scholarly journals JOINT SUB-CLASSIFIERS ONE CLASS CLASSIFICATION MODEL FOR AVIAN INFLUENZA OUTBREAK DETECTION

2011 ◽  
Vol 10 (04) ◽  
pp. 425-443
Author(s):  
JIE ZHANG ◽  
JIE LU ◽  
GUANGQUAN ZHANG
Keyword(s):  
Avian Influenza ◽  
Domain Knowledge ◽  
Classification Model ◽  
Outbreak Detection ◽  
Features Selection ◽  
Influenza Outbreak ◽  
Knowledge Based ◽  
H5n1 Avian Influenza ◽  
The One ◽  
One Class Classification

H5N1 avian influenza outbreak detection is a significant issue for early warning of epidemics. This paper proposes domain knowledge-based joint one class classification model for avian influenza outbreak. Instead of focusing on manipulations of the one class classification model, we delve into the one class avian influenza dataset, divide it into sub-classes by domain knowledge, train the sub-class classifiers and unify the result of each classifier. The proposed joint method solves the one class classification and features selection problems together. The experiment results demonstrate that the proposed joint model definitely outperforms the normal one class classification model on the animal avian influenza dataset.

H5N1 Avian Influenza Outbreak in the Far East of Russia in 2008: New Introduction

2010 ◽  
Vol 54 (s1) ◽  
pp. 509-512 ◽  
Author(s):  
Timofey B. Manin ◽  
Ilya A. Chvala ◽  
Sergey N. Kolosov ◽  
Inna P. Pchelkina ◽  
Victor N. Irza ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Far East ◽  
Far East Of Russia ◽  
Influenza Outbreak ◽  
The Far East ◽  
H5n1 Avian Influenza

scholarly journals Dynamics of Price Transmission in the Presence of a Major Food Safety Shock: Impact of H5N1 Avian Influenza on the Turkish Poultry Sector

2008 ◽  
Vol 40 (3) ◽  
pp. 1015-1031 ◽  
Author(s):  
Sayed H. Saghaian ◽  
Gökhan Özertan ◽  
Aslıhan D. Spaulding
Keyword(s):  
Avian Influenza ◽  
Marketing Channel ◽  
Price Transmission ◽  
Exogenous Shock ◽  
Influenza Outbreak ◽  
Price Adjustments ◽  
H5n1 Avian Influenza ◽  
Supply Channel ◽  
Shock Impact ◽  
Historical Decomposition

This article addresses the dynamic impact of the 2005 H5N1 avian influenza outbreak on the Turkish poultry sector. Contemporary time-series analyses with historical decomposition graphs are used to address differences in monthly price adjustments between market levels along the Turkish poultry supply channel. The empirical results show that price adjustments are asymmetric with respect to both speed and magnitude along the marketing channel. Results also reveal a differential impact of the exogenous shock on producers and retailers. The findings have critical efficiency and equity implications for the supply-chain participants.

A- AND V-UNCERTAINTY: AN EXPLORATION ABOUT UNCERTAINTY MODELING FROM A KNOWLEDGE ENGINEERING PERSPECTIVE

2007 ◽  
Vol 16 (02) ◽  
pp. 161-194 ◽  
Author(s):  
PIETRO BARONI ◽  
GIOVANNI GUIDA ◽  
MASSIMILIANO GIACOMIN
Keyword(s):  
Domain Knowledge ◽  
Knowledge Engineering ◽  
Uncertainty Modeling ◽  
Uncertain Reasoning ◽  
Correct Treatment ◽  
Knowledge Based ◽  
Uncertainty Sources ◽  
Concrete Experience ◽  
The One ◽  
Uncertainty Models

This paper presents a concrete experience of Knowledge Engineering, which, starting from a specific problem which occurred during the development of ASTRA, a knowledge-based system for preventive diagnosis of power transformers, turned out to provide significant insights concerning modeling of uncertain knowledge. In particular, it was observed that there are (at least) two conceptually distinct types of uncertainty affecting knowledge, namely uncertainty about applicability (A-uncertainty, for short) and uncertainty about validity (V-uncertainty, for short), which are different in nature and play different roles in uncertain reasoning. The concepts of A- and V-uncertainty are applicable in any context where uncertainty affecting domain knowledge can be ascribed to two kinds of sources: on the one hand, the existence of exceptions, on the other hand, deep-rooted doubts about the foundations themselves of the relevant domain knowledge. The introduction of these concepts allows one to define articulated uncertainty models, supporting the representation of the reasoning mechanisms used by experts in domains where both such uncertainty sources are present. This general claim was confirmed by the experience developed with ASTRA, where the explicit representation and management of A- and V-uncertainty enabled the correct treatment of some critical diagnostic cases.

scholarly journals A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Fangdong Zhu ◽  
Wen Chen ◽  
Hanli Yang ◽  
Tao Li ◽  
Tao Yang ◽  
...  
Keyword(s):  
Big Data ◽  
Negative Selection ◽  
Classification Model ◽  
Generation Process ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Testing Stage ◽  
Low Efficiency ◽  
The One ◽  
One Class Classification

Negative selection algorithm (NSA) is an important kind of the one-class classification model, but it is limited in the big data era due to its low efficiency. In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional “Random-Discard” model to the “Computing-Designated” model by VorNSA. Furthermore, we present an immune detection process of VorNSA under Map/Reduce framework (VorNSA/MR) to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one-class classifiers. The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset.

H5N1 Avian Influenza Outbreak in the Far East of Russia in 2008: New Introduction

2010 ◽  
Vol 5 (s1) ◽  
pp. e111-e112
Author(s):  
Timofey B. Manin ◽  
Ilya A. Chvala ◽  
Sergey N. Kolosov ◽  
Inna P. Pchelkina ◽  
Victor N. Irza ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Far East ◽  
Far East Of Russia ◽  
Influenza Outbreak ◽  
The Far East ◽  
H5n1 Avian Influenza

scholarly journals Confirmed H5N1 avian influenza outbreak on a poultry farm in England, February 2007

2007 ◽  
Vol 12 (6) ◽  
Author(s):  
Collective Editorial team
Keyword(s):  
United Kingdom ◽  
Avian Influenza ◽  
Poultry Farm ◽  
Influenza Outbreak ◽  
The United Kingdom ◽  
H5n1 Avian Influenza

An outbreak of H5N1 avian influenza which occurred on 1 February 2007 has been confirmed on a large closed poultry farm in Suffolk, east England, according to the United Kingdom ministry of agriculture.

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

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