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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Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China by Junzhi Liu, Zheng Duan, Jingchao Jiang, A-Xing Zhu

“…This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. …”
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Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). The results indicate a promising predictive capacity of both models, showcasing their effectiveness in capturing intricate patterns within the dataset. …”
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A New GLLD Operator for Mass Detection in Digital Mammograms by N. Gargouri, A. Dammak Masmoudi, D. Sellami Masmoudi, R. Abid

“…We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. …”
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Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices by Henrique Frulani de Paula Barbosa, Andreas Schander, Andika Asyuda, Luka Bislich, Sarah Bornemann, Björn Lüssem

“…Abstract Artificial Neural Networks (ANN) require a better platform to reduce their energy consumption and achieve their full potential. …”
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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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Design of Morlet Wavelet Neural Networks for Solving the Nonlinear Van der Pol–Mathieu–Duffing Oscillator Model by Ali Hasan Ali, Muhammad Amir, Jamshaid Ul Rahman, Ali Raza, Ghassan Ezzulddin Arif

“…The proposed technique utilizes artificial neural networks to model equations and optimize error functions using global search with a genetic algorithm (GA) and fast local convergence with an interior-point algorithm (IPA). …”
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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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Improving Fuel Consumption Prediction for Marine Diesel Engines Using Hierarchical Neural Networks and Pulsating Exhaust Models by Anibal Aguillon Salazar, Georges Salameh, Pascal Chesse, Nicolas Bulot, Yoann Thevenoux

“…This study introduces a hybrid model where artificial neural networks replace engine block elements, while the 1D gas circuit and turbocharger models are retained. …”
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Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review by Bilal Kazmi, Syed Ali Ammar Taqvi, Dagmar Juchelkov, Guoxuan Li, Salman Raza Naqvi

“…It examines artificial neural networks, deep learning models, and support vector machines for predicting solubility in ILs, and presents valuable results demonstrating the potential of these techniques. …”
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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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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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ZleepNet: A Deep Convolutional Neural Network Model for Predicting Sleep Apnea Using SpO2 Signal by Hnin Thiri Chaw, Thossaporn Kamolphiwong, Sinchai Kamolphiwong, Krongthong Tawaranurak, Rattachai Wongtanawijit

“…The accuracy of the proposed CNN is 91.30% in which training data are 83% and testing data are 17% when compared with artificial neural networks (ANN).…”
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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 basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

“…Traditional forecasting techniques, such as Linear Mixed Models, Random Forest and standard Artificial Neural Networks, often fail to account for the time-dependent nature of tree growth and utilize simple architectures. …”
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Lung Diseases Diagnosis-Based Deep Learning Methods: A Review by Shahad A. Salih, Sadik Kamel Gharghan, Jinan F. Mahdi, Inas Jawad Kadhim

“…DL methods, which utilize artificial neural networks to extract features from medical images automatically, have shown great promise in improving the accuracy and efficiency of lung disease diagnosis. …”
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Integrating AI and statistical methods for enhancing civil structural practices: current trends, practical issues, and future direction by Asraar Anjum, Meftah Hrairi, Abdul Aabid Shaikh, Noorfazrina Yatim, Maisarah Ali

“…This review systematically examines how advanced optimization techniques, including artificial neural networks (ANNs), Design of Experiments (DOE), and fuzzy logic (FL), are transforming civil engineering practices. …”
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