The Application of a Fuzzy Approach to the Analysis of OSH Practitioners Level of Risk Acceptance

2014 ◽  
Author(s):  
Matilde A. Rodrigues ◽  
Celina P. Leão ◽  
Eusébio Nunes ◽  
Sérgio Sousa ◽  
Pedro Arezes
Keyword(s):  
Fuzzy Logic ◽  
Occupational Safety ◽  
Fuzzy Inference ◽  
Risk Level ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Risk Acceptance ◽  
Input Variables ◽  
Accident Scenarios ◽  
Level Of Risk

Organizations need constantly to take decisions about risk. In this process, Occupational Safety & Health (OSH) practitioners’ judgments have a great importance. If on one hand they have the technical knowledge about risk, on the other hand the decisions can be dependent on their level of risk acceptance. In view of this, this paper analyzes the views of the OSH practitioners about the level of risk acceptance, using the Fuzzy logic approach. A questionnaire to the analysis of the reported level of risk acceptance was developed and applied. The questionnaire included 79 risk scenarios, each accounted for the frequency of an accident with more lost workdays than a given magnitude. Through the two-step cluster analysis three groups of OSH practitioners were identified: Unacceptable, Tolerable and Realistic groups. A further analysis of the realistic group judgments about risk was performed, using the Fuzzy logic approach. The fuzzy sets of inputs and output variables were determined and the relationship between the variables was mapped through fuzzy rules. After that, the Min–Max fuzzy inference method was used. The obtained results show that the risk level is acceptable when input variables are at the lowest value and unacceptable when the risk level is high. Furthermore, the results obtained allow to better understand the uncertainty related with the OSH practitioners judgments being an important step to better understand the modeling of judgments about risk acceptance level allowing to know the different risk acceptance levels for the different accident scenarios.

A Fuzzy Logic Approach in the Definition of Risk Acceptance Boundaries in Occupational Safety and Health

2016 ◽  
Vol 2 (4) ◽  
Author(s):  
Matilde A. Rodrigues ◽  
Celina P. Leão ◽  
Eusébio Nunes ◽  
Sérgio Sousa ◽  
Pedro Arezes
Keyword(s):  
Fuzzy Logic ◽  
Occupational Safety ◽  
Fuzzy Inference ◽  
Occupational Safety And Health ◽  
Risk Level ◽  
Safety And Health ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Risk Acceptance ◽  
Level Of Risk

Organizations need to make decisions about risk acceptance, to decide about the need of risk-reducing measures. In this process, the personal judgments of occupational safety and health (OSH) practitioners have great importance. If on one hand, they have the technical knowledge about risk; on the other hand, the decisions can be dependent on their level of risk acceptance. This paper analyzes judgments of OSH practitioners about the level of risk acceptance, using the fuzzy logic approach. A questionnaire to analyze the reported level of risk acceptance was applied. The questionnaire included 79 risk scenarios, each accounting for the frequency of an accident with more lost workdays than a given magnitude. Through the two-step cluster analysis, three groups of OSH practitioners were identified: unacceptable, tolerable, and realistic groups. A further analysis of the realistic group judgments about risk was performed, using the fuzzy logic approach. The fuzzy sets of input and output variables were determined, and the relationship between the variables was mapped through fuzzy rules. After that, the min–max fuzzy inference method was used. The obtained results show that the risk level is acceptable when input variables are at the lowest value and unacceptable when the risk level is high. The obtained results allow us to better understand the modeling of OSH practitioners’ judgments about risk acceptance, noting the uncertainty related to these judgments.

scholarly journals Examining child obesity risk level using fuzzy inference system

2021 ◽  
Vol 10 (3) ◽  
pp. 679
Author(s):  
Febrina Sari ◽  
Desyanti Desyanti ◽  
Teuku Radillah ◽  
Siti Nurjannah ◽  
Julimar Julimar ◽  
...  
Keyword(s):  
Childhood Obesity ◽  
Fuzzy Inference System ◽  
Fast Food ◽  
Fuzzy Inference ◽  
Risk Level ◽  
Food History ◽  
Inference System ◽  
Input Variables ◽  
Level Of Risk ◽  
Obesity In Children

The doctor will determine the risk level of childhood obesity by using standard calculations, namely measuring the child's weight and height, and many other factors. Then the doctor will calculate the child's body mass index (BMI). The results of calculations made by the doctor will be compared with standard/normal values set by FAO/WHO, to obtain the level of risk of obesity in children. This study aims to analyze the risk level of obesity in children using the Sugeno method of Fuzzy Inference system, using the trapezoidal membership function and involving six input variables such as exercise habits, consumption of fast food, history of obesity of parents, and others. The application of the fuzzy inference system Sugeno method can help doctors to analyze the risk level of childhood obesity quickly and accurately with an accuracy rate of 85%. The results of the implementation of the Sugeno method of Fuzzy Inference system showed that out of 140 children who were the object of the study, 119 children received a diagnosis of the level of risk of obesity which was the same as the diagnosis made by a doctor.

scholarly journals The Performance of Fuzzy Logic How to Fixed The Risk Level of Bug System as Hard-working Quality Software

2019 ◽  
Vol 2 (2) ◽  
pp. 80-91
Author(s):  
Agus Pamuji
Keyword(s):  
Fuzzy Logic ◽  
Risk Level ◽  
Critical Levels ◽  
End User ◽  
Software Production ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Almost All ◽  
Working Quality

The quality of software production is considered important when testing which is involves several IT Staff such as IT development, operation, end-user. One of the issue was having today is a bug processing where it almost all platforms too difficult to avoid from the bugs and even might be full of the risks. In the main of Our focus is on how to measure and attempt to reduce the number were indicated as bugs from low up to critical levels. Furthermore, we were propose with a method already known as a fuzzy logic approach to measure the severity of the presence of bugs during the testing process. there are 20 thousand even more bugs have been reported and be supposed removed with the fuzzy logic approach with various levels. As The end result is that we have found a gradual 20% reduction in various criteria in the testing process as experimentally. Therefore, fuzzy logic is considered as  effective enough to be able to improve existing methods and support to reduce bugs significantly.

scholarly journals Penempatan Posisi Multi Kamera Berdasarkan Gaya Sutradara Berbasis Logika Fuzzy

2018 ◽  
Vol 5 (6) ◽  
pp. 687
Author(s):  
Hartarto Junaedi ◽  
Jaya Pranata ◽  
Mochamad Hariadi ◽  
I Ketut Eddy Purnama
Keyword(s):  
Fuzzy Logic ◽  
Computer Games ◽  
Computer Game ◽  
Point Of View ◽  
Virtual Camera ◽  
Fuzzy Logic Approach ◽  
Multi Agent ◽  
Logic Approach ◽  
Input Variables ◽  
Game Simulation

<p class="Abstrak">Teknologi komputer saat ini telah banyak digunakan dalam pengembangan animasi atau permainan komputer. Salah satu teknologi itu adalah <em>machinima </em>yaitu suatu sistem yang menggunakan teknologi mesin grafik 3D untuk menghasilkan produk sinematik secara <em>real time</em>. Dalam proses pembuatan produk sinematik itu penempatan posisi kamera sangat memegang peranan penting. Penempatan posisi kamera ini tentu harus sesuai dengan kaidah-kaidah sinematografi. Penelitian ini akan mengusulkan sebuah pendekatan agen cerdas dengan multi perilaku untuk menempatkan kamera <em>virtual</em> dalam lingkungan <em>virtual </em>secara otomatis sesuai dengan gaya seorang sutradara. Setiap kamera <em>virtual </em>itu akan memiliki perilaku yang berbeda berdasarkan kaidah sinematografi sehingga memiliki <em>Point of View</em> (POV) yang berbeda. Untuk memberikan perilaku pada kamera <em>virtual</em> akan digunakan pendekatan berbasis logika fuzzy dengan menggunakan metode <em>mamdani</em>. Jumlah variabel masukan yang digunakan sejumlah tiga dan variabel keluaran sejumlah tiga dengan <em>membership function </em>antara tiga sampai lima. Penelitian ini akan menggunakan simulasi permainan komputer dengan tiga kamera <em>virtual</em> dengan perilaku yang berbeda untuk merekam adegan yang sama dan hasilnya akan divalidasi berdasarkan hasil pengamatan dengan komunitas juru foto.  Pada akhirnya dapat diambil kesimpulan bahwa pendekatan logika fuzzy dapat digunakan untuk memberikan sebuah perilaku atau gaya sutradara pada kamera <em>virtual</em>.</p><p class="Abstrak"><strong>Abstract</strong></p><p class="Abstract">Computer technology is has been used widely in the development of animation or computer games. One of the technologies is machinima, a system that uses reak time 3D graphics engine technology to produce cinematic products. In the process of develop a cinematic product, camera positioning is a very important component. The camera positioning must be comply with cinematography’s rule. This research will propose an intelligent multi agent behavior to positining a virtual camera in a virtual environment automatically according to the director’s style. Each virtual camera will have a different behavior based on cinematographic rules so that it has a different Point of View (POV). To assign a behavior on the virtual camera will be based on  fuzzy logic using the mamdani method. The number of input variables are three and the output variables are three with the number membership functions between three to five. This research will program  a computer game simulation with three multi behavior virtual cameras to capture some scene and the results will be validated based on observations with the photographer community. Finally it can be concluded that the fuzzy logic approach can be used to assign some behavior to a virtual camera.</p>

Benchmarking fuzzy logic and ANFIS approaches for leanness evaluation in an Indian SME

2017 ◽  
Vol 24 (4) ◽  
pp. 973-993 ◽  
Author(s):  
Rohit Agrawal ◽  
P. Asokan ◽  
S. Vinodh
Keyword(s):  
Fuzzy Logic ◽  
Lean Manufacturing ◽  
Fuzzy Inference ◽  
Evaluation Model ◽  
Medium Size ◽  
Inference System ◽  
Content Type ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Leanness Evaluation

Purpose The purpose of this paper is to present a study that is focused on application of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) approaches for leanness evaluation in an Indian small- and medium-size enterprise (SME). Design/methodology/approach Lean manufacturing concepts are being adopted by SMEs to sustain in the competitive manufacturing landscape. Performance of lean system needs to be assessed using appropriate methods. A model for measuring lean performance is proposed with five enablers, 30 criteria and 90 attributes. Leanness index is computed using fuzzy logic approach and benchmarked with ANFIS approach. Findings Leanness index computed using fuzzy logic approach is found to be (4.47, 5.97, 7.55) and that of ANFIS approach is found to be 5.84 to facilitate benchmarking of leanness evaluation. After finding weaker areas, certain improvement initiatives are being deployed. Research limitations/implications The developed model for leanness evaluation has been test implemented in an SME. In future, the model could be test implemented in several SMEs. Practical implications A case study conducted in an SME involved in heavy engineering fabrication is presented. Therefore, the inferences derived from the study has practical propensity. Originality/value The development of leanness evaluation model for SMEs and deployment in an industrial scenario are the original contributions of the authors.

scholarly journals Aplikasi Fuzzy Logic dalam Diagnosa Penyakit Diabetes Mellitus pada PUSKESMAS di Jakarta Timur

2016 ◽  
Vol 2 (3) ◽  
pp. 21-30 ◽  
Author(s):  
Zaimatun Niswati ◽  
Aulia Paramita ◽  
Fanisya Alva Mustika
Keyword(s):  
Diabetes Mellitus ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fuzzy Inference ◽  
Treatment Decision ◽  
Inference System ◽  
Family History Of Diabetes ◽  
Patients With Diabetes ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Abstract— Patients with diabetes mellitus increased from year to year. This is due to delays in diagnosis of the disease and also because of unhealthy lifestyles. This study aims to create an application of decision support systems in the field of health, namely the diagnosis of the disease Diabetes Mellitus with Fuzzy Inference System (FIS) Mamdani, so that a layman can perform early diagnosis and immediate treatment. Decision Support System Techniques developed to improve the effectiveness of decision-makers. Samples are six puskesmas in East Jakarta. This application uses five variables as inputs consisting of glucose 2 hours after a meal, Diastolic blood pressure, body mass index, family history of diabetes, total pregnancies and one variable as output. The data obtained be processed using fuzzy logic approach to programming Matlab and made Graphical User Interface (GUI). Result is an expert system for diagnosis of Diabetes Mellitus .The trial results by midwives and nurses puskesmas is 100% of these applications in accordance with the doctor”s diagnosis. It is to help improve the quality of service in the Puskesmas in East Jakarta, thus satisfying the users and puskesmas be able to compete both nationally and internationally. 

scholarly journals Course recommendation system using fuzzy logic approach

2020 ◽  
Vol 17 (1) ◽  
pp. 365
Author(s):  
Mohd Suffian Sulaiman ◽  
Amylia Ahamad Tamizi ◽  
Mohd Razif Shamsudin ◽  
Azri Azmi
Keyword(s):  
Fuzzy Logic ◽  
Recommendation System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Education Environment ◽  
User Design ◽  
Interest Level ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
The Right

Course selection is a key for success in student’s academic path. In today’s education environment, various courses offered by different academic institutions required the students to explore the course outline manually. Most of them are lacking in knowledge, having dilemma and making blind selections to choose the right course. Therefore, it is essential to have a course recommendation to provide guidance to a student to choose the course related with their interest and skill. This paper proposed to develop a course recommendation system using fuzzy logic approach. The development methodology of this system involves several phases include requirements planning, user design and construction for prototyping, testing and cutover. This study used the fuzzy rules technique in order to calculate each associated student’s skill and interest level based on Mamdani fuzzy inference system method. Then, the rules will generate final outcome which recommend the suitable course path and provide the details to a user based on their course test. The result shows the functionality of this system has been achieved and works well. This study is significantly helping the students to choose their course based on the interest and skill.

scholarly journals Determination of Difficulty Level for Garment Model with Fuzzy Logic Method

2021 ◽  
Vol 28 (121) ◽  
pp. 39-47
Author(s):  
Hilal Bilgiç ◽  
Yusuf Kuvvetli ◽  
Pınar Duru Baykal
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Real Life ◽  
Operation Time ◽  
Difficulty Level ◽  
Decision Framework ◽  
Inference System ◽  
Fuzzy Logic Approach ◽  
Input Variables ◽  
Fabric Weight

The purpose of this study is a rule-based fuzzy logic approach is proposed for determining model difficulty in manufacturing top clothing for ladies. A decision framework concerned with different scenarios (main pattern types and material types) is proposed for determining the model difficulty. Each scenario modeled as a Mamdani type fuzzy inference system which is known as one of the best approximator fuzzy logic models. The fuzzified input variables are unit operation time, second quality rate and fabric weight. Moreover, two different defuzzification methods which are centroid and middle of maxima are compared for finding best fuzzy logic structure over the six different test instances. According to the results, both deffuzzification methods find similar model difficulty determinations. A graphical user interface of the proposed decision framework is designed in order to apply this to real-life applications. Finally, six different clothing models are identified to be simple, medium-hard, hard and very hard. The results of this study showed that defuzzification methods is not significantly effected the model difficulty decisions off is systems regarding different test instances. The model difficulty values range between 0-10. In order to find a useful difficulty assignment (linguistic), the model difficulty is determined by using the closeness to center value (a2) of membership functions. This research offers a solution to determine the difficulty levels of the garment models.

scholarly journals Fuzzy Logic Approach for Routing in Internet of Things Network

2021 ◽  
Vol 15 (1) ◽  
pp. 18-24
Author(s):  
Reena P. Pingale ◽  
S. N. Shinde
Keyword(s):  
Fuzzy Logic ◽  
Network Lifetime ◽  
Network Performance ◽  
Fuzzy Inference ◽  
Residual Energy ◽  
Link Quality ◽  
Inference System ◽  
Input And Output ◽  
Fuzzy Logic Approach ◽  
Logic Approach

A performance of network is evaluated by considering different parameters. The network lifetime depends on many factors Residual energy, Link lifetime and Delay. The Major Challenge in IoT is to the increased lifetime of low power and lossy network (RPL).The process considering input and output to evaluate Network performance by considering the above factors. The proposed system makes use of FIS (Fuzzy Inference System) for selecting the best path to maximize network lifetime. The outcome obtained by using MATLAB and Network performance is increased. The excellent route is selected if Residual Energy is 194, Link quality is 51.2 and Delay is 1.05 then excellent route quality is 73.4%.

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