Online Real Time Fuzzy Inference System Based Human Health Monitoring and Medical Decision Making

2017 ◽  
Author(s):  
Aqeel Majeed Humadi ◽  
Alaa Khalaf Hamoud
Keyword(s):  
Decision Making ◽  
Human Health ◽  
Real Time ◽  
Health Monitoring ◽  
Fuzzy Inference System ◽  
Medical Decision Making ◽  
Fuzzy Inference ◽  
Medical Decision ◽  
Inference System

scholarly journals M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 707 ◽  
Author(s):  
Tran Manh Tuan ◽  
Luong Thi Hong Lan ◽  
Shuo-Yan Chou ◽  
Tran Thi Ngan ◽  
Le Hoang Son ◽  
...  
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Inference System ◽  
Granular Computing ◽  
Fuzzy Inference ◽  
Fuzzy Theory ◽  
Rule Base ◽  
Inference System ◽  
Fuzzy Measures ◽  
Fuzzy Similarity

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

scholarly journals A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 164899-164921
Author(s):  
Luong Thi Hong Lan ◽  
Tran Manh Tuan ◽  
Tran Thi Ngan ◽  
Le Hoang Son ◽  
Nguyen Long Giang ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Knowledge Graph ◽  
Inference System ◽  
Fuzzy Knowledge

scholarly journals Analysis and Modeling for the Real-Time Condition Evaluating of MOSFET Power Device Using Adaptive Neuro-Fuzzy Inference System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 6510-6518 ◽  
Author(s):  
Shengyou Xu ◽  
Xin Yang ◽  
Minyou Chen ◽  
Wei Lai ◽  
Yueyue Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Power Device ◽  
Inference System ◽  
Time Condition ◽  
The Real ◽  
Neuro Fuzzy

scholarly journals An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Ali Safa Sadiq ◽  
Norsheila Binti Fisal ◽  
Kayhan Zrar Ghafoor ◽  
Jaime Lloret
Keyword(s):  
Decision Making ◽  
Wireless Networks ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Weight Vector ◽  
Point Load ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
Handover Decision ◽  
Seamless Mobility

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

scholarly journals A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jose M. Gonzalez-Cava ◽  
José Antonio Reboso ◽  
José Luis Casteleiro-Roca ◽  
José Luis Calvo-Rolle ◽  
Juan Albino Méndez Pérez
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Real Data ◽  
Clinical Variable ◽  
Decision Making Process ◽  
Drug Supply ◽  
Inference System ◽  
Surgical Interventions ◽  
Physiological Signal

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

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