Dini Handayani

Critical success factors for IS project implementation in Malaysian institutions of higher learning

What do Information System (IS) project managers consider as critical success factors (CSFs) in IS project implementation? Project implementation is a complex process which requires simultaneous attention to a variety of factors - people, technology and processes. To determine whether a project is a success or a failure, one needs to know what are the important criteria of a project implementation in the eyes of the stakeholders. For this study, we are identifying the CSFs from a project managers view. This study tries to identify which processes, people, tools and environment factors do experienced project managers regard as CSFs for IS projects implementation. Lessons learnt from these experienced project managers provide an important framework for establishing a sense of IS project implementation success, so that others can learn and take the necessary steps. This study is far from being exhaustive, but it can contribute to project managers by identifying and establishing the CSFs for IS project implementation, as there are not many studies concerning this being carried out for Malaysian Institution of Higher Learning (IHL).

Systematic review of computational modeling of mood and emotion

In the recent years, more studies that aim to make computers understand, experience and respond to affects accordingly through computational models have been widely researched. Although many studies have defined and distinguished the words affect, mood and emotion, such terms are still used interchangeably. Thus, in this study, a systematic literature review was implemented to summarize and evaluate the current states of the arts on computational modeling of mood. From three online databases including IEEE Xplore, ScienceDirect and Springer Link, 825 scientific articles were extracted. Furthermore, through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta Analyses) Statement, 9 articles were selected for the review. These resulting articles were reviewed based on several categories including the aim of the study, the study population, the measurement of mood, the basic emotion dimension, and proposed computational model, as well as evaluation. As a result, the systematic literature review has provided a good starting point in the study of the computational modeling of mood and emotion.

Anomaly Detection in Vessel Tracking Using Support Vector Machines (SVMs)

The paper is devoted to supervise method approach to identify the vessel anomaly behaviour in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behaviour. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behaviour. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.

Design and development of the FMCW Radar Scene Generator

Like other radar systems, Frequency Modulated Continuous Wave (FMCW) maritime radar can measure simultaneously the range, velocity, reflectivity and azimuth of targets on the sea. However, compared with conventional pulse radar, FMCW radar has many advantages. It is relatively small and simple, offers high resolution, provides data over wide bandwidths, and is relative inexpensive. On the other hand, the fast development in technologies of radar, electronic charts and further navigational aids requires permanent training. Such training can be conducted with the aid of a radar simulator. The benefit of using a radar simulator is that the radar environment is electronically synthesized, and therefore exactly repeatable. Furthermore, a good physics-based FMCW radar simulator requires a radar scene generator. This scene generator will provide a real-world test and training environment for the operation of the simulated radar system. The aim of this work is to develop a two-dimensional FMCW radar scene generator that can be used as an aid in the ship navigation training. In this work a suitable graphical user interface (GUI) is also designed and developed. Using this GUI the user can easily generate static and dynamic components of the radar scene generator.

Statistical Approach for a Complex Emotion Recognition Based on EEG Features

This paper presents electroencephalogram (EEG) signals and normal distribution technique to recognize the complex emotion. In the recent years, there has been a trend towards recognizing human emotions, however not many researcher aware that human can recognize more than one emotion at one time. Thus, in this study, normal distribution is utilized to recognize the expected emotion. The feature extraction and classification were obtained using a Mel-frequency cepstral coefficients (MFCC) and multilayer perceptron (MLP). The correlation between human emotion and mood is also the essential point, since the mood can affected to the human emotion. The results show that the human emotions is strongly influenced by his initial mood.

Identification of Vessel Anomaly Behavior Using Support Vector Machines and Bayesian Networks

In this work, a model based on Support Vector Machines (SVMs) classification to identify vessel anomaly behavior has been proposed and implemented. The results are compared to Bayesian Networks (BNs). The real world Automated Identification System (AIS) vessel reporting data is used in this work. The results shows that SVMs can achieve higher accuracy compared to BNs in both memory-test and blind-test. The effect of holdout method which are partitioned size of training and testing data set on the accuracy result are also investigated in this study. The proposed classifier demonstrates to be a viable tool for identifying the vessel anomaly behavior by its accuracy.

Prototyping of a Situation Awareness System in the Maritime Surveillance

This paper discusses about the design of a Situation Awareness (SA) system to support vessel crews and control room operators in improving the decision making process. The architecture of the system is ontology based. The vessel crews and control room operators may face a loss of SA. They may have limited cognitive abilities which make it difficult to make a decision in a high stress level, short time availability and continuously evolving situation with incomplete information. In this work, we describe the application of Semantic Web Rule Language to represent corresponding knowledge in the maritime surveillance domain. The result of this research will demonstrate that an ontology based system can be used to remodel the information into a meaningful and valuable form to predict the future states of SA and improve the decision making process.

An implementation of natural language processing on ontology-based electronic health website: A case of Female-related diseases domain

The advent of Internet has changed the way people live and search for information. Study showed that browsing on health information is amongst popular activities on the Internet, particularly among women. Despite a wide range of information one could gathered from the Internet, no website provides facility for the user to query the information using a natural language. It is the aim of this research to develop a prototype that allows individuals to query information from the web using natural language processing. This prototype will serve as a tool that allows both individuals and medical practitioners to refer and enquire to using natural language, and also enable them to share knowledge on Female-related diseases.