Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…Quantitative analysis demonstrates that the CO-RF model surpasses other techniques, Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network, and standalone Random Forest (RF), both in the training and testing phases of SR prediction. …”
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A review on inverse analysis models in steel material design by Yoshitaka Adachi, Ta‐Te Chen, Fei Sun, Daichi Maruyama, Kengo Sawai, Yoshihito Fukatsu, Zhi‐Lei Wang

“…Key models discussed include the convolutional neural network–artificial neural network‐coupled model, which employs convolutional neural networks for feature extraction; the Bayesian‐optimized generative adversarial network–conditional generative adversarial network model, which generates diverse virtual microstructures; the multi‐objective optimization model, which concentrates on process–property relationships; and the microstructure–process parallelization model, which correlates microstructural features with process conditions. …”
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Factors Affecting Digital Financial Service Adoption in Bangladesh: Evidence from SEM-ANN Approaches by Nafis Sadik, Md. Mominur Rahman

“…This study investigates the key drivers affecting the adoption of DFS in Bangladesh by employing Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) approaches. Using survey data collected from 340 DFS users, the SEM analysis validates the proposed relationships, identifying financial literacy, trust, access to capital, digital payment usage, and digital financial inclusion as significant factors driving DFS adoption. …”
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Predictive Modeling of Fracture Behavior in Ti6Al4V Alloys Manufactured by SLM Process by Mohsen Sarparast, Majid Shafaie, Mohammad Davoodi, Ahmad Memaran Babakan, Hongyan Zhang

“…The research explores the impact of Artificial Neural Network (ANN) architecture, specifically hidden layers and neurons, on predicting fracture parameters. …”
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Online multi‐object tracking based on time and frequency domain features by Mahbubeh Nazarloo, Meisam Yadollahzadeh‐Tabari, Homayun Motameni

“…The features are given for learning vector quantization, which is a supervised artificial neural network (ANN). It is used to classify the dataset. …”
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Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN by Ali Reza Ghanizadeh, Mansour Fakhri

“…Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. …”
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Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models by Laurentiu Fara, Alexandru Diaconu, Dan Craciunescu, Silvian Fara

“…This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. …”
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Development of a Medical Information System with Data Storage and Intelligent Image Analysis by D. A. Yakimov, N. V. Vygovskaya, I. V. Drozdov

“…Conditions have been prepared for the implementation of the second stage – integration of an automated workstation of medical information systems into the existing technological process in a medical institution and detection of pathology using an artificial neural network.…”
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Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement by Wenhua Fan, Hyun Joo Min

“…The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements. …”
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A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device by Debajyoti Pal, Vajirasak Vanijja

“…We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.…”
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Improving Electron Density Predictions in the Topside of the Ionosphere Using Machine Learning on In Situ Satellite Data by S. Dutta, M. B. Cohen

“…This research focuses on predicting the electron density in the topside of the ionosphere using satellite data, in particular from the Defense Meteorological Satellite Program, a collection of 19 satellites that have been polar orbiting the Earth for various lengths of times, fully covering 1982 to the present. An artificial neural network was developed and trained on two solar cycles worth of data (113 satellite‐years), along with global drivers and indices such as F10.7, interplanetary magnetic field, and Kp to generate an electron density prediction. …”
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Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features by Emmanuel Adetiba, Oludayo O. Olugbara

“…This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. …”
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Energy-Saving and Sustainable Separation of Bioalcohols by Adsorption on Bone Char by Oslery Becerra-Pérez, Stavros Georgopoulos, Maria Lanara, Hilda Elizabeth Reynel-Ávila, Maria Papadaki, Adrián Bonilla-Petriciolet, Didilia Ileana Mendoza-Castillo

“…A model based on an artificial neural network was proposed to correlate both single and binary adsorption isotherms of these bioalcohols with bone char. …”
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Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model by Nhat-Duc Hoang, Anh-Duc Pham, Quoc-Lam Nguyen, Quang-Nhat Pham

“…Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.…”
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Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control by Yufei Ruan, Huang Gao, Dequn Li

“…Once the optimal compensation time is learned, a deep Q-learning algorithm which combined Q-learning with an artificial neural network (ANN) is proposed to learn the optimal compensation quantity. …”
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Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique by Saud Altaf, Muhammad Waseem Soomro, Mirza Sajid Mehmood

“…This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. …”
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Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes by Ehsan Homayouny, Rasoul Mahdavifar Khayati, Seyed Massood Nabavi, Vania Karami

“…Therefore, a Bayesian classifier based on the adaptive mixture method was used to segment all lesions, and an artificial neural network (ANN) employed a multi‐layer Perceptron as a subtype classifier. …”
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Discrimination of Melanoma Using Laser-Induced Breakdown Spectroscopy Conducted on Human Tissue Samples by Muhammad Nouman Khan, Qianqian Wang, Bushra Sana Idrees, Geer Teng, Xutai Cui, Kai Wei

“…Chemometric methods, artificial neural network (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least square discriminant analysis (PLS-DA) are used to build the classification models. …”
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Automatic Tracking and Characterization of Cumulonimbus Clouds from FY-2C Geostationary Meteorological Satellite Images by Yu Liu, Du-Gang Xi, Zhao-Liang Li, Chun-Xiang Shi

“…First, a seeded region growing (SRG) algorithm is used with artificial neural network (ANN) cloud classification as preprocessing to identify consistent homogeneous Cb patches from infrared images. …”
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Design and Optimization of Iron Cow Stem with Flaps by Finite Element Method and Genetic Algorithm by Khaled Kamal Oude, Ali Adelkhani

“…Then, using an artificial neural network, a model was presented to estimate the von Mises tension based on the information related to the cross-sectional area and stem curvature, and this model was able to estimate the maximum von Mises tension with an accuracy of 99%. …”
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