Advancement of Data Processing Methods for Artificial and Computing IntelligenceКНИГИ » ОС И БД
Название: Advancement of Data Processing Methods for Artificial and Computing Intelligence Автор: Seema Rawat, V. Ajantha Devi, Praveen Kumar Издательство: River Publishers Серия: River Publishers Series in Computing and Information Science and Technology Год: 2024 Страниц: 431 Язык: английский Формат: pdf (true) Размер: 36.7 MB
This book emphasizes the applications of advances in data processing methods for Artificial Intelligence in today's fast-changing world, as well as to serve society through research, innovation, and development in this field. This book is applicable to a wide range of data that contribute to data science concerns and can be used to promote research in this high-potential new field. People's perceptions of the world and how they conduct their lives have changed dramatically as a result of technological advancements. The world has been gripped by technology, and the advances that are being made every day are undeniably transforming the planet. In the domains of Big Data, engineering, and Data Science, this cutting-edge technology is ready to support us.
Artificial Intelligence (AI) is a current research topic because it can be applied to a wide range of applications and disciplines to solve complicated problems and find optimal solutions. In research, medicine, technology, and the social sciences, the benefits of AI have already been proven. Data Science, also known as pattern analytics and mining, is a technique for extracting useful and relevant information from databases, enabling better decision-making and strategy formulation in a range of fields. As a result of the exponential growth of data in recent years, the combined notions of Big Data and AI have given rise to many study areas, such as scale-up behaviour from classical algorithms. Furthermore, combining numerous AI technologies from other areas (such as vision, security, control, and biology) in order to build efficient and durable systems that interact in the real world is a new problem. Despite recent improvements in fundamental AI technologies, the integration of these skills into larger, trustworthy, transparent, and maintainable systems is still in its development. Both conceptually and practically, there are a number of unanswered issues.
Streaming highway traffc alerts using Twitter API: A proposed application for streaming analytics, which is a type of Big Data application, is covered in this chapter. The goal is to use real-time data analytics to forecast traffc by collecting traffc-related tweets from Twitter. The user would input a city name and the application would generate tweets about traffc in that city for the past seven days, alerting the user to avoid routes with blockages or accidents. The tweets are collected from the Twitter API using TweePy, and then classifed as “traffc” or “non-traffc” using a model. The tweets also need to be pre-processed and lemmatized to improve the effciency and accuracy of the model. The model is trained using a pre-classifed dataset and then used to classify the tweets collected earlier.
The impact of Artificial Intelligence (AI) and Data Science on various fields, specifcally how it contributes to solving common problems, is discussed in this chapter. It is explained that AI and Data Science enable machines to exhibit human-like characteristics such as learning and problem-solving and that the healthcare and finance industries are examples of where it has a signifcant impact. The abstract also mentions that chatbots and virtual assistants are other examples of applications of AI and Data Science, where they are used for effective communication and completing tasks with simple voice commands. Additionally, it mentions that there are many other real-life applications of AI and Data Science such as e-mail spam fltering, recommendation systems, autocomplete, and face recognition.
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