scholarly journals Risk of fraud classification

4open ◽  
2020 ◽  
Vol 3 ◽  
pp. 9
Author(s):  
Jesús Enrique García ◽  
Verónica Andrea González-López ◽  
Hugo Helito da Silva ◽  
Thainá Soares Silva
Keyword(s):  
Classification Method ◽  
Success Rates ◽  
Consistent Method ◽  
Client Risk

In this article, we define consumers’ profiles of electricity who commit fraud. We also compare these profiles with users’ profiles not classified as fraudsters in order to determine which of these clients should receive an inspection. We present a statistically consistent method to classify clients/users as fraudsters or not, according to the profiles of previously identified fraudsters. We show that it is possible to use several characteristics to inspect the classification of fraud; those aspects are represented by the coding performed in the observed series of clients/users. In this way, several encodings can be used, and the client risk can be constructed to integrate complementary aspects. We show that the classification method has success rates that exceed 77%, which allows us to infer confidence in the methodology.

scholarly journals The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 915
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Spectral Data ◽  
Uv Spectroscopy ◽  
Moving Average ◽  
Food Product ◽  
Classification Method ◽  
Economic Losses ◽  
Food Fraud ◽  
Authentication System ◽  
Uv Visible

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.

Study on Effect of Spheroidization on Hazardous Classification of ADN

2021 ◽  
Vol 881 ◽  
pp. 71-76
Author(s):  
Jian Yang ◽  
Hong Bin Li ◽  
Song Tao Ren ◽  
Peng Gang Jin ◽  
Zan Gao
Keyword(s):  
Temperature Stability ◽  
Impact Sensitivity ◽  
Classification Method ◽  
Ammonium Dinitramide ◽  
Classification Procedure ◽  
Research Results ◽  
Before And After ◽  
Hazard Classification ◽  
Friction Sensitivity

In order to determine the influence of spheroidization process of Ammonium dinitramide’s hazard grade, the hazardous division of Ammonium dinitramide before and after spheroidization is studied by using hazard classification procedure for combustible and explosive substances and articles standard (WJ20405) and hazard classification method and criterion for combusitible and explosive substances and articles standard (WJ20404). The research results show that spheroidization process can significantly improve the temperature stability of Ammonium dinitramide and significantly reduce friction sensitivity and impact sensitivity of Ammonium dinitramide. So spheroidization process can reduce the hazardous of Ammonium dinitramide and improve the safe character of Ammonium dinitramide.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals Automatic Morphological Classification of Galaxies

1994 ◽  
Vol 161 ◽  
pp. 243-247
Author(s):  
S. Okamura ◽  
M. Doi ◽  
M. Fukugita ◽  
N. Kashikawa ◽  
M. Sekiguchi ◽  
...  
Keyword(s):  
Surface Brightness ◽  
Classification Method ◽  
Morphological Classification

We study the performance and limitations of the morphological classification method based on luminosity concentration and mean surface brightness. In particular, the effects of the different colour bands and of a finite seeing are investigated.

scholarly journals Classification of Early Type Stars on the Dominion Astrophysical Observatory Photometric System

1973 ◽  
Vol 50 ◽  
pp. 152-161
Author(s):  
S. C. Morris ◽  
G. Hill ◽  
G. A. H. Walker ◽  
H. I. B. Thompson
Keyword(s):  
Classification Method ◽  
Photometric System ◽  
Early Type ◽  
Balmer Series ◽  
Early Type Stars ◽  
Independent Parameters

A classification method has been developed for early–type stars observed on the Dominion Astrophysical Observatory photometric system. Two reddening-independent parameters, Q(35) and Q(38), are used. Q(35) is a measure of the Balmer discontinuity, while Q(38) is a measure of the strength of the upper members of the Balmer series. A preliminary calibration of Q(35) and Q(38) in terms of spectral types and luminosity classes is given, and applications to several groups of stars are shown.

scholarly journals Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
F. A. Bastiaan von Meijenfeldt ◽  
Ksenia Arkhipova ◽  
Diego D. Cambuy ◽  
Felipe H. Coutinho ◽  
Bas E. Dutilh
Keyword(s):  
Dna Sequences ◽  
De Novo ◽  
Taxonomic Classification ◽  
Classification Method ◽  
Reference Database ◽  
Annotation Tool ◽  
Multiple Signals

Abstract Current-day metagenomics analyses increasingly involve de novo taxonomic classification of long DNA sequences and metagenome-assembled genomes. Here, we show that the conventional best-hit approach often leads to classifications that are too specific, especially when the sequences represent novel deep lineages. We present a classification method that integrates multiple signals to classify sequences (Contig Annotation Tool, CAT) and metagenome-assembled genomes (Bin Annotation Tool, BAT). Classifications are automatically made at low taxonomic ranks if closely related organisms are present in the reference database and at higher ranks otherwise. The result is a high classification precision even for sequences from considerably unknown organisms.

scholarly journals A Comparative Study on Classification of Image Pixels

2015 ◽  
Vol 20 (2) ◽  
pp. 128-135
Author(s):  
Santosh Ghimire
Keyword(s):  
Comparative Study ◽  
Science And Technology ◽  
Classification Method ◽  
Image Pixels ◽  
Better Than

In this paper, we perform the comparison between the classification method introduced by Ghimire-Wang and the classification method developed by Liao-Akritas in many images. We show that in all the considered images, the method introduced by Ghimire-Wang works better than the method of Liao-Akritas.Journal of Institute of Science and Technology, 2015, 20(2): 128-135  

scholarly journals A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 518
Author(s):  
Carlos Dafonte ◽  
Alejandra Rodríguez ◽  
Minia Manteiga ◽  
Ángel Gómez ◽  
Bernardino Arcay
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Approach ◽  
Success Rates ◽  
Stellar Spectra ◽  
Knowledge Based ◽  
Astronomical Surveys ◽  
Classification Project ◽  
Intelligent Models ◽  
The Individual

This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan–Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today’s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.

Classification of Airborne Lidar Data by Echo

2012 ◽  
Vol 500 ◽  
pp. 696-700 ◽  
Author(s):  
Sheng Yao Wang ◽  
Xi Min Cui ◽  
De Bao Yuan ◽  
Jing Jing Jin ◽  
Qiang Zhang
Keyword(s):  
Data Collection ◽  
Current Data ◽  
Airborne Lidar ◽  
Classification Method ◽  
Lidar Data ◽  
Collection System ◽  
Continuous Development ◽  
Data Collection System ◽  
Airborne Lidar Data

With the continuous development of Airborne Lidar hardware, the current data collection system will not only collect information on a single echo, multiple echo information also can be available. Through the analysis and discussion of echo principle, this paper compares and elaborates the characteristics of single-echo and multiple echo information, and introduces a filter classification method based on echo information, and illustrates that the method is simple and effective according to an example.

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