scholarly journals Applying Deep Learning in the Training of Communication Design Talents Under University-Industrial Research Collaboration

2021 ◽  
Vol 12 ◽  
Author(s):  
Rui Zhou ◽  
Zhihua He ◽  
Xiaobiao Lu ◽  
Ying Gao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Research Collaboration ◽  
Moving Average ◽  
Supply And Demand ◽  
Industrial Research ◽  
Social Needs ◽  
Communication Design ◽  
Moving Average Model ◽  
Bp Model

The purpose of the study was to solve the problem of the mismatching between the supply and demand of the talents that universities provide for society, whose major is communication design. The correlations between social post demand and university cultivation, as well as between social post demand and the demand indexes of enterprises for posts, are explored under the guidance of University-Industrial Research Collaboration. The backpropagation neural network (BPNN) is used, and the advantages of the Seasonal Autoregressive Integrated Moving Average model (SARIMA) model are combined to design the SARIMA-BPNN (SARIMA-BP) model after the relevant parameters are adjusted. Through the experimental analysis, it is found that the error of the root mean square of the designed SARIMA-BP model in post prediction is 7.523 and that of the BPNN model is 16.122. The effect of the prediction model that was designed based on deep learning is smaller than that of the previous model based on the neural network, and it can predict future posts more accurately for colleges and universities. Guided by the “University-Industrial Research Collaboration,” students will have more practice in the teaching process in response to social needs. “University-Industrial Research Collaboration” guides the teaching direction for communication design majors and can help to cultivate communication design talents who are competent for the post provided.

scholarly journals Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3059
Author(s):  
Myeong-Hun Jeong ◽  
Tae-Young Lee ◽  
Seung-Bae Jeon ◽  
Minkyo Youm
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Moving Average ◽  
Computational Cost ◽  
Nonlinear Problems ◽  
Transportation Systems ◽  
Mobility Data ◽  
Challenges And Opportunities ◽  
Moving Average Model ◽  
Gated Recurrent Unit

Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.

A Structural Identification Method Based on Recurrent Neural Network and Auto-Regressive and Moving Average Model

2012 ◽  
Vol 256-259 ◽  
pp. 2261-2265
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Moving Average ◽  
Structural Identification ◽  
Modal Parameters ◽  
Average Model ◽  
Identification Method ◽  
Moving Average Model ◽  
Nonlinear Relation ◽  
Auto Regressive

The work presented a structural identification method based on recurrent neural network and auto-regressive and moving average model. The proposed approach involves two steps. The first step is to build a recurrent neural network to map the complex nonlinear relation between the excitations and responses of the structure-unknown system by on-line learning . The second step is to propose a procedure to determine the modal parameters of the structure from the trained neural networks. The dynamic characteristics of the structure are directly evaluated from the weighting matrices of the trained recurrent neural network. Furthermore, a illustrative example demonstrates the feasibility of using the proposed method to identify modal parameters of structure-unknown systems.

scholarly journals Hybrid of convolutional neural network algorithm and autoregressive integrated moving average model for skin cancer classification among Malaysian

2021 ◽  
Vol 10 (3) ◽  
pp. 707
Author(s):  
Chee Ka Chin ◽  
Dayang Azra binti Awang Mat ◽  
Abdulrazak Yahya Saleh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Cell Carcinoma ◽  
Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Neural Network Algorithm

Skin cancer is a widely spreading cause of mortality among the people specifically living on or near the equatorial belt. Early detection of skin cancer significantly improves the recovery prevalence and the chance of surviving. Without the assist of computer-aided decision (CAD) system, skin cancer classification is the challenging task for the dermatologist to differentiate the type of skin cancer and provide the suitable treatment. Recently, the development of machine learning and pretrained deep neural network (DNN) shows the tremendous performance in image classification task which also provide the promising performance in medical field. However, these machine learning methods cannot get the deep features from network flow which resulting in low accuracy and the pretrained DNN has the complex network with a huge number of parameters causes the limited classification accuracy. This paper focuses on the classification of skin cancer to identify whether it is basal cell carcinoma, melanoma or squamous cell carcinoma by using the development of hybrid convolutional neural network algorithm and autoregressive integrated moving average model (CNN-ARIMA). The CNNARIMA model was trained and found to produce the best accuracy of 92.25%.

scholarly journals Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

2018 ◽  
Vol 10 (7) ◽  
pp. 2552 ◽  
Author(s):  
Minglu Ma ◽  
Min Su ◽  
Shuyu Li ◽  
Feng Jiang ◽  
Rongrong Li
Keyword(s):  
South Africa ◽  
Moving Average ◽  
Supply And Demand ◽  
Grey Model ◽  
Average Model ◽  
Total Energy Consumption ◽  
Coal Consumption ◽  
Autoregressive Integrated Moving Average ◽  
Non Linear ◽  
Moving Average Model

South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy.

scholarly journals Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3089 ◽  
Author(s):  
Ke Yan ◽  
Xudong Wang ◽  
Yang Du ◽  
Ning Jin ◽  
Haichao Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Consumption ◽  
Real World ◽  
Electricity Market ◽  
Moving Average ◽  
Short Term ◽  
Auto Regressive ◽  
Short Term Forecasting ◽  
Deep Learning Neural Network

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

scholarly journals Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

2019 ◽  
Vol 11 (3) ◽  
pp. 695 ◽  
Author(s):  
Shuyu Li ◽  
Xuan Yang ◽  
Rongrong Li
Keyword(s):  
Moving Average ◽  
Supply And Demand ◽  
Grey Model ◽  
Average Annual Rate ◽  
Average Model ◽  
Coal Consumption ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Combined Models ◽  
Relative Errors

India’s coal consumption is closely related to greenhouse gas emissions and the balance of supply and demand in energy trading markets. Most existing research on India focuses on total energy, renewable energy and energy intensity. To fill this gap, this study used two single forecasting models: the metabolic grey model (MGM) and the Back-Pro-Pagation Network (BP) to make predictions. In addition, based on these two single models, this study also developed the ARIMA correction principle and derived two combined models: the metabolic grey model, the Autoregressive Integrated Moving Average model (MGM-ARIMA) and Back-Pro-Pagation Network; and the Autoregressive Integrated Moving Average model (BP-ARIMA). After fitting India’s coal consumption during 1995–2017, the average relative errors of the four models were 2.28%, 1.53%, 1.50% and 1.42% respectively. The forecast results show that coal consumption in India will continue to increase at an average annual rate of 2.5% during the period from 2018–2030.

scholarly journals Deep Learning Based Customer Preferences Analysis in Industry 4.0 Environment

2022 ◽  
Author(s):  
Qindong Sun ◽  
Xingyu Feng ◽  
Shanshan Zhao ◽  
Han Cao ◽  
Shancang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Moving Average ◽  
Demand Forecasting ◽  
Customer Relationship ◽  
Event Analysis ◽  
Market Place ◽  
Customer Preferences ◽  
Autoregressive Moving Average Model ◽  
Catering Industry ◽  
Moving Average Model

AbstractCustomer preferences analysis and modelling using deep learning in edge computing environment are critical to enhance customer relationship management that focus on a dynamically changing market place. Existing forecasting methods work well with often seen and linear demand patterns but become less accurate with intermittent demands in the catering industry. In this paper, we introduce a throughput deep learning model for both short-term and long-term demands forecasting aimed at allowing catering businesses to be highly efficient and avoid wastage. Moreover, detailed data collected from a business online booking system in the past three years have been used to train and verify the proposed model. Meanwhile, we carefully analyzed the seasonal conditions as well as past local or national events (event analysis) that could have had critical impact on the sales. The results are compared with the best performing forecast methods Xgboost and autoregressive moving average model (ARMA), and they suggest that the proposed method significantly improves demand forecasting accuracy (up to 80%) for dishes demand along with reduction in associated costs and labor allocation.

scholarly journals Implementation of Stacking Based ARIMA Model for Prediction of Covid-19 Cases in India

2020 ◽  
Author(s):  
Aman Swaraj ◽  
Arshpreet Kaur ◽  
Karan Verma ◽  
Ghanshyam Singh ◽  
Ashok Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Moving Average ◽  
Critical Role ◽  
Arima Model ◽  
Essential Information ◽  
Moving Average Model ◽  
Nonlinear Features ◽  
Novel Coronavirus ◽  
Nonlinear Components

Abstract Background: Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely.Methods: We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data.Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters.Result:The hybrid combination displayed significant reduction in RMSE(16.23%), MAE(37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries.Conclusion: Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

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