| 000 | 01096npc a2200157Ia 4500 | ||
|---|---|---|---|
| 008 | 140613s2013 xx 000 0 und d | ||
| 060 | _a004.692 | ||
| 100 | _aNONYELUM, OGWUELEKA FRANCISCA | ||
| 245 | _aMultilayered perceptron feed forward artificial neural network approach for E-mail classification | ||
| 260 | _c2013 | ||
| 300 | _a7-29 | ||
| 520 | _aE-mail messages are originally designed to be sent and accumulated in repository for periodical use which amounts to the details of an event or a meeting's upcoming agenda for a particular organization. These messages range from static organizational knlowledge to conversations and pose a lot of difficulties to users in terms of prioritizing and processing of the contents of both stored and new incoming messages. This research has established a classification model which classifies the accumulated e-mails in the mail inbox known as dataset into four classes : critical, urgent, important and others. | ||
| 653 | _aCOMPUTER SCIENCE | ||
| 773 |
_oP14782 _nM _933643 _011231 _tIUP: JOURNAL OF INFORMATION TECHNOLOGY |
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| 942 |
_2ddc _cARTCL |
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| 999 |
_c11721 _d11721 |
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