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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Personalized recommendation model with multi-level latent features by Qing SHEN, Wenbin GUO, Jungang LOU, Qiangguo YU

“…Personalized recommendation has become one of the most effective means to solve information overload, and it is also a hot technology in the research field of massive data mining.However, traditional recommendation algorithms often only use the user’s rating information on the item, and lack a comprehensive consideration of the potential characteristics of the user and the item.The factorization machine, wide neural network, crossover network and deep neural network were combined to extract the shallow latent features, low-order nonlinear latent features, linear cross latent features, and high-order nonlinear latent features of users and items.Thus, a new deep learning personalized recommendation model with multilevel latent features was established.The experimental results on four commonly used data sets show that considering the multi-level potential features of users and items can effectively improve the prediction accuracy of personalized recommendations.Finally, the influence of factors such as the dimensions of the embedding layer and the number of neurons on the prediction performance of the new model was studied.…”
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Research on neuromorphic vision sensor and its applications by Yongsheng SANG, Renhao LI, Yaoqian LI, Qiangwei WANG, Yao MAO

“…Neuromorphic vision sensor is a biologically inspired artificial neural system that mimics algorithmic behavior of biological vision systems,which has numerous advantages over standard vision sensors,such as high temporal resolution,low latency,low power,high dynamic range,etc.At first,a brief introduction to neuromorphic engineering,neuromorphic chips,and vision sensors was given.Then the main computing methods for neuromorphic vision were reviewed,including probability and statistics,spiking neural network and deep neural network.Finally,several kinds of applications based on neuromorphic vision sensors were given,such as simultaneous localization and mapping (SLAM),image reconstruction (IR),etc.A review of hardware,computing methods and applications for neuromorphic vision sensors was given,which provided a comprehensive reference for researchers.…”
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Bearing Fault Diagnosis Method based on Sensitive Component and MCPG by Mingliang Zhang, Hongkun Li, Yue Ma, Gangjin Huang, Yuchen Xu

“…Firstly， the Empirical Mode Decomposition （EMD） is used to decompose the original signal into multiple Intrinsic Mode Function（IMF）， and the discrete Fréchet distance is used as the measurement index， the fault sensitive components are selected as the fault data sources representing different fault types. Then， a MCPG deep neural network architecture is proposed， and sensitive data sources are used to train and test the model to achieve the data-driven bearing fault diagnosis. …”
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Construction of Intelligent Building Integrated Evaluation System Based on BIM Technology by Mengmeng Song

“…The software communicates with the monitoring system through 5G public network and applies the unique advantages of deep neural network in classification to the assessment of the health status of old bridges with the help of the multiclassification convolutional neural network embedded in the software. …”
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6G knowledge system construction: academic knowledge mining and on-demand application for full domains and omni scenarios by Zifan SHA, Nan CHENG, Yilong HUI, Wenwei YUE, Yuchuan FU, Ruijin SUN

“…At present, the concepts related to 6G have not been unified, and there is an urgent need for consistent cognition and definition.Academics and industries lack a clear understanding of the overall development of 6G and the research progress in related fields.Therefore, the 6G knowledge base and knowledge system was constructed.Firstly, the existing 6G academic documents were automatically screened and stored in a structured way.Secondly, a 6G knowledge base was constructed on the basis of labeling and standardizing text data.In addition, a comprehensive statistical analysis was conducted across all domains of 6G based on the knowledge base and the technologies such as natural language processing, deep neural network and latent tree model were used to realize the extraction and generation of 6G knowledge.Finally, on the basis of large-scale model training, the on-demand knowledge application was realized for diversified service requirements.…”
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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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DNN-based Sub-6 GHz assisted millimeter wave network power allocation algorithm by Changyin SUN, Liyan LIU, Fan JIANG, Jing JIANG

“…Aimed at the problems of the signaling cost and power consumption in the power control measurement of the millimeter wave system, as well as the complexity caused by iteration operations, a millimeter wave link power allocation prediction algorithm using the Sub-6 GHz frequency band was proposed.Firstly, the mapping between the Sub-6 GHz band channel information and the optimal power allocation of the millimeter wave band was analyzed.Then, a deep neural network (DNN) model was utilized to realize this mapping function.To predict the power allocation of millimeter wave channel with Sub-6 GHz channel as input, the neural network was trained with the weighted mean square error minimization method (WMMSE) as the supervisor in different scenarios.The simulation results show that compared with the WMMSE algorithm in millimeter wave band, the proposed algorithm can obtain more than 97% of its sum-rate performance while taking less than 0.1% of the time.…”
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An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning by Fernando Mattioli, Daniel Caetano, Alexandre Cardoso, Eduardo Naves, Edgard Lamounier

“…The choice of a good topology for a deep neural network is a complex task, essential for any deep learning project. …”
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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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Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm by Fuxing He

“…This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. …”
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Application of Full Vector Deep Learning in Bearing Fault Diagnosis by Chen Chaoyu, Chen Lei, Zhang Wang, Han Jie

“…Then,a full-vector deep neural network is built on this basis,combining sparse coding and de-noising coding algorithm,the fault features can be extracted automatically. …”
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Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention by Biao Yang, Jinmeng Cao, Rongrong Ni, Ling Zou

“…We propose an anomaly detection approach by learning a generative model using deep neural network. A weighted convolutional autoencoder- (AE-) long short-term memory (LSTM) network is proposed to reconstruct raw data and perform anomaly detection based on reconstruction errors to resolve the existing challenges of anomaly detection in complicated definitions and background influence. …”
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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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PSR-SQUARES: SQL reverse synthesis system based on program space reducer by Quansheng DOU, Shun ZHANG, Hao PAN, Huixian WANG, Huanling TANG

“…In order to address the issue of rapid growth of program space in SQUARES, which led to low efficiency in program synthesis, a program space reducer based on deep neural network (DNN) was introduced into the SQUARES framework.A given ＆lt;Queried tables, Query result＆gt; pair was represented as a 2D tensor which was used as input for a DNN.And the output of the DNN was the relevance vector of the target SQL statement synthesis rules.Based on the output of the DNN, the last N rules with weak correlation to the target SQL statement were eliminated, thereby shrinking the program search space and improving the system synthesis efficiency.The architecture of DNN, the method of generating training datasets, and the training process of DNN were described in detail.Furthermore, experimental comparisons between PSR-SQUARES and other representative SQL reverse synthesis systems were conducted.The results show that the overall performance of PSR-SQUARES is superior to other synthesis systems to varying degrees, with the average synthesis time reduced from 251 s in SQUARES to 130 s and the target program synthesis success rate increased from 80% to 89%.…”
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Research on Personal Loan Default Risk Assessment Based on Machine Learning by Liu Guangsen

“…Among them, the Deep Neural Network has the best overall performance compared to the other three machine learning models. …”
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Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN by Zhen Tang, Xiaobin Fu, Pingping Xiao

“…We also investigate the outage probability (OP) performance and derive OP expressions. Employing the deep neural network (DNN), an OP intelligent prediction algorithm is proposed. …”
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Deep learning for stage prediction in neuroblastoma using gene expression data by Aron Park, Seungyoon Nam

“…Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. …”
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Analyzing information sharing behaviors during stance formation on COVID-19 vaccination among Japanese Twitter users. by Sho Cho, Shohei Hisamitsu, Hongshan Jin, Masashi Toyoda, Naoki Yoshinaga

“…We constructed a dataset of all Japanese posts mentioning vaccines for five months since the beginning of the vaccination campaign in Japan and carried out a stance detection task for all the users who wrote the posts by training an original deep neural network. Investigating the users' stance formations using this large dataset, it became clear that some neutral users became pro-vaccine, while almost no neutral users became anti-vaccine in Japan. …”
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