A machine learning model using the snapshot ensemble approach for soil respiration prediction in an experimental Oak Forest by S.N. Ferdous, J.P. Ahire, R. Bergman, L. Xin, E. Blanc-Betes, Z. Zhang, J. Wang

“…We then used the Artificial Neural Network (ANN) regression model to correct the forecasting model errors and perform the final prediction using the snapshot ensemble approach. …”
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Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images by K. Lakshmi, Sibi Amaran, G. Subbulakshmi, S. Padmini, Gyanenedra Prasad Joshi, Woong Cho

“…Furthermore, the Bayesian regularized artificial neural network (BRANN) model is used to identify the presence of BTs. …”
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Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study by Ping Zheng, Ting Pan, Ya Gao, Juan Chen, Liren Li, Yan Chen, Dandan Fang, Xuechun Li, Fei Gao, Yilei Li

“…Ten algorithms, including Random Forest, XGBoost, LightGBM, Gradient Boosting Decision Tree, CatBoost, Artificial Neural Network, Grandient Boosting Machine, Transformer, Wide&Deep, and TabNet, were employed for modeling based on two, three, or four concentrations of MPA. …”
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Magnetic field influence on heat transfer of NEPCM in a porous triangular cavity with a cold fin and partial heat sources: AI analysis combined with ISPH method by Munirah Aali Alotaibi, Weaam Alhejaili, Abdelraheem M. Aly, Samiyah Almalki

“…This study employs the Incompressible Smoothed Particle Hydrodynamics (ISPH) method and an Artificial Neural Network (ANN) model to examine the thermal and fluid dynamics behavior of nano-enhanced phase change material (NEPCM) within a triangular cavity containing a fin. …”
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Effect of Molarity of Sodium Hydroxide on the Strength Behavior of Fiber-Reinforced Geopolymer Concrete Exposed to Elevated Temperature by Abbasali Saffar, Mohammad Ehsanifar, Seyed Mohammad Mirhoseini, Mohammad Javad Taheri Amiri

“…Beside, post-fire strength of FRGPC was predicted using artificial neural network (ANN) and support vector machines (SVM) with the integration of water cycle algorithm (WCA). …”
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Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach by Junwei Ma, Xiaoxu Niu, Huiming Tang, Yankun Wang, Tao Wen, Junrong Zhang

“…The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. …”
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Machine Learning Models for Predicting the Compressive Strength of Concrete with Shredded PET Bottles and M-Sand as Fine Aggregate by Altamashuddinkhan Nadimalla, Siti Aliyyah Masjuki, Abdullah Gubbi, Anjum Khan, Imran Mokashi

“…The study employs Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Decision Tree (DT) models, using the experimental data for predictive analysis. …”
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Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers by Garrett S Bullock, Charles A Thigpen, Gary S Collins, Nigel K Arden, Thomas K Noonan, Michael J Kissenberth, Ellen Shanley

“…Support vector machine RMSE was 10° and calibration was 1.13 (95% CI: 1.08, 1.18). Artificial neural network RMSE was 15° and calibration was 1.03 (95% CI: 0.97, 1.09)…”
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Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms by Fude Duan, Mahdiyeh Eslami, Mohammad Khajehzadeh, Ali Basem, Dheyaa J. Jasim, Sivaprakasam Palani

“…In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. …”
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Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica by René Tobar-Díaz, Yan Gao, Jean François Mas, Víctor Hugo Cambrón-Sandoval

“…Para dicha revisión se utilizaron únicamente artículos científicos publicados entre el año 2000 al 2020 y que consideraran alguno de los siguientes algoritmos para la clasificación de UCS: k vecinos más cercanos (K-nearest neighbor-KNN), bosque aleatorio (random forest-RF), máquina de soporte de vectores (support vector machine-SVM), redes neuronales artificiales (artificial neural network-ANN) y árboles de decisión (decision trees-DT). …”
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Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data by Le Ngoc Son, Nguyen The Duc, Sumihiko Murata, Phan Ngoc Trung

“…After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. …”
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Analysis and Identification of Factors Influencing the Survival of Burn Injury Patients with an Artificial Intelligence Approach by Sara Hashemi, Shahla Faramarzi, Laya Rahmani Pirouz, Azita Yazdani

“…Among these studies, 33% have indicated that artificial neural network algorithms and random forest show the best performance. …”
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Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor... by Mehdi Tlija, Muhammad Sana, Anamta Khan, Sana Hassan, Muhammad Umar Farooq

“…A full factorial design of experiments with two levels, resulting in 16 experiments, is performed with statistical parametric significance analysis to better control process variability. Multiple artificial neural network (ANN) architectures are generated to accurately model the non-linearity of the process for better prediction of key characteristics. …”
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Testing General Relativity Using Large-scale Structure Photometric Redshift Surveys and the Cosmic Microwave Background Lensing Effect by Shang Li, Jun-Qing Xia

“…In this formulation, we reconstruct the growth rate of structure, fσ _8 ( z ), using the artificial neural network method, while simultaneously utilizing model-independent constraints on the parameter bσ _8 ( z ), directly obtained from the DES collaboration. …”
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Analyzing the impact of non-Newtonian nanofluid flow on pollutant discharge concentration in wastewater management using an artificial computing approach by Sidra Jubair, Jie Yang, Bilal Ali, Bandar Bin-Mohsin, Hamiden Abd El-Wahed Khalifa

“…These equations (ODEs) are solved using the Levenberg Marquardt back-propagation optimization algorithm (LMBOA) of the artificial neural network (ANN). The Matlab package “bvp4c” is used for generating the dataset in order to validate the results of the ANN-LMBOA. …”
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Assessment of using transfer learning with different classifiers in hypodontia diagnosis by Tansel Uyar, Didem Sakaryalı Uyar

“…Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). The dataset was divided into 80% for training and 20% for testing. …”
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Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan by Kai Lin, Qingming Zhan, Wei Xue, Yulong Shu, Yixiao Lu

“…Then, the ANN (artificial neural network)–CA (Cellular Automata) model is employed to establish three distinct development scenarios (Ecological Priority, Tight Growth, and Natural Growth) to predict future urban expansion. …”
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Advances in the Study of AI-assisted Diagnosis and Rehabilitation of ASD by Bosen MA, Xiaorong ZENG, Ran GONG, Farui LI

“…The AI-assisted diagnosis research analyzes relevant data mainly by training the artificial neural network. The data include: ①brain imaging data from the ASD group and the matched group.②multimodal data of speech, movements and facial expressions produced by infants or children with ASD and data of the diagnostic process. …”
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Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan by Hurriyatul Fitriyah, Rizal Maulana

“…Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. …”
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Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches by Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A. Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The performance of the KNN model was compared with Artificial neural network (ANN) and support vector machine (SVM) models. …”
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