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The use of a conversational agent to relay information on behalf of individuals has gained worldwide acceptance. The conversational agent in this study was developed using Retrieval-based Model and Deep Learning to enhance the user experience. Nevertheless, the successfulness of the conversational agent could only be determined upon the evaluation. Thus, the testing was performed in the quantitative approach via questionnaire survey to capture user experience upon the usage of the conversational agent in terms of Usability, Usefulness and Satisfaction. The questionnaire survey was tested via statistical tool for reliability and validation test and proven to be carried out. The test results indicate positive experience towards the usage of the conversational agent and the outcome of the testing showed promising results and proof the success of this study, with immense contributions to the field of conversational agent.

University timetabling construction is a complicated task that is encountered by universities in the world. In this study, a hybrid approach has been developed to produce timetable solution for the university examination timetabling problem. Black Hole Algorithm (BHA), a population-based approach that mimics the black hole phenomenon has been introduced in the literature recently and successfully applied in addressing various optimization problems. Although its effectiveness has been proven, there still exists inefficiency regarding the exploitation ability where BHA is poor in fine tuning search region in reaching for good quality of solution. Hence, a hybrid framework for university examination timetabling problem that is based on BHA and Hill Climbing local search is proposed (hybrid BHA). The aim of this hybridization is to improve the exploitation ability of BHA in fine tuning the promising search regions and convergence speed of the search process. A real-world university examination benchmark dataset has been used to evaluate the performance of hybrid BHA. The computational results demonstrate that hybrid BHA capable of generating competitive results and recording best results for three instances, compared to the reference approaches and current best-known recorded in the literature. Other than that, findings from the Friedman tests show that the hybrid BHA ranked second and third in comparison with hybrid and meta-heuristic approaches (total of 27 approaches) reported in the literature, respectively.

This paper describes research work in implementing a conversational intelligent agent on the web focusing on a top-down natural language query approach. While the present World-Wide Web provides a distributed hypermedia interface to the vast amount of information on the Internet, there is a lack of appropriate metadata to that content. Instead of being a giant library as intended, increasing sections of the Web are looking like a giant dump. A multi-level natural language query system is described in this paper for the representation of knowledge in specific and open domains. The six layers system includes spell check, Natural Language Understanding and Reasoning, FAQChat, Metadata Index Search, Pattern matching and case-based reasoning, and, semi-automated matching approach. Extracts from queries on the field of pandemic crisis, Bird Flu H5N1 is demonstrated.