Modified Mamdani-fuzzy inference system for predicting the cost overrun of construction projects

Al-Nahhas, Yaman Saeid; Hadidi, Laith A.; Islam, Muhammad Saiful; Skitmore, Martin; Abunada, Ziyad

doi:10.1016/j.asoc.2023.111152

Cited by 4 publications

(1 citation statement)

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“…The fuzzy inference system used in this research is Mamdani [24], There are several steps in the fuzzy logic system, starting with the input data process, which consists of two variables: temperature and humidity. Next, the fuzzification process was performed, which involved transforming the input variables from crisp sets into fuzzy sets.…”

Section: Fig 2 Data Communications Systemmentioning

confidence: 99%

Smart Drip Irrigation System Based on IoT Using Fuzzy Logic

Walid,

Ashar,

Wahyudi

2024

intensif

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The absence of a water drip rate control system in drip irrigation systems has impacted water use efficiency and normalization of soil moisture. Therefore, this research aims to develop an intelligent system using the fuzzy logic method to control the rate of water droplets in a drip irrigation system and maintain soil moisture in normal conditions. The DHT22 sensor is used to obtain temperature and humidity values, which are then used as input data and processed by the ESP32 microcontroller, which includes a fuzzy system. The Internet of Things (IoT) is also used to send data from the microcontroller to the Thingspek web server. The Blynk application is used to make it easier to monitor temperature, humidity, and water droplet rate values. The results of this research show that the temperature accuracy values produced using the MSE evaluation were 6.66667 and RMSE were 2.58199, while for temperature, the values for MSE were 0.128333 and RMSE were 0.358236. The average value of soil moisture produced in the planting medium is 44.46%; this value is within normal conditions for chili plants, where normal soil moisture conditions range between 40% - 60%

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Section: Fig 2 Data Communications Systemmentioning

confidence: 99%