A deep learning approach for forecasting non-stationary big remote sensing time series

Rhif, Manel; Abbes, Ali Ben; Martínez, Beatriz; Farah, Imed Riadh

doi:10.1007/s12517-020-06140-w

Cited by 17 publications

(5 citation statements)

References 25 publications

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“…e result is input to the lower layer map, with a real-number range of [0, 1]. e function value represents the probability of a positive class [35]. Its expression is shown in the following equation:…”

Section: Input Layer Hidden Layermentioning

confidence: 99%

Optimizing Film Companies’ Marketing Strategy Using Blockchain and Recurrent Neural Network Model

Liu

2022

Computational Intelligence and Neuroscience

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With the growing scale of the domestic film industry, the output of domestic films is also increasing. The design and execution problems in ticket marketing strategies adversely affect the revenue of domestic films. This paper aims to propose solutions to optimize film companies’ marketing strategies by analyzing the marketing environment and current situation to increase the income of domestic films. Firstly, the current situation of BJ’s marketing is analyzed, and the main problems are clarified. Secondly, the necessity and feasibility of applying blockchain technology to the operation and management of film companies are introduced. New marketing strategies and safeguards have been developed by analyzing the target market and optimizing the program. A quantitative method is used to predict a new movie’s box office in its premiere month. The proposed method introduces three factors affecting the box office: new film positioning, film marketing, and film prerating, as input variables. Besides, a Recurrent Neural Network (RNN) is implemented to predict the monthly box office of the premiere of new films. The results show that the predicted monthly box office of the movie adopting the optimized marketing strategy is 1,451,718.6 CNY, which is smaller than the target box office of 1414029.8. The Mean Absolute Error (MAE) is only 0.026. The proposed model’s Root Mean Square Error (RMSE) is 0.45 for the long-term prediction of a single movie price. The MAE is 0.106, and the accuracy is 0.80. The model proposed beats the unimproved Long Short-Term Memory (LSTM) model and the Autoregressive Moving Average (ARMA) model. This paper provides a reference for optimizing the film company’s marketing system.

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Section: Input Layer Hidden Layermentioning

confidence: 99%

Optimizing Film Companies’ Marketing Strategy Using Blockchain and Recurrent Neural Network Model

Liu

2022

Computational Intelligence and Neuroscience

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“…Before the system design, the research on the control method becomes critical. The current chopping control, Pulse Width Modulation (PWM) control, and angle control are interpreted below [15].…”

Section: Control Strategies Of the Sr Motormentioning

confidence: 99%

Application of Avr Power Automatic Single-Chip Microcomputer to the Electromechanical Measurement and Control System

2022

IJOMAM

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The study aims to reduce the production cost and improve the electromechanical management level of enterprises. A switching type reluctance drive system based on Advanced Virtual RISC (Reduced Instruction Set Computer) (AVR) is designed. In addition, the control circuit of the switched reluctance drive (SRD) system needs to be designed. Control circuit design includes the minimum system of the Single Chip Microcomputer (SCM), position sensor circuit, position signal channel, current detection circuit, and current simulation control circuit. Among them, the current simulation control circuit is the most critical hardware. The performance of the designed SRD system is tested through simulation experiments. The results show that when the rated speed is lower than 750 rpm, the rotation multiple is between 1.5 and 2. And when the speed is slow, the matrix output ability will be better. When the speed ratio is 20% more than the rated value, the matrix output ability has 1.32 times the ability of the torque. It can be concluded that the torque output trend and matrix output value conform to the requirements of matrix performance. Combined with the test results of the current waveform, the SRD system based on the AVR microcontroller has excellent control and detection ability. The study provides a technical reference for the design of electromechanical control system and plays an important role in developing the electromechanical measurement and control system.

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“…For instance, Huang et al (2017) proposed the use of Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models to improve NDVI prediction. Meanwhile, for predicting NDVI from nonstationary big remote sensing time series long short-term memory (LSTM) neural networks have been proposed by Reddy and Prasad (2018) and Rhif et al (2020) and conventional LSTM (ConvLSTM) for crop forecasting by Ahmad et al (2020b). The Elman recurrent neural network model (ERNN) has been used for short-term NDVI index forecasting (Stepchenko and Chizhov, 2015).…”

Section: Use Of Machine Learning For Spatiotemporal Data Fusion Ndvi-...mentioning

confidence: 99%

“…For instance, CNN and RF display good performance in vegetation growth predictions from NDVI (Ayhan et al, 2020;Li et al, 2021;Mishra and Shahi, 2021;Ferchichi et al, 2022). The performance of machine learning models can be evaluated through a range of approaches, including Root Mean Square Error (RMSE), coefficient of determinates (R 2) , Pearson correlation (R), and structural similarity (SSIM), which have been used by Rhif et al (2020), Ahmad et al (2020b), Arab et al (2021), Htitiou et al (2021), Mishra andShahi (2021), andRoy (2021). Htitiou et al (2021) use NDVI values extracted from spatial transects created across the study site to compare the performance of Very Deep Super-Resolution (VDSR) against the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method in producing high resolution NDVI time series datasets.…”

Section: Use Of Machine Learning For Spatiotemporal Data Fusion Ndvi-...mentioning

confidence: 99%

Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems?

et al. 2022

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Systematic tools and approaches for measuring climate change adaptation at multiple scales of spatial resolution are lacking, limiting measurement of progress toward the adaptation goals of the Paris Agreement. In particular, there is a lack of adaptation measurement or tracking systems that are coherent (measuring adaptation itself), comparable (allowing comparisons across geographies and systems), and comprehensive (are supported by the necessary data). In addition, most adaptation measurement efforts lack an appropriate counterfactual baseline to assess the effectiveness of adaptation-related interventions. To address this, we are developing a “Biomass Climate Adaptation Index” (Biomass CAI) for agricultural systems, where climate adaptation progress across multiple scales can be measured by satellite remote sensing. The Biomass CAI can be used at global, national, landscape and farm-level to remotely monitor agri-biomass productivity associated with adaptation interventions, and to facilitate more tailored “precision adaptation”. The Biomass CAI places focus on decision-support for end-users to ensure that the most effective climate change adaptation investments and interventions can be made in agricultural and food systems.

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A deep learning approach for forecasting non-stationary big remote sensing time series

Cited by 17 publications

References 25 publications

Optimizing Film Companies’ Marketing Strategy Using Blockchain and Recurrent Neural Network Model

Optimizing Film Companies’ Marketing Strategy Using Blockchain and Recurrent Neural Network Model

Application of Avr Power Automatic Single-Chip Microcomputer to the Electromechanical Measurement and Control System

Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems?

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