scholarly journals Parallel Induction of Modular Classification Rules

2009 ◽  
pp. 343-348 ◽  
Author(s):  
Frederic Stahl ◽  
Max Bramer ◽  
Mo Adda
Keyword(s):  
Classification Rules

Learning classification rules from an ion chromatography database using a genetic based classifier system

1997 ◽  
Vol 344 (1-2) ◽  
pp. 1-15 ◽  
Author(s):  
A.H.C. van Kampen ◽  
Z. Ramadan ◽  
M. Mulholland ◽  
D.B. Hibbert ◽  
L.M.C. Buydens
Keyword(s):  
Ion Chromatography ◽  
Classification Rules ◽  
Classifier System

KDIGO 2012 Clinical Practice Guideline CKD classification rules out creatinine clearance 24 hour urine collection?

2016 ◽  
Vol 49 (1-2) ◽  
pp. 85-89 ◽  
Author(s):  
A. Ognibene ◽  
G. Grandi ◽  
M. Lorubbio ◽  
S. Rapi ◽  
B. Salvadori ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Creatinine Clearance ◽  
Clinical Practice Guideline ◽  
Practice Guideline ◽  
Urine Collection ◽  
Classification Rules

scholarly journals A Context-Aware Accurate Wellness Determination (CAAWD) Model for Elderly People Using Lazy Associative Classification

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1613 ◽  
Author(s):  
Farhan Sabir Ujager ◽  
Azhar Mahmood
Keyword(s):  
Elderly People ◽  
Data Extraction ◽  
Contextual Information ◽  
Elderly Person ◽  
The Elderly ◽  
Sensor Nodes ◽  
Behavior Patterns ◽  
Context Aware ◽  
Classification Rules ◽  
Training Time

Wireless Sensor Network (WSN) based smart homes are proving to be an ideal candidate to provide better healthcare facilities to elderly people in their living areas. Several currently proposed techniques have implementation and usage complexities (such as wearable devices and the charging of these devices) which make these proposed techniques less acceptable for elderly people, while the behavioral analysis based on visual techniques lacks privacy. In this paper, a context-aware accurate wellness determination (CAAWD) model for elderly people is presented, where behavior monitoring information is extracted by using simple sensor nodes attached to household objects and appliances for the analysis of daily, frequent behavior patterns of elderly people in a simple and non-obtrusive manner. A contextual data extraction algorithm (CDEA) is proposed for the generation of contextually comprehensive behavior-training instances for accurate wellness classification. The CDEA presents an activity’s spatial–temporal information along with behavioral contextual correlation aspects (such as the object/appliance of usage and sub-activities of an activity) which are vital for accurate wellness analysis and determination. As a result, the classifier is trained in a more logical manner in the context of behavior parameters which are more relevant for wellness determination. The frequent behavioral patterns are classified using the lazy associative classifier (LAC) for wellness determination. The associative nature of LAC helps to integrate spatial–temporal and related contextual attributes (provided by CDEA) of elderly behavior to generate behavior-focused classification rules. Similarly, LAC provides high accuracy with less training time of the classifier, includes minimum-support behavior patterns, and selects highly accurate classification rules for the classification of a test instance. CAAWD further introduces the ability to contextually validate the authenticity of the already classified instance by taking behavioral contextual information (of the elderly person) from the caregiver. Due to the consideration of spatial–temporal behavior contextual attributes, the use of an efficient classifier, and the ability to contextually validate the classified instances, it has been observed that the CAAWD model out-performs currently proposed techniques in terms of accuracy, precision, and f-measure.

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