scholarly journals Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

2022 ◽  
Vol 32 (1) ◽  
pp. 87-99
Author(s):  
Meenakshi Malik ◽  
Rainu Nandal ◽  
Surjeet Dalal ◽  
Vivek Jalglan ◽  
Dac-Nhuong Le
Keyword(s):  
Neural Network ◽  
Pattern Analysis ◽  
Behavioral Pattern ◽  
And Performance ◽  
Network Approaches

Methods of Forecasting Solar Radiation

2013 ◽  
pp. 437-461
Author(s):  
Rubita Sudirman ◽  
Muhammad Noorul Anam Mohd Norddin
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Solar Radiation ◽  
Moving Average ◽  
Extensive Study ◽  
Network Approach ◽  
Neural Network Approach ◽  
Process Comparison ◽  
And Performance ◽  
Network Approaches

Extreme demands on the methods used for forecasting solar radiation has been the driving force behind the efforts to find the best method available. An extensive study of different techniques available was conducted. Methods studied in this research can be classified as time series and neural network approach. Time series approaches considered are autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA). In neural network approaches, multi-layer perceptron networks are used. The error back-propagation learning algorithm is utilized in the training process. Comparison of methods and performance of different methods are presented in the result and discussion section of this chapter. The solar radiation data used were a collection of past data acquired throughout the US continent for 10 years period. These data were used to forecast future solar radiation based on the past trend observed from the database using both time series and neural network approaches. Finally, this chapter makes general comparison among the methods used and outlines some advantages and disadvantages of using the neural network approach.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals Outlier detection in scatterometer data: neural network approaches

2003 ◽  
Vol 16 (3-4) ◽  
pp. 419-426 ◽  
Author(s):  
Robert J. Bullen ◽  
Dan Cornford ◽  
Ian T. Nabney
Keyword(s):  
Neural Network ◽  
Outlier Detection ◽  
Network Approaches

scholarly journals International agricultural trade forecasting using machine learning

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Munisamy Gopinath ◽  
Feras A. Batarseh ◽  
Jayson Beckman ◽  
Ajay Kulkarni ◽  
Sei Jeong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Agricultural Trade ◽  
Supervised Machine Learning ◽  
Trade Patterns ◽  
Subjective Assessments ◽  
History Of ◽  
Network Approaches ◽  
Traditional Approaches

Abstract Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

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