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Machine Learning for Environmental Noise Classification in Smart Cities (Synthesis Lectures on Engineering, Science, and Technology) - Softcover
Synopsis
We present a Machine Learning (ML) approach to monitoring and classifying noise pollution. Both methods of monitoring and classification have been proven successful. MATLAB and Python code was generated to monitor all types of noise pollution from the collected data, while ML was trained to classify these data. ML algorithms showed promising performance in monitoring the different sound classes such as highways, railways, trains and birds, airports and many more. It is observed that all the data obtained by both methods can be used to control noise pollution levels and for data analytics. They can help decision making and policy making by stakeholders such as municipalities, housing authorities and urban planners in smart cities. The findings indicate that ML can be used effectively in monitoring and measurement. Improvements can be obtained by enhancing the data collection methods. The intention is to develop more ML platforms from which to construct a less noisy. The second objective of this study was to visualize and analyze the data of 18 types of noise pollution that have been collected from 16 different locations in Malaysia. All the collected data were stored in Tableau software. Through the use of both qualitative and quantitative measurements, the data collected for this project was then combined to create a noise map database that can help smart cities make informed decisions.
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About the Author
Ali Othman Albaji received a bachelor’s degree in electrical engineering specializing in “General communications” from the Civil Aviation Higher College, Tripoli, Libya, in 2007, and a Master’s degree in electronics and telecommunication engineering from University Technology Malaysia *UTM*, Johor Bahru, Malaysia in 2022. His research interests are Machine Learning (ML), IoT, Wireless Sensor Networks (WSN), VSAT, SCADA Systems, Optical Networking, Wireless Communications, Deep Learning (DL), Artificial intelligence (AI), Web design, Robotics, and Programming Languages expert / Traineron ( Python, MATLAB, JAVA, JAVA Script, SQL, Data Base MSQL, C++, HTML, and....ETC).
From the Back Cover
We present a Machine Learning (ML) approach to monitoring and classifying noise pollution. Both methods of monitoring and classification have been proven successful. MATLAB and Python code was generated to monitor all types of noise pollution from the collected data, while ML was trained to classify these data. ML algorithms showed promising performance in monitoring the different sound classes such as highways, railways, trains and birds, airports and many more. It is observed that all the data obtained by both methods can be used to control noise pollution levels and for data analytics. They can help decision making and policy making by stakeholders such as municipalities, housing authorities and urban planners in smart cities. The findings indicate that ML can be used effectively in monitoring and measurement. Improvements can be obtained by enhancing the data collection methods. The intention is to develop more ML platforms from which to construct a less noisy. The second objective of this study was to visualize and analyze the data of 18 types of noise pollution that have been collected from 16 different locations in Malaysia. All the collected data were stored in Tableau software. Through the use of both qualitative and quantitative measurements, the data collected for this project was then combined to create a noise map database that can help smart cities make informed decisions.
In addition, this book:
- Machine learning-based sound classifier for environmental noise
- Qualitative analysis of community perceptions based on a noise pollution survey
- Create an interactive web dashboard and data warehousing for intelligent analytics reporting
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Machine Learning for Environmental Noise Classification in Smart Cities
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Springer, Berlin, Springer Nature Switzerland, Springer, 2025
ISBN 10: 3031546695
ISBN 13: 9783031546693
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -We present a Machine Learning (ML) approach to monitoring and classifying noise pollution. Both methods of monitoring and classification have been proven successful. MATLAB and Python code was generated to monitor all types of noise pollution from the collected data, while ML was trained to classify these data. ML algorithms showed promising performance in monitoring the different sound classes such as highways, railways, trains and birds, airports and many more. It is observed that all the data obtained by both methods can be used to control noise pollution levels and for data analytics. They can help decision making and policy making by stakeholders such as municipalities, housing authorities and urban planners in smart cities. The findings indicate that ML can be used effectively in monitoring and measurement. Improvements can be obtained by enhancing the data collection methods. The intention is to develop more ML platforms from which to construct a less noisy. The second objective of this study was to visualize and analyze the data of 18 types of noise pollution that have been collected from 16 different locations in Malaysia. All the collected data were stored in Tableau software. Through the use of both qualitative and quantitative measurements, the data collected for this project was then combined to create a noise map database that can help smart cities make informed decisions. 170 pp. Englisch. Seller Inventory # 9783031546693
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Machine Learning for Environmental Noise Classification in Smart Cities
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Machine Learning for Environmental Noise Classification in Smart Cities
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Springer Nature Switzerland, Springer Nature Switzerland, 2025
ISBN 10: 3031546695
ISBN 13: 9783031546693
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Taschenbuch
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - We present a Machine Learning (ML) approach to monitoring and classifying noise pollution. Both methods of monitoring and classification have been proven successful. MATLAB and Python code was generated to monitor all types of noise pollution from the collected data, while ML was trained to classify these data. ML algorithms showed promising performance in monitoring the different sound classes such as highways, railways, trains and birds, airports and many more. It is observed that all the data obtained by both methods can be used to control noise pollution levels and for data analytics. They can help decision making and policy making by stakeholders such as municipalities, housing authorities and urban planners in smart cities. The findings indicate that ML can be used effectively in monitoring and measurement. Improvements can be obtained by enhancing the data collection methods. The intention is to develop more ML platforms from which to construct a less noisy. The second objective of this study was to visualize and analyze the data of 18 types of noise pollution that have been collected from 16 different locations in Malaysia. All the collected data were stored in Tableau software. Through the use of both qualitative and quantitative measurements, the data collected for this project was then combined to create a noise map database that can help smart cities make informed decisions. Seller Inventory # 9783031546693
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Machine Learning for Environmental Noise Classification in Smart Cities
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Machine Learning for Environmental Noise Classification in Smart Cities
Published by
Springer Nature Switzerland, Springer International Publishing Apr 2025, 2025
ISBN 10: 3031546695
ISBN 13: 9783031546693
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Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. Neuware -We present a Machine Learning (ML) approach to monitoring and classifying noise pollution. Both methods of monitoring and classification have been proven successful. MATLAB and Python code was generated to monitor all types of noise pollution from the collected data, while ML was trained to classify these data. ML algorithms showed promising performance in monitoring the different sound classes such as highways, railways, trains and birds, airports and many more. It is observed that all the data obtained by both methods can be used to control noise pollution levels and for data analytics. They can help decision making and policy making by stakeholders such as municipalities, housing authorities and urban planners in smart cities. The findings indicate that ML can be used effectively in monitoring and measurement. Improvements can be obtained by enhancing the data collection methods. The intention is to develop more ML platforms from which to construct a less noisy. The second objective of this study was to visualize and analyze the data of 18 types of noise pollution that have been collected from 16 different locations in Malaysia. All the collected data were stored in Tableau software. Through the use of both qualitative and quantitative measurements, the data collected for this project was then combined to create a noise map database that can help smart cities make informed decisions.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 188 pp. Englisch. Seller Inventory # 9783031546693
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