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Victor Chang

Other affiliations: University of Liverpool, Leeds Beckett University, IBM more
Bio: Victor Chang is an academic researcher from Teesside University. The author has contributed to research in topics: Cloud computing & Big data. The author has an hindex of 50, co-authored 391 publications receiving 10184 citations. Previous affiliations of Victor Chang include University of Liverpool & Leeds Beckett University.

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TL;DR: The state-of-the-art communication technologies and smart-based applications used within the context of smart cities are described and a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified.

774 citations

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TL;DR: It is proposed that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other, and needs a multi-layer architecture.

725 citations

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TL;DR: The application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence are explored.

304 citations

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TL;DR: CCAF multi-layered security blends with policy, services and business activities, and can be beneficial for volume, velocity, and veracity of big data services operated in the cloud.

288 citations

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TL;DR: A secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations, is formally proved secure, and extensive comparison and simulations demonstrate its efficiency.

267 citations

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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Jan 2002

9,314 citations

Reference EntryDOI
15 Oct 2004

2,118 citations