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We explore the problem of locating documents pertaining to critical technologies (e.g., restricted, proprietary, or sensitive technical information) from among a massive and highly heterogeneous collection of largely unimportant files. We present a system that employs the use of supervised machine learning (i.e.
user preferred learning material. Searching through keywords or metadata of learning material will result in display of huge quantity of information. Thus there is an earnest need to identify the techniques that can provide more efficient mechanism for information retrieval. Recommendation techniques have shown to be
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.