Development of an adaptive neuro fuzzy inference system based vehicular traffic noise prediction model

Singh, Daljeet; Upadhyay, Rahul; Pannu, Husanbir Singh; Leray, Dimitri

doi:10.1007/s12652-020-02431-y

Cited by 28 publications

(11 citation statements)

References 41 publications

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“…The hardware component is implemented in the laboratory, where different light-emitting diode (LED) indicators blink based on various output. The intelligent model, on the other hand, is designed using statistical and fuzzy inference techniques [31], and the proposed algorithm is shown as follows:…”

Section: System Implementationmentioning

confidence: 99%

Intelligent Cloud IoMT Health Monitoring-Based System for COVID-19

Al-Qaheri¹,

Sarkar²,

Gupta³

et al. 2022

Computers, Materials &Amp; Continua

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The most common alarming and dangerous disease in the world today is the coronavirus disease 2019 . The coronavirus is perceived as a group of coronaviruses which causes mild to severe respiratory diseases among human beings. The infection is spread by aerosols emitted from infected individuals during talking, sneezing, and coughing. Furthermore, infection can occur by touching a contaminated surface followed by transfer of the viral load to the face. Transmission may occur through aerosols that stay suspended in the air for extended periods of time in enclosed spaces. To stop the spread of the pandemic, it is crucial to isolate infected patients in quarantine houses. Government health organizations faced a lack of quarantine houses and medical test facilities at the first level of testing by the proposed model. If any serious condition is observed at the first level testing, then patients should be recommended to be hospitalized. In this study, an IoT-enabled smart monitoring system is proposed to detect COVID-19 positive patients and monitor them during their home quarantine. The Internet of Medical Things (IoMT), known as healthcare IoT, is employed as the foundation of the proposed model. The least-squares (LS) method was applied to estimate the linear model parameters for a sequential pilot survey. A statistical sequential analysis is performed as a pilot survey to efficiently collect preliminary data for an extensive survey of COVID-19 positive cases. The Bayesian approach is used, based on the assumption of the random variable for the priori distribution of the data sample. Fuzzy inference is used to construct different rules based on the basic symptoms of COVID-19 patients to make an expert decision to detect COVID-19 positive cases. Finally, the performance of the proposed model was determined by applying a four-fold cross-validation technique.

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Section: System Implementationmentioning

confidence: 99%

Intelligent Cloud IoMT Health Monitoring-Based System for COVID-19

Al-Qaheri¹,

Sarkar²,

Gupta³

et al. 2022

Computers, Materials &Amp; Continua

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“…Thus, the system will select the appropriate sentence and predict the date of the incident in relation to multiple information. Nonetheless, the traffic volume corresponding to the estimated date is determined by the data related to the event and the other traffic volume used as the traffic volume without the event, because the traffic volume is divided into two: one that is influenced by the event information, and one that is not affected by the event information [17][18][19][20].…”

Section: Proposed System Architecturementioning

confidence: 99%

Traffic Inference System Using Correlation Analysis with Various Predicted Big Data

Kim

Chung

2021

Electronics

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Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information system that reflects various kinds of environmental information related to people’s safety must be able to reflect not only the existing economic goals but also a safe traffic environment. The traffic environment can be thought of as safety and direct information such as rainfall, including information on specific days when many people are scheduled to be gathered for certain events nearby. Intelligent transportation systems using this information can provide safety-related information for traveling to a specific area or for business trips. In addition, traffic congestion is a social problem and is directly related to a comfort life for individuals. Therefore, addressing various social and environmental factors could make human life more stable and reduce stress as a result. To do that, we need to estimate the impact on traffic based on environmental Big Data. The data can generally be divided into structured data and unstructured data. In inference, structured data analysis is relatively easy due to the precise meaning of the data. Nonetheless, it can be very difficult to predict environmentally sensitive data, such as traffic volume in intelligent transportation systems. To cope with this problem, there are a few systems for handling unstructured data to find out specific events that affect the traffic volume and improve its reliability. This paper shows that it is possible to estimate the exact volume of traffic using correlation analysis with various predicted data. Thus, we may apply this technique to the existing intelligent transportation system to predict the exact volume of traffic with environmentally sensitive data including various unstructured data.

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“…In addition, they presented the application of emotional artifcial neural network as a new generation neural network method for modelling road trafc noise [18]. To predict the noise level, an adaptive neurofuzzy inference system was developed and a detailed comparative analysis was performed with conventional soft-computing techniques such as ANNs, generalized linear model, random forests, decision trees, and support vector machines [19].…”

Section: Introductionmentioning

confidence: 99%

Sound Pressure Level at Terminals through Data Mining

Monteza¹,

Rivera²,

Huatangari³

et al. 2023

Scientific Programming

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The land transportation is a cause of noise in cities, thus breaking the natural balance and bringing with it physiological and mental illnesses, as well as occupational accidents. In this sense, the objective of the research was to estimate the sound pressure in land terminals in the city of Jaen, Peru, using data mining algorithms. The methodology consisted in environmentally monitoring six terminals in the city of Jaen, during 2019, using a class 1 sound level meter; the exploratory analysis of the collected variables that influence the noise of the terminals (minimum and maximum sound pressure level, number of light and heavy vehicles, and equivalent sound pressure level) was performed, which were grouped into three groups of variables for the purpose of using data mining algorithms. Three algorithms were used, namely, artificial neural network, linear regression, and M5Rules, using the free software Weka. Considering all variables, the M5Rules method performed the best, because the value of the mean absolute error (0.7462), the root mean square error (1.0575), and uncertainty analysis (0.09) was the smallest compared to the other two methods. However, for the two remaining groups of variables, the linear regression model showed the lowest mean absolute error and mean square root of the error; in addition to presenting coefficients of determination close to one. The algorithms show good behavior when estimating the sound pressure of the terminals in the city of Jaen.

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Development of an adaptive neuro fuzzy inference system based vehicular traffic noise prediction model

Cited by 28 publications

References 41 publications

Intelligent Cloud IoMT Health Monitoring-Based System for COVID-19

Intelligent Cloud IoMT Health Monitoring-Based System for COVID-19

Traffic Inference System Using Correlation Analysis with Various Predicted Big Data

Sound Pressure Level at Terminals through Data Mining

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