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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Deep Ensemble Learning for Human Action Recognition in Still Images by Xiangchun Yu, Zhe Zhang, Lei Wu, Wei Pang, Hechang Chen, Zhezhou Yu, Bin Li

“…Firstly, we construct an end-to-end NCNN-based model by attaching the nonsequential convolutional neural network (NCNN) module to the top of the pretrained model. …”
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Skull vibration induced nystagmus, velocity storage and self-stability by Ian S. Curthoys, David S. Zee, Georges Dumas, Georges Dumas, Christopher J. Pastras, Julia Dlugaiczyk

“…Within the indirect pathway there is a unique neural mechanism called the velocity storage integrator (VSI) which is part of a neural network generating prolonged nystagmus, afternystagmus and the sensation of self-motion and its converse self-stability. …”
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ConvXGB: A novel deep learning model to predict recurrence risk of early-stage cervical cancer following surgery using multiparametric MRI images by Ji Wu, Jian Li, Bo Huang, Sunbin Dong, Luyang Wu, Xiping Shen, Zhigang Zheng

“…We designed a novel deep learning model called “ConvXGB” for predicting recurrence risk by combining the convolutional neural network (CNN) and eXtreme Gradient Boost (XGBoost). …”
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Adverse events in the nervous system associated with blinatumomab: a real-world study by Wen Gao, Jingwei Yu, Yifei Sun, Zheng Song, Xia Liu, Xue Han, Lanfan Li, Lihua Qiu, Shiyong Zhou, Zhengzi Qian, Xianhuo Wang, Huilai Zhang

“…The reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence interval progressive neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms were utilized for data mining. …”
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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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Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment by Mimouna Abdullah Alkhonaini, Manal Abdullah Alohali, Mohammed Aljebreen, Majdy M. Eltahir, Meshari H. Alanazi, Ayman Yafoz, Raed Alsini, Alaa O. Khadidos

“…Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. …”
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Characterization of Low Visibility and Forecasting Model in Chongqing Central Area by Yu Han, Yi Liu, Yaping Zhang, Jun He, Yan Zhang, Qu Guo, Huan Wang

“…The visibility prediction model was established by using the neural network method, and the effect of introducing the PM2.5 concentration factor on low visibility prediction was analyzed and compared. …”
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A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning by Bin Hu, Yaohui Han, Wenhui Zhang, Qingyang Zhang, Wen Gu, Jun Bi, Bi Chen, Lishun Xiao

“…Classical deep learning models including recurrent neural network (RNN), long and short-term memory (LSTM), gated recurrent unit (GRU) and temporal convolutional network (TCN) are initially trained, then RNN, LSTM and GRU are integrated with a new attention mechanism and transfer learning to improve the performance. …”
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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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Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods by Sahika Betul Yayli, Kutay Kılıç, Salih Beyaz

“…(2) How do seven convolutional neural network (CNN) architectures perform across four distinct deep learning tasks? …”
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A Mobile Image-Driven PM2.5 Estimation Framework Using Deep Learning Techniques by Anupam Kamble, Somrawee Aramkul, Paskorn Champrasert

“…The EfficientNet-B1 neural network is applied in the image feature vector extraction process. …”
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Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…Results: The random forest regressor (MAE: 0.416 mSv; MAPE: 7%; and R²: 0.98) showed best performances over the neural network and the support vector machine. However, all three machine learning algorithms outperformed effective dose estimation using k-factors (MAE: 2.06; MAPE: 26%) or multiple linear regression (MAE: 0.98; MAPE: 44.4%). …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…<h4>Methods</h4>Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. …”
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A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore, Osamah Ibrahim Khalaf, Sarita Simaiya, Carlos Andrés Tavera Romero, Ghaida Muttashar Abdulsahib, Poongodi M, Dinesh Kumar

“…The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. …”
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A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series by Hui Hu, Jianfeng Zhang, Tao Li

“…The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. …”
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Machine learning and AVO class II workflow for hydrocarbon prospectivity in the Messinian offshore Nile Delta Egypt by Nadia Abd-Elfattah, Aia Dahroug, Manal El Kammar, Ramy Fahmy

“…Machine learning techniques, specifically neural network models, were trained to differentiate seismic features such as low-amplitude gas sand from background-amplitude water sand and shale. …”
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