Application of neural and statistical classifiers to the problem of seafloor characterization

1993 ◽  
Vol 93 (4) ◽  
pp. 2401-2401
Author(s):  
Zoi‐Heleni Michalopoulou ◽  
Dimitri Alexandrou
Keyword(s):  
Statistical Classifiers ◽  
Seafloor Characterization

Application of neural and statistical classifiers to the problem of seafloor characterization

1995 ◽  
Vol 20 (3) ◽  
pp. 190-197 ◽  
Author(s):  
Z.-H. Michalopoulou ◽  
D. Alexandrou ◽  
C. de Moustier
Keyword(s):  
Statistical Classifiers ◽  
Seafloor Characterization

scholarly journals The Use of Drone Photo Material to Classify the Purity of Photovoltaic Panels Based on Statistical Classifiers

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 483
Author(s):  
Tomasz Czarnecki ◽  
Kacper Bloch
Keyword(s):  
Renewable Energy Sources ◽  
Base Material ◽  
Weather Conditions ◽  
High Rate ◽  
Solar Panels ◽  
Soil Dust ◽  
Photovoltaic Panels ◽  
Statistical Classifiers ◽  
Study Results ◽  
The Subject

The subject of this work is the analysis of methods of detecting soiling of photovoltaic panels. Environmental and weather conditions affect the efficiency of renewable energy sources. Accumulation of soil, dust, and dirt on the surface of the solar panels reduces the power generated by the panels. This paper presents several variants of the algorithm that uses various statistical classifiers to classify photovoltaic panels in terms of soiling. The base material was high-resolution photos and videos of solar panels and sets dedicated to solar farms. The classifiers were tested and analyzed in their effectiveness in detecting soiling. Based on the study results, a group of optimal classifiers was defined, and the classifier selected that gives the best results for a given problem. The results obtained in this study proved experimentally that the proposed solution provides a high rate of correct detections. The proposed innovative method is cheap and straightforward to implement, and allows use in most photovoltaic installations.

Seafloor characterization using texture

2002 ◽  
Author(s):  
S. Subramaniam ◽  
H. Barad ◽  
A.B. Martinez ◽  
B. Bourgeois
Keyword(s):  
Seafloor Characterization

DEVELOPEMENT AN ALGORITM FOR RECOGNIZING TEXT DATA IN DIGITAL GRAPHIC IMAGES

2021 ◽  
pp. 75-78
Author(s):  
Н.Н. Беляев ◽  
О.А. Бебенина ◽  
В.Е. Бородкина
Keyword(s):  
Frequency Domain ◽  
Recognition Algorithm ◽  
Statistical Characteristics ◽  
Discrete Cosine Transformation ◽  
Text Data ◽  
Jpeg Images ◽  
Statistical Classifiers ◽  
Distribution Of Values ◽  
Data Content ◽  
Graphic Images

Предложен алгоритм распознавания, реализующий процедуры: обучения выбранных классификаторов и распознавания текстовых данных, учитывающие статистические характеристики распределения коэффициентов частотной области цифровых графических изображениях формата JPEG. The article presents an approach to development an algorithm for recognizing text data within JPEG format digital graphic images. Considered a hypothesis about influence text data content in JPEG digital graphic images on the distribution of values of the discrete cosine transformation coefficients in the frequency domain JPEG images of the format. Statistical classifiers models that provide a solution to the problem of recognition of text data in JPEG images based on analysis of its frequency domain have been determined. A recognition algorithm is proposed that implements the following procedures: training of selected classifiers and recognition of text data, taking into account the statistical characteristics of the distribution of frequency domain coefficients in JPEG format images.

A Framework on Data Mining on Uncertain Data with Related Research Issues in Service Industry

Data Mining ◽  
2013 ◽  
pp. 515-529
Author(s):  
Edward Hung
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Service Industry ◽  
Uncertain Data ◽  
Research Work ◽  
Real Life ◽  
Mean Value ◽  
Customer Relationship ◽  
Statistical Classifiers ◽  
Targeted Marketing

There has been a large amount of research work done on mining on relational databases that store data in exact values. However, in many real-life applications such as those commonly used in service industry, the raw data are usually uncertain when they are collected or produced. Sources of uncertain data include readings from sensors (such as RFID tagged in products in retail stores), classification results (e.g., identities of products or customers) of image processing using statistical classifiers, results from predictive programs used for stock market or targeted marketing as well as predictive churn model in customer relationship management. However, since traditional databases only store exact values, uncertain data are usually transformed into exact data by, for example, taking the mean value (for quantitative attributes) or by taking the value with the highest frequency or possibility. The shortcomings are obvious: (1) by approximating the uncertain source data values, the results from the mining tasks will also be approximate and may be wrong; (2) useful probabilistic information may be omitted from the results. Research on probabilistic databases began in 1980s. While there has been a great deal of work on supporting uncertainty in databases, there is increasing work on mining on such uncertain data. By classifying uncertain data into different categories, a framework is proposed to develop different probabilistic data mining techniques that can be applied directly on uncertain data in order to produce results that preserve the accuracy. In this chapter, we introduce the framework with a scheme to categorize uncertain data with different properties. We also propose a variety of definitions and approaches for different mining tasks on uncertain data with different properties. The advances in data mining application in this aspect are expected to improve the quality of services provided in various service industries.

Discrimination of Benign from Malignant Breast Lesions Using Statistical Classifiers

2006 ◽  
pp. 543-546 ◽  
Author(s):  
Konstantinos Koutroumbas ◽  
Abraham Pouliakis ◽  
Tatiana Mona Megalopoulou ◽  
John Georgoulakis ◽  
Anna-Eva Giachnaki ◽  
...  
Keyword(s):  
Breast Lesions ◽  
Statistical Classifiers ◽  
Malignant Breast

scholarly journals Data Security Platform Model in Networked Medical IT Systems based on Statistical Classifiers and ANN

2020 ◽  
Vol 176 ◽  
pp. 3682-3691
Author(s):  
Miroslaw Hajder ◽  
Janusz Kolbusz ◽  
Piotr Hajder ◽  
Mariusz Nycz ◽  
Mateusz Liput
Keyword(s):  
Data Security ◽  
Statistical Classifiers ◽  
It Systems
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