Study on Ductility of Ti Aluminide Using Artificial Neural Network by R. K. Gupta, Rama Mehta, Vijaya Agarwala, Bhanu Pant, P. P. Sinha

“…Using the reported data, the present paper aims to optimize the experimental conditions through computational modeling using artificial neural network (ANN). Ductility database were prepared, and three parameters, namely, alloy type, grain size, and heat treatment cycle were selected for modeling. …”
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Efficient Artificial Neural Network for Smart Grid Stability Prediction by Saeed Mohsen, Mohit Bajaj, Hossam Kotb, Mukesh Pushkarna, Sadam Alphonse, Sherif S. M. Ghoneim

“…In this paper, an artificial neural network (ANN) is proposed to predict a smart grid stability for Decentral Smart Grid Control (DSGC) systems. …”
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Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks by Mehdi Nikoo, Farshid Torabian Moghadam, Łukasz Sadowski

“…Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). …”
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Streamflow prediction using artificial neural networks and soil moisture proxies by Robert Edwin Rouse, Doran Khamis, Scott Hosking, Allan McRobie, Emily Shuckburgh

Subjects: “…artificial neural networks…”
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Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees by Ünal Kızıl, Hakkı Fırat Altınbilek, Sefa Aksu, Hakan Nar

“…In the Decision Tree (DT) a model score of 0.96 was obtained by choosing the maximum depth of 20. The artificial neural network (ANN) yielded a classification score of 0.92 using 4 hidden layers and 100 epochs in the artificial neural network model.…”
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Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks by Aminmohammad Saberian, H. Hizam, M. A. M. Radzi, M. Z. A. Ab Kadir, Maryam Mirzaei

“…This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power. …”
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Real Lithuanian economic growth forecasting using artificial neural networks by Audronė Jakaitienė, Minija Tamošiūnaitė

“…Prognoses obtained by means of linear models and artificial neural networks are compared. …”
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Real-Time Evaluation of Compaction Quality by Using Artificial Neural Networks by Weidong Cao, Shutang Liu, Xuechi Gao, Fei Ren, Peng Liu, Qilun Wu

“…It can be found that artificial neural networks show good performance and huge potential for the problem of compaction quality control.…”
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Producing secure multimodal biometric descriptors using artificial neural networks by Dogu Cagdas Atilla, Raghad Saeed Hasan Alzuhairi, Cagatay Aydin

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Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction by Ayodele Ariyo Adebiyi, Aderemi Oluyinka Adewumi, Charles Korede Ayo

“…This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. …”
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Gender Classification Based on Iris Recognition Using Artificial Neural Networks by Basna Mohammed Salih, Adnan Mohsin Abdulazeez, Omer Mohammed Salih Hassan

Subjects: “…Gender prediction, Iris biometrics, Artificial Neural Networks, Canny Edge Detection.…”
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Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks by Daniele Kauctz Monteiro, Letícia Fleck Fadel Miguel, Gustavo Zeni, Tiago Becker, Giovanni Souza de Andrade, Rodrigo Rodrigues de Barros

“…This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. …”
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In situ training of an in-sensor artificial neural network based on ferroelectric photosensors by Haipeng Lin, Jiali Ou, Zhen Fan, Xiaobing Yan, Wenjie Hu, Boyuan Cui, Jikang Xu, Wenjie Li, Zhiwei Chen, Biao Yang, Kun Liu, Linyuan Mo, Meixia Li, Xubing Lu, Guofu Zhou, Xingsen Gao, Jun-Ming Liu

“…Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). …”
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Determination of the deformation modulus of binary composite using artificial neural network by E. S. Klimanova

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Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification by Mhd Saeed Sharif, Maysam Abbod, Abbes Amira, Habib Zaidi

“…The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. …”
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HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm by AbdoulAhad Validi, Jyh-Yuan Chen, Akbar Ghafourian

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Drought Prediction Based on Artificial Neural Network and Support Vector Machine by ZHAO Guoyang, TU Xinjun, WANG Tian, XIE Yuting, MO Xiaomei

“…Drought has aggravated in the humid areas of South China due to climate warming.Drought prediction is of great significance for the optimal management of water resources and the alleviation of drought.Based on the standardized precipitation evapotranspiration index (SPEI) of different time scales for drought evaluation,this paper constructs the artificial neural network (ANN) and support vector regression (SVR) models to predict droughts in the prediction periods of 1 to 3 months,and builds the EMD-ANN and EMD-SVR coupling models to increase the prediction precision for the SPEI1 with the scale of 1 month.The results showed that:The ANN and SVR models have good prediction precision for SPEI with the scales of 3 months.In addition,the prediction precision of the SVR model is slightly better than that of ANN model.The shorter the prediction period is,the higher the prediction precision is.The coefficient of determination of the ANN and SVR models for the drought prediction period of 1 month accounts for 0.834~0.911.The ANN and SVR models are not suitable for the prediction of the SPEI1 with scale of 1 month.After processing by EMD and wavelet denoising,the prediction precision of the SPEI1 by the EMD-ANN and EMD-SVR models is significantly increased.…”
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Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction by Nor Irwin Basir, Kathlyn Kaiyun Tan, Danny Hartanto Djarum, Zainal Ahmad, Dai-Viet N. Vo, Zhang Jie

“…The performance of these autoencoder models is also compared with other models, such as feedforward artificial neural networks (FANN) and principal component analysis (PCA). …”
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Classification of Layers Using Artificial Neural Networks in the Province of Kurdistan (Iran) by Semko Arefpanah, Alireza Sharafi, Alireza Gholamian

“…Artificial Neural Networks (ANN) is a field that combines science, technology, and ancient and modern knowledge. …”
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Comparison of artificial neural network models of categorized daily electric load by Vildan Evren, İlker Ali Ozkan

Subjects: “…artificial neural network…”
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