Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function by Fang Jia, Boli Yang

“…In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. …”
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SBT Approach towards Analog Electronic Circuit Fault Diagnosis by V. Manikandan, N. Devarajan

“…The artificial neural network classifiers are then used for the classification of fault. …”
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Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware by Valentina Baruzzi, Giacomo Indiveri, Silvio P. Sabatini

“…By developing a recurrent spiking neural network model of the retinocortical visual pathway, we show how such noisy and heterogeneous computing substrate can produce linear receptive fields tuned to visual stimuli with specific orientations and spatial frequencies. …”
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A Global Perspective on Lunar Granular Flows by V. T. Bickel, S. Loew, J. Aaron, N. Goedhart

“…Here, we build and deploy a convolutional neural network and map 28,101 flow features between 60°N and S by scanning through ∼150,000 Lunar Reconnaissance Orbiter images. …”
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Neural quantum propagators for driven-dissipative quantum dynamics by Jiaji Zhang, Carlos L. Benavides-Riveros, Lipeng Chen

“…In this work, we develop driven neural quantum propagators (NQP), a universal neural network framework that solves driven-dissipative quantum dynamics by approximating propagators rather than wave functions or density matrices. …”
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Visualization of Multivariate Time-Series Characteristics of Ground Loss Caused by Shield Tunneling by Zhu Wen, Xiaoli Rong, Fei Gao, Zhen Wang, Dong An

“…A method of visualizing MTS features based on a residual network and multichannel fully convolutional neural network is also presented. The validity of the proposed ground-loss model is verified via calculation and comparison with 13 EPB shield construction projects carried out in typical urban areas featuring soft soil. …”
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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

“…Initially, image enhancement techniques are integrated with neural network-based visual feature extraction. Subsequently, employs a high-dimensional error-state optimizer coupled with a low-dimensional height filter to achieve high-precision localization of the UAV in under-canopy environments. …”
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Research on Fault Diagnosis Method of Planetary Gearboxes Based on DPD-1DCNN by Zhang Bowen, Pang Xinyu, Guan Chongyang

“…In view of the above shortcomings, a new fault diagnosis method based on data probability density and one-dimensional convolutional neural network (DPD-1DCNN) is proposed. It has two characteristics: ①the density feature of the extracted signal resists the redundancy of the data; ②adapt redundant signals of different lengths as input to the diagnostic model. …”
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DEEP-SEA LANDING VEHICLE SHAPE DRAG ANALYSIS AND BOW MODELED LINE OPTIMIZATION DESIGN (MT) by ZHANG ZiYao, ZHOU Yue, SUN Yu, LAN YanJun, GUO Wei

“…The optimal latin Hypercube method is used to select sample points for the direct navigation resistance calculation, an approximate model of design variable-resistance was established based on the radial basis function neural network, and the optimal design of landing vehicle bow modeled line was carried out by using the adaptive simulated annealing algorithm. …”
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Laser-Induced Breakdown Spectroscopy-Based Coal-Rock Recognition: An <italic>in Situ</italic> Sampling and Recognition Method by Cong Liu, Yimin Luo, Qizhi Zhang

“…Finally, SSM and neural network classifiers are used to recognize coal and rock, and the results are presented and discussed. …”
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Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection by Haojie Lou, Yuanlin Zheng, Wenqian Chen, Haiwen Liu

“…Firstly, we use a Siamese neural network to extract the multi-scale features of reference image and detection image. …”
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Development of particle flow algorithm with GNN for Higgs factories by Murata Tatsuki, Suehara Taikan

“…We have studied machine learned particle flow model using Graph Neural Network based algorithm developed in the context of CMS HGCAL clustering. …”
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Digital twin based intelligent urban traffic forecasting and guidance strategy by Xiwen LIAO, Supeng LENG, Yujun MING, Tianyang LI

“…As the technology of ubiquitous Internet of things and artificial intelligence improves by leaps and bounds, the transportation system revolution is flourishing and bringing new opportunities and challenges.Considering the defect in the existing navigation system, and the neglect of the temporal and spatial characteristics of traffic flow, the macro traffic network and micro vehicle network were modeled and their coupling relationship was mined.Then, a digital twin based urban traffic forecasting and guidance method was proposed to alleviate the problem of traffic congestion.The spatial-temporal traffic flow information was predicted through the diffusion convolution recurrent neural network, which was explicitly applied to the vehicle path planning decision.On this basis, a spatial-temporal collaborative deep reinforcement learning method was proposed to implement the future-oriented collaborative path planning of vehicles.It also guided the underlying vehicle twins to select the optimal strategy for the real world.With SUMO for simulation verification, the experimental results show that the proposed method is significantly better than the existing algorithms in improving the travel completion ratio and congestion relief, and can improve the efficiency of urban traffic travel.…”
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Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. by Mwenge Mulenga, Arutchelvan Rajamanikam, Suresh Kumar, Saharuddin Bin Muhammad, Subha Bhassu, Chandramathi Samudid, Aznul Qalid Md Sabri, Manjeevan Seera, Christopher Ifeanyi Eke

“…This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. …”
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Comparing ChatGPT And LSTM In Predicting Changes In Quarterly Financial Metrics by Vitali Chaiko

“…The performance of ChatGPT is compared against Long Short-Term Memory (LSTM) neural network models developed as part of this research. …”
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Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS-SVM Optimized by PSO by Jungang Du

“…We also compare the prediction results with the wavelet neural network algorithm, and the results show that the generalization ability of LS-SVM Optimized by PSO is better, and the prediction accuracy of small samples is higher.…”
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Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition by Xi Cheng

“…To further improve the accuracy of smoke detection, an automatic feature extraction and classification method based on fast regional convolution neural network (fast R–CNN) was introduced in the study. …”
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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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Logistics Information Traceability Mechanism of Fresh E-Commerce Based on Image Recognition Technology by Xin Zhang, Pengfei Shao

“…Based on the dimensional features and regional-pixel similarity factor, it is verified using the deep neural network. This learning process identifies dimensional variations due to logistics displacement and position suppressing the similarity variations. …”
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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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