Ong Sing Goh

Intelligent agent technology in e-commerce

Use of Internet has surged at an exponential rate in recent years. In particular, it has led to many new and innovative applications in the area of E-Commerce. In this paper, we introduce an intelligent agent termed AINI, the Artificial Intelligent Solution Humanoid. We also show how current e-commerce technological trends can be enhanced by using AINI. AINI is a chatterbot integrated with 3D animated agent character, Speech Technology and Artificial Intelligence Markup Language (AIML) is utilised. This agent technology is mainly used to improve customer services and to reduce customer reliance on human operators. By using artificial intelligence techniques, AINI is able to provide appropriate answers to service inquiries. In this paper, issues on Intelligent Agents Technology, Speech Technology, AIML and the use of 3D animated character in E-Commerce are discussed.

Intelligent meters for improved system operation and customer relationship management

Since the time. that electric power meters were introduced in the 1870s, the basic function of the meters has remained more or less unchanged. Many developed countries are still using the same technology that has existed for more than a century. In particular, meter readings for residential services are normally taken manually once a month or every two months. While automatic meter reading (AMR) has gradually been introduced in many places, the cost involves in retrofitting the existing systems may not be justified if they are used merely for meter reading. This paper proposes two approaches to enhance the functions of the meters intelligently thereby improving the operation of the electrical supply system and customer relationship management. Data mining (DM) techniques are first discussed for information extraction and an intelligent agent (IA) technique is also proposed for front-end customer services.

An Analysis of Man-Machine Interaction in Instant Messenger

The availability of multiple media channels through the Internet has added new dimensions of communication between people or communities who are geographically separated. In the environment of informal communication on the Internet, chat applications are popular in which a user may be represented only by a nickname or an alias. This suggests that a person may be able to communicate more freely when his or her identity is concealed. Popular chatting or instant messaging (IM) systems such as Microsoft MSN Messenger, America Onlines Instant Messenger, Yahoo! Messenger, and GoogleTalk have changed the way that a user may communicate with friends, acquaintances, and business colleagues. Once limited to desktop personal computers (PCs) or laptops, popular instant messaging systems are finding their way onto handheld devices and mobile phones. This allows a user to chat from virtually anywhere. Nowadays, IM is found on almost every personal PC connected to the Internet as well as on many corporate desktops.

An Embodied Conversational Agent for Intelligent Web Interaction on Pandemic Crisis Communication

In times of crisis, an effective communication mechanism is paramount in providing accurate and timely information to the community. In this paper we study the use of an intelligent embodied conversational agent (ECA) as the front end interface with the public for a crisis communication network portal (CCNet). The proposed system, CCNet, is an integration of the intelligent conversation agent, AINI, and an automated knowledge extraction agent (AKEA). The AKEA retrieves first hand information from relevant sources such as government departments and news channels. In this paper, we compare the interaction of AINI against two popular search engines, two question answering systems and two conversational systems

Embodied Conversational Agents for H5N1 Pandemic Crisis

This paper presents a novel framework for modeling embodied conversational agent for crisis communication focusing on the H5N1 pandemic crisis. Our system aims to cope with the most challenging issue on the maintenance of an engaging while convincing conversation. What primarily distinguishes our system from other conversational agent systems is that the human-computer conversation takes place within the context of H5N1 pandemic crisis. A Crisis Communication Network, called CCNet, is established based on a novel algorithm incorporating natural language query and embodied conversation agent simultaneously. Another significant contribution of our work is the development of a Automated Knowledge Extraction Agent (AKEA) to capitalize on the tremendous amount of data that is now available online to support our experiments. What makes our system differs from typical conversational agents is the attempt to move away from strictly task-oriented dialogue.

A Review on Automatic Text Summarization Approaches

It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

Single channel sEMG muscle fatigue prediction: An implementation using least square support vector machine

Surface electromyogram (sEMG) signal is commonly used for muscle fatigue analysis in clinical rehabilitation studies. Prediction results based on sEMG signals are promising because muscle contradiction can be easily characterized using sEMG signals. However, the prediction results usually deteriorate significantly when noise exist during data acquisition. Noise happens due to many factors ranging from hardware, software to procedure flaws. This investigation is aimed to assess the performance of the Least Square SVM model in predicting muscle fatigue using single channel sEMG signal. The root mean square, median frequency, and mean frequency features were extracted from two sets of raw sEMG signals captured at the multifidus (for low back pain) and flexor carpi radialis (for forearm muscle fatigue) muscles. The proposed LS-SVM technique were used to build the prediction rule-base separately for both the datasets. The implementation, testing and verification were performed in Matlab environment. The k-nearest neighbour and artificial neural network were used as the benchmarking techniques in results comparison and analysis. LS-SVM technique is proven good against the benchmarking techniques on classification accuracy and area under ROC curve. The ANOVA and Tukey HSD post hoc test were used to further validate the significant of the comparison results on both accuracy and AUC measurements.

Query based intelligent web interaction with real world knowledge

This paper describes an integrated system based on open-domain and domain-specific knowledge for the purpose of providing query-based intelligent web interaction. It is understood that general purpose conversational agents are not able to answer questions on specific domain subject. On the other hand, domain specific systems lack the flexibility to handle common sense questions. To overcome the above limitations, this paper proposed an integrated system comprises of an artificial intelligent conversation software robot or chatterbot, called Artificial Intelligence Natural-language Identity (hereafter, AINI), and an Automated Knowledge Extraction Agent (AKEA) for the acquisition of real world knowledge from the Internet. The objective of AKEA is to retrieve real world knowledge or information from trustworthy websites. AINI is the mechanism used to manage the knowledge and to provide appropriate answer to the user. In this paper, we compare the performance of the proposed system against two popular search engines, two question answering systems and two other conversational systems.

Text Summarization Based on Classification Using ANFIS

The information overload faced by today’s society has created a big challenge for people who want to look for relevant information from the internet. There are a lot of online documents available and digesting such large texts collection is not an easy task. Hence, automatic text summarization is required to automate the process of summarizing text by extracting only the salient information from the documents. In this paper, we propose a text summarization model based on classification using Adaptive Neuro-Fuzzy Inference System (ANFIS). The model can learn to filter high quality summary sentences. We then compare the performance of our proposed model with the existing approaches which are based on neural network and fuzzy logic techniques. ANFIS was able to alleviate the limitations in the existing approaches and the experimental finding of this study shows that the proposed model yields better results in terms of precision, recall and F-measure on the Document Understanding Conference (DUC) data corpus.

Voting Models for Summary Extraction from Text Documents

Electronic information - web pages, text documents, etc. are rapidly expanding due to the exponential growth of the World Wide Web (WWW). Information which are available through online search often provide readers with large collection of texts. Although easy access to online information had made great impact to the people, on the other hand, it has also caused them problem in facing information overload. Providing a solution to digest various information sources is indeed necessary to treat such problem. Especially in the case concerning online text sources, one study which is being actively researched is the field of automatic text summarization. In this paper, we propose the use of voting models, an effective approach in ranking aggregates tasks, to treat text summarization. Here, we will discuss how voting models can be adapted to the task of sentence ranking to generate text summaries.