Machine Learning for Environmental Noise Classification in Smart Cities
暫譯: 智慧城市環境噪音分類的機器學習

Albaji, Ali Othman

Machine Learning for Environmental Noise Classification in Smart Cities
  • 出版商: Springer
  • 出版日期: 2024-03-23
  • 售價: $2,240
  • 貴賓價: 9.5 折 $2,128
  • 語言: 英文
  • 頁數: 170
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031546660
  • ISBN-13: 9783031546662
  • 相關分類: Machine Learning

    • 立即出貨(限量) (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

我們提出了一種機器學習（Machine Learning, ML）方法來監測和分類噪音污染。監測和分類的兩種方法已被證明是成功的。生成了 MATLAB 和 Python 代碼來監測從收集的數據中獲得的各類噪音污染，同時訓練 ML 來對這些數據進行分類。ML 算法在監測不同聲音類別方面顯示出良好的性能，例如高速公路、鐵路、火車和鳥類、機場等。觀察到通過這兩種方法獲得的所有數據都可以用來控制噪音污染水平和進行數據分析。這些數據可以幫助市政府、住房管理機構和城市規劃者等利益相關者在智慧城市中進行決策和政策制定。研究結果表明，ML 可以有效地用於監測和測量。通過改善數據收集方法，可以獲得進一步的改進。本研究的第二個目標是可視化和分析從馬來西亞16個不同地點收集的18種類型的噪音污染數據。所有收集的數據都存儲在 Tableau 軟體中。通過使用定性和定量測量，為本項目收集的數據隨後被結合以創建一個噪音地圖數據庫，這可以幫助智慧城市做出明智的決策。

作者簡介

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).

 

 

 

作者簡介(中文翻譯)

阿里·奧斯曼·阿爾巴吉（Ali Othman Albaji）於2007年在利比亞的的黎波里民航高等學院獲得電機工程學士學位，專攻「一般通信」。他於2022年在馬來西亞柔佛州的馬來西亞科技大學（University Technology Malaysia, UTM）獲得電子與電信工程碩士學位。他的研究興趣包括機器學習（Machine Learning, ML）、物聯網（IoT）、無線感測器網路（Wireless Sensor Networks, WSN）、VSAT、SCADA系統、光纖網路、無線通信、深度學習（Deep Learning, DL）、人工智慧（Artificial Intelligence, AI）、網頁設計、機器人技術，以及程式語言專家/訓練師（Python、MATLAB、JAVA、JavaScript、SQL、資料庫MSQL、C++、HTML等）。

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