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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Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches by Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad, Amr Yousef, Tagrid Abdullah N. Alshalali, Zaffar Ahmed Shaikh

“…In this work, we implemented a transfer learning approach, building upon the DenseNet-121 convolutional neural network to detect diabetic retinopathy, followed by Bayesian extensions to the trained model. …”
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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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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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Assessing the possibility of using portable and stationary non-mydriatic fundus cameras for diabetic retinopathy screening assisted by an artificial intelligence-based software pla... by A.O. Nevska, O.A. Pohosian, K.O. Goncharuk, O.O. Chernenko, I.V. Hymanyk, A.R. Korol

“…Results: In group 1 and group 2, there were 37 eyes and 339 eyes, respectively, whose images could not be processed by the neural network. DR was found in 15 subjects (5.17%) in group 1 and 8 subjects (2.51%) in group 2. …”
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Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques by Sahar R. Al-Sakini, Ghassan A. Bilal, Ahmed T. Sadiq, Wisam Abed Kattea Al-Maliki

“…The MLMs used for transformer fault diagnosis were random forest (RF), backpropagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB). …”
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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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Lighting Spectrum Optimization With Deep Learning for Moss Species Classification by Kenichi Ito, Pauli Falt, Markku Hauta-Kasari, Shigeki Nakauchi

“…Hence, we propose a method for obtaining spectral information on moss in the forest using a deep learning model to train convolutional neural network models while optimizing a suitable light source for moss identification. …”
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Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients by Dominique J. Monlezun, Alfred T. Samura, Ritesh S. Patel, Tariq E. Thannoun, Prakash Balan

“…Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. …”
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An OSNR Monitoring Scheme based on Mean and Variance Value of Signal Amplitude by CHEN Fang, LI Zifan, ZHU Yanyuan, LI Bozhong, LONG Han, WU Jianjun, DUAN Mingxiong

“…【Methods】To achieve a simple, efficient, and high-precision OSNR monitoring, based on the mean and variance of the signal amplitude histogram, combined with a Deep Neural Network (DNN), the article proposes a highly accurate OSNR monitoring method. …”
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Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data by Vahid Nourani, Ramin Ahmadi, Yongqiang Zhang, Dominika Dąbrowska

“…This study applies several machine learning (ML) models—including a backpropagation neural network (BPNN), an adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and long short-term memory (LSTM)—to simulate PML-V2 ET in the Ahar Chay basin, Northwestern Iran. …”
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