scholarly journals GEOGRAPHIC RATEMAKING WITH SPATIAL EMBEDDINGS

2021 ◽  
pp. 1-31
Author(s):  
Christopher Blier-Wong ◽  
Hélène Cossette ◽  
Luc Lamontagne ◽  
Etienne Marceau
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Convolutional Neural Network ◽  
Predictive Model ◽  
Spatial Data ◽  
Spatial Interpolation ◽  
Census Data ◽  
Historical Experience ◽  
Management Processes ◽  
Source Of Information

Abstract Spatial data are a rich source of information for actuarial applications: knowledge of a risk’s location could improve an insurance company’s ratemaking, reserving or risk management processes. Relying on historical geolocated loss data is problematic for areas where it is limited or unavailable. In this paper, we construct spatial embeddings within a complex convolutional neural network representation model using external census data and use them as inputs to a simple predictive model. Compared to spatial interpolation models, our approach leads to smaller predictive bias and reduced variance in most situations. This method also enables us to generate rates in territories with no historical experience.

scholarly journals Tax Risk Prediction of Real Estate Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Meilin Yin ◽  
Ning Luo
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Real Estate ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Risk Prediction ◽  
Risk Identification ◽  
Risk Prediction Model ◽  
The Real Estate ◽  
The Times

Risk management is an important link in tax administration. From China’s taxation practice, risk identification has become the weakness of tax management. With the complexity of massive data and the secrecy of modern transactions, traditional tax risk identification can no longer adapt to the development of the times. In the past, most risk researches focused on the basic machine learning stage. There are gaps in the application of deep learning in tax risk management. Based on the tax risk management indicators, this paper took the real estate industry as an example. We used convolutional neural network (CNN) to construct a tax risk prediction model. The experiment shows that a tax risk prediction model based on CNN has higher accuracy in tax risk identification and has a stronger ability to process tax data. The model has a certain reference value for tax authorities to reduce tax risk and tax loss.

scholarly journals Surgical Tool Classification in Laparoscopic Videos Using Convolutional Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 407-410 ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Training Data ◽  
Negative Effects ◽  
Surgical Tools ◽  
Surgical Tool ◽  
Context Aware System ◽  
Source Of Information ◽  
Laparoscopic Videos

AbstractLaparoscopic videos are a very important source of information which is inherently available in minimally invasive surgeries. Detecting surgical tools based on that videos have gained increasing interest due to its importance in developing a context-aware system. Such system can provide guidance assistance to the surgical team and optimise the processes inside the operating room. Convolutional neural network is a robust method to learn discriminative visual features and classify objects. As it expects a uniform distribution of data over classes, it fails to identify classes which are under-presented in the training data. In this work, loss-sensitive learning approach and resampling techniques were applied to counter the negative effects of imbalanced laparoscopic data on training the CNN model. The obtained results showed improvement in the classification performance especially for detecting surgical tools which are shortly used in the procedure.

CVaR Prediction Model of the Investment Portfolio Based on the Convolutional Neural Network Facilitates the Risk Management of the Financial Market

2022 ◽  
Vol 30 (7) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Financial Market ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Investment Portfolio ◽  
Model Based ◽  
Swarm Algorithm

In summary, firstly, a method for establishing a portfolio model is proposed based on the risk management theory of the financial market. Then, a prediction model for CVaR is established based on the convolutional neural network, and the improved particle swarm algorithm is employed to solve the model. The actual data analysis is implemented to prove the feasibility of CVaR prediction model based on deep learning and particle swarm optimization algorithm in financial market risk management. The test results show that the investment portfolio CVaR prediction model based on the convolutional neural network can obtain the optimal solution in the 18th generation at the fastest after using the improved particle swarm algorithm, which is more effective than the traditional algorithm. The CVaR prediction model of the investment portfolio based on the convolutional neural network facilitates the risk management of the financial market.

scholarly journals A 3D-2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Douglas Omwenga Nyabuga ◽  
Jinling Song ◽  
Guohua Liu ◽  
Michael Adjeisah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Spatial Data ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Experimental Result ◽  
Computational Time ◽  
Hyperspectral Image Classification

As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless, due to a lack of distinctiveness in the hyperspectral pixels of separate classes, there is a recurrent inseparability obstacle in the primary space. Additionally, an open challenge stems from examining efficient techniques that can speedily classify and interpret the spectral-spatial data bands within a more precise computational time. Hence, in this work, we propose a 3D-2D convolutional neural network and transfer learning model where the early layers of the model exploit 3D convolutions to modeling spectral-spatial information. On top of it are 2D convolutional layers to handle semantic abstraction mainly. Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. The experimental result shows that our model can efficiently improve the hyperspectral imagery classification, including an instantaneous representation of the spectral-spatial information. Our model evaluation on five publicly available hyperspectral datasets, Indian Pines (IP), Pavia University Scene (PU), Salinas Scene (SA), Botswana (BS), and Kennedy Space Center (KSC), was performed with a high classification accuracy of 99.85%, 99.98%, 100%, 99.82%, and 99.71%, respectively. Quantitative results demonstrated that it outperformed several state-of-the-arts (SOTA), deep neural network-based approaches, and standard classifiers. Thus, it has provided more insight into hyperspectral image classification.

VEdge_Detector: Automated coastal vegetation edge detection using a convolutional neural network

2021 ◽  
Author(s):  
Martin Rogers ◽  
Tom Spencer ◽  
Mike Bithell ◽  
Sue Brooks
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Edge Detection ◽  
Convolutional Neural Network ◽  
Aerial Photographs ◽  
Coastal Vegetation ◽  
Management Decisions ◽  
Shoreline Position ◽  
Decadal Scale ◽  
Coastal Risk

<p>Coastal communities, land covers and intertidal habitats are vulnerable receptors of erosion, flooding or both in combination. This vulnerability is likely to increase with sea level rise and greater storminess over future decadal-scale time periods. The accurate, rapid and wide-scale determination of shoreline position, and its migration, is therefore imperative for future coastal risk adaptation and management. Developments in the spectral and temporal resolution and availability of multispectral satellite imagery opens new opportunities to rapidly and repeatedly monitor change in shoreline position to inform coastal risk management decisions. This presentation discusses the development and application of an automated tool, VEdge_Detector, to extract the coastal vegetation line from high spatial resolution (Planet's 3 – 5 m) remote sensing imagery, training a very deep convolutional neural network (Holistically-Nested Edge Detection) to predict sequential vegetation line locations on annual/decadal timescales. The VEdge_Detector outputs were compared with vegetation lines derived from ground-referenced positional measurements and manually digitised aerial photographs, revealing a mean distance error of <6 m (two image pixels) and > 84% producer accuracy at six out of the seven sites. Extracting vegetation lines from Planet imagery of the rapidly retreating cliffed coastline at Covehithe, Suffolk, UK identified a mean landward retreat rate >3 m a<sup>-1</sup> (2010 - 2020). Plausible vegetation lines were successfully retrieved from images of other global locations, which were not used to train the neural network; although significant areas of exposed rocky coastline proved to be less well recovered by VEdge_Detector. The method therefore promises the possibility of generalising to estimate retreat of sandy coastlines in otherwise data-poor areas, which lack ground-referenced measurements. Vegetation line outputs derived from VEdge_Detector are produced rapidly and efficiently compared to more traditional non-automated methods. These outputs also have the potential to inform upon a range of future coastal risk management decisions, including hazard and risk mapping considering future shoreline change.</p>

A novel fusion approach of deep convolution neural network with auto-encoder and its application in planetary gearbox fault diagnosis

2020 ◽  
pp. 1748006X2096461
Author(s):  
Fafa Chen ◽  
Lili Liu ◽  
Baoping Tang ◽  
Baojia Chen ◽  
Wenrong Xiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Spatial Data ◽  
Engineering Application ◽  
Learning Ability ◽  
Stationary Condition ◽  
Planetary Gearbox ◽  
Practical Engineering ◽  
Envelope Spectrum

The fault features of gearbox are often influenced and interwoven with each other under the non-stationary condition. The traditional shallow intelligent diagnosis models are difficult to detect and identify gearbox faults with selected features according to prior knowledge. To solve this problem, a novel deep convolutional auto-encoding neural network is designed based on the fusion of the convolutional neural network with the automatic encoder in this research. The vibration signals of gearbox are transformed into Hilbert envelope spectrum by using Hilbert transform and Fourier transform, and the different characteristics of spectral spatial data are automatically learned by convolutional auto-encoding neural network with multiple convolution kernels. The parameters of the convolutional neural network are fine-tuned through a fully connected neural network with a small number of labeled samples. Through the analysis for gearbox fault experiments, the effectiveness and practicability of the proposed method in equipment fault diagnosis are verified. The deep convolutional neural network embedded in the auto-encoder has stronger learning ability, and the diagnosis performance is more stable and reliable in practical engineering application.

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