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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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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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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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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Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models by Salwa Sahnoun, Rihab Souissi, Sirine Chiboub, Aziza Chabchoub, Mohamed Khalil Baazaoui, Ahmed Fakhfakh, Faouzi Derbel

“…This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. …”
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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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Application of artificial intelligence for feature engineering in education sector and learning science by Chao Wang, Tao Li, Zhicui Lu, Zhenqiang Wang, Tmader Alballa, Somayah Abdualziz Alhabeeb, Maryam Sulaiman Albely, Hamiden Abd El-Wahed Khalifa

“…In order to tackle this issue, we utilized three sophisticated machine learning methodologies: Adaptive Lasso (ALasso), Artificial Neural Networks (ANN), and Support Vector Regression (SVR). …”
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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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Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns by Khudhayr A. Rashedi, Mohd Tahir Ismail, Nawaf N. Hamadneh, S. AL Wadi, Jamil J. Jaber, Muhammad Tahir

“…These data are further used to train artificial neural network in conjunction with particle swarm optimization algorithm. …”
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Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning by Xiaoqing Liu, Miaoran Wang, Rui Wen, Haoyue Zhu, Ying Xiao, Qian He, Yangdi Shi, Zhe Hong, Bing Xu

“…An 80/20 train-test split was implemented for model development and validation, employing various machine learning classifiers, including artificial neural networks (ANN), random forest (RF), XGBoost (XGB), and LASSO regression. …”
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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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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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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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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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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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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

“…Seven models such as multiple linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs), K-nearest neighbor (KNNs), decision trees (DT), and ensemble methods combining DT with boosting and bootstrapping were employed. …”
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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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