用大数据的机器学习来解决现实问题

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Data Science and Analytics Pub Date : 2023-04-23 DOI:10.59615/jda.2.1.9
M. Rahmaty
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引用次数: 2

摘要

机器学习算法使用大数据来学习未来趋势,并为企业做出预测。在了解消费者模式可以带来巨大改进的行业中,机器学习可以非常有效地破译数据。机器学习的使用对企业来说是一个巨大的飞跃,不能简单地集成为顶层。这需要重新定义工作流、架构、数据收集和存储、分析和其他模块。应该评估系统改革的规模,并清楚地传达给适当的利益相关者。机器学习的主要重点是开发能够访问数据并使用数据进行学习的计算机程序。学习过程从观察或数据开始,在数据中找到一种模式,做出更好的决策。使用机器学习进行数据分析的主要目标是,它允许计算机在没有人为干预和帮助的情况下自动学习,并可以相应地调整其行为。考虑到数据分析在现实世界中发现的许多应用,因此,在本文中,机器学习作为人工智能工具之一的基本应用的回顾已经完成,重点是大数据分析。本文的目的是了解在现实世界中使用机器学习的维度、组件和应用程序以及挑战。
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Machine learning with big data to solve real-world problems
Machine learning algorithms use big data to learn future trends and predict them for businesses. Machine learning can be very efficient for deciphering data in industries where understanding consumer patterns can lead to big improvements. The use of machine learning can be a giant leap for businesses and cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. The magnitude of the system overhaul should be assessed and clearly communicated to the appropriate stakeholders. The main focus of machine learning is to develop computer programs that can access data and use it to learn. The learning process starts with observations or data, to find a pattern in the data and make better decisions. The main goal of data analysis using machine learning is that it allows the computer to learn automatically without human intervention and help and can adjust its actions accordingly. Considering the many applications that data analysis has found in the real world, therefore, in this article, a review of the basic applications of machine learning as one of the tools of artificial intelligence has been done with an emphasis on big data analysis. The purpose of this article is to understand the dimensions, components and applications, and challenges of using machine learning in the real world.
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来源期刊
CiteScore
6.40
自引率
8.30%
发文量
72
期刊介绍: Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social sci­ence, and lifestyle. The field encompasses the larger ar­eas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new sci­entific chal­lenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and vis­ualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The jour­nal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applica­tions of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interopera­bility, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics
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