A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction by Yajiao Tang, Junkai Ji, Yulin Zhu, Shangce Gao, Zheng Tang, Yuki Todo

“…Among them, Artificial Neural Networks (ANNs) have been widely and effectively applied in bankruptcy prediction. …”
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Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning by Min Liang, Zhiwen Zhang, Langming Wu, Mafeng Chen, Shifan Tan, Jian Huang

“…Predictive models were built using Random Forest, XGBoost, Decision Trees, and Artificial Neural Networks, with their performance evaluated via metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, brier score, and decision curve analysis (DCA). …”
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ANN-based software cost estimation with input from COCOMO: CANN model by Chaudhry Hamza Rashid, Imran Shafi, Bilal Hassan Ahmed Khattak, Mejdl Safran, Sultan Alfarhood, Imran Ashraf

“…This research aims to identify the factors that influence the software effort estimation using the constructive cost model (COCOMO), and artificial neural networks (ANN) model by introducing a novel cost estimation approach, COCOMO-ANN (CANN), utilizing a partially connected neural network (PCNN) with inputs derived from calibrated values of the COCOMO model. …”
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Improvement of Propeller Hydrodynamic Prediction Model Based on Multitask ANN and Its Application in Optimization Design by Liang Li, Yihong Chen, Lu Huang, Qing Hai, Denghai Tang, Chao Wang

“…A multitask learning (MTL) model based on artificial neural networks (ANNs) is proposed in this study to improve the prediction accuracy and physical reliability of marine propeller hydrodynamic performance. …”
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γ-H2AX: A Novel Prognostic Marker in a Prognosis Prediction Model of Patients with Early Operable Non-Small Cell Lung Cancer by E. Chatzimichail, D. Matthaios, D. Bouros, P. Karakitsos, K. Romanidis, S. Kakolyris, G. Papashinopoulos, A. Rigas

“…The use of artificial neural networks in prediction problems is well established in human medical literature. …”
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Evaluation of Satellite Rainfall Products over the Mahaweli River Basin in Sri Lanka by Helani Perera, Shalinda Fernando, Miyuru B. Gunathilake, T. A. J. G. Sirisena, Upaka Rathnayake

“…Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) outperformed among all SRPs, while Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products showed dire performances. …”
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“…Landslide susceptibility assessment and factor effect analysis: bad propagation artificial neural networks and comparison with frequency ratio and bivariate logistic regression modeling. …”
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The structure of the local detector of the reprint model of the object in the image by A. A. Kulikov

“…These networks are called capsules. Artificial neural networks should use local capsules that perform some rather complex internal calculations on their inputs, and then encapsulate the results of these calculations in a small vector of highly informative outputs. …”
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Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms by Rafael Pires de Lima, Yue Chen, Youzuo Lin

“…Furthermore, based on several kinds of linear and artificial neural networks algorithms, a list of models was constructed, trained, validated, and tested with 42‐month MeV electron observations from Van Allen Probes. …”
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A comprehensive analysis of advanced solar panel productivity and efficiency through numerical models and emotional neural networks by Ali Basem, Serikzhan Opakhai, Zakaria Mohamed Salem Elbarbary, Farruh Atamurotov, Natei Ermias Benti

“…A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. …”
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Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps by Torsten Mattfeldt, Hubertus Wolter, Danilo Trijic, Hans‐Werner Gottfried, Hans A. Kestler

“…Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. …”
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A review of artificial intelligence techniques for optimizing friction stir welding processes and predicting mechanical properties by Roosvel Soto-Diaz, Mauricio Vásquez-Carbonell, Jose Escorcia-Gutierrez

“…Artificial intelligence (AI) techniques, including artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), were utilized to predict mechanical properties such as ultimate tensile strength (UTS) and optimize pivotal welding parameters, such as rotational speed, feed rate, axial force, and tilt angle. …”
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Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction by Chandan Kumar, Gabriel Walton, Paul Santi, Carlos Luza

“…This experiment was conducted on regional landslide susceptibility prediction using different ML models: logistic regression (LR), k-nearest neighbor (KNN), linear discriminant analysis (LDA), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and C5.0. …”
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Improving Health Through Indoor Environmental Quality Monitoring: A Review of Data-Driven Models and Smart Sensor Innovations by Kidari Rachid, Tilioua Amine

“…Numerous cutting-edge deep learning techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), and deep neural networks (DNNs), are incorporated into the hybrid framework. …”
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Different pixel sizes of topographic data for prediction of soil salinity. by Shima Esmailpour, Ebrahim Mahmoudabadi, Mohammad Ghasemzadeh Ganjehie, Alireza Karimi

“…This study was aimed to examine the accuracy of soil salinity prediction model integrating ANNs (artificial neural networks) and topographic factors with different cell sizes. …”
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Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

“…This article provides a critical analysis of the following cutting-edge methods: DTC with Space Vector Modulation (DTC-SVM), DTC based on Fuzzy Logic (DTC-FL), DTC using Artificial Neural Networks (DTC-ANN), DTC optimized by Genetic Algorithms (DTC-GA), DTC with Ant Colony Optimization (DTC-ACO), DTC with rooted tree optimization (DTC-RTO), Sliding Mode Control (DTC-SMC), and Predictive DTC (P-DTC). …”
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Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Fatemeh Mohammadinia, Amirjavad Borhani

“…Furthermore, ML algorithms such as Artificial Neural Networks (ANN), Bayesian networks, Random Forest (RF), Support Vector Machine (SVM), LSBoost, and Linear Regression (LR) were employed to estimate MMP. …”
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Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability by Muhammad Paend Bakht, Mohd Norzali Haji Mohd, Babul Salam KSM Kader Ibrahim, Nuzhat Khan, Usman Ullah Sheikh, Ab Al-Hadi Ab Rahman

“…Their performance was then validated against commonly used artificial neural networks (ANN) and support vector machines (SVM) using multiple evaluation metrics including prediction accuracy, error rates, and interpretability. …”
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Letter and Person Recognition in Freeform Air-Writing Using Machine Learning Algorithms by Huseyin Kunt, Zeki Yetgin, Furkan Gozukara, Turgay Celik

“…Fourier and wavelet transforms are used to extract features and the performances of various machine learning algorithms, namely Decision Tree, Random-Forest, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks, and SubSpace KNN, are comparatively studied. …”
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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

“…To address this gap, we applied two machine learning algorithms—random forest (RF), and artificial neural networks (ANN) to predict benthic chl-a concentrations by incorporating these specific P fractions as separate variables. …”
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