Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. by Xiaohua Zeng, Changzhou Liang, Qian Yang, Fei Wang, Jieping Cai

“…The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. …”
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A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning by Zhen Yuan, Jinfeng Liu

“…With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. …”
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Application of Big Data Analysis in Cost Control of Marine Fishery Breeding by Yunsong Gu

“…We establish the cost analysis model of marine fishery, use the big data correlation analysis method to conduct distributed mining on the cost characteristics of marine fishery, deeply grasp the relevance of data characteristics, use the information fusion method to build the constraint parameter analysis model of aquaculture cost operation, limit the amount of calculation, and use the adaptive neural network weighted training method to adaptively optimize the cost control to avoid falling into local optimization. …”
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Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning by Si Chen

“…The electronic economy is very dangerous and must be approached with great caution, so as to avoid or minimize the risks that occur in such cases. Deep neural network (DNN) algorithm was improved to predict the Bitcoin price and then achieve the main goal of reducing financial risks to proceed with electronic business, and good estimation was achieved by using informative data such as transactions and currency return. …”
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A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots by Qili Tang

“…This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. …”
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LSTM-Based Deep Model for Investment Portfolio Assessment and Analysis by Haohua Yang

“…The standard long short-term memory (LSTM) neural network has the shortcoming of low effectiveness of the fiscal cycle sequence. …”
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The Application of the Depth Model of Precise Matching between People and Posts Based on Ability Perception in Human Resource Management by Hui Cao

“…In order to solve the problem that job seekers’ job-seeking ability is difficult to match the job requirements, this paper combines neural network with traditional HRM (human resource management) algorithm based on ability perception and designs a depth model of accurate matching of people and posts in HR field, which can improve the quality of data training of traditional algorithm. …”
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Research on Literary Translation Based on the Improved Optimization Model by Hongjian Liu

“…In order to improve the accuracy of the intelligent translation literature of the semantic ontology optimization model, the conversion layer, including the forward neural network layer, residual connection layer, and normalization layer, is added between the encoder and decoder of the semantic ontology optimization model. …”
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Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics by Hayam Alamro, Mohammed Maray, Jawhara Aljabri, Saad Alahmari, Monir Abdullah, Jehad Saad Alqurni, Faiz Abdullah Alotaibi, Abdelmoneim Ali Mohamed

“…Besides, the attention long short-term memory neural network (ALSTM-NN) model is employed to detect and classify cyberattacks. …”
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Impact of carbapenem-resistant infections on mortality in mechanically ventilated acute respiratory distress syndrome patients: A comparison with hypoxemia severity – An observatio... by Thejesh Srinivas, Shwethapriya R, Gagana Hanumaiah, Pratibha Todur, Souvik Chaudhuri, Ganesh Paramasivam, Prithvishree Ravindra, Vinutha R. Bhat, Sagar Shanmukhappa Maddani, Shobha U. Kamath, Danavath Nagendra, Vishwas P, Likith Hanumaiah, Pratik Paran Medhi, Prabha Prakash

“…Univariate and multivariable logistic regression analyses and artificial neural network model, were employed to analyze mortality outcomes. …”
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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…Currently, hybrid convolutional neural network-transformer architectures achieve state-of-the-art performances on benchmark datasets over standalone models. …”
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Wetland vegetation mapping improved by phenological leveraging of multitemporal nanosatellite images by Lucas T. Fromm, Laurence C. Smith, Ethan D. Kyzivat

“…Maximum Likelihood (MLC), Support Vector Machine (SVM), and Artificial Neural Network (ANN) classification algorithms are tested on individual, monthly- and multi-seasonal composite images. …”
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Guaranteeing Prescribed Performance Control for Gyrostabilized Platform with Unknown Control Direction Preceded by Hysteresis by Yexing Wang, Humin Lei, Jikun Ye, Xiangwei Bu, Yali Xue

“…Besides, through ingenious transformation, radial basis function neural network (RBFNN) is applied to estimate the unknown control gains preceded by hysteresis. …”
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Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination by Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie

“…Here, we address this issue by employing a physics-informed deep learning (PIDL) approach that quantifies uncertainty in two-dimensional seismic velocity structure modeling and its propagation to hypocenter determination by introducing neural network ensembles trained on active seismic survey data, earthquake observation data, and the physical equation of wavefront movement. …”
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An artificial intelligence optimization of NOx conversion efficiency under dual catalytic mechanism reaction based on multi-objective gray wolf algorithm by Zhiqing Zhang, Zicheng He, Yuguo Wang, Feng Jiang, Weihuang Zhong, Bin Zhang, Yanshuai Ye, Zibin Yin, Dongli Tan

“…In this study, a fuzzy gray relational analysis coupled with random forest (RF) and back propagation artificial neural network (BP-ANN) model was developed. This model was trained based on the Langmuir-Hinshelwood and Eley-Rideal coupled mechanism for SCR reaction mechanism, and had good fitting effect on the heat transfer rate, catalytic efficiency and ammonia (NH3) slip rate of the catalytic reaction under loading conditions. …”
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Research and Implementation of Fast-LPRNet Algorithm for License Plate Recognition by Zhichao Wang, Yu Jiang, Jiaxin Liu, Siyu Gong, Jian Yao, Feng Jiang

“…Aiming at the problems of traditional license plate recognition algorithms such as the low accuracy, slow speed, and the recognition rate being easily affected by the environment, a Convolutional Neural Network- (CNN-) based license plate recognition algorithm-Fast-LPRNet is proposed. …”
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Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method by Imran Khan, Hakeem Ullah, Hussain AlSalman, Mehreen Fiza, Saeed Islam, Muhammad Shoaib, Muhammad Asif Zahoor Raja, Abdu Gumaei, Farkhanda Ikhlaq

“…In this article, an effective computing approach is presented by exploiting the power of Levenberg-Marquardt scheme (LMS) in a backpropagation learning task of artificial neural network (ANN). It is proposed for solving the magnetohydrodynamics (MHD) fractional flow of boundary layer over a porous stretching sheet (MHDFF BLPSS) problem. …”
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One-Shot M-Array Pattern Based on Coded Structured Light for Three-Dimensional Object Reconstruction by Xiaojun Jia, Zihao Liu

“…A deep convolution neural network (DCNN) and chain sequence features are used to accurately classify pattern elements and key points (KPs), respectively. …”
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Research Progress of Synthesis and Modification Methods Based on Dynamic Substructures by Jintao Su, Bangdong Wang

“…In terms of substructure modification, the reference datum method, function dynamic modification method, neural network model modification, and frequency response function modification are analyzed, and the shortcomings of the dynamic substructure modification method are summarized. …”
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Improving source code suggestion with code embedding and enhanced convolutional long short‐term memory by Yasir Hussain, Zhiqiu Huang, Yu Zhou

“…First, DeepSN uses an enhanced hierarchical convolutional neural network combined with code‐embedding to automatically extract the top‐notch features of the source code and to learn useful semantic information. …”
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