Blockchain, artificial intelligence (AI), and the internet of things (IoT) come together to offer a transformative opportunity to increase automation, effectiveness, and security across a variety of industries. Blockc...
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Blockchain, artificial intelligence (AI), and the internet of things (IoT) come together to offer a transformative opportunity to increase automation, effectiveness, and security across a variety of industries. Blockc...
详细信息
ISBN:
(数字)9798331518578
ISBN:
(纸本)9798331518585
Blockchain, artificial intelligence (AI), and the internet of things (IoT) come together to offer a transformative opportunity to increase automation, effectiveness, and security across a variety of industries. Blockchain ensures decentralised trust, data integrity, and safe transactions, while AI enables analytics in real time, predictive insights, and intelligent decision-making. IoT enables smooth communication among the digital and physical worlds by connecting and gathering enormous volumes of data from smart devices. By combining these innovations, a synergistic ecosystem is produced in which AI safely analyses data provided by the Internet of Things and stores it on blockchain networks, guaranteeing transparency and immutability. Smart cities, healthcare, supply chain management, and autonomous systems are just a few of the many fields where this incorporation can be used to promote innovation and operational resilience. To fully utilise this trinity, however, issues like flexibility, compatibility, and privacy concerns need to be resolved. This study examines the advantages, drawbacks, and potential avenues for further research of the synergy amongst blockchain, AI, and the IoT.
This paper introduces a recently published Python data mining book (chapters, topics, samples of Python source code written by its authors) to be used in data mining via world wide web and any specific database in sev...
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This paper introduces a recently published Python data mining book (chapters, topics, samples of Python source code written by its authors) to be used in data mining via world wide web and any specific database in several disciplines (economic, physics, education, marketing. etc). The book started with an introduction to data mining by explaining some of the data mining tasks involved classification, dependence modelling, clustering and discovery of association rules. The book addressed that using Python in data mining has been gaining some interest from data miner community due to its open source, general purpose programming and web scripting language; furthermore, it is a cross platform and it can be run on a wide variety of operating systens such as Linux, Windows, FreeBSD, Macintosh, Solaris, OS/2, Amiga, AROS, AS/400, BeOS, OS/390, z/OS, Palm OS, QNX, VMS, Psion, Acorn RISC OS, VxWorks, PlayStation, Sharp Zaurus, Windows CE and even PocketPC. Finally this book can be considered as a teaching textbook for data mining in which several methods such as machine learning and statistics are used to extract high-level knowledge from real-world datasets.
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