Quantification of modal mineralogy in molybdenite-bearing drill-core samples by laser-induced breakdown spectroscopy by Jonnathan Álvarez, Germán Velásquez, Iván Arévalo, Jorge Yáñez, Claudio Sandoval-Muñoz, Benjamín Sepúlveda

“…The selected spectral signals are defined as “mineralogical patterns”, which are processed using supervised chemometrics methods, such as artificial neural networks, to enable an automated mineral classification. …”
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Simulation and explanatory analysis of dissolved oxygen dynamics in Lake Ulansuhai, China by Fan Zhang, Xiaohong Shi, Shengnan Zhao, Ruonan Hao, Biao Sun, Guohua Li, Shihuan Wang, Hao Zhang

“…Study focus: After implementing the optimal noise reduction strategies based on wavelet transform for the high-frequency monitoring data, hybrid models coupling random forests, support vector machines, and artificial neural networks were employed to simulate the dissolved oxygen in the lake during both the open-water and ice-covered periods. …”
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Neural network quantification for solar radiation prediction: An approach for low power devices by Brenda Alejandra Villamizar-Medina, Angelo Joseph Soto Vergel, Byron Medina-Delgado, Darwin Orlando Cardozo-Sarmiento, Dinael Guevara-Ibarra, Oriana Alexandra Lopez-Bustamante

“… Accurate solar radiation prediction leverages various machine learning techniques, with artificial neural networks (ANN) being the most common and precise due to their ability to detect and learn relationships between meteorological variables and solar radiation. …”
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Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream by Waseem Shariff, Paul Kielty, Joe Lemley, Peter Corcoran

“…The paper proposes a hybrid architecture combining Spiking Neural Networks (SNNs) and Artificial Neural Networks (ANNs). This approach leverages the energy efficiency and low-latency of SNNs while maintaining the high accuracy of ANNs, resulting in a highly efficient and accurate face detection system. …”
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Comparative Study of Statistical Features to Detect the Target Event During Disaster by Madichetty Sreenivasulu, M. Sridevi

“…Additionally, different classifiers such as Artificial Neural Networks (ANN), decision tree, and K-Nearest Neighbor (KNN) are compared by using these two features. …”
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Investigation of the application of an automated monitoring system for detecting transmission cable deterioration in Nigeria: A case study of transmission cable lines between Offa... by C.S. Omoniabipi, R. Agbadede, K.C. Emmanuel, O.J. Adewuni, I. Allison

“…Both forward propagation and backpropagation techniques were adopted for training Artificial Neural Networks (ANNs), and the gradient descent with momentum algorithm was employed for optimization. …”
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A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities by José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Tuxpan Vargas, José Alfredo Ramos Leal, Janete Morán Ramírez

“…It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). …”
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E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review by Abed Mutemi, Fernando Bacao

“…Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.…”
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Machine learning-assisted prediction of durability behavior in pultruded fiber-reinforced polymeric (PFRP) composites by Ammar A. Alshannaq, Mohammad F. Tamimi, Muath I. Abu Qamar

“…The results reveal that Decision Trees, Artificial Neural Networks, and Random Forests are the best models to predict behavior of pultruded composites under environmental exposure, which achieved R2 values of 0.9647, 0.9537, and 0.8970, respectively for the case of flexural strength. …”
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Ensemble Deep Learning Technique for Detecting MRI Brain Tumor by Rasool Fakhir Jader, Shahab Wahhab Kareem, Hoshang Qasim Awla

“…In recent years, a variety of computational algorithms for segmentation and classification have been developed with improved results to get around the issue. Artificial neural networks (ANNs) have the capability and promise to classify in this regard. …”
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Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin by iran salehvand, amir gandomkar, ebrahim fatahi

“…Due to the nonlinear behavior of rainfall, artificial neural networks were used for modeling. Factor analysis was used to determine the best architecture for entering the neural network. …”
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Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy by Muhammad Shoaib Saleem, Naeem Abas

“…Techniques used for electricity demand forecasting encompass artificial intelligence, artificial neural networks, trend line extrapolations, fuzzy logic, vector support machines, genetic algorithms and expert systems. …”
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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

“…In this study, we focus on CKD classification and creatinine prediction using three sets of features: at-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. …”
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Daily reference evapotranspiration prediction using empirical and data-driven approaches: A case study of Adana plain by Semin Topaloğlu Paksoy, Deniz Levent Koç

“…The objective of this research was to examine the effectiveness of five different data-driven techniques, including artificial neural networks "multilayer perceptron" (ANN), gene expression programming (GEP), random forest (RF), support vector machine "radial basis function" (SVM), and multiple linear regression (MLR) to model the daily ET0. …”
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Künstliche Intelligenz im Englischunterricht – Grundwissen und Praxisbeispiele by Inez De Florio-Hansen

“…What are the meanings of fundamental concepts such as algorithms, machine learning, and artificial neural networks? What are the best practices for inputting prompts (prompt engineering), and how can prompt quality be improved to achieve desired outcomes? …”
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Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height by Tzu-Chia Chen, Zryan Najat Rashid, Biju Theruvil Sayed, Arif Sari, Ahmed Kateb Jumaah Al-Nussairi, Majid Samiee-Zenoozian, Mehrdad Shokatian-Beiragh

“…This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). …”
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Seismic Vulnerability Assessment of Reinforced Concrete Educational Buildings Using Machine Learning Algorithm by Tapan Kumar, Mohammad Al Amin Siddique, Raquib Ahsan

“…Random forest regression (RFR), support vector regression (SVR), and artificial neural networks (ANNs) are employed to determine the SSR of existing educational RC buildings. …”
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Impact of morphological traits and irrigation levels on fresh herbage yield of sorghum x sudangrass hybrid: Modelling data mining techniques. by Halit Tutar, Senol Celik, Hasan Er, Erdal Gönülal

“…For this purpose, Artificial Neural Networks (ANN), Automatic Linear Model (ALM), Random Forest (RF) Algorithm and Multivariate Adaptive Regression Spline (MARS) Algorithm were used, and the prediction performances of these methods were compared. …”
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Cognitive Feature Extraction of Puns Code-Switching Based on Neural Network Optimization Algorithm by Jing Zhang, Qiaoyun Liao, Lipei Li

“…It is generally believed that the human brain’s thinking is divided into three basic ways: abstract (logical) thinking, image (intuitive) thinking, and inspiration (awareness) thinking. Artificial neural networks are the second way to simulate human thinking. …”
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Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…Four ML models are trained: Random Forest Regression (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Artificial Neural Networks (ANN). To address the low interpretability of complex ML models, the Shapley Additive Explanations (SHAP) method is introduced to clarify all input features. …”
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