Fahad Anwar

A Framework for Using Web Usage Mining to Personalise E-learning

Web usage mining can contribute to finding significant educational knowledge. It can play a vital role in the personalisation aspects of any domain. We propose a framework for personalizing e-learning that necessitates careful attention towards individual learning styles. We focus on identifying learning patterns of learners and the sequence of choosing learning resources in relation to their learning styles. A prototype for an adaptive Web based course has been developed where the learning environment is modifying its behaviour to reflect learning styles.

Efficient periodicity mining of sequential patterns in a post-mining environment

Sequential pattern mining approaches mainly deal with finding the positive behaviour of a sequential pattern that can help in predicting the next event after a sequence of events. In addition, sequential patterns may exhibit periodicity as well, i.e. during weekends 80% of people who watch a movie in cinemas will have a meal in a restaurant afterwards. This is a problem that has not been studied in the literature. To confront the problem of discovering periodicity for sequential patterns we adopt and extend a periodic pattern mining approach which has been utilised in association rule mining. However, due to the sequential/temporal nature of sequential patterns, the process of finding the periodicity of a given sequential pattern increases the complexity of the above mentioned association rule mining approach considerably. As a key attribute of any data mining strategy we provide a comprehensive and flexible problem definition framework for the above mentioned problem. Two main mining techniques are introduced to facilitate the mining process. The Interval Validation Process (IVP) is introduced to neutralise complexities which emerge due to the temporal/sequential nature of sequential patterns, whereas the Process Switching Mechanism (PSM) is devised to increase the efficiency of the mining process by only scanning relevant data-sets from the source database. The approach proposed in this paper is based on a post-mining environment, where the identification of sequential patterns from a database has already taken place.

Entity appearance model generation for multimedia events in surveillance videos

Traditionally, surveillance systems only focus on a small number of entities (such as humans, entrance and exit areas, etc.) and appearance models of these entities are uploaded manually in the system. However, as the end users are becoming more aware of the vision based technologies, there is ever growing demand for advanced surveillance systems which can detect complex abnormal events on different aspects of operational activities and can also provide intelligence to improve their operational management process. To achieve this goal, we proposed the event mining framework which explores the relationship between entity feature-sets and associated text strings to generate appearance models of all the entities automatically and can update them dynamically.

Visual Recognition of Manual Tasks Using Object Motion Trajectories

Motion trajectories are powerful cues for event detection and recognition. In this paper we present a system for manual task analysis that distinguishes between skin and object motion and learns activity patterns through analysing object trajectories. It is particularly suited to the recognition of common object handling tasks. Our vision system performs hand skin detection and object segmentation for each frame in a sequence. The object trajectories are then modelled as motion time series. We have compared the performance of several different time series indexing schemes: symbolic, polynomial and orthonormal basis functions used for trajectory similarity retrieval and classification. We then attempt to cluster objectcentred motion patterns in the coefficient feature space. The proposed technique is validated on two different datasets, Australian Sign Language and object handling data obtained in the laboratory. Applications to task recognition and motion data mining in industrial surveillance applications are envisaged.

Discovery of events with negative behavior against given sequential patterns

The dramatic drop in the prices of data collection and storage devices has not only enabled organisations to store almost every activity of their business processes, they can also retain every state of these activities as well. Availability of these masses of data also means that by implementing different data mining techniques we can yield more accurate and useful information to be used for important decision making. One of the key mining techniques on such data is to discover sequential patterns. Most of the existing sequential pattern mining approaches mainly deal with finding the positive behaviour of a sequential pattern that can help in predicting the next event after a sequence of events. In this paper we propose the concept of Negative Behaviour Against the Sequential Pattern (NBASP) that is to discover the events/event-sets which are unlikely to follow the given sequential pattern and discuss its applications in a variety of domains. A comprehensive problem definition and efficient algorithm to discover NBASP is presented.

Discovery of Anomalous Event against Frequent Sequence of Video Events

Events occurring in observed scenes are one of the most important semantic entities that can be extracted from videos (Anwar and Naftel, 2008). Most of the work presented in the past is based upon finding frequent event patterns or deals with discovering already known abnormal events. In contrast in this paper we present a framework to discover unknown anomalous events associated with a frequent sequence of events (AEASP); that is to discover events which are unlikely to follow a frequent sequence of events. This information can be very useful for discovering unknown abnormal events and can provide early actionable intelligence to redeploy resources to specific areas of view (such as PTZ camera or attention of a CCTV user). Discovery of anomalous events against a sequential pattern can also provide business intelligence for store management in the retail sector.

Accumulation, storage and obtainment of generalized net tokens characteristics history

In this paper is represented one idea for an extension to the existing software for generalized net (GN) models simulation. Given solution for storing of the characteristics of the tokens during their movements through the GN-models improves the existing GN-interpreter. The software tool is designed to be the independent central component of GN enabled applications. In this paper the idea for adding a new functionality is presented and the bases for storing tokens characteristics development are laid.