H. A. Omar

Support vector machine for classification of stress subjects using EEG signals

Stress is a mental condition that can effects the brain electrical activity to be different from the normal state. This brain cognitive change can be measured using EEG. The objective of this paper is to classify stress subjects based on EEG signal using SVM. The data which are used to represent stress subjects were taken from the residents of Pusat Darul Wardah; a shelter centre for troubled women. SVM is used to classify the EEG Alpha band data for Power Spectral Density and Energy Spectral Density. Using 5-fold cross validation, the classification rate are 83.33% for ESD data using RBF kernel function.

Classification of orchid species using Neural Network

Orchid species have a largest families among the botanical plant. Basically, a species of orchid are visually recognizing from its color, root, petal shape or even the size. However, there are several orchid species that really look alike and the type could be falsely classified. The aim of this paper is to classify two species of orchids which are physically look identical, i.e. Dendrobium Madame Pampadour and Dendrobium Cqompactum using image processing techniques. Using Neural Network, the classification rate is 85.7%.

An online system to support collaborative knowledge acquisition for ontology development

Ontology development is an important practice required in any knowledge engineering process. Before knowledge can be integrated and put into good use by computer applications, knowledge acquisition and modeling has to be done. This signifies the adoption of ontology; which is defined as conceptualization or formal specifications of a domain; as the core framework for a knowledge-based system. For faster ontology development process, it is normally done in a collaborative manner and the challenge of having an environment that supports collaborative work is manifold. This includes the challenge of forming a collaborative procedure in acquiring and modeling the knowledge, allocating a platform that is accessible to geographical distributed parties and to handle update conflicts during the development. This paper presents a system in supporting initial collaborative ontology development works between ontology developers without requiring the presence of domain experts, aiming at accelerating the ontology development process. This system is designed in such a way that it incorporates and formalizes a novel collaborative knowledge acquisition and modeling procedure.

The investigation of alpha frontal energy asymmetry on normal and stress subjects after listening to the binaural beats 10 Hz

In this modern era, life is becoming more challenging and faster paced, where we are highly demanded with the complex needs of our daily life that requires more focus and efforts. Such daily needs will eventually lead us to stress if not handled properly. These negatives effects if go undiagnosed, can be fatal. However, it can be turned into positive effects if we know how to manage it. One way of stress management is through meditation. However, to some people, this act is hard to perform because their brains refuse to obey to be silence and focus. Thus, one way for them to get into the mood easier and quicker is through brainwave entrainment; binaural beat specifically. Our study focuses on the effect of the binaural beats, 10Hz on the fontal alpha energy asymmetry of normal and stress subjects. The total numbers of subjects involved are 42 which comprises of 18 subjects in normal group and 24 subjects in stress group. The EEG recording is done in two sessions that are before and after listening to the binaural beats. Normal subjects experience an increment of 90.1% in their frontal alpha asymmetry while stress subjects experience only 1.37%. The results suggested that frontal alpha energy asymmetry could be an indicator to show the positive effects of the binaural beats sound to human brainwave.

Semantic-based Bayesian Network to determine correlation between binaural-beats features and entrainment effects

In coping with hectic everyday lives, people have tried many different ways to reduce stress and depression. Such effort has also lead to meditation practice, which is believed and proven able to help. While meditation is beneficial, for some people, meditation is just hard to perform. These people may switch to other alternatives that give the same effects as meditation, such as through binaural beats entrainment. Many studies on brainwave entrainment have demonstrated that the brain responds by synchronizing its own electrical cycles to the same rhythm of the stimulating binaural-beats audio. The results of binaural beats entrainment towards the brainwave can be determined by monitoring the EEG readings, which can be analyzed to capture the altered brainwave patterns and qualities they exhibit. In relation to the monitoring process, our work focuses on capturing and analyzing the correlations between different binaural beats features to resulting EEG and perceived mental states. A general methodology is presented while detailing further on the proposed Semantic-based Bayesian Network Engine, which is the core mechanism employed in capturing the correlations. This novel approach is proposed firstly due to the well-known capability of Bayesian Network in modeling the elements of causal and effects. Secondly, with the introduction of semantic notion, the engine is enhanced even more for allowing dynamic-construction of Bayesian Network based on its semantics.

Throughput analysis of LAN and WAN network based on socket buffer length using JPerf

Cluster computing involves connection between a server and multiple hosts in one single environment and multi-cluster in inter-cluster environments. One of the crucial factors of a clustering is its network communication. Relying for support of a server-client internetworking, cluster computing uses the TCP/IP socket communication mechanism extensively. Mechanism that controls the data transfer interruptions between a server and client is referred to as a flow control. Theoretically, if the receive window size for TCP/IP buffers is too small, the receive window buffer is frequently overrun, and the flow control mechanism stops the data transfer until the receive buffer is empty. Thus, as a result flow control activity contributes to unnecessary additional network latency which is unacceptable in any server-client networking especially in clustering environment where it involves highly reliable data transfer. Among the solutions to this problem is by adjusting buffer size to avoid or reduce the potential for flow control to occur. This paper describes the analysis of TCP/IP socket buffer length in Local Area Network (LAN) and Wide Area Network (WAN) clustering. It details the process and flow of setting up the environment, implementing as well as analyzing the throughput performance of the measured buffering.

Depression Diagnostic and Screening Tools Using Android OS Platform

Depression affects all walks of life and is a common form of mental health illness. Some of the common methods to diagnose depression are usually through a session with certified psychiatrist or with the aid of depression rating scales. This paper seeks to provide both clinicians and patients with an Android based mobile application that may store and calculate results based on depression rating scales i.e. DASS and MINI. A novel approach of combining both questionnaire statistics is proposed in one solution. As such, a tablet friendly application that uses a scoring algorithm and a series of psychiatric questionnaires as an indicator to a person’s mental state or depression level is developed by means of an android platform.

Profiling system for depressive disorder patient using web based approaches

This paper presents the work of design, development and implementation of profiling system for depressive disorder patience. Using a web based approach; the proposed system offers medical organizations that diagnose and treat mental disorders patients to begin the implementation of its automated medical record. The work presented here will replace the existing manual patient profiling. It has profound implications for future records of valuable information about the patient profile, pharmacy activities, and accurate medication history to the concerned health care professionals more quickly and efficiently.

Semantic-based online Outcome-based education measurement system

This paper proposes an online Outcome-based Education (OBE) Measurement System, which promotes a centralized OBE data repository for an institution, allowing concurrent online transactions between distributed multi-users. A well-known OBE framework currently being followed by most higher learning institutions in Malaysia is based on the four to seven levels of learning outcomes; a hierarchy commonly composed of course outcomes, program outcomes, program educational objectives, learning outcomes and soft-skill outcomes. The last two learning-outcomes were outlined by the Ministry of Higher Education serving as a standard guideline to be followed by all higher learning institutions in Malaysia. In connecting between the different levels of learning-outcomes, mappings or links between the different levels were pre-defined. Based on these mappings, OBE measurement activities could be carried out across all levels, indicating learning performance achievement reflected at different degrees and perspectives. To accelerate such evaluation processes, we propose a novel online-system design for our OBE measuring engine, adopting semantic notion in defining the concepts of learning-outcomes and their mappings.