Ali Shariq Imran

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In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted from the lecture transcript. The(More)
Lecture videos contain most of the instructional content. These videos contents are in general non-scripted and unedited and thus do not provide the required level of interactivity from such material. Therefore, these videos fail to captivate students' attention for long and thus their effective use remains a challenge. In this regard, Media Learning Object(More)
The usage of non-scripted lecture videos as a part of learning material is becoming an everyday activity in most of higher education institutions due to the growing interest in flexible and blended education. Generally these videos are delivered as part of Learning Objects (LO) through various Learning Management Systems (LMS). Currently creating these(More)
This paper proposes a new objective metric called the SEMCON to enrich existing concepts in domain ontologies for describing and organizing multimedia documents. The SEMCON model exploits the document contextually and semantically. The preprocessing module collects a document and partitions that into several passages. Then a morpho-syntatic analysis is(More)
In this paper, we propose a novel approach to understand the high level semantics of instructional video by identifying mid-level features from the lecture content. The lecture content in instructional videos can be divided into text, equations and figures. In unscripted lecture video, these visual contents can be useful visual cues to understand the high(More)
This paper proposes a game based e-Learning tool called The forensic challenger (TFC) to teach digital forensic investigation. By combining elements from game theory with the use of e-Learning, the authors are able to provide a solution that offers a more efficient way of learning how to perform digital forensics investigations. Contrary to traditional(More)
In this paper, we address the problem of content classification for chalkboard images. Unlike document images, classifying chalkboard content into different categories is a challenging task. The task gets even tougher with varying handwriting styles and arbitrary drawings. We therefore, propose a tool with a set of functions to distinguish equations from(More)