Satellite Parametric Description to Ontology Concepts and Semantic Classification of Satellite Data

2016 ◽  
Vol 12 (2) ◽  
pp. 53-75 ◽  
Author(s):  
Sunitha Abburu ◽  
Nitant Dube
Keyword(s):  
Satellite Data ◽  
Phase 1 ◽  
Research Work ◽  
Data Classification ◽  
Data Retrieval ◽  
Satellite Observation ◽  
Two Phase ◽  
Semantic Classification ◽  
Kalpana Satellite

Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.

Satellite Parametric Description to Ontology Concepts and Semantic Classification of Satellite Data

2019 ◽  
pp. 1198-1222
Author(s):  
Sunitha Abburu ◽  
Nitant Dube
Keyword(s):  
Satellite Data ◽  
Phase 1 ◽  
Research Work ◽  
Data Classification ◽  
Data Retrieval ◽  
Satellite Observation ◽  
Two Phase ◽  
Storage And Retrieval ◽  
Kalpana Satellite

Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.

PROBLEMS OF CLASSIFICATION OF CULTURAL HERITAGE OBJECTS

2020 ◽  
Vol 10 (2) ◽  
pp. 158-168
Author(s):  
SVETLANA IVANOVA ◽  
Keyword(s):  
Cultural Heritage ◽  
Cultural Values ◽  
Research Work ◽  
Cultural Property ◽  
Cultural Inheritance ◽  
Federal Laws ◽  
Cultural Heritage Objects ◽  
Cultural Monuments

The purpose of the research work is to analyze the norms of Federal laws, as well as the laws of the Russian Federation's constituent entities, devoted to the definitions and classification of the concepts “cultural heritage”, “historical and cultural monuments”, “cultural values”. Conclusions obtained in the course of the research: based on the study of current legislation, it is concluded that the definitions of “cultural values”, “cultural property”, “objects of cultural inheritance” contained in various normative legal acts differ in content. Based on the research, the author proposes the concept of “cultural values”.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

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