Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network by Thuy-Anh Nguyen, Hai-Bang Ly, Hai-Van Thi Mai, Van Quan Tran

“…In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later-age concrete. …”
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Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers by Alon Kukliansky, Marko Orescanin, Chad Bollmann, Theodore Huffmire

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Antiperiodic Solutions to Impulsive Cohen-Grossberg Neural Networks with Delays on Time Scales by Yanqin Wang, Maoan Han

“…We use the method of coincidence degree and construct suitable Lyapunov functional to investigate the existence and global exponential stability of antiperiodic solutions of impulsive Cohen-Grossberg neural networks with delays on time scales. Our results are new even if the time scale T=R or Z. …”
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Respiratory disease detection in lung auscultation with convolutional neural networks and CVAE augmentation by D.V. Panaskin, S.H. Stirenko, D.S. Babko

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Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients by Mikkel Bonde, Alexander Bonde, Haytham Kaafarani, Andreas Millarch, Martin Sillesen

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AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM by Trần Thị Hương, Phạm Văn Hạnh

“…In this paper, we present a model based on the combination of recurrent neural networks and rule sets for the network intrusion detection problem. …”
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Horseshoe Chaos in a 3D Neural Network with Different Activation Functions by Fangyan Yang, Song Tang, Guilan Xu

“…This paper studies a small neural network with three neurons. First, the activation function takes the sign function. …”
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Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network by Kazuhiko Hiramoto, Taichi Matsuoka, Katsuaki Sunakoda

“…We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. …”
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New Results on Stability of Delayed Cohen–Grossberg Neural Networks of Neutral Type by Ozlem Faydasicok

“…This research work conducts an investigation of the stability issues of neutral-type Cohen–Grossberg neural network models possessing discrete time delays in states and discrete neutral delays in time derivatives of neuron states. …”
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On the Prediction of Product Aesthetic Evaluation Based on Hesitant-Fuzzy Cognition and Neural Network by Xinying Wu, Minggang Yang, Zishun Su, Xinxin Zhang

“…By measuring the cognitive complexity of the product, this research establishes the relationship between the complexity and aesthetics of the product using an artificial neural network. Hence the prediction of product beauty is achieved, which guides design decisions. …”
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GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks by Irene Palazzoli, Serena Ceola, Pierre Gentine

“…In this study, we develop GRAiCE, a set of four global monthly TWSA reconstructions from 1984 to 2021 at 0.5° spatial resolution, using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks. Our models accurately reproduce GRACE/GRACE-FO observations at the global scale and effectively capture the impacts of climate extremes. …”
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Multi-component Runoff Simulation in Arid Area Based on BP Neural Network by HAN Zichen, WANG Yi, HE Bin

Subjects: “…BP neural network…”
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Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network by Xiaoxuan Wang, Jingjing Wang, Ying Zhang, Yixing Du

“…This article proposes a method of selecting macroeconomic early-warning indicators using self-organizing competitive neural networks and designs a macroeconomic nonlinear early warning model of self-organizing competitive neural networks; using fuzzy logic reasoning to introduce economic experts’ experience into macroeconomic early warning analysis, the system has the ability to deal with nonlinear and uncertain problems and realizes the intelligence of the early-warning process, uses the national macroeconomic indicator data from January 1997 to March 2008 for empirical analysis, and compares the self-organizing competitive neural network method with the traditional KL information method. …”
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Artificial Neural Network for the Clustering of Vibration Signals for Condition Monitoring of Rotating Machines by Eyere Emagbetere, Samson Uwatse, Omonzokpia Goerge Okoidigun

“…However, analysis of vibration signals using artificial neural network (ANN) is mostly via development of classification models, which cannot be suitably applied to several varied machine types and specifications. …”
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Atmospheric Turbulence Intensity Image Acquisition Method Based on Convolutional Neural Network by Yuan Mu, Liangping Zhou, Shiyong Shao, Zhiqiang Wang, Pei Tang, Zhiyuan Hu, Liwen Ye

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Advanced Earthquake Magnitude Prediction Using Regression and Convolutional Recurrent Neural Networks by Asep Id Hadiana, Rifaz Muhammad Sukma, Eddie Krishna Putra

“…This study presents a novel earthquake magnitude prediction model by integrating regression analysis with Convolutional Recurrent Neural Networks (CRNNs). It utilises Convolutional Neural Networks (CNNs) for spatial feature extraction from 2-dimensional seismic signal images and Long Short-Term Memory (LSTM) networks to capture temporal dependencies. …”
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Forecast of Chemical Export Trade Based on PSO-BP Neural Network Model by Na Li, Meng Li

“…Although the traditional linear prediction method has the advantages of intuitiveness, simplicity, and strong interpretability, it is difficult to deal with the prediction problem of dynamic and complex nonlinear systems. The neural network is a nonlinear dynamic system, with strong nonlinear mapping ability, strong robustness, and fault tolerance. …”
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Retracted: Risk Prediction of Sports Events Based on Gray Neural Network Model by null Complexity

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The Application of Speech Synthesis Technology Based on Deep Neural Network in Intelligent Broadcasting by Jihong Yang

“…To improve the sound quality of speech synthesis technology in intelligent broadcasting, a deep neural network-based method is proposed. It also proved the effectiveness of the DNN discrimination s/u/v and completed the conversion of the HMM synthesis spectrum parameter to original speech. …”
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Recurrent neural networks with transient trajectory explain working memory encoding mechanisms by Chenghao Liu, Shuncheng Jia, Hongxing Liu, Xuanle Zhao, Chengyu T. Li, Bo Xu, Tielin Zhang

“…Even though many recurrent neural networks (RNNs) have been proposed to simulate WM, most networks are designed to match respective experimental observations and show either transient or persistent activities. …”
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