Intelligence algorithm for the treatment of gastrointestinal diseases based on immune monitoring and neuroscience: A revolutionary tool for translational medicine by Liangyu Li, Xuewen Qin, Guangwei Wang, Siyi Li, Xudong Li, Lizhong Guo, Javier Santos, Ana María Gonzalez-Castro, Yanyang Tu, Yi Qin

“…The research team has developed an information system based on clinical blood cell analysis and designed and implemented highly innovative algorithms. A neural network model was created based on these feature data of the blood cell population. …”
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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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Tourism Growth Prediction Based on Deep Learning Approach by Xiaoling Ren, Yanyan Li, JuanJuan Zhao, Yan Qiang

“…The outcome of this study showed that the performance of the adopted deep learning framework was better than that of artificial neural network and support vector regression models. Practitioners can rely on the identified relevant features from the developed framework to understand the nature of the relationships between the predictive factors of tourist demand and the actual volume of tourist arrival.…”
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Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation by Shubo Wang, Haisheng Yu, Xuehui Gao, Na Wang

“…This model is incorporated into an augmented neural network (NN) to account for the unknown nonlinearities. …”
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Optimal Exponential Synchronization of Chaotic Systems with Multiple Time Delays via Fuzzy Control by Feng-Hsiag Hsiao

“…This study presents an effective approach to realize the optimal exponential synchronization of multiple time-delay chaotic (MTDC) systems. First, a neural network (NN) model is employed to approximate the MTDC system. …”
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A Novel Relative Navigation Control Strategy Based on Relation Space Method for Autonomous Underground Articulated Vehicles by Fengqian Dou, Yu Meng, Li Liu, Qing Gu

“…In the RSM, a self-organizing, competitive neural network is used to identify the space around the vehicle, and the spatial geometric relationships of the identified space are used to determine the vehicle’s optimal driving direction. …”
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Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting by Qiushuang Lin, Chunxiang Li

“…In this paper, an adaptive residual convolutional neural network (CNN) is developed, aiming at achieving not only high precision but also high adaptivity for various wind signals with varying complexity. …”
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Gold Price Forecasting: A Novel Approach Based on Text Mining and Big-Data-Driven Model by Poor Saeed Kian, Fattahi Shahram, Hajian Mohsen

“…To explain the gold price prognosis, the convolutional neural network (CNN) also uses linguistic criteria for news items about gold. …”
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Detection of Two-Level Inverter Open-Circuit Fault Using a Combined DWT-NN Approach by Bilal Djamel Eddine Cherif, Azeddine Bendiabdellah

“…This paper work addresses a diagnostic technique based on the discrete wavelet transform (DWT) algorithm and the approach of neural network (NN), for the detection of an inverter IGBT open-circuit switch fault. …”
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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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Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA-III by Peijian Wu, Yulu Chen

“…First, we developed a model based on the NAR neural network to predict order requests. Subsequently, we used the improved NSGA-III based on good point set theory to construct a multiobjective optimization model to minimize resource loss, maximize efficiency in goods selection, and maximize goods accumulation. …”
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The Underwater Projectile Launching Process Prediction Based on Reduced Order Method by Ke Deng, Yi Jiang, Huan-Xing Liu

“…The prediction of the initial stage of the launching velocity (which is the most important part of the launching process) is realized by establishing the neural network between the working condition parameters and the POD basis coefficients. …”
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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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Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis by Shihua Luo, Jiangyou Huo, Zian Dai

“…Using the daily closing price data of SSE (Shanghai Stock Exchange) Composite Index and Shenzhen Component Index as samples, compared with conventional wavelet prediction model, ARIMA model, and BP neural network model, the empirical results show that the new algorithm M-ARIMA-BP can improve the accuracy of volatility forecasting and perform better in predicting prices rising and falling.…”
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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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Forecasting Directions, Dates, And Causes of Future Technological Revolutions concerning the Growth of Human Capital by Ning Chen, Masoud Vaseei, Ali Peivandizadeh

“…Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. …”
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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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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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