Manufacturing optimization using Tsukamoto fuzzy inference system method: A case study in block paving and solid concrete block industry

Sayuti, M; Juliananda,; Khairani, Sofyan Diana

doi:10.1051/matecconf/201820402002

Cited by 2 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fuzzy sets of temperature variable are divided into three, namely hypothermia (≤35 C), normal (35.1 to 37.9 C), and hyperthermic (≥ 38 C) [12]. This sets are used as a reference in the formation of the fuzzy membership function for temperature variable.…”

Section: A Fuzzyfication Processmentioning

confidence: 99%

“…The fuzzy sets of human respiratory rate based on age are divided into 4, namely age < 2 months, age 2 months to 11 months, age 1 year to 5 years, and age over 5 years [12]. The aged used in this study is limited to starting from the age of 5.…”

Section: Picture 4 the Fuzzy Sets Of O2 Saturation Variablementioning

confidence: 99%

“…This research uses the Fuzzy Inference System (FIS)-Tsukamoto method. This method gives more optimal results compared to the Fuzzy Inference System-Sugeno [12]. FIS-Tsukamoto also suitable for the use of real-world problems which are mostly non-binary and non-linear.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Self-Isolation Monitoring of COVID-19 Patients Using Fuzzy Inference System-Tsukamoto

Roshinta¹,

Masbahah²

2022

SINTECH Journal

View full text Add to dashboard Cite

In self-isolation of Covid-19 patients, it is very important to carry out regular condition checks. Currently, the examination of severity of patien’s condition can be carried out by the patient himself online with the tools as measurement provided by public health center, and the data can be monitored by medic team. Several applications for monitoring the daily condition of Covid-19 patients have been developed but the parameters used in the monitoring application are not standardized and the accuracy of the application is unknown. This study aims to develop a Covid-19 patient monitoring application using more complete and accurate parameters. The input parameters used are body temperature, O2 saturation, pulse rate, and respiratory rate. The output is the level of the Covid-19 patient's condition which is divided into mild, moderate, and severe, as well as information on the actions that must be taken. This research uses the Fuzzy Inference System-Tsukamoto method. The test results between the system output and expert testing related to the condition of Covid-19 patients show that this self-checking application for monitoring has an accuracy of 95%.

show abstract

Section: A Fuzzyfication Processmentioning

confidence: 99%

Section: Picture 4 the Fuzzy Sets Of O2 Saturation Variablementioning

confidence: 99%

See 1 more Smart Citation

Self-Isolation Monitoring of COVID-19 Patients Using Fuzzy Inference System-Tsukamoto

Roshinta¹,

Masbahah²

2022

SINTECH Journal

View full text Add to dashboard Cite

show abstract

“…Fuzzyfication, rule "IF-THEN", inference and defuzzyfication processes are steps of the model. The end result is calculated using weighted average [46].…”

Section: Fuzzy Modelsmentioning

confidence: 99%

Evolutionary neural networks for improving the prediction performance of recommender systems

Seref¹,

Bostancı²,

Güzel³

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving performance of them. In this study, prediction performances of neural networks were evaluated and their performances were improved using genetic algorithms. Performance of this study was compared with other studies, after that, superiority of this study was shown. While multilayer perceptron, generalized feed-forward network and CANFIS (Coactive Neuro Fuzzy Inference Systems) were used as a neural network algorithms, Movielens 100K and Movielens 1M datasets which are preferred in recommender systems studies mostly were used to train and test the system. Mean square error and root mean square error were employed as performance metrics. As a result, it was observed that genetic algorithm improves performance of neural networks and prediction performance of hybrid combination of neural networks and genetic algorithm is superior to prediction performance of recommender systems available in the literature.

show abstract

Manufacturing optimization using Tsukamoto fuzzy inference system method: A case study in block paving and solid concrete block industry

Cited by 2 publications

References 4 publications

Self-Isolation Monitoring of COVID-19 Patients Using Fuzzy Inference System-Tsukamoto

Self-Isolation Monitoring of COVID-19 Patients Using Fuzzy Inference System-Tsukamoto

Evolutionary neural networks for improving the prediction performance of recommender systems

Contact Info

Product

Resources

About