Application of Chaos and Neural Network in Power Load Forecasting by Li Li, Liu Chong-xin

“…Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of artificial neural network (ANN). Improved back propagation (BP) algorithm based on genetic algorithm (GA) is used to train and forecast. …”
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Channel modeling of molecular communication via free diffusion with multiple receiver by Zhuo SUN, Xu BAO, Jie LIN, Wence ZHANG

“…A coexistence scenario with a point source, a pair of absorbing and transparent receiver was considered, an interference factor was introduced in the proposed channel model based on the receiving molecular probability in the transparent receiver considering the influences of the absorbing receiver on the transparent one.Furthermore, the channel model of point source and transparent receiver had been proposed by using Levenberg-Marquardt algorithm combined with artificial neural network to study and predict channel model parameters.The simulation results not only verify the effectiveness of the proposed channel model, but also show that the peak time of any point in the environment is directly proportional to the square of the distance from the point source to the receiver, and inversely proportional to the molecular diffusion coefficient, and the peak time is not affected by the absorbing receiver in the environment.…”
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Prediction of groundwater level Sharif Abad catchment of Qom using WANN and GP models

“…To compare the results of the hybrid model of wavelet analysis-neural network (WNN), genetic programming (GP) multiple linear regression (MLR) and artificial neural network (ANN), two criteria of root mean squared error (RMSE) and nash-sutcliffe coefficient of efficiency (E) is used. …”
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Dynamics of specialization in neural modules under resource constraints by Gabriel Béna, Dan F. M. Goodman

“…Using a simple, toy artificial neural network setup that allows for precise control, we find that structural modularity does not in general guarantee functional specialization (across multiple measures of specialization). …”
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Tourism Growth Prediction Based on Deep Learning Approach by Xiaoling Ren, Yanyan Li, JuanJuan Zhao, Yan Qiang

“…The outcome of this study showed that the performance of the adopted deep learning framework was better than that of artificial neural network and support vector regression models. …”
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Forecasting Directions, Dates, And Causes of Future Technological Revolutions concerning the Growth of Human Capital by Ning Chen, Masoud Vaseei, Ali Peivandizadeh

“…Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. …”
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Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks by Marcin Bernas, Bartłomiej Płaczek

“…For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). …”
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Automatic diagnosis of selected retinal diseases based on OCT B-scan by Tomasz Marciniak, Agnieszka Stankiewicz

“…The prepared software allows the reader to familiarize themselves with the topic of current artificial neural network solutions.…”
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Optimization of Solar Panel Deployment Using Machine Learning by Shoaib Kamal, P. S. Ramapraba, Avinash Kumar, Bikash Chandra Saha, M. Lakshminarayana, S. Sanal Kumar, Anitha Gopalan, Kuma Gowwomsa Erko

“…The study takes into concern several topologies that includes series parallel topology, parallel topology, bridge link topology, honeycomb topology, and total cross tied. The artificial neural network-based topology reconfiguration strategy allows for optimal working conditions for PV arrays. …”
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Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study by Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, Itaru Hosaka, Keisuke Endo, Toshifumi Ogawa, Tatsuya Sato, Toru Suzuki, Toshiyuki Yano, Hirofumi Ohnishi, Nagisa Hanawa, Masato Furuhashi

“…The AUCs of ML models were 0.765–0.825, and discriminatory capacity was significantly improved in the artificial neural network model compared to that in the logistic regression model.Conclusions The development of hypertension can be simply and accurately predicted by each ML model using systolic blood pressure, age and FLI as selected features. …”
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Flexural Strength Prediction of Welded Flange Plate Connections Based on Slenderness Ratios of Beam Elements Using ANN by Amir A. Hedayat, Ehsan Jazebi, Saman AsadAbadi, Amin Iranpour

“…Proposed theoretical formulas and artificial neural network- (ANN-) based models developed in this study were able to adequately predict the connection strength.…”
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The Impacts of Internet + Rural Financial Industry on County Economy and Industrial Growth Algorithm by Yuanhui Huang, Yimao Ye

“…To better develop rural finance and county economy, this paper constructs an artificial neural network model and FA model to predict the rural finance industry, and four indicators are selected to test the performance measurement of the model. …”
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Prediction-Based Maintenance of Existing Bridges Using Neural Network and Sensitivity Analysis by Pengyong Miao

“…This study proposed a methodology to resolve these issues by integrating an artificial neural network (ANN) and sensitivity analysis method. …”
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An Application of ANN Ensemble for Estimating of Precipitation Using Regional Climate Models by Dongwoo Jang

“…In this study, the precipitation of five regional climate models and actual observed precipitation provided in Korea are applied to ANN (artificial neural network), which suggests ways to improve prediction accuracy for precipitation. …”
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Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework by Mousmita Sarma, Kandarpa Kumar Sarma

“…This paper describes an Artificial Neural Network (ANN) based algorithm developed for the segmentation and recognition of the vowel phonemes of Assamese language from some words containing those vowels. …”
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Forecasting-Aided Monitoring for the Distribution System State Estimation by S. Carcangiu, A. Fanni, P. A. Pegoraro, G. Sias, S. Sulis

“…In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. …”
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Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model by Geleta T. Mohammed, Jane A. Aduda, Ananda O. Kube

“…This work shown as the fuzzy-EGARCH-ANN (fuzzy-exponential generalized autoregressive conditional heteroscedastic-artificial neural network) model does not require continuous model calibration if the corresponding DE algorithm is used appropriately, but other models such as GARCH, EGARCH, and EGARCH-ANN need continuous model calibration and validation so they fit the data and reality very well up to the desired accuracy. …”
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An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings by Zeyu Luo, Xian-Bo Wang, Zhi-Xin Yang

“…The autoregressive neural network (ARNN) is an early idea to combine the artificial neural network (ANN) and the autoregressive (AR) model for forecasting, but the model is limited to linear terms. …”
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MODELING OF SOLAR RADIATION WITH A NEURAL NETWORK by VALENTIN STOYANOV, IVAYLO STOYANOV, TEODOR ILIEV

“… Modeling of solar radiation with neural network could be used for real-time calculations of the radiation on tilted surfaces with different orientations. In the artificial neural network (ANN), latitude, day of the year, slope, surface azimuth and average daily radiation on horizontal surface are inputs, and average daily radiation on tilted surface of definite orientation is output. …”
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Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight by Hao Zhou, Tawfiqur Rahman, Wanchun Chen

“…In order to ensure terminal velocity constraint, a prediction of the terminal velocity is required, based on which, the approximated Bézier curve is adjusted. An artificial neural network is used for this prediction of the terminal velocity. …”
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