An intelligent hybrid neuro-fuzzy rule-based system for prognostic decision making in prostate cancer patients

2003 ◽  
Author(s):  
H. Seker ◽  
M.O. Odetayo ◽  
D. Petrovic ◽  
R.N.G. Naguib ◽  
F.C. Hamdy
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Cancer Patients ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Rule Based System ◽  
Neuro Fuzzy

Fuzzy-Rule Based Adaptive Data Warehouse

2012 ◽  
Vol 3 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Rajdev Tiwari ◽  
Anubhav Tiwari ◽  
Manu Pratap Singh
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Knowledge Workers ◽  
Fuzzy Rule ◽  
Data Warehouses ◽  
Large Reservoir ◽  
Inference System ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
New Generation

Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

A fuzzy rule-based system to assess e-government technical interoperability maturity level

2014 ◽  
Vol 8 (3) ◽  
pp. 335-356 ◽  
Author(s):  
Andreiwid Sheffer Corrêa ◽  
Alexandre de Assis Mota ◽  
Lia Toledo Moreira Mota ◽  
Pedro Luiz Pizzigatti Corrêa
Keyword(s):  
Fuzzy Rule ◽  
Assessment Process ◽  
Maturity Model ◽  
Maturity Level ◽  
Rule Based ◽  
Content Type ◽  
Management Level ◽  
Rule Based System ◽  
Brazilian Government ◽  
Information And Communication

Purpose – The purpose of this study is to present a system called NEBULOSUS, which is a fuzzy rule-based expert system for assessing the maturity level of an agency regarding technical interoperability. Design/methodology/approach – The study introduces the use of artificial intelligence and fuzzy logic to deal with the imprecision and uncertainty present in the assessment process. To validate the system proposed and demonstrate its operation, the study takes into account the Brazilian technical interoperability maturity model, based on the Brazilian Government Interoperability Framework (GIF). Findings – With the system proposed and its methodology, it could be possible to increase the assessment process to management level and to provide decision-making support without worrying about technical details that make it complex and time-consuming. Moreover, NEBULOSUS is a standalone system that offers an easy-to-use, open and flexible structuring database that can be adapted by governments throughout the world. It will serve as a tool and contribute to governments’ expectations for continuous improvement of their technologies. Originality/value – This study contributes toward filling a gap in general interoperability architectures, which is a means to provide an objective method to evaluate GIF adherence by governments. The proposed system allows governments to configure their technical models and GIF to assess information and communication technology resources.

scholarly journals Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 110 ◽  
Author(s):  
Gadelhag Mohmed ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Neural Networks ◽  
Human Activities ◽  
Finite State Machine ◽  
Fuzzy Rule ◽  
State Machine ◽  
Machine Learning Techniques ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
Finite State ◽  
Learning Capabilities

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

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