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.

scholarly journals Fuzzy Inference System Using Tsukamoto Method For Making Decision of Production (Case Study: PT Waru Kaltim Plantation)

METIK JURNAL ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 76-82
Author(s):  
Dominggus Norvindes Dellas ◽  
Ika Purnamasari ◽  
Nanda Arista Rizki
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Palm Oil ◽  
Oil Palm ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Decision Making Process ◽  
Inference System

The decision-making process using a fuzzy inference system (FIS) logic can use one of the methods called the Tsukamoto method. The process carried out in this method is the same as the fuzzy method in general, namely the formation of fuzzy sets, the fuzzification process, defuzzification, and measuring the accuracy of the result. The purpose of this study was to apply the Tsukamoto method to predict the yield of oil palm production at PT. Waru Kaltim Plantation. Based on the analysis using the Tsukamoto method, 36 fuzzy rules were obtained for each data from February 2013 to December 2015. The prediction results of palm oil production in 2013 did not change, except for May and August. In February, March, June, and August 2014 the level of production is constant, and almost throughout 2015, there was constant. The predicted MAPE for oil palm production was 31,522%, or in the fairly good category.

Fuzzy Logic Modeling for Decision Making Processes Using MATLAB

2014 ◽  
Vol 984-985 ◽  
pp. 425-430 ◽  
Author(s):  
Thangavel Ramya ◽  
A.C. Kannan ◽  
R.S. Balasenthil ◽  
B. Anusuya Bagirathi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Decision Making Process ◽  
Computing Environment ◽  
Output Variable ◽  
Inference System ◽  
Logic Modeling ◽  
Decision Making Processes

— This paper demonstrates to build a Fuzzy Inference System (FIS) for any model utilizing the Fuzzy Logic Toolbox graphical user interface (GUI) tools. A different conception for decision making process, based on the fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description about the Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes the second section. Based on various assumptions the authors construct the rule statements which are then converted into fuzzy rules and the GUI tools of the Fuzzy Logic Toolbox built using MATLAB numeric computing environment is used to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzy sets in Mamdani-type inference.Defuzzification of fuzzy set for each output variable generated after the aggregation process has to be carried out. Application of information technology for Decisions in today's environment which is highly competitive are undeniable principles of organizations and helps managers in making useful decisions meaningfully.

APPLICATION OF ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN THE PROCESS OF TRANSPORTATION SUPPORT

2013 ◽  
Vol 30 (02) ◽  
pp. 1250053 ◽  
Author(s):  
DRAGAN PAMUČAR ◽  
VESKO LUKOVAC ◽  
SNEŽANA PEJČIĆ-TARLE
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Technological Development ◽  
Decision Making Process ◽  
Inference System ◽  
Wrong Decision ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Military Units

The possibility for more confidential predictions, leaning on scientific methods and accomplishments of information technology leaves more time for the realization of logistic needs. Longstanding ambitions to acquire desired levels of efficiency within the system with minimal costs of resources, materials, energy and money are the features of executive structures of logistic systems. A successful logistic process is based on validation of technological development, indicating the need for a faster and more confidential integration of logistic systems and "instilling confidence" with military units that provide critical support (supply, transport and maintenance) will be reliably realized according to relevance and priority. Conclusions like these impose the necessity that the decision-making process of logistic organs is accessed carefully and systematically, since any wrong decision leads to a reduced state of readiness for military units. To facilitate the day-to-day operation of the Army of Serbia and the completion of both scheduled and unscheduled tasks it is necessary to satisfy the wide range of transport requirements. In this paper, the Adaptive Neuro Fuzzy Inference System (ANFIS) is described, thus making possible a strategy of coordination of transport assets to formulate an automatic control strategy. This model successfully imitates the decision-making process of the chiefs of logistic support. As a result of the research, it is shown that the suggested ANFIS, which has the ability to learn, has a possibility to imitate the decision-making process of the transport support officers and show the level of competence that is comparable with the level of their competence.

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 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 Fuzzy Inference System for Management Control Tools

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2145
Author(s):  
Carolina Nicolas ◽  
Javiera Müller ◽  
Francisco-Javier Arroyo-Cañada
Keyword(s):  
Decision Making ◽  
Organizational Performance ◽  
Control Systems ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Small And Medium Enterprises ◽  
Belief Systems ◽  
Management Control ◽  
Inference System ◽  
Medium Enterprises

Despite the importance of the role of small and medium enterprises (SMEs) in developing and growing economies, little is known regarding the use of management control tools in them. In management control in SMEs, a holistic system needs to be modeled to enable a careful study of how each lever (belief systems, boundary systems, interactive control systems, and diagnostic control systems) affects the organizational performance of SMEs. In this article, a fuzzy logic approach is proposed for the decision-making system in management control in small and medium enterprises. C. Mamdani fuzzy inference system (MFIS) was applied as a decision-making technique to explore the influence of the use of management control tools on the organizational performance of SMEs. Perceptions data analysis is obtained through empirical research.

scholarly journals PERHITUNGAN TINGKAT KESIAPAN IMPLEMENTASI SMART CITY DALAM PERSPEKTIF SMART GOVERNANCE DENGAN METODE FIS MAMDANI

Dinamik ◽  
2017 ◽  
Vol 22 (1) ◽  
pp. 39-48
Author(s):  
Sri Eniyati ◽  
Rina Candra Noor Santi ◽  
Retnowati Retnowati ◽  
Sri Mulyani ◽  
Khristma Martha
Keyword(s):  
Decision Making ◽  
Research Methods ◽  
Social Services ◽  
Fuzzy Inference System ◽  
Smart City ◽  
Fuzzy Inference ◽  
Political Strategies ◽  
Inference System ◽  
Participation In Decision Making ◽  
Smart Governance

Smart City adalah skonsep tata kota yang mengoptimalkan teknologi informasi dan digital untuk meningkatkan kesejahteraan dan kebahagiaan masyarakat, serta meningkatkan layanan Pemerintah. Kota Pekalongan sedang berupaya untuk mempersiapkan diri dalam proses implementasi Smart City. Dalam referensi diketahui bahwa salah satu indikator kesiapan implementasi Smart City adalah Smart Governance, yang terdiri atasi empat indikator utama yaitu Participation in decision-making, public and social services, Transparent Governance, political strategies and perspectives. Dari keempat indikator tersebut diperjelas ke dalam indikator operasional yang lebih mudah diukur secara kuantitatif. Oleh sebab itu metode penelitian dipilih mix research methods karena data yang diperoleh dilakukan melalui cara kualitatif dengan wawancara kepada narasumber. Hasil data dikelola dan diolah menggunakan cara kuantitatif. Cara kuantitatif tersebut adalah metode Fuzzy Inference System (FIS) Mamdani. Dari keempat indikator utama diturunkan menjadi 21 variabel input Hasil yang diperoleh adalah tingkat kesiapan Kota Pekalongan dalam mengimplementasikan Smart City dari Perspektif Smart Governance adalah 1,5 (Sedang).

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