Global stability of delayed Hopfield neural networks under dynamical thresholds by Fei-Yu Zhang, Wan-Tong Li

“…We study dynamical behavior of a class of cellular neural networks system with distributed delays under dynamical thresholds. …”
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Multi-scale Logo detection algorithm based on convolutional neural network by Yuchao JIANG, Lixin JI, Chao GAO, Shaomei LI

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Spiking Neural Networks for Energy-Efficient Acoustic Emission-Based Monitoring by Federica Zonzini, Wenliang Xiang, Luca de Marchi

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Neural network model for dependency parsing incorporating global vector feature by Hengjun WANG, Nianwen SI, Yulong SONG, Yidong SHAN

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Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review by Nafizul Alam, Sk Hasan, Gazi Abdullah Mashud, Subodh Bhujel

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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. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). …”
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Method for generating pseudo random numbers based on cellular neural network by Li-hua DONG, Guo-li YAO

“…To overcome the degradation characteristics of chaos system due to finite precision effect and improve the sta-tistical performance of the random number,a new method based on 6th-order cellular neural network (CNN) was given to construct a 64-bit pseudo random number generation (PRNG).In the method,the input and output data in every iteration of 6th-order CNN were controlled to improved the performance of the random number affected by chaos degradation.Then the data were XORed with a variable parameter and the random sequences generated by a Logistic map,by which the repeat of generated sequences was avoided,and the period of output sequences and the key space were expended.Be-sides,the new method was easy to be realized in the software and could generate 64 bit random numbers every time,thus has a high generating efficiency.Test results show that the generated random numbers can pass the statistical test suite NIST SP800-22 completely and thus has good randomness.The method can be applied in secure communication and other fields of information security.…”
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Performance Evaluation of Laboratory Management System Based on BP Neural Network by Anjie Su, Zhigang Wu, Yifeng Yin

“…Based on the exploration of neural network, this paper combines BP neural network with performance evaluation and applies BP neural network to the performance evaluation of university laboratories. …”
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Neural network and Markov based combination prediction algorithm of video popularity by Xuesen MA, Shuyou CHEN, Xiangdong XU, Zhaokun CHU

“…Caching popular video into user-side in advance improves the user experience and reduces operator costs, which is a common practice in the industry.How to effectively predict the popularity of videos has become a hot issue in the industry.On account of the shortcomings of traditional prediction algorithms such as poor nonlinear mapping ability, low prediction accuracy and weak adaptability, a video popularity prediction algorithm based on a neural network and Markov combined model (Mar-BiLSTM) was proposed.Information dependencies were preserved by constructing bidirectional memory network model (bi-directional long short-term memory, BiLSTM), the prediction accuracy of the model was further improved by using Markov properties while avoiding the increase of the complexity of the model caused by the introduction of external variables.Experimental results show that compared with traditional time series and classic neural network algorithms, the proposed algorithm improves predicting accuracy, effectiveness and reduces the amount of calculation.…”
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State Estimation for Discrete-Time Stochastic Neural Networks with Mixed Delays by Liyuan Hou, Hong Zhu, Shouming Zhong, Yong Zeng, Lin Shi

“…This paper investigates the analysis problem for stability of discrete-time neural networks (NNs) with discrete- and distribute-time delay. …”
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Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays by Weisong Zhou, Zhichun Yang

“…A class of dynamical neural network models with time-varying delays is considered. …”
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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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Intelligent prediction method of network performance based on graph neural network by Yijiang LI, Huibiao YE, Renhua XIE, Jiali LOU, Danna ZHUANG, Chuanhuang LI

Subjects: “…graph neural network…”
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Fixed-Time Synchronization of Delayed Memristive Neural Networks with Discontinuous Activations by Hao Pu, Fengjun Li

“…In this paper, the fixed-time synchronization problem for a class of memristive neural networks with discontinuous neuron activation functions and mixed time-varying delays is investigated. …”
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Dynamical Behaviors of the Stochastic Hopfield Neural Networks with Mixed Time Delays by Li Wan, Qinghua Zhou, Zhigang Zhou, Pei Wang

“…This paper investigates dynamical behaviors of the stochastic Hopfield neural networks with mixed time delays. The mixed time delays under consideration comprise both the discrete time-varying delays and the distributed time-delays. …”
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DEEP NEURAL NETWORK-BASED APPROACH FOR COMPUTING SINGULAR VALUES OF MATRICES by Diyari A. Hassan

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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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Synaptic energy drives the information processing mechanisms in spiking neural networks by Karim El Laithy, Martin Bogdan

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Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network by Jying-Nan Wang, Jiangze Du, Chonghui Jiang, Kin-Keung Lai

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Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network by Muhamad Farhin Harun, Azurah A Samah, Muhammmad Imran Ahmad Shabuli, Hairudin Abdul Majid, Haslina Hashim, Nor Azman Ismail, Syiral Mastura Abdullah, Aspalilah Alias

“…This study has developed a malocclusion classification model using the cut-out method and Convolutional Neural Network (CNN). The cut-out method restructures the input images by standardising the sizes and highlighting the incisor sections of the images which assisted the CNN in accurately classifying the malocclusion. …”
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