A fuzzy system for cloacal temperature prediction of broiler chickens

Cloacal temperature (CT) of broiler chickens is an important parameter to classify its comfort status; therefore its prediction can be used as decision support to turn on acclimatization systems. The aim of this research was to develop and validate a system using the fuzzy set theory for CT prediction of broiler chickens. The fuzzy system was developed based on three input variables: air temperature (T), relative humidity (RH) and air velocity (V). The output variable was the CT. The fuzzy inference system was performed via Mamdani's method which consisted in 48 rules. The defuzzification was done using center of gravity method. The fuzzy system was developed using MAPLE® 8. Experimental results, used for validation, showed that the average standard deviation between simulated and measured values of CT was 0.13°C. The proposed fuzzy system was found to satisfactorily predict CT based on climatic variables. Thus, it could be used as a decision support system on broiler chicken growth.

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Fuzzy Evaluation of Crowd Safety Based on Pedestrians’ Number and Distribution Entropy

Entropy ◽

10.3390/e22080832 ◽

2020 ◽

Vol 22 (8) ◽

pp. 832

Author(s):

Xuguang Zhang ◽

Qinan Yu ◽

Yuxi Wang

Keyword(s):

Public Management ◽

Fuzzy Inference ◽

Video Monitoring ◽

Video Sequences ◽

Fuzzy Evaluation ◽

Output Variable ◽

Inference System ◽

Small Areas ◽

Distribution Uniformity ◽

Input Variables

Crowd video monitoring and analysis is a hot topic in computer vision and public management. The pre-evaluation of crowd safety is beneficial to the prediction of crowd status to avoid the occurrence of catastrophic events. This paper proposes a method to evaluate crowd safety based on fuzzy inference. Pedestrian’s number and distribution uniformity are considered in a fuzzy inference system as two kinds of attributes of a crowd. Firstly, the pedestrian’s number is estimated by the number of foreground pixels. Then, the distribution uniformity of a crowd is calculated using distribution entropy by dividing the monitoring scene into several small areas. Furthermore, through the fuzzy operation, the fuzzy system is constructed by using two input variables (pedestrian’s number and distribution entropy) and one output variable (crowd safety status). Finally, inference rules between the crowd safety state and the pedestrian’s number and distribution uniformity are constructed to obtain the pre-evaluation of the safety state of the crowd. Three video sequences extracted from different scenes are used in the experiment. Experimental results show that the proposed method can be used to evaluate the safety status of the crowd in a monitoring scene.

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APLIKASI SISTEM INFERENSI FUZZY METODE TSUKAMOTO DALAM PENILAIAN MUTU BENIH PADI

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2015.7.1.2896 ◽

2015 ◽

Vol 7 (1) ◽

pp. 19

Author(s):

Diasta Risi Esa Annisa ◽

Mutia Nur Estri

Keyword(s):

Fuzzy Inference System ◽

Seed Quality ◽

Fuzzy Inference ◽

Fuzzy Rules ◽

Rice Seed ◽

Output Variable ◽

Inference System ◽

Rice Seeds ◽

Input Variables

In this paper, we use the Fuzzy inference system of Tsukamoto method to determine the rice seeds quality. The input variables are production average, the age of plant, and fallen seeds, and the output variable is rice seed quality. The output is determined through 4 steps i.e. fuzzification, determine fuzzy rules, and defuzzification. The results show that the best quality of rice seed is IR 64 and the worst is Lusi.

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SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PADA IKAN HIAS AIR TAWAR DENGAN FUZZY INFERENCE SYSTEM

JOUTICA ◽

10.30736/jti.v4i1.284 ◽

2019 ◽

Vol 4 (1) ◽

pp. 194

Author(s):

Indahsari Dewi Rina ◽

Dina Komar Lia

Keyword(s):

Fuzzy Inference ◽

Expert Knowledge ◽

Fish Farming ◽

Ornamental Fish ◽

Fish Disease ◽

Inference Process ◽

Output Variable ◽

Inference System ◽

The Media ◽

Input Variables

One of the main causes of failure in aquaculture activities is due to disease factors. The emergence of disease disorders in fish farming is a biological risk that must always be anticipated. The emergence of diseases in fish is generally the result of complex / unbalanced interactions between the three components in the aquatic ecosystem, namely weak hosts (fish), malignant pathogens and deteriorating environmental quality. Fish cultivators must obtain fast information related to diseases that infect their fish, and how to deal with them. In this study an expert system was created to diagnose ornamental fish disease using the media website, so that it can be used at any time without having to see a doctor / expert. Knowledge base involves 23 symptoms and 5 diseases that are common in freshwater ornamental fish, using a decision table producing 20 Rule. The inference process uses the Tsukamoto fuzzy, the modeling has 23 input variables and 1 output variable. Each input variable has 3 sets and the output variable has 5 sets. The implementation results indicate that the system built can provide diagnostic results with an 85% accuracy rate.

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Artificial Intellegence Untuk Mendeteksi Penyakit Kelenjar Getah Bening (Lymphadennopathy) Menggunakan Fuzzy Inference System (Fis) Di Kota Batam

JURNAL ILMIAH INFORMATIKA ◽

10.33884/jif.v6i01.434 ◽

2018 ◽

Vol 6 (01) ◽

pp. 54

Author(s):

Sestri Novia Sestri ◽

Algifanri Maulana

Keyword(s):

Fuzzy System ◽

Fuzzy Inference ◽

Weighted Average ◽

Inference System ◽

Average Value ◽

Fuzzy Methods ◽

System Output ◽

Input Variables ◽

To Come ◽

The City

Health is a basic thing to be maintained in life, because with a strong health and physical man can run his life. Batam is a rapidly growing city seen from many residents who live in the city of batam, but most of them are less healthy so that the disease is easy to come, another thing that is less his expert in the lymph node and costly in his treatment. The dangerous condition that causes swollen glands is a blood infection. A person suffering from a blood infection will look very weak. And will also experience a fever that will worsen and also accompanied by a body that feels shivering. This infection is caused by a bacterial attack and someone who experienced it should be treated as soon as possible in hospital. The clear-spoken cleavage is part of the human body's defense system. output Solving production problems using the Sugeno and Sugeno fuzzy methods corrects the weaknesses of the pure fuzzy system to add a simple mathematical calculation as part of THEN. In this change, the fuzzy system has a weighted average value (Values) in the IF-THEN fuzzy rules section. The Sugeno fuzzy system also has a disadvantage, especially in the THEN part, that is, by mathematical calculations that it can not provide a natural framework for representing human knowledge in fact. This method uses the mathematical constants or functions of the input variables, and in the defuzzification process uses the centralized mean method.

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PENILAIAN MUTU DOSEN TERHADAP TRI DHARMA PERGURUAN TINGGI DENGAN MENERAPKAN LOGICA FUZZY LOGIC DI STIKES NAN TONGGA

Jurnal Akademika Baiturrahim Jambi ◽

10.36565/jab.v8i2.167 ◽

2019 ◽

Vol 8 (2) ◽

pp. 175

Author(s):

Tri Monarita Johan ◽

Renty Ahmalia

Keyword(s):

Higher Education ◽

Fuzzy Logic ◽

Fuzzy Inference ◽

Teaching Staff ◽

Output Variable ◽

Inference System ◽

Quality Value ◽

Input Variables ◽

Collection Methods ◽

Functional Position

Tri Dharma of Higher Education is an activity that must be carried out by every Lecturer. In this study an application was designed to apply Fuzzy logic to calculate the quality value of Lecturers on the implementation of Higher Education Tri Dharma. Higher Education has the aim of producing quality qualifications. Therefore we need competent teaching staff needed. The background of this research is to study the results obtained from the application and calculation using Fuzzy logic, also help the lecturer evaluation in the field of quality control. The Mamdani Method is often also known as the Max-Min Method. This method was introduced by Ebrahim Mamdani in 1975. To get results, four stages are needed: 1. The formation of the fuzzy set; 2. Application function implications (rules); 3. Composition of rules; 4. Affirmation (deffuzy). The results obtained in this study the value of the function that has been optimized where lecturers will get the best in performance. Data collection methods in the fuzzy inference system function meeting, the author requires input data consisting of three variables and one output variable. Input variables consist of: 1. Research Variables 2. Dedication Variables 3. Teaching Variables. 4. Functional Position Variables After calculations and experiments, the results obtained using the Fuzzy Mamdani method with Matlab

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Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

Journal of Marine Science and Engineering ◽

10.3390/jmse9010049 ◽

2021 ◽

Vol 9 (1) ◽

pp. 49

Author(s):

Tanja Brcko ◽

Andrej Androjna ◽

Jure Srše ◽

Renata Boć

Keyword(s):

Fuzzy Logic ◽

Collision Avoidance ◽

Decision Model ◽

Fuzzy Inference ◽

Weather Conditions ◽

Target Vessel ◽

Planning Tool ◽

Inference System ◽

International Regulations ◽

Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

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Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-06-2016-0021 ◽

2017 ◽

Vol 10 (2) ◽

pp. 166-182 ◽

Cited By ~ 2

Author(s):

Shabia Shabir Khan ◽

S.M.K. Quadri

Keyword(s):

Pancreatic Cancer ◽

Fuzzy Inference System ◽

Fuzzy System ◽

Fuzzy Inference ◽

Patient Survival ◽

Data Partitioning ◽

Structure Identification ◽

Inference System ◽

Content Type ◽

Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

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