LiDAR-Based Road Cracking Detection: Machine Learning Comparison, Intensity Normalization, and Open-Source WebGIS for Infrastructure Maintenance by Nicole Pascucci, Donatella Dominici, Ayman Habib

“…Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. …”
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Predictive analytics framework for accurate estimation of child mortality rates for Internet of Things enabled smart healthcare systems by Muhammad Islam, Muhammad Usman, Azhar Mahmood, Aaqif Afzaal Abbasi, Oh-Young Song

“…These real-world data sets have been tested using machine learning classifiers, such as Naïve Bayes, decision tree, rule induction, random forest, and multi-layer perceptron, for the prediction task. …”
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Estimation of Cylinder Grasping Contraction Force of Forearm Muscle in Home-Based Rehabilitation Using a Stretch-Sensor Glove by Adhe Rahmatullah Sugiharto Suwito P, Ayumi Ohnishi, Tsutomu Terada, Masahiko Tsukamoto

“…This study employed support vector machine (SVM), multi-layer perceptron (MLP), and random forest (RF) to construct the estimation model. …”
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CNN-GRU-ATT Method for Resistivity Logging Curve Reconstruction and Fluid Property Identification in Marine Carbonate Reservoirs by Jianhong Guo, Hengyang Lv, Qing Zhao, Yuxin Yang, Zuomin Zhu, Zhansong Zhang

“…Using logging data from the marine carbonate oil layers, the reconstructed deep resistivity curve is compared with actual measurements to determine reservoir fluid properties. …”
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Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines by Hongbo Liu, Xiangzhao Meng

“…The results indicate that, compared with Random Forest, LightGBM, Support Vector Machine, gradient boosting regression tree, and Multi-Layer Perceptron, the BO-XGBoost model exhibits the best prediction performance, with MAPE, R², and RMSE values of 5.5%, 0.971, and 1.263, respectively. …”
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Failure Probability Analysis of Composite Pressure Tanks Using Subset Simulation by Youcef Sid Amer, Samir Benammar, Kong Fah Tee, Zouhir Iourzikene

“…The model was developed in two steps, first, the development of limit state functions for hoop and helical layers using netting analysis, and afterwards, a probabilistic computation with six random variables. …”
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Comparing performance, morphological, physical, and chemical properties of eggs produced by 1940 Leghorn or a commercial 2016 Leghorn fed representative diets from 1940 to 2016 by Dannica C. Wall, Ramon D. Malheiros, K.E. Anderson, N. Anthony

“…The factors consisted of 2 leghorn genetic strains that were a 2016 commercial layer (W36) and a 1940 random-bred leghorn line, then 2 diets based on 2016 and 1940 dietary standards. …”
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Machine Learning-Driven Consensus Modeling for Activity Ranking and Chemical Landscape Analysis of HIV-1 Inhibitors by Danishuddin, Md Azizul Haque, Geet Madhukar, Qazi Mohammad Sajid Jamal, Jong-Joo Kim, Khurshid Ahmad

“…Predictive models were built using Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP). …”
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Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers by Natenaile Asmamaw Shiferaw, Zefree Lazarus Mayaluri, Prabodh Kumar Sahoo, Ganapati Panda, Prince Jain, Adyasha Rath, Md. Shabiul Islam, Mohammad Tariqul Islam

“…Subsequently, the softmax layer is replaced with machine learning classifiers, including Random Forest, XGBoost, and Support Vector Machine (SVM), while freezing the pretrained feature extractors. …”
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BCDnet: Parallel heterogeneous eight-class classification model of breast pathology. by Qingfang He, Guang Cheng, Huimin Ju

“…After the information output by the fully connected layer of the two convolutional bases is fused, it is classified and output by the SoftMax function. …”
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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River by Shuguang Li, Sultan Noman Qasem, Shahab S. Band, Rasoul Ameri, Hao-Ting Pai, Saeid Mehdizadeh

“…To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. …”
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“Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region” by Ankit Singh, Nitesh Dhiman, K. C. Niraj, Dericks Praise Shukla

“…Efficient machine learning method such as random forest (RF) and multi-layer perceptron (MLP) was used to train the models in both areas, statistical measure such as AUC-ROC, precision, recall, F-score, and accuracy were used to evaluate the performance of the LSMs. …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…It evaluates 35 factors playing a role in gold price fluctuations. GARCH and random fluctuation models are used to extract gold price fluctuations. …”
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Latitudinal gradient and environmental drivers of soil organic carbon in permafrost regions of the Headwater Area of the Yellow River by Mingxin Yang, Shizhen Li, Shouxin Wang, Qingdongzhi Huang, Qi Shen, Yanbin Kang, Mingming Shi, Yafei Zhang, Dongliang Luo

“…Moreover, the spatial distribution of key permafrost parameters was simulated: temperature at the top of permafrost (TTOP), active layer thickness (ALT), and maximum seasonal freezing depth (MSFD) using the TTOP model and Stefan Equation. …”
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SCM-DL: Split-Combine-Merge Deep Learning Model Integrated With Feature Selection in Sports for Talent Identification by Didem Abidin, Muhammed G. Erdem

“…After feature selection, our novel SCM-DL deep learning classifier model (apart from the architectures in literature, this model is constructed internally with parallel layers and carries a combinatorial layer that is beyond the combination of existing techniques) is applied and compared with Random Forest, Decision Tree, Extra Tree, and Support Vector Classifiers. …”
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Assessment of carbon sequestration and its economic value in Iranian oak forests: case study Bisetoon protected area by Mohammad Yousefi, Mahmud khoramivafa, Abdolmajid Mahdavi Damghani, Gholamreza Mohammadi, Ali Beheshti Alagha

“…Therefore amount of CO2 captured and saved in wood tissue and organic matter of residual in bottom of tree layer was 5841.61, and 6431.29 kgha-1yer-1 in coppice and single stem forms. …”
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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

“…Among all the ML algorithms, multi-layer perceptron and deep learning performed the best, with an F1 score of more than 99%.In all three evaluation scenarios of 5fold and 10fold cross-validation, the K-nearest neighbors (KNN), random forest (RF), AdaBoost, XGBoost, and logistic regression (LR) had an F1 score of over 0.95, while this value was around 0.90 for support vector machine (SVM), and the lowest values were below 0.90 for decision tree (DT). …”
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A Comprehensive Investigation of the Two-Phonon Characteristics of Heat Conduction in Superlattices by Pranay Chakraborty, Milad Nasiri, Haoran Cui, Theodore Maranets, Yan Wang

“…By systematically varying acoustic contrast, interatomic bond strength, and average layer thickness, we examine the interplay between coherent and incoherent phonon transport in these systems. …”
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Predicting breast cancer recurrence using deep learning by Deepa Kumari, Mutyala Venkata Sai Subhash Naidu, Subhrakanta Panda, Jabez Christopher

“…Utilizing the Wisconsin Diagnostic Breast Cancer and Wisconsin Prognostic Breast Cancer datasets, the framework integrates multiple deep learning architectures- Multi layer Perceptron (MLP), Visual Geometry Group (VGG), Residual Network (ResNet), and Extreme Inception (Xception)-with traditional machine learning models such as Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). …”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

“…Then, we applied four machine learning methods—random forest, gradient boosting decision tree, extreme gradient boosting, and multi-layer perceptron—to develop binary classification models for N-dealkylation. …”
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