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Mosquitoes are of great concern for occasionally carrying noxious diseases (dengue, malaria, zika, and yellow fever). To control mosquitoes, it is very crucial to effectively monitor their behavioral trends and presence. Traditional mosquito repellent works by heating small pads soaked in repellant, which then diffuses a protected area around you, a great alternative to spraying yourself with insecticide. But they have limitations, including the range, turning them on manually, and then waiting for the protection to kick in when the mosquitoes may find you. This research aims to design a fuzzy-based controller to solve the above issues by automatically determining a mosquito repellent’s speed and active time. The speed and active time depend on the repellent cartridge and the number of mosquitoes. The Mamdani model is used in the proposed fuzzy system (FS). The FS consists of identifying unambiguous inputs, a fuzzification process, rule evaluation, and a defuzzification process to produce unambiguous outputs. The input variables used are the repellent cartridge and the number of mosquitoes, and the speed of mosquito repellent is used as the output variable. The whole FS is designed and simulated using MATLAB Simulink R2016b. The proposed FS is executed and verified utilizing a microcontroller using its pulse width modulation capability. Different simulations of the proposed model are performed in many nonlinear processes. Then, a comparative analysis of the outcomes under similar conditions confirms the higher accuracy of the FS, yielding a maximum relative error of 10%. The experimental outcomes show that the root mean square error is reduced by 67.68%, and the mean absolute percentage error is reduced by 52.46%. Using a fuzzy-based mosquito repellent can help maintain the speed of mosquito repellent and control the energy used by the mosquito repellent. [ABSTRACT FROM AUTHOR]

For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested model demonstrate that the relative error is abated to a sufficient range (≤ ± 10%) and that the mean absolute percentage error (MPAE) is reduced by around 66.2%. The proposed strategy to reduce MAPE using an FS improves the system’s performance and control accuracy. By using the best input and output MFs protocol, the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs. The proposed fuzzy system performed far better than other modern days approaches available in the literature. [ABSTRACT FROM AUTHOR]