machine learning models (decision trees. SVMs, and random forests) in predicting online course participation. …”
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A fair and efficient two-step procedure for sugarcane properties prediction based on near-infrared spectra by Luiz Alexandre Peternelli, Andréa Carla Bastos Andrade, Cristina Silva Dias, Reinaldo Francisco Teófilo

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A review of machine learning applications in heart health by Ava Perrone, Taghi M. Khoshgoftaar

“…The field of healthcare holds particularly strong potential for improvement from integration with machine learning. In the future, clinicians will likely utilize machine learning to enhance the efficiency of diagnosis and prognosis, optimizing the delivery of care. …”
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An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model by Nisha Y., Jagadeesh Gopal

“…So, this research introduces a hybrid DL model for improving prediction performance andreducing time consumption compared to the machine learning (ML)model.Describing a pre-processing method utilizing statistical co-relational evaluation to improve the classifier’s accuracy.The features are then extracted from the Region of Interest (ROI) images using the wrapping technique and a fast discrete wavelet transform (FDWT). …”
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Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMI... by Zhengqiu Yu, Lexin Fang, Yueping Ding

“…Abstract Objectives This study aimed to develop and validate an explainable machine learning (ML) model to predict 28-day all-cause mortality in immunocompromised patients admitted to the intensive care unit (ICU). …”
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Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images by Xintao Ling, Gui Zhang, Ying Zheng, Huashun Xiao, Yongke Yang, Fang Zhou, Xin Wu

“…The value of each target variable and that of its corresponding independent variable constituted a sample. Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. …”
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