Khairil Imran Ghauth

Building an E-learning Recommender System Using Vector Space Model and Good Learners Average Rating

An enormous amount of learning materials in e-learning has led to the difficulty on locating suitable learning materials for a particular learning topic, creating the need for content recommendation tools within learning context. In this paper, we aim to address this need by proposing a novel framework for an e-learning recommender system. Our proposed framework works on the idea of recommending learning materials based on the similarity of content items (using Vector Space Model) and good learners’ average rating strategy. This paper presents the overall architecture of the proposed system and its potential implementation via a prototype design.

A graph-based web usage mining method considering client side data

To improve website, we need to evaluate current usage of it. Web usage mining and statistical analysis are two ways to evaluate usage of website. The combination of web usage mining and statistical analysis gives more accurate information about web usage. Through web usage mining methods, graph mining covers complex web browsing behaviors such as parallel browsing. Through statistical analysis methods, analyzing page browsing time gives valuable information about website and its users. This paper presents a web usage mining method which combines web usage mining and statistical analysis considering client side data. In other words, it combines graph based web usage mining and browsing time analysis with taking client side data into account. It helps us to reconstruct user session exactly as it has been and based on these data, we find web usage patterns with more accuracy.

Using service-based content adaptation platform to enhance mobile user experience

The ability to retrieve device capabilities provides huge potential in enhancing contents to user. In this paper, we propose service-based content adaptation platform (SCAP) that adapts content for display on mobile devices. It serves the objectives to provide adaptive content to user without direct user input; to provide an enhanced user experience by offering value-added content and also to provide flexible and scalable service-based content delivery mechanism. This paper discusses the implementation modules which include service-based device information retrieval and content negotiation. Performance analysis, benchmarking comparison and caching for proposed platform are scheduled for the ongoing works.

A Semantic Content-Based Forum Recommender System Architecture Based on Content-Based Filtering and Latent Semantic Analysis

The rapidly increasing popularity of social computing has encouraged Internet users to interact with online discussion forums to discuss various topics. Online discussion forums have been used as a medium for collaborative learning that supports knowledge sharing and information exchanging between users. One of the serious problems of such environments is high volume of shared data that causes a difficulty for users to locate relevant content to their preferences. In this paper, we propose an architecture of a forum recommender system that recommends relevant post messages to users based on content-based filtering and latent semantic analysis which in turn will increase the dynamism of online forums, help users to discover relevant post messages, and prevent them from redundant post messages as well as bad content post messages.

Adopting Big Data Analytics Strategy in Telecommunication Industry

Nowadays, adopting big data is a reality. Telecommunication company or telco must find the right solution to store all information available across the organization to maximize revenue using the analytics. The solution must be able to harness the large volume, variety, and velocity of the data available. One of the challenging actions is how to perform decision making and analysis in real-time. Some of the operational decisions may not comply with the corporation policy which makes it hard to keep up with the modern evolving business environment. Telco needs a platform to improve the business process and sustainable and profitable growth. The significant impacts should involve improvements of the customer experience and more reliable network quality, thereby reducing the customer churn rate. Big data and machine learning represent todays trends for the analytics. With big data analytics, the service provider can utilize the full potential of their data set by correlating, processing, and deciphering the hidden information from it. The conventional machine learning tools without big data are becoming inadequate as the trends shift towards distributed and real-time processing. The service provider needs the solution big data-driven which supports them to achieve timely manner and more accurate insights via the predictive analytics, text mining, and optimization. This paper also explains the characteristics of big data, and several uses of case implementing machine learning inside the big data platform related to telco operation such as mobile fraud detection. A well-known big data processing framework such as Hadoop indicated that there is an integration with machine learning tools such as Mahout, H2O.ai, R-Hadoop components, and KNIME. The advantages of these tools are evaluated based on their scalability, ease of use and extensibility features.

Text Censoring System for Filtering Malicious Content Using Approximate String Matching and Bayesian Filtering

Information obtained nowadays often contains malicious contents. These malicious contents such as profane words have to be censored as they can influence the minds of the young ones and create hate among people. In censoring the profane words, this paper introduces a hybrid text censoring method which is based on Bayesian Filtering and Approximate String Matching techniques. The Bayesian filtering technique is used to detect the malicious contents (profane words) while the Approximate String Matching technique is used to enhance the effectiveness of detecting profane words. In evaluating the performance of the proposed system, the evaluation metrics of Precision, Recall, F-measure and MAE were used. The results show that Bayesian filtering technique can be used to filter profane words.