Creation of Data Classification System for Local Administration

2019 ◽  
pp. 761-768
Author(s):  
Raissa Uskenbayeva ◽  
Aiman Moldagulova ◽  
Nurzhan K. Mukazhanov
Keyword(s):  
Classification System ◽  
Data Classification ◽  
Local Administration

Intelligent and secure RFID multilevel fuzzy inference system for client to banker profiling

2020 ◽  
Vol 39 (3) ◽  
pp. 2991-3010
Author(s):  
Sonam Devgan Kaul ◽  
Dimitrios Hatzinakos
Keyword(s):  
Fuzzy Inference System ◽  
Classification System ◽  
Fuzzy Inference ◽  
Data Classification ◽  
Fuzzy Data ◽  
Inference System ◽  
Staff Members ◽  
Profile Matching ◽  
Comprehensive Framework ◽  
Financial Transactions

In this work, we will be investigating, developing and implementing an intelligent RFID system in conjunction with a fuzzy data classification system, to greatly enhance and secure financial transactions and improve operational efficiency in the banking environment. The innovative part of this research is to provide an efficient solution to the challenge that may arise from the need to expertly and automatically match the profile of customer and banker and solve the vagueness in customer/banking profiling. Our proposal offers an expert, secure, efficient and comprehensive framework, methodology and its application in financial environments to develop customer to banker profile matching and availability via an expert agent multi level fuzzy data classification system. Foremost, according to clients and banking staff members weighted attributes, exact match has been established according to highest degree of relevance by utilizing Matlab fuzzy inference system. Then, to communicate output of a match profile engine from one party to another, to show profiling effectiveness and to do implementation; secure, privacy preserving, and comprehensive intelligent RFID profiling authentication system has been designed and verified by Scyther tool.

scholarly journals Time Conserving Multi-Label Classification System by Incorporating Pyramid Data Structure

2019 ◽  
Vol 8 (6) ◽  
pp. 1379-1384
Keyword(s):  
Data Structure ◽  
Classification System ◽  
Data Classification ◽  
Classification Systems ◽  
Recall Accuracy ◽  
Single Entity ◽  
Research Areas ◽  
Numerous Data ◽  
Multi Class Classification ◽  
The Relationship

Data classification is one of the evergreen research areas of data analysis. Numerous data classification approaches exist in the literature and most of the classification systems are based on binary and multi-class classification. Multi-label classification system attempts to suggest multiple labels for a single entity. However, it is complex to attain a better multi-label classification system. Taking this as a challenge, this work proposes a multi-label classification system, which extracts the features of both entities and labels. The relationship between them are organised in the pyramid data structure. As the features are organized effectively, the interrelated labels are present in the same tier. This feature makes it simple for suggesting multiple labels for a single entity. The performance of this work is analysed over three different datasets and compared against existing approaches in terms of precision, recall, accuracy and time consumption.

Testing the GDPR in Relation to Biobanking

2021 ◽  
pp. 148-158
Author(s):  
Dara Hallinan
Keyword(s):  
Classification System ◽  
Personal Data ◽  
Data Classification ◽  
Genetic Data ◽  
Classification Systems ◽  
General Data Protection Regulation ◽  
Data Processor ◽  
General Data ◽  
Data Controller ◽  
Data Subject

This chapter addresses how the biobanking process—in the instances in which it falls within the scope of the General Data Protection Regulation (GDPR)—is classified under the GDPR's classification systems. These classification systems do not, themselves, constitute substantive provisions; they do not consist of rights or obligations. They are, however, key in determining the types of actors to whom substantive provisions apply and the way in which substantive provisions apply. The chapter begins with a detailed elaboration of the GDPR's two key classification systems: the actor classification system and the personal data classification system. It then describes how the actor classification system applies to actors involved in the biobanking process, focusing on the applicability of the concepts of ‘data subject’, ‘data controller’, and ‘data processor’. Finally, the chapter considers how the personal data classification system applies to personal data processed in biobanking, looking, in particular, at the applicability of the concepts of ‘genetic data’ and ‘data concerning health’.

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