Design and Implementation of an IoT Prototype for the Detection of Carbon Monoxide

Irid, Sidi Mohammed Hadj; Hadjila, Mourad; Adardour, Haroun Errachid

doi:10.1109/ispa48434.2019.8966882

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…In a similar study, [13] outlined some of these effects including eye, skin, and respiratory tract irritation amongst others. It was shown in [10] that the addition of CO would likely develop a close bond with haemoglobin within the blood that may reduce the blood's circulation of oxygen to all parts of the body, thus may cause dizziness, nausea, headaches, central nervous system abnormalities, reduced body movements, irregular lung and heart function, unconsciousness, respiratory crisis, and even death at high concentrations [14].…”

Section: Gas Pollutionmentioning

confidence: 99%

Voting Ensemble Learning Model (VELM) for Harmful Gas Detection Systems: A Proposed Model

Akpan,

Ajayi,

Adelowo

2023

CTICTR

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An increase in industrialization has caused the release of harmful gases into the atmosphere and this has resulted in environmental pollution that greatly affects the health of both plants and animals alike. Over time, sensor-based technology has been used to develop gas detection systems, but they were faced with some major challenges, such as sensor failure and poor performance that have greatly affected their performance. Also, the use of machine learning techniques has been developed for this purpose by leveraging the powers of sensor-based technology. However, they were faced with issues, such as poor feature selection criteria and missing data, which resulted in delays and inaccurate prediction performance of the models. This study, therefore, seeks to survey existing ensemble learning models and to design a Voting Ensemble Learning Model (VELM) for harmful gas detection systems. The methodology adopted for this study is a comparative analysis of the classification models used for the study. The study concludes that there is a need to develop advanced machine learning models to be used for detecting harmful gas and other detection systems such as earthquake, volcano, and landslide detection systems, in real-time that can inform future research. Also, a recommendation was made for the implementation of the developed model based on its low variance and low bias when dealing with widely dispersed data points in a dataset in addition to its ability to deal with local minima and overfitting that affect classic machine learning. Keywords: Detection System, Ensemble Learning, Machine Learning, Sensor

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Section: Gas Pollutionmentioning

confidence: 99%

Voting Ensemble Learning Model (VELM) for Harmful Gas Detection Systems: A Proposed Model

Akpan,

Ajayi,

Adelowo

2023

CTICTR

View full text Add to dashboard Cite

show abstract

“…1, 3), we can predict the speed of sound waves in this IMR using the linear interpolation method. The predictive values will be computed on the basis of Table 1 [34,35].…”

Section: Measuring the Temperature Of Environment In Which Ultrasound Is Spreadmentioning

confidence: 99%

Embedded System Prototype to Fight Covid-19 Pandemic Contamination with Less Cost

Adardour

Hadjila

Irid

et al. 2021

Wireless Pers Commun

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In the shadow of the coronavirus (Covid-19) pandemic, the sterilization has become a major necessity for humans to avoid exponential Infection. In order to avoid touching contaminated surfaces, we develop an embedded system prototype, which allows the bottle or any other medium to pour the sterilization product without touching it. The human hands are detected using two sensors HC-SR04 and LM35. To overcome the stability problem due to the problem of sensor noise and enhance the system performance a Kalman filter algorithm is implemented to ensure stable hands detection. The efficiency of the prototype mounted on an Arduino board is checked. After the completion of the prototype, a comprehensive cost analysis is conducted.

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Android Application Test for GPS Geolocation Using CN

Irid

Hachemi

Adardour

et al. 2022

Innovations in Smart Cities Applications Volume 5

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Design and Implementation of an IoT Prototype for the Detection of Carbon Monoxide

Cited by 4 publications

References 10 publications

Voting Ensemble Learning Model (VELM) for Harmful Gas Detection Systems: A Proposed Model

Voting Ensemble Learning Model (VELM) for Harmful Gas Detection Systems: A Proposed Model

Embedded System Prototype to Fight Covid-19 Pandemic Contamination with Less Cost

Android Application Test for GPS Geolocation Using CN

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