Smart cities machine learning (233 résultats)

XVII, 998 p. Softcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Advances in Intelligent Systems and Computing, 1245. Sprache: Englisch.…

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Taschenbuch. Etat : Neu. Neuware - Imagine a world where cities not only grow but thrive, where the safety of every citizen is monitored and protected through cutting-edge technologies. AI Public Safety Systems offers readers a transformative journey into the heart of AI-enhanced urban living. This book is your key to unlocking the future of smart city innovations and securing a safer, more efficient world.In this insightful guide, you'll discover how leading-edge AI technologies are revolutionizing the way we think about public safety. Delve into the integration of smart technologies that are not just designed to protect but to enhance the overall quality of life in urban environments. You stand on the cusp of becoming an expert in the realm of AI-driven safety systems.Whether you're a seasoned expert or a curious newcomer, this book provides clarity, utility, and a pathway to implement AI solutions in urban infrastructures. It's crafted to elevate your understanding and equip you with the tools needed for profound change.AI Public Safety Systems distills complex ideas into manageable parts, making it accessible to all. From real-time threat detection to emergency response optimization, this book is packed with the wisdom needed to foster safer cities.Gain deep insights into the public safety frameworks shaping our future. Transform your understanding with practical, real-world applications that inspire confidence in implementing AI systems effectively.? Master the art of building resilient safety systems? Eliminate uncertainty with predictive analytics? Prevent crimes with cutting-edge AI strategies? Secure urban environments through innovative solutions? Build trust with transparent AI engagements? Control city safety with adaptive technology? Enhance collaboration with smart community modelsWith over 5 years in the ICT field, the author brings a wealth of experience and authority to this essential guide. This book is not just a read; it's an invitation to engage with the future of urban safety.Claim your copy and become a pioneer in AI smart city development. Empower yourself today and influence tomorrow's cityscapes.…

Hardcover. Etat : new. Hardcover. 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. We present a Machine Learning (ML) approach to monitoring and classifying noise pollution. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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HRD. Etat : Used - Very Good. Used - Like New Book. Shipped from UK. Established seller since 2000.

HRD. Etat : Used - Very Good. Used - Like New Book. Shipped from UK. Established seller since 2000.

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Etat : New. In English.

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Paperback / softback. Etat : New. New copy - Usually dispatched within 4 working days.

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Etat : New. In English.

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Etat : New. 2024th edition NO-PA16APR2015-KAP.

Hardcover. Etat : Brand New. 187 pages. 9.25x6.10x9.45 inches. In Stock.

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Taschenbuch. Etat : Neu. Role Of big Data and Machine Learning In Development of Smart Cities | Hiba Iftikhar | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203924497 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Etat : 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.…

Buch. Etat : 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.…

Paperback. Etat : Brand New. 322 pages. 9.18x6.12x9.21 inches. In Stock.

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Taschenbuch. Etat : Neu. Machine Learning for Environmental Noise Classification in Smart Cities | Ali Othman Albaji | Taschenbuch | Synthesis Lectures on Engineering, Science, and Technology | xvii | Englisch | 2025 | Springer | EAN 9783031546693 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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