Fused Contextual Data with Threading Technology to Accelerate Processing in Home UbiHealth

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Data Fusion ◽  
Home Environment ◽  
Data Set ◽  
Contextual Data ◽  
Dempster Shafer Theory ◽  
Computing Paradigm ◽  
Fusion Methods ◽  
Input Type ◽  
Shafer Theory ◽  
Identical Type

According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents’ health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. In some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. The employment of threading technology accelerates the computational process and carrying out benchmarks on publicly available data set, is shown to be more efficient. Thus, threading technology proved promising for home UbiHealth applications by lowering the number of required cooperating computers.

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

Analyzing Strategic Stance in Public Services Management

2011 ◽  
pp. 344-359 ◽  
Author(s):  
Malcolm J. Beynon ◽  
Martin Kitchener
Keyword(s):  
Public Management ◽  
Missing Values ◽  
Public Services ◽  
System Development ◽  
Uncertain Reasoning ◽  
Term Care ◽  
Data Set ◽  
Dempster Shafer Theory ◽  
And Performance ◽  
Shafer Theory

This chapter describes the utilization of an uncertain reasoning-based technique in public services strategic management analysis. Specifically, the nascent NCaRBS technique (developed from Dempster-Shafer theory) is used to categorize the strategic stance of each state’s public long-term care (LTC) system to prospector, defender or reactor. Missing values in the data set are termed ignorant evidence and withheld in the analysis rather than transformed through imputation. Optimization of the classification of states, using trigonometric differential evolution, attempts to minimize ambiguity in their prescribed stance but not the concomitant ignorance that may be inherent. The graphical results further the elucidation of the uncertain reasoning-based analysis. This method may prove a useful means of moving public management research towards a state where LTC system development can be benchmarked and the relations between strategy processes, content, and performance examined.

scholarly journals Membership Functions for Fuzzy Focal Elements

2016 ◽  
Vol 26 (3) ◽  
pp. 395-427 ◽  
Author(s):  
Sebastian Porębski ◽  
Ewa Straszecka
Keyword(s):  
Membership Function ◽  
Data Driven ◽  
Membership Functions ◽  
Data Set ◽  
Dempster Shafer Theory ◽  
Elimination Algorithm ◽  
Rule Sets ◽  
Shafer Theory ◽  
Iris Data

Abstract The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.

A Cloud Intrusion Detection System Using Novel PRFCM Clustering and KNN Based Dempster-Shafer Rule

2016 ◽  
Vol 6 (4) ◽  
pp. 18-35 ◽  
Author(s):  
Partha Ghosh ◽  
Shivam Shakti ◽  
Santanu Phadikar
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection System ◽  
Cloud Environment ◽  
Dempster Shafer Theory ◽  
Computing Paradigm ◽  
Rule Set ◽  
Shafer Theory

Cloud computing has established a new horizon in the field of Information Technology. Due to the large number of users and extensive utilization, the Cloud computing paradigm attracts intruders who exploit its vulnerabilities. To secure the Cloud environment from such intruders an Intrusion Detection System (IDS) is required. In this paper the authors have proposed an anomaly based IDS which classifies an incoming connection by taking the deviation of it from the normal behaviors. The proposed method uses a novel Penalty Reward based Fuzzy C-Means (PRFCM) clustering algorithm to generate a rule set and the best rule set is extracted from it using a modified approach for KNN algorithm. This best rule set is used in evidential reasoning of Dempster Shafer Theory for classification. The IDS has been trained and tested with NSL-KDD dataset for performance evaluation. The results prove the proposed IDS to be highly efficient and reliable.

scholarly journals Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications

2020 ◽  
Vol 162 ◽  
pp. 113887
Author(s):  
Nimisha Ghosh ◽  
Rourab Paul ◽  
Satyabrata Maity ◽  
Krishanu Maity ◽  
Sayantan Saha
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence
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