Smart articles classifier (SAC)‎

Joint Authors

Nasir, Rim S.
Radwan, Khulud S.
Salihah, Ahlam M.
Husayn, Muhammad T.

Source

المؤتمر الدولي الهندسي الرابع : “نحو هندسة القرن الحادي و العشرين” : الذي نظمته كلية الهندسة بالجامعة الإسلامية-غزة يومي الاثنين و الثلاثاء الخامس عشر و السادس عشر من تشرين أول / أكتوبر 2012.

Publisher

Islamic University of Gaza Faculty of Engineering

Publication Date

2012-10-31

Country of Publication

Palestine (Gaza Strip)

No. of Pages

16

Main Subjects

Engineering & Technology Sciences (Multidisciplinary)

English Abstract

SAC articles classifier is the task of automatically sorting a set of documents into categories from a predefined set.

This task has several applications, including automated indexing of scientific articles according to predefined thesauri of technical terms.

Automated classification is attractive because it frees organizations from the need of manually organizing document bases, which can be too expensive, or simply not feasible given the time constraints of the application or the number of documents involved.

This paper proposes the use accuracy of modern text classification systems rivals that of trained human professionals, thanks to a combination of information retrieval (IR) technology and machine learning (ML) technology.

The proposed approach will be implemented by using three different algorithms from machine learning include Naïve Bayes classifies and artificial neural network (ANN).

These algorithms achieved different accuracy according to its mechanism.

Classification based on naïve bayes assumption include Multinomial model which specifies that a document is represented by the set of word occurrences from the document, and Bernoulli model which specifies that a document is represented by a vector of binary attributes indicating which words occur and do not occur in the document.

However classification based on neural network assumption consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation.

The main advantage of creating a classification scheme is that a gateway is able to create a customized scheme, adapted to its specific content and user groups, which should be able to meet all of its specific requirements.

This research paper opens the prospect of great techniques that are more about building strength, flexibility and ease in dealing with data.

And develop methods to extract and retrieve data by means for dispensing with the usual.

Data Type

Conference Papers

Record ID

BIM-777412

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