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    <title>Digital Technologies</title>
    <link>http://www.sciepub.com/journal/DT</link>
    <description>The Odessa National Academy of Telecommunications n. a. A. S. Popov and The Institution of Radio, Television, Electronics, which is a part of the Academy, undertook to publish this period-ical edition. The aim is to enable scientists to present their scientific achievements in the field of digital technology, to increase discussions for solving urgent problems of infocommunication, to display new tendencies of digital technology, television and radio broadcasting, mobile and satel-lite communications, information and telecommunication systems, etc.</description>
    <dc:publisher>Science and Education Publishing</dc:publisher>
		<dc:language>en</dc:language>
		<dc:rights>2013 Science and Education Publishing Co. Ltd All rights reserved.</dc:rights>
		<prism:publicationName>Digital Technologies</prism:publicationName>
		3
		1
		January 2018
		<prism:copyright>2013 Science and Education Publishing Co. Ltd All rights reserved.</prism:copyright>
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<title>
Modeling the Computational Solution of Market Basket Associative Rule Mining Approaches Using Deep Neural Network
</title>
<link>http://pubs.sciepub.com/dt/3/1/1</link>
<description>
<![CDATA[Data is an important property to everyone and lots of it is generated daily. The large amount of data available in the world today, is stored in repositories, databanks, data warehouses etc. Generated data is further on the rise with the Internet, resulting in the consequent explosion of data and its usage. Data convergence over the Internet, has made it more imperative to analyze data relations due to the tremendous sizes that scales up to petabytes of data. But, there exists inherent challenges of extracting useful data from these large repositories. Thus, focal point of this study is to model a rule-based computational solution to the inherent challenge. We thus propose the use of a market basket dataset mining using a hybrid deep learning associative rule mining heuristic.]]>
</description>
<dc:creator>
A.A.  Ojugo, A.O.  Eboka
</dc:creator>
<dc:date>2018-11-09</dc:date>
<dc:publisher>Science and Education Publishing</dc:publisher>
<prism:publicationDate>2018-11-09</prism:publicationDate>
<prism:number>1</prism:number>
<prism:volume>3</prism:volume>
<prism:startingPage>1</prism:startingPage>
<prism:endingPage>8</prism:endingPage>
<prism:doi>10.12691/dt-3-1-1</prism:doi>
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<title>
Comparative Evaluation for High Intelligent Performance Adaptive Model for Spam Phishing Detection
</title>
<link>http://pubs.sciepub.com/dt/3/1/2</link>
<description>
<![CDATA[Modern day technology, daily seeks to better data processing activities through features such as improved speed, better functionality, higher mobility, portability and improved data access – all of which is extended via smart computing. The widespread use of smartphone has led to an exponential growth in the volumes of emails, alongside great success in phishing attacks carried out more effective via spam inbox mails to unsuspecting users – soliciting for funds. Many mail apps today, offers automatic filters as a set of rules to help better organize and dispose (as spam, if necessary) incoming mails based through the checking of certain keywords detected in a message’s header or body. Achieving such programming filter feature is quite mundane and also inefficient, as spams often evade such filters, slipping into inbox again and again. The study seeks to provide an intelligent adaptive mail support that learns user’s preference via an evolutionary unsupervised model(s) as a computational alternative that adapts the data locality feat as well as compares convergence results yielded by the unsupervised hybrid classifiers. It achieves such feats by building local decision heuristics into their classification processes so that such spam filter(s) are embedded with a design that allows for email genres.]]>
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<dc:creator>
A.A.  Ojugo, A.O.  Eboka
</dc:creator>
<dc:date>2018-11-09</dc:date>
<dc:publisher>Science and Education Publishing</dc:publisher>
<prism:publicationDate>2018-11-09</prism:publicationDate>
<prism:number>1</prism:number>
<prism:volume>3</prism:volume>
<prism:startingPage>9</prism:startingPage>
<prism:endingPage>15</prism:endingPage>
<prism:doi>10.12691/dt-3-1-2</prism:doi>
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