File Name: deep learning applications and challenges in big data analytics .zip
Big data is no mere buzzword—big data is here to stay. Organizations that bypassed the initial hype now see the need to make decisions as to whether to intertwine big data with their future organizational culture. Others are experiencing pressure from the first movers. Organizations can implement big data and benefit from data science by learning from best practices and adapting those that have the deepest potential for meaningful insight. They can also proactively and innovatively search for big data sources that could help answer the most urgent research needs.
This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Big data analytics is the process of examining large and varied data sets - i. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics.
Metrics details. Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process.
Interest in big data has swelled within the scholarly community as has increased attention to the internet of things IoT. Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students. Buy Hardcover. Add to Cart.
We discuss the new challenges and directions facing the use of big data and artificial intelligence AI in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education.
PDF | Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Najafabadi and Flavio Villanustre and T.
The use of deep learning DL for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper or more hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health mHealth.
Deep learning is one of the most active research fields in machine learning community. It has gained unprecedented achievements in fields such as computer vision, natural language processing and speech recognition. The ability of deep learning to extract high-level complex abstractions and data examples, especially unsupervised data from large volume data, makes it attractive a valuable tool for Big Data analytics. In this paper, discuss the challenges posed by Big Data analysis. Next, presented typical deep learning models, which are the most widely used for Big Data analysis and feature learning. Finally, have been outlined some open issues and research trends. Aliyeva A.
This section of the Web site provides theses and projects proposals for students. The goal of this thesis is to identify novel Bayesian Optimization methods to build performance models for various big data and deep learning applications based on Spark, the most promising big data framework which will probably dominate the big data market in the next years. The aim of this research work is to building accurate machine learning models to estimate the performance of Spark applications possibly running on GPU clusters by considering only few test runs on reference systems and identify optimal or close to optimal configurations. Bayesian methods will be mixed with traditional techniques for performance modelling, which includes computer systems simulations or bounding techniques. Nowadays, Big Data are becoming more and more important. Many sectors of our economy are now guided by data-driven decision processes.
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