scholarly journals Ontology and rule based knowledge representation for situation management and decision support

2009 ◽  
Author(s):  
Neelakantan Kartha ◽  
Aaron Novstrup
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Rule Based

scholarly journals Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Agnieszka Nowak-Brzezińska
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Support System ◽  
Inference Algorithm ◽  
Rule Based ◽  
New Knowledge ◽  
The Impact ◽  
Base Structure

Decision support systems founded on rule-based knowledge representation should be equipped with rule management mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new knowledge) from rules and facts already known. The author defines and analyses four various representative generation methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a decision support system with such knowledge representation. In order to do this, four representative generation methods and various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results.

scholarly journals An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty

2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Experimental Result ◽  
Rule Base ◽  
Rule Based ◽  
Belief Rule Base

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

Ontological Knowledge Representation and Inference Rules for MTM Decision Support System

2014 ◽  
Author(s):  
Rahul Renu ◽  
Gregory Mocko
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Support System ◽  
Time Estimation ◽  
Time Measurement ◽  
Inference Rules ◽  
Assembly Time ◽  
Time Estimates ◽  
Definition Of

The objective of the research presented is to develop and implement an ontological knowledge representation for Methods-Time Measurement assembly time estimation process. The knowledge representation is used to drive a decision support system that provides the user with intelligent MTM table suggestions based on assembly work instructions. Inference rules are used to map work instructions to MTM tables. An explicit definition of the assembly time estimation domain is required. The contribution of this research, in addition to the decision support system, is an extensible knowledge representation that models work instructions, MTM tables and mapping rules between the two which will enable the establishment of assembly time estimates. Further, the ontology provides an extensible knowledge representation framework for linking time studies and assembly processes.

scholarly journals Penentuan Prioritas Pengambilan Pesanan Barang Oleh Angkutan Kota dengan Metode Rule-Based System

2018 ◽  
Vol 8 (2) ◽  
pp. 81
Author(s):  
Nur Aini Rakhmawati ◽  
Aditya Septa Budi ◽  
Faizal Johan Altetiko ◽  
Fajar Ramadhani ◽  
Nanda Kurnia Wardati ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Public Transportation ◽  
Storage Capacity ◽  
Urban Transportation ◽  
Urban Transport ◽  
Rule Based ◽  
Rule Based System ◽  
Passenger Transportation

Angkotin is a system that provides an alternative for urban transport to not only be used for passenger transportation, but also as freight service. Therefore, it needs a decision support system for taking order to delivery to the destination according to each criterion from urban transportation. The method used to develop this decision support system is a rule-based system. The result of this research is a decision support system that can help public transportation to find orders that can be taken based on four factors, such as distance, direction, route code, and status of storage capacity. Based on these four factors, the system can provide an order recommendation under the appropriate conditions through the Angkotin application. Based on our experiment, our system performs on 7 seven cases as expected.   

Sign in / Sign up

Export Citation Format

Share Document