Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

“…We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. …”
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Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol by Mauro Podda, Adolfo Pisanu, Gianluca Pellino, Adriano De Simone, Lucio Selvaggi, Valentina Murzi, Eleonora Locci, Matteo Rottoli, Giacomo Calini, Stefano Cardelli, Fausto Catena, Carlo Vallicelli, Raffaele Bova, Gabriele Vigutto, Fabrizio D’Acapito, Giorgio Ercolani, Leonardo Solaini, Alan Biloslavo, Paola Germani, Camilla Colutta, Savino Occhionorelli, Domenico Lacavalla, Maria Grazia Sibilla, Stefano Olmi, Matteo Uccelli, Alberto Oldani, Alessio Giordano, Tommaso Guagni, Davina Perini, Francesco Pata, Bruno Nardo, Daniele Paglione, Giusi Franco, Matteo Donadon, Marcello Di Martino, Dario Bruzzese, Daniela Pacella

“…The model includes the following steps: the spatial transformation of variables using kernel Principal Component Analysis (kPCA), the creation of 2D images from transformed data, the application of convolutional filters, max-pooling, flattening, and final risk prediction via a fully connected layer. …”
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Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields by Larona Keabetswe, Yiyin He, Chao Li, Zhenjiang Zhou

“…Motivated by the robustness of deep learning models, this study employed two hybrid models that integrate Convolution Neural Network with either Random Forests (CNN-RF) or Support Vector Machine (CNN-SVR) to estimate potato ETc act using a limited set of input features. …”
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Effective Land Use Classification Through Hybrid Transformer Using Remote Sensing Imagery by Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ul-Haq, David Blake, Naeem Janjua

“…The uneven distribution of land cover introduces spectral-spatial variability, causing inter- and intra-class similarity. …”
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Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence by Liliana Ibeth Barbosa-Santillán, Bertha Patricia Guzman-Velazquez, Ma. Teresa Orozco-Aguilera, Leticia Flores-Pulido

“…This study introduces a computational framework to identify such content using convolutional neural networks of weapon-related images. …”
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The role of spectral characteristics of urine in bladder cancer diagnostics by Martina Velísková, Dominika Masarovičová, Iveta Waczulíková, Boris Kollárik, Juraj Jacko, L’uba Hunáková, Milan Zvarík

“…In both EEMs and chromatograms, statistically significant peaks and areas were identified, which were evaluated using various statistical methods and machine learning techniques (logistic regression, OPLS-DA, convolutional neural networks). The analysis of urine EEMs did not yield satisfactory results; the highest accuracy was achieved using convolutional neural networks, with a maximum accuracy of 72.1% for the training model. …”
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Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

“…Tabular-to-2D image data transformation was achieved by creating a feature matrix from encoded labels of the input variables arranged according to their correlation coefficient. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. …”
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Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models by Hongkun Fu, Jian Lu, Jian Li, Wenlong Zou, Xuhui Tang, Xiangyu Ning, Yue Sun

“…By adjusting the key parameters of the Convolutional Neural Network (CNN) with IGWO, the prediction accuracy is significantly enhanced. …”
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Optimizing physical education schedules for long-term health benefits by Liang Tan, Qin Chen, Jianwei Wu, Mingbang Li, Tianyu Liu

“…The developed DL model integrates convolutional neural network (CNN) layers to capture spatial features and long short-term memory (LSTM) layers to extract temporal patterns from demographic and activity-related variables. …”
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Machine Learning for Fire Safety in the Built Environment: A Bibliometric Insight into Research Trends and Key Methods by Mehmet Akif Yıldız

“…Multiple regression analysis was applied to support this metric’s theoretical basis and determine the impact levels of variables affecting the metric’s value (such as total citation count, publication year, and number of articles). …”
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Deep learning-based assessment of pulp involvement in primary molars using YOLO v8. by Aydin Sohrabi, Nazila Ameli, Masoud Mirimoghaddam, Yuli Berlin-Broner, Hollis Lai, Maryam Amin

“…The YOLOv8m-cls model architecture included convolutional and classification layers, and performance was evaluated using top-1 and top-5 accuracy metrics. …”
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Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review by Carlos M. Ferreira-Vanegas, Jorge I. Vélez, Guisselle A. García-Llinás

“…Although the negative binomial regression method was used for several years, we noticed that other regression models as well as methods based on deep learning, convolutional neural networks, transfer learning, 5G technology, Internet of Things, and intelligent transport systems have recently emerged as suitable alternatives for RTA analysis. …”
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Impact of Safety Signage Placement on Evacuation Behavior in Virtual Fire Scenarios Based on EDA Data by Wenqi Song, Xu Feng, Yu Song

“…Five features are extracted from the EDA signal: PhasicData, PhasicDriver, Skin Conductance (SC), TonicData, and TonicDriver. Three variables are evaluated, signage height (1m, 0.5m, and 0m), spacing (5m and 10m), and presence of active fire, using a hybrid classification model that integrates an im-proved convolutional neural network (CNN), a Transformer-based sequence encoder, and a multi-layer spiking neural network. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…The findings underscore the effectiveness of machine learning methods, particularly XGBoost, LightGBM, and CatBoost, in forecasting adsorption levels with high precision while offering actionable insights into key variables driving adsorption mechanisms.…”
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Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques by Raouf Hassan, Alireza Baghban

“…This research aims to develop robust machine learning models to capture the intricate relationships influencing CO2 adsorption, driven by variables like pressure, temperature, and the biochar’s chemical and physical properties. …”
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Micro-Mobility Safety Assessment: Analyzing Factors Influencing the Micro-Mobility Injuries in Michigan by Mining Crash Reports by Baraah Qawasmeh, Jun-Seok Oh, Valerian Kwigizile

“…In addition, the findings emphasize the overall effect of many different variables, such as improper lane use, violations, and hazardous actions by micro-mobility users. …”
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Machine learning and deep learning in medicine and neuroimaging by Iván Sánchez Fernández, Jurriaan M. Peters

“…The emphasis of this review is the application of convolutional neural networks for image classification and for image segmentation in neuroimaging. …”
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AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images. by Parsa Delavari, Gulcenur Ozturan, Eduardo V Navajas, Ozgur Yilmaz, Ipek Oruc

“…Here we examine whether DR manifests differently in male and female patients, using a dataset of retinal images and leveraging convolutional neural networks (CNN) integrated with explainable artificial intelligence (AI) techniques. …”
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An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks by Zhifeng Ma, Zhanjun Hao, Zhenya Zhao

“…Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. …”
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