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1. |
Record Nr. |
UNINA990009102560403321 |
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Autore |
Randa, Anton |
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Titolo |
Das Eigenthumsrecht mit besonderer Rücksicht auf die Werthpapiere des Handelsrechtes nach österreichischem Rechte mit Berücksichtigung des gemeinen Rechtes und der neueren Gesetzbücher / Anton Randa |
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Pubbl/distr/stampa |
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Leipzig : Breitkopf und Härtel, 1893 |
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Descrizione fisica |
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Locazione |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910917787303321 |
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Autore |
Fowdur Tulsi Pawan |
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Titolo |
AI Solutions for the United Nations Sustainable Development Goals (UN SDGs) : A Practical Approach Using JavaScript / / by Tulsi Pawan Fowdur, Lavesh Babooram |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (355 pages) |
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Altri autori (Persone) |
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BabooramLavesh |
LaliteshDobee |
AshvenSanghan |
GyaneetaLuchmunparsad |
AdnaanKurmally Mohammad |
AvishayeDomah |
DheerajRadjoo |
VandanaHanumunthadu |
MaadhaveeMohadeb Sai |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Java (Computer program language) |
Programming languages (Electronic computers) |
Artificial Intelligence |
Java |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Chapter 1: Introduction to Machine Learning Applications Development and the UN SDGs -- Chapter 2: Utilizing Machine Learning Algorithms for Power generation prediction and classification in Wind Farms -- Chapter 3: Crop Recommendation System Using Machine Learning Algorithms for achieving SDGs 2, 9, and 12 -- Chapter 4: Aligning Manufacturing Emissions with SDGs 9 and 13 Using Machine Learning Algorithms -- Chapter 5: Water Potability Testing Using Machine Learning -- Applying Machine Learning for Air Quality Monitoring Targeting SDG 3 and 13 -- Chapter 7: Clustering the Development of Worldwide Internet Connectivity with Unsupervised Learning for SDGs 7, 9, and 11. |
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Sommario/riassunto |
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Learn the United Nations Sustainable Development Goals (UN SDGs) and see how machine learning can significantly contribute to their realization. This book imparts both theoretical knowledge and hands-on experience in comprehending and constructing machine learning-based applications for addressing multiple UN SDGs using JavaScript. The reading begins with a delineation of diverse UN SDG targets, providing an overview of previous successful applications of machine learning in solving realistic problems aligned with these targets. It thoroughly explains fundamental concepts of machine learning algorithms for prediction and classification, coupled with their implementation in JavaScript and HTML programming. Detailed case studies examine challenges related to renewable energy, agriculture, food production, health, environment, climate change, water quality, air quality, and telecommunications, corresponding to various UN SDGs. Each case study includes related works, datasets, machine learning algorithms, programming concepts, and comprehensive explanations of JavaScript and HTML codes used for web-based machine learning applications. The results obtained are meticulously analyzed and discussed, showcasing the pivotal role of machine learning in advancing the relevant SDGs. By the end of this book, you’ll have a firm understanding of SDG fundamentals and the practical application of machine learning to address diverse challenges associated with these goals. You will: Understand the fundamental concepts of the UN SDGs, AI, and machine learning algorithms. Employ the correct machine learning algorithms to address challenges on the United Nations Sustainable Development Goals (UN SDGs)? Develop web-based machine learning applications for the UN SDGs using Javascript, and HTML. Analyze the impact of a machine learning-based solution on a specific UN SDG. . |
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