Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm by Hanxi Jia, Junqi Lin, Jinlong Liu

“…This paper proposed a rapid assessment method for bridge seismic damage based on the random forest algorithm (RF) and artificial neural networks (ANN). This method evaluated the relative importance of each uncertain influencing factor of the seismic damage to the girder bridges and arch bridges, respectively. …”
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Existence and Exponential Stability of Periodic Solution for a Class of Generalized Neural Networks with Arbitrary Delays by Yimin Zhang, Yongkun Li, Kuohui Ye

“…By the continuation theorem of coincidence degree and M-matrix theory, we obtain some sufficient conditions for the existence and exponential stability of periodic solutions for a class of generalized neural networks with arbitrary delays, which are milder and less restrictive than those of previous known criteria. …”
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Research on Energy-Efficient Building Design Using Target Function Optimization and Genetic Neural Networks by Youxiang Huan

“…This paper utilizes the EnergyPlus API to directly call the simulation engine from within the optimization algorithm. The genetic neural network algorithm iteratively modifies design parameters (e.g., building orientation, insulation levels etc) and evaluates the resulting energy performance using EnergyPlus. …”
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A BP Neural Network Method for Grade Classification of Loose Damage in Semirigid Pavement Bases by Bei Zhang, Jianyang Liu, Yanhui Zhong, Xiaolong Li, Meimei Hao, Xiao Li, Xu Zhang, Xiaoliang Wang

“…Based on the finite-difference time-domain (FDTD) method, a backpropagation (BP) neural network identification method for loose damage of a semirigid base is presented. …”
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Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach by Mohammad Reza Zakerzadeh, Mohsen Firouzi, Hassan Sayyaadi, Saeed Bagheri Shouraki

“…Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. …”
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H∞ Synchronization of Semi-Markovian Jump Neural Networks with Randomly Occurring Time-Varying Delays by Mengping Xing, Hao Shen, Zhen Wang

“…Based on the Lyapunov stability theory, this paper mainly investigates the H∞ synchronization problem for semi-Markovian jump neural networks (semi-MJNNs) with randomly occurring time-varying delays (TVDs). …”
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A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US by Vladimir M. Krasnopolsky, Ying Lin

“…A novel multimodel ensemble approach based on learning from data using the neural network (NN) technique is formulated and applied for improving 24-hour precipitation forecasts over the continental US. …”
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Neural network analysis for hotel service design in Madrid: the 3Ps methodology and the frontline staff by Javier De Esteban Curiel, Mª Luisa Delgado Jalón, Sandra Flores Ureba, José Ángel Rivero Menéndez

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Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network by Xihua Chen, Xing Yang

“…And it is realized by image semantic processing of colorimetric sensor array and deep convolutional neural network processing of imaging. And through the experimental experiments based on convolutional neural network image segmentation processing, the results show that the efficiency of extracting features corresponding to different layers in the convolutional neural network is that the extraction efficiency of feature 1 and feature 2 is higher in the processing of 4 layers. …”
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Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect by Guowei Yang, Yonggui Kao, Changhong Wang

“…This paper considers dynamical behaviors of a class of fuzzy impulsive reaction-diffusion delayed cellular neural networks (FIRDDCNNs) with time-varying periodic self-inhibitions, interconnection weights, and inputs. …”
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Convergence and Stability of the Split-Step θ-Milstein Method for Stochastic Delay Hopfield Neural Networks by Qian Guo, Wenwen Xie, Taketomo Mitsui

“…A new splitting method designed for the numerical solutions of stochastic delay Hopfield neural networks is introduced and analysed. Under Lipschitz and linear growth conditions, this split-step θ-Milstein method is proved to have a strong convergence of order 1 in mean-square sense, which is higher than that of existing split-step θ-method. …”
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Stability of Impulsive Cohen-Grossberg Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms by Jinhua Huang, Jiqing Liu, Guopeng Zhou

“…This work concerns the stability of impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms as well as Dirichlet boundary condition. …”
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LoCS-Net: Localizing convolutional spiking neural network for fast visual place recognition by Ugur Akcal, Ugur Akcal, Ugur Akcal, Ivan Georgiev Raikov, Ekaterina Dmitrievna Gribkova, Ekaterina Dmitrievna Gribkova, Anwesa Choudhuri, Anwesa Choudhuri, Seung Hyun Kim, Mattia Gazzola, Rhanor Gillette, Rhanor Gillette, Ivan Soltesz, Girish Chowdhary, Girish Chowdhary, Girish Chowdhary

Subjects: “…spiking neural networks…”
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Investigation into the Prediction of Ship Heave Motion in Complex Sea Conditions Utilizing Hybrid Neural Networks by Yuchen Liu, Xide Cheng, Kunyu Han, Zhechun Liu, Baiwei Feng

“…Consequently, this paper proposes a hybrid neural network method that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory Networks (BiLSTMs), and an Attention Mechanism to predict the heaving motion of ships in moderate to complex sea conditions. …”
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Intelligent Prediction of Flood Disaster Risk Levels Based on Knowledge Graph and Graph Neural Networks by Peisheng Yang, Xiaohua Xu, Meilan Shao, Yewei Liu

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Capsule neural network and adapted golden search optimizer based forest fire and smoke detection by Luling Liu, Li Chen, Mehdi Asadi

Subjects: “…Capsule neural networks…”
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Application of Entropy Hierarchy Analysis and Deep Neural Network Algorithm Combination in Enterprise Economic Management by Dan Liu, Xuefei Zheng, Chunqian Dai

“…In order to solve this problem, this paper proposes an evaluation method that integrates entropy weight analytic hierarchy process and deep neural network algorithm. Through objective entropy weight and imitation, the deep neural network fusion of subjective judgment of experts combines the subjective and objective factors of the evaluation index, then uses the AHP method as the evaluation index of performance evaluation according to the index weight, and finally realizes the accurate evaluation of enterprise economic management.…”
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Forecasting energy production of a PV system connected by using NARX neural network model by Marwa M. Ibrahim, Amr A. Elfeky, Amal El Berry

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Iraqi Stock Market Prediction Using Artificial Neural Network and Long Short-Term Memory by Sama Hayder Abdulhussein AlHakeem, Nashaat Jasim Al-Anber, Hayfaa Abdulzahra Atee, Mahmod Muhamad Amrir

“…In this paper, two models were proposed to predict the Iraqi stock markets index through the use of artificial neural networks (ANN) and a long short-term memory (LSTM) algorithm where Iraqi stock market data were used from 2017 to 2021 and good results were achieved in the prediction where the long short-term memory (LSTM) algorithm reached a mean square error (MSE) rate of as little as 0.0016 while the artificial neural network (ANN) algorithm reached error rate 0.0055. …”
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Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture by Hemkant Nehete, Sandeep Soni, Tharun Kumar Reddy Bollu, Balasubramanian Raman, Brajesh Kumar Kaushik

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