Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves by Majid Niazkar, Mohammad Zakwan

“…As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP-GRG technique. …”
Get full text

Bioinsecticide Production from Cigarette Wastes by Badhane Gudeta, Solomon K, M. Venkata Ratnam

“…In addition, artificial neural network (ANN) studies with MATLAB were used to accurately forecast extraction yield. …”
Get full text

A study comparing energy consumption and environmental emissions in ostrich meat and egg production by Behrooz Behboodi, Mohammad Gholami Parashkoohi, Davood Mohammad Zamani, Saeed Firouzi

“…This study delves into the impact of egg and meat production on human health, revealing a slight difference of 0.23 disability adjusted life years (DALY), hinting that egg production could potentially have marginally more negative health effects than meat production. Artificial neural network (ANN) analysis indicates that optimizing machinery, diesel fuel, and energy usage can enhance the productivity of meat production. …”
Get full text

Efficiency-House Optimization to Widen the Operation Range of the Double-Suction Centrifugal Pump by Wenjie Wang, Majeed Koranteng Osman, Ji Pei, Shouqi Yuan, Jian Cao, Fareed Konadu Osman

“…A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. …”
Get full text

Intelligent Classification of Stable and Unstable Slope Conditions Based on Landslide Movement by Long Tsang, Ali Ghorbani, Seyed Mohammad Hossein Khatami, Dmitrii Ulrikh

“…Three models of Tree, Adaboost and artificial neural network (ANN) were developed for classification into two categories, stable and unstable. …”
Get full text

Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns by Aya Mohsen Handousa, Mohamed Abdellatief, Fikry Abdo Salem, Nabil Mahmoud, Mohamed Ghannam

“…Furthermore, the study proposed two machine-learning models, namely Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to estimate the ultimate compressive strength of square CFDST columns. …”
Get full text

A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF by Zenzen, Roumaissa, Ayadi, Ayoub, Benaissa, Brahim, Belaidi, Idir, Sukic, Enes, Khatir, Tawfiq

“…Comparative evaluations with conventional optimisation algorithms, namely Cuckoo, Bat, and Particle Swarm Optimisation, reveal similar Mean Percentage Error values but with increased result variability, whereas Deep Artificial Neural Network models with varied hidden layer sizes.…”
Get full text

Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis by Gabriel Henrique Danielsson, Leonardo Nogueira Fontoura da Silva, Joelson Lopes da Paixão, Alzenira da Rosa Abaide, Nelson Knak Neto

“…The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. …”
Get full text

An artificial intelligence optimization of NOx conversion efficiency under dual catalytic mechanism reaction based on multi-objective gray wolf algorithm by Zhiqing Zhang, Zicheng He, Yuguo Wang, Feng Jiang, Weihuang Zhong, Bin Zhang, Yanshuai Ye, Zibin Yin, Dongli Tan

“…In this study, a fuzzy gray relational analysis coupled with random forest (RF) and back propagation artificial neural network (BP-ANN) model was developed. This model was trained based on the Langmuir-Hinshelwood and Eley-Rideal coupled mechanism for SCR reaction mechanism, and had good fitting effect on the heat transfer rate, catalytic efficiency and ammonia (NH3) slip rate of the catalytic reaction under loading conditions. …”
Get full text

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).…”
Get full text

Advanced neural network modeling with Levenberg–Marquardt algorithm for optimizing tri-hybrid nanofluid dynamics in solar HVAC systems by A. Aziz, S.A.H. Shah, H.M.S. Bahaidarah, T. Zamir, T. Aziz

“…To address this, we propose a nanofluid-based thermal cooling model and develop an advanced computational solver using an Artificial Neural Network (ANN) trained with the Levenberg–Marquardt algorithm (LMA-TNN). …”
Get full text

Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning by Jian Wang, Bo Zhang, Dong Yin, Jinxin Ouyang

“…Moreover, the proposed method has significant advantages over the impedance method and artificial neural network method for fault section identification and fault distance estimation. …”
Get full text

Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan by John Karongo, Joseph Ivivi Mwaniki, John Ndiritu, Victor Mokaya

“…We use five Machine Learning (ML) techniques, including Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGboost), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to predict 2021 end-of-season sorghum yield in conflict affected Upper Nile and Western Bahr El Gazal states. …”
Get full text

Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms by Sobhan Maleky, Maryam Faraji, Majid Hashemi, Akbar Esfandyari

“…The random forest model with the highest accuracy (R 2 = 0.986) was the best prediction model, while logistic regression (R 2 = 0.98), decision tree (R 2 = 0.979), K-nearest neighbor (R 2 = 0.968), artificial neural network (R 2 = 0.955), and support vector machine (R 2 = 0.928) predicted GWQI with lower accuracy. …”
Get full text

Utilizing deterministic smart tools to predict recovery factor performance of smart water injection in carbonate reservoirs by Ali Maghsoudian, Amin Izadpanahi, Zahra Bahmani, Amir Hossein Avvali, Ali Esfandiarian

“…In this paper, three predictive algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and multigene genetic programming (MGGP) are developed to predict the RF of smart water flooding in carbonate reservoirs. …”
Get full text

A Tuning Method for the Supplementary Voltage Controller of Dual-Side Grid Forming Converters in Distributed Storage Systems by Angel Perez-Basante, Asier Gildemuro, Ander Ordono, Salvador Ceballos, Eneko Unamuno, Jon Andoni Barrena

“…Real-time estimation of the optimum controller gains by making use of an artificial neural network is proposed. Simulation and experimental results are presented to validate the method.…”
Get full text

Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations by Badriah Alkalifah, Muhammad Tariq Shaheen, Johrah Alotibi, Tahani Alsubait, Hosam Alhakami

“…To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. …”
Get full text

Advancing hospital healthcare: achieving IoT-based secure health monitoring through multilayer machine learning by Ke Qi

“…Results This cloud-based smart C-IoT system shows the results approximately with 91% accuracy while using Artificial Neural Network (ANN) algorithms. This smart C-IoT-based health issue diagnostic model is one step ahead toward the modernization of society 5.0. …”
Get full text

Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks by F. Siciliano, G. Consolini, R. Tozzi, M. Gentili, F. Giannattasio, P. De Michelis

“…To forecast SYM‐H, we built two artificial neural network (ANN) models and trained both of them on two different sets of input parameters including interplanetary magnetic field components and magnitude and differing for the presence or not of previous SYM‐H values. …”
Get full text

Multi-level lag scheme significantly improves training efficiency in deep learning: a case study in air quality alert service over sub-tropical area by Benedito Chi Man Tam, Su-Kit Tang, Alberto Cardoso

“…In multivariate time series (MTS) models, the predictive accuracy of artificial neural network ANN-type models can be improved by including more features. …”
Get full text