Levenberg–Marquardt Backpropagation for Numerical Treatment of Micropolar Flow in a Porous Channel with Mass Injection by Hakeem Ullah, Imran Khan, Hussain AlSalman, Saeed Islam, Muhammad Asif Zahoor Raja, Muhammad Shoaib, Abdu Gumaei, Mehreen Fiza, Kashif Ullah, Sk. Md. Mizanur Rahman, Muhammad Ayaz

“…The proposed model is evaluated by conducting experiments on a dataset acquired from the OHA method. …”
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Dark Ship Detection via Optical and SAR Collaboration: An Improved Multi-Feature Association Method Between Remote Sensing Images and AIS Data by Fan Li, Kun Yu, Chao Yuan, Yichen Tian, Guang Yang, Kai Yin, Youguang Li

“…Subsequently, an advanced JVC global optimization algorithm is employed to ensure high-precision association in dense scenes. …”
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Determination of disintegration time using formulation data for solid dosage oral formulations via advanced machine learning integrated optimizer models by Mohammed Ghazwani, Umme Hani

“…Data preprocessing involved Min-Max normalization, outlier detection via Elliptic Envelope, and feature selection using Conditional Mutual Information, with hyperparameters optimized through the Water Cycle Algorithm. Performance was assessed using R², RMSE, and MAE across train, validation, and test sets, with 95% confidence intervals confirming robust predictions. …”
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Hybrid Machine Learning Model for Electricity Consumption Prediction Using Random Forest and Artificial Neural Networks by Witwisit Kesornsit, Yaowarat Sirisathitkul

“…An open geospatial data gathered from a real service as well as geographical, climatic, industrial, household information are used to train, evaluate, and validate these models. Machine learning methods such as principal component analysis (PCA), stepwise regression (SWR), and random forest (RF) are used to determine the significant predictor variables. …”
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Is the concept of mammalian epigenetic clocks universal and applicable to invertebrates? by Ryszard Maleszka

“…Certain aspects of animal ageing can be quantified using molecular clocks or machine learning algorithms that are trained on specific omics data, with epigenetic clocks based on DNA methylation (DNAm) garnering the most attention. …”
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Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury by Moa Pontén, Oskar Flygare, Martin Bellander, Moa Karemyr, Jannike Nilbrink, Clara Hellner, Olivia Ojala, Johan Bjureberg

“…The aim of this study was to explore clinician predictions of which adolescents would abstain from nonsuicidal self-injury after treatment as well as how these predictions match machine-learning algorithm predictions. Methods Data from a recent trial evaluating an internet-delivered emotion regulation therapy for adolescents with nonsuicidal self-injury was used. …”
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Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media by Imran Shafi, Sadia Din, Zahid Hussain, Imran Ashraf, Gyu Sang Choi

“…The performance of the proposed method is evaluated against other popular feature selection/extraction algorithms like term frequency-inverse document frequency, Gini Index (GI), Chi square statistics, and PCA. …”
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Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data by Necati Aksoy, Alper Yilmaz, Gokay Bayrak, Mehmet Koc

“…The second case study applied the NeuralProphet-based model to a large-scale dataset of nationwide solar power generation in Germany, spanning five years and collected at 15-minute intervals. Models trained on this dataset achieved R-squared values exceeding 0.99, highlighting the algorithm’s capacity to effectively capture seasonal and temporal patterns at a national scale. …”
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Development of an Intelligent System for Processing Semistructured Data: Industry Structuring and Advanced Analysis of Information Extracted from Comments to Video Clips in Social... by A. A. Poguda, H. Tape

“…The aim of this research is to develop an intelligent system for processing semistructured data from comments on social media videos using structuring algorithms targeting different industries. The research aims to create an efficient method to analyze tone, clustering and extract key themes from comments in order to evaluate the impact of video content on the audience. …”
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ML modeling of ultimate and relative bond strength for corroded rebars based on concrete and steel properties by Alireza Hosseinzadeh Kashani, Mansour Ghalehnovi, Hossein Etemadfard

“…A comprehensive dataset was compiled from experimental studies, and six ML algorithms, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GBoost), and Extreme Gradient Boosting (XGBoost), were trained to forecast UBS and RBS simultaneously. …”
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Predicting shear capacity of Recycled Aggregate Concrete beams using Artificial Neural Network by Ha HOANG, Tuan-Dung PHAM, Xuan-Tung NGUYEN, Minh Van NGO

“…This study investigates the application of an Artificial Neural Network (ANNs) utilizing a Multi-Layer Perceptron (MLP) architecture to predict the shear capacity of Recycled Aggregate Concrete (RAC) beams. The ANNs model was trained using the Levenberg-Marquardt algorithm with a comprehensive dataset comprising 232 experimental shear tests, reflecting a wide range of variables relevant to RAC beam performance. …”
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Dual-Channel CNN-Based Framework for Automated Rebar Detection in GPR Data of Concrete Bridge Decks by Sepehr Pashoutani, Mohammadsajjad Roudsari, Jinying Zhu

“…The models were evaluated using GPR data collected from three bridges with different overlay types. …”
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A New Support Vector Machine Based on Convolution Product by Wei-Chang Yeh, Yunzhi Jiang, Shi-Yi Tan, Chih-Yen Yeh

“…., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time. …”
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Persistent Homology Combined with Machine Learning for Social Network Activity Analysis by Zhijian Zhang, Yuqing Sun, Yayun Liu, Lin Jiang, Zhengmi Li

“…Numerical experiments are conducted to evaluate the performance of clustering quality metrics such as profile coefficients. …”
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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete by Omobolaji Opafola, Abisola Olayiwola, Ositola Osifeko, Adekunle David, Ajibola Oyedejı

“…The developed GB model achieved R-squared values of 91.60%, 91.43%, and 90.18% for the 10-fold, 5-fold, and 3-fold cross-validations, respectively, with mean absolute error, root mean squared error, and mean absolute percentage error values of 2.6776, 4.3523, and 9.19%, respectively. The GB model trained and evaluated was deployed to a web application using Streamlit for real-time prediction of the concrete compressive strength. …”
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Neural network based active control of base isolated structure considering isolator nonlinearity by Nour Elhouda Ghanemi, Mahdi Abdeddaim, Abdelhafid Ounis, Michela Basili

“…An artificial neural network (ANN) is employed, trained via supervised learning using the Levenberg-Marquardt backpropagation algorithm to minimize displacement demands during strong earthquakes. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Background and Objective: In recent years, DNA methylation-tumor classification based on artificial intelligence algorithms has led to a notable improvement in diagnostic accuracy compared to traditional machine learning methods. …”
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Genotype Prediction from Retinal Fundus Images Using Deep Learning in Eyes with Age-Related Macular Degeneration by Avishai Halev, PhD, Denis Huang, MD, Shahbaz Rezaei, PhD, Sean Banks, BS, John D. McPherson, PhD, Suma P. Shankar, MD, PhD, Xin Liu, PhD, Glenn Yiu, MD, PhD

“…Participants: Thirty-one thousand two hundred seventy-one retinal color fundus photographs of 1754 participants from the Age-Related Eye Disease Study. Methods: We trained deep learning models including convolution neural networks and vision transformers (ViTs) to classify patients into high-risk (homozygous high-risk alleles) or low-risk (heterozygous or homozygous low-risk alleles) genotypes for CFH or ARMS2, then evaluated algorithm performance on an independent test set. …”
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High-throughput phenotyping tools for blueberry count, weight, and size estimation based on modified YOLOv5s by Xingjian Li, Sushan Ru, Zixuan He, James D. Spiers, Lirong Xiang

“…The first pipeline used traditional algorithms such as Hough Transform, Watershed, and filtering. …”
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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning by Md. Hasan Imam Bijoy, Md. Jueal Mia, Md. Mahbubur Rahman, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…The proposed models were trained on five pre-processed CKD datasets using four robust feature selection techniques, including Lasso, Fisher score, Information Gain, and Relief. …”
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