A User-Centric Decision Support Model for Cloud of Things Adoption Using Ellipsoidal Fuzzy Inference System

2018 ◽  
pp. 255-261
Author(s):  
Ademola Olaniyi ◽  
Babatunde Akinkunmi ◽  
Olufade Onifade
Keyword(s):  
Decision Support ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Decision Support Model ◽  
Inference System ◽  
User Centric ◽  
Support Model

scholarly journals Effective Prediction of Rheumatoid Arthritis (Ra) Diagnosis Using Hybrid Harmony Search with Adaptive Neuro Fuzzy Inference System

2021 ◽  
Author(s):  
S SHANMUGAM ◽  
C Saravanabhavan ◽  
T Arunkumar
Keyword(s):  
Rheumatoid Arthritis ◽  
Fuzzy Inference System ◽  
Predictive Value ◽  
Fuzzy Inference ◽  
Search Algorithm ◽  
Early Stage ◽  
Harmony Search ◽  
Decision Support Model ◽  
Inference System ◽  
Neuro Fuzzy

Abstract One of the severe auto immune diseases that affects the entire human body is Rheumatoid Arthritis (RA), the disease triggers one’s immune system to attack the inner linings of bones and causes severe inflammation of the synovium. The continuous erosion of bone lining leads to permanent loss of the joint, accounting this severity the early prognosis of the disease is a significant and inevitable process. But, the sign and symptoms of the disease are always uncertain. The symptom of RA disease is similar to other inflammatory diseases, so highly experienced experts can identify the disease in its early stage. To support the clinicians and technicians for early prognosis of the disease, a computer-aided decision support model based on Harmony Search –Adaptive Neuro Fuzzy Inference System is presented in this study. The Harmony search algorithm is employed to select the optimal features, and ANFIS is adopted to perform classification. To demonstrate the effectiveness of the model, metrics such as Accuracy, Sensitivity, Specificity, Precision, Recall, F-measure, Positive Predictive Value, Negative Predictive Value, Root Mean Square Error, and Mean Absolute Error are employed and evaluated in MATLAB simulation environment. The proposed HS-ANFIS outperformed other models developed in this research and existing works of literature.

scholarly journals A FUZZY DECISION SUPPORT SYSTEM FOR SUSTAINABLE CONSTRUCTION PROJECT SELECTION: AN INTEGRATED FPP-FIS MODEL

2020 ◽  
Vol 26 (3) ◽  
pp. 247-258 ◽  
Author(s):  
Alireza Fallahpour ◽  
Kuan Yew Wong ◽  
Srithar Rajoo ◽  
Ezutah Udoncy Olugu ◽  
Mehrbakhsh Nilashi ◽  
...  
Keyword(s):  
Decision Support ◽  
Fuzzy Inference ◽  
Construction Project ◽  
Project Selection ◽  
Sustainable Construction ◽  
Second Phase ◽  
Decision Support Model ◽  
Inference System ◽  
Business Goals ◽  
The Right

Sustainability has become a key concern for project selection in construction industries. Determining the best sustainable project based on various sustainability attributes is a very complicated decision. Accordingly, developing a suitable decision support framework can be very helpful for decision makers to attain planned business goals and complete projects at the right time with good quality. This research develops a decision support model which helps managers to understand the concept of sustainability in construction project selection and choose the best project using a new integrated Multi-Criteria Decision Making (MCDM) approach under uncertainty by integrating Fuzzy Preference Programming (FPP) as a modification of Fuzzy Analytical Hierarchy Process (FAHP), with Fuzzy Inference System (FIS) as a fuzzy rulebased expert system. In the first phase of the research, fifteen sustainability attributes were selected. In the second phase, the final weight of each attribute was computed by using FPP. In the last phase, the most appropriate project was selected by running the weighted FIS. The results showed that Project 3 (P3) is the best project. Finally, two different evaluative tests were also applied to verify the validity and robustness of the developed model.

scholarly journals A Development of Clinical Decision Support System for Video Head Impulse Test Based on Fuzzy Inference System

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Dao Thi Anh Nguyen ◽  
Insu Won ◽  
Kyusung Kim ◽  
Jangwoo Kwon
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Fuzzy Inference System ◽  
Clinical Decision Support System ◽  
Fuzzy Inference ◽  
Clinical Decision ◽  
Head Impulse Test ◽  
Video Head Impulse Test ◽  
Inference System ◽  
Intact Side

This paper represents the clinical decision support system for video head impulse test (vHIT) based on fuzzy inference system. It examines the eye and head movement recorded by the eye movement tracking device, calculates the vestibulo-ocular reflex (VOR) gain, and applies fuzzy inference system to output the normality and artifact index of the test result. The position VOR gain and the proportion of covert and overt catch-up saccades (CUS) within the dataset are used as the input of the inference system. In addition, this system yields one more factor, the artifact index, which represents the current interference in the dataset. Data of fifteen vestibular neuritis patients and two of normal subjects were evaluated. The artifact index appears to be very high in the lesion side of vestibular neuritis (VN) patients, indicating highly theoretical contradictions, which are low gain but without CUS, or normal gain with the appearance of CUS. Both intact side and normal subject show high normality and low artifact index, even though the intact side has slightly lower normality and higher artifact index. In conclusion, this is a robust system, which is the first one that takes gain and CUS into account, to output not only the normality of the vHIT dataset, but also the artifacts.

scholarly journals Decision Support System for Green Supplier Selection Using the Fuzzy Inference System Method in Abrasive Companies

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Defi Norita ◽  
Ririn Regiana Dwi Satya ◽  
Andary Asvaroza Munita ◽  
Asep Endih Nurhidayat
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference System ◽  
Supplier Selection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Green Supplier Selection ◽  
System Method ◽  
Green Suppliers

The development of information and communication technology makes it easier for users in the industrial world to make decisions in choosing environmentally friendly suppliers more easily. This study aims to determine the selection of green suppliers of all the criteria that have been determined and make a decision support system for selecting green suppliers with the Fuzzy Inference System method. The method used in making identification of green supplier selection is to create criteria based on fuzzy rules and to make digital business modeling using business process modeling notation. Decision support there are 4 criteria used, namely price, reject quality, late delivery and environmental management. Based on the results of research conducted it is known that with the fuzzy inference system method that is assisted using matlab software, the optimization results on the fuzzy inference system show that prices are 20.5%, quality is 5.5%, environment is 5.5%, and material delays are 3%, then supplier performance in selecting green suppliers with a decision making system of 55% so that green supplier selection is obtained at abrasive companies.

scholarly journals Semi-Automatic Generation of a Fuzzy Inference System in a Reshoring Context

2020 ◽  
Author(s):  
Anders Adlemo ◽  
Per Hilletofth
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Automatic Generation ◽  
Inference Rules ◽  
Inference System ◽  
Product Delivery

Reshoring can be regarded as offshoring in reverse. While offshoring mainly has been driven by cost aspects, reshoring considers multiple aspects, such as higher quality demands, faster product delivery and product mass-customization. Where to locate manufacturing is usually a purely manual activity that relies on relocation experts, hence, an automated decision-support system would be extremely useful. This paper presents a decision-support system for reshoring decision-making building a fuzzy inference system. The construction and functionality of the fuzzy inference system is briefly outlined and evaluated within a high-cost environment considering six specific reshoring decision criteria, namely cost, quality, time, flexibility, innovation and sustainability. A challenge in fuzzy logic relates to the construction of the so called fuzzy inference rules. In the relocation domain, fuzzy inference rules represent the knowledge and competence of relocation experts and are usually generated manually by the same experts. This paper presents a solution where fuzzy inference rules are automatically generated applying one hundred reshoring scenarios as input data. Another important aspect in fuzzy logic relates to the membership functions. These are mostly manually defined but, in this paper, a semi-automatic approach is presented. The reshoring decision recommendations produced by the semi-automatically configured fuzzy inference system are shown to be as accurate as those of a manually configured fuzzy inference system.

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