A Dynamic Prediction Model of Financial Distress in the Financial Sharing Environment by Jie Zhu, Hong Zhu, Nan Lin

“…Then, based on the probabilistic neural network (PNN), this study constructed a dynamic prediction model of financial distress and used experimental results to verify the effectiveness and feasibility of the constructed model.…”
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A Novel Pigeon-Inspired Optimized RBF Model for Parallel Battery Branch Forecasting by Yanhui Zhang, Shili Lin, Haiping Ma, Yuanjun Guo, Wei Feng

“…This paper proposes a radial basis function neural network model based on the pigeon-inspired optimization method and successfully applies the algorithm to predict the parallel branch current of the battery pack. …”
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3D Large-Pose Face Alignment Method Based on the Truncated Alexnet Cascade Network by Qian Zhang, Hao Zheng, Tao Yan, Jiehui Li

“…The parallel convolution pooling layers are added for concatenating parallel results in the original deep convolution neural network, which improves the accuracy of the output. …”
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Adaptive Neural Control and Modeling for Continuous Stirred Tank Reactor with Delays and Full State Constraints by Dongjuan Li, Dongxing Wang, Ying Gao

“…In this paper, an adaptive neural network control method is described to stabilize a continuous stirred tank reactor (CSTR) subject to unknown time-varying delays and full state constraints. …”
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Research on forecast and recommendation technology of taxi passengers based on time-varying Markov decision process by Tong WANG, Shan GAO, Huiwen GONG, Bo SUN

“…To solve the problems of unloading rate caused by blind passenger search of taxis, the hotspot recommendation strategy of taxi passengers was proposed.The proposed strategy could optimize the process of matching passengers to the greatest extent to increase the efficiency of passenger search.Based on the historical trajectory data of taxis and the time series characteristics of hotspot passenger information, a segment prediction method was proposed based on recurrent neural network (SPBR) and a passenger recommendation model was proposed based on time-varying Markov decision process (TMDP).Experimental results show that the RMSE predicted by SPBR algorithm is 67.6%, 71.1% and 64.5% lower than the SVR, CART and BPNN algorithms.The expected return of taxis based on the TMDP algorithm is 35.9% higher than historical expectations.…”
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A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport by Yingmiao Qian, Shuhang Chen, Jianchang Li, Qinxin Ren, Jinfu Zhu, Ruijia Yuan, Hao Su

“…The mathematical methods such as function equation, BP neural network algorithm, and queuing theory were used to establish a complete decision-making model for taxi drivers and an optimization model of dispatching efficiency at the airport. …”
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Proposed Detection Face Model by MobileNetV2 Using Asian Data Set by Phat Nguyen Huu, Vinh Tran Quang, Chau Nguyen Le Bao, Quang Tran Minh

“…Therefore, we propose a model capable of distinguishing between masked and nonmasked faces using a convolutional neural network (CNN) based on deep learning (DL)—MobileNetV2 in this paper. …”
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Evaluation and Analysis of Multimedia Collaborative Building Design Relying on Particle Swarm Optimization Algorithm by Tao Yang

“…It also proposes the import of “with tutor” type data of particle swarm optimization algorithm as well as the self-organizing neural network evaluation model and calculation method of “no tutor” type resource import. …”
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Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modelin... by Yeonjin Park, Jungpyo Hong

“…This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. …”
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Implementation of a low‐power LVQ architecture on FPGA by Najoua Chalbi, Mohamed Boubaker, Mohamed Hedi Bedoui

“…This study presents an architecture‐optimising methodology for embedding an learning vector quantization (LVQ) neural network on an field programmable gate array (FPGA) device. …”
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Analysis of Surrounding Rock Creep Effect on the Long-Term Stability of Tunnel Secondary Lining by Xiaoqian Zhang, Chengmin Wei, Heng Zhang

“…Therefore, the combination of in situ stress measurement and neural network inversion is used to analyze the distribution characteristics of in situ stress. …”
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Price Forecasting Through Multivariate Spectral Analysis: Evidence for Commodities of BM&Fbovespa by Carlos Alberto Orge Pinheiro, Valter de Senna

“…ABSTRACT This study aimed to forecast the prices of a group of commodities through the multivariate spectral analysis model and compare them with those obtained by classical forecasting and neural network models. The choice of commodities such as ethanol, cattle, corn, coffee and soy was due to the emphasis in the exports in 2013. …”
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Spatio-temporal data analysis and accessibility method for IoV in an urban scene by Jiujun CHENG, Guiyuan YUAN, Jie CUI, Aiguo ZHOU, Bo LYU, Guangyao LI

“…In order to solve the problems of diversity spatio-temporal data and low connectivity efficiency in a single road side unit for Internet of vehicles (IoV) in an urban scene, a spatio-temporal data analysis and accessibility method was presented.First, a spatio-temporal data analysis method based on de-noising and data filling was introduced, and a tensor factor aggregation-based neural network was constructed to predict connectivity intensity among vehicles.Then, a connectivity intensity prediction-based accessibility method was proposed.The simulation results demonstrate that the proposed connectivity intensity prediction method can accurately predict connectivity intensity among vehicles, and the proposed accessibility method can effectively reduce connectivity redundancy and loads of road side units.…”
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A Hybrid Temporal-Spatio Forecasting Approach for Passenger Flow Status in Chinese High-Speed Railway Transport Hub by Zhengyu Xie, Limin Jia, Yong Qin, Li Wang

“…The approach combined temporal forecasting based on radial basis function neural network (RBF NN) and spatio forecasting based on spatial correlation degree. …”
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Maximum Power Point Tracking of PV Grids Using Deep Learning by K. Rafeeq Ahmed, Farrukh Sayeed, K. Logavani, T. J. Catherine, Shimpy Ralhan, Mahesh Singh, R. Thandaiah Prabu, B. Bala Subramanian, Adane Kassa

“…In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. …”
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Feature Recognition of Crop Growth Information in Precision Farming by Hanqing Sun, Xiaohui Zhang, Zhou Yu, Gang Xi

“…Finally, the classification method of BP neural network is used to classify the obtained feature vectors. …”
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Communication, latéralité et cerveau chez les primates humains et non humains : vers une origine gestuelle ou multimodale du langage ? by Adrien Meguerditchian, Hélène Cochet, Catherine Wallez, Jacques Vauclair

“…Most language functions are under the dominance of the left cerebral hemisphere and involve a complex neural network in which some cerebral regions play a key-role such as Broca’s and Wernicke's areas within the frontal and the temporal lobes respectively. …”
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Recursive deep reinforcement learning-based collaborative caching relay algorithm in mobile vehicular edge network by WU Honghai, WANG Baibing, MA Huahong, XING Ling

“…Vehicle trajectories were predicted using graph neural network, and the connectivity stability between vehicles was measured to select those that could serve as caching nodes. …”
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Time series generation model based on multi-discriminator generative adversarial network by Yanhui LU, Han LIU, Hang LI, Guangxu ZHU

“…Aiming at the problems of expensive collection cost and missing data due to the privacy and continuity of time series data set, a multi-discriminator generative adversarial network model based on recurrent neural network was proposed, which could synthesize time series dataset that were approximately distributed with real data of a small scale dataset.Multi-discriminator included four discriminators in time domain, frequency domain, time-frequency domain and autocorrelation.Different discriminators could effectively recognize the features of the time series in different domains.In the experiment, the convergence of loss function, principal component analysis and error analysis were performed to evaluate the performance of the model from qualitative and quantitative perspectives.The experimental results show that the proposed model has better performance than other reference models.…”
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Hierarchical micro-blog sentiment classification based on feature fusion by Xianying ZHU, Zhen LIU, Wei JIN, Tingting LIU, Cuijuan LIU, Yanjie CHAI

“…Sentiment classification is an important issue of opinion mining.It has a high application value to classify sentiment in micro-blogs.As traditional feature selection method has semantic gap,a neural network language model was used to propose a deep feature representation method based on probability model to distribute weight to the word vector.Using this method,text semantic vector could be built.In order to avoid the semantic gap,the deep features and shallow features of text were integrated and feature vector that contained semantic information was constructed.With SVM hierarchical classification model,a variety of sentiments could be classified.Experimental results show that the hierarchical sentiment classification method based on feature fusion can improve the accuracy of sentiment classification in micro-blogs.…”
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