Noise Modeling In Universiti Sains Malaysia And Offshore Oil And Gas Platform [TD892. H239 2007 f rb].

Dalam beberapa dekad lepas, pencemaran bunyi bising telah meningkat secara berterusan disebabkan oleh perkembangan perbandaran dan perindustrian yang pesat. Ia telah dikategorikan sebagai salah satu masalah utama alam sekitar dan juga dikaitkan dengan isu-isu bagi kesihatan fizikal dan mental. Over the last few decades, noise pollution has steadily increased due to rapid urbanization and industrialization. It has been categorized as a major environmental problem as well as being related to physical and mental health issues

Massive training artificial immune recognition system for lung nodules detection

In the early detection and diagnosis of lung nodule, computer aided detection (CAD) has become crucial to assist radiologists in interpreting medical images and decision making. However, some limitations have been found in the existing CAD algorithms for detecting lung nodules, such as imprecision classification due to inaccurate segmentation and lengthy computation time. In this research, Massive Training Artificial Immune Recognition System (MTAIRS) is proposed to detect lung nodules on Computed Tomography (CT) scans. MTAIRS is developed based on the pixel machine learning and artificial immune-based system-Artificial Immune Recognition System (AIRS). Two versions of proposed algorithms have been investigated in the study: MTAIRS 1 and MTAIRS 2. Since segmentation and feature calculation are not implemented in the pixel-based machine learning, the loss of information can be avoided during the data training in MTAIRS 1 and MTAIRS 2. The experiment and analysis find that MTAIRS 1 and MTAIRS 2 have successfully reduced the computation time and accomplished good accuracy in the detection of lung nodules on CT scans compared to other well-known pixel-based classification algorithms. Furthermore, MTAIRS 1 and MTAIRS 2 are investigated to improve their performance in eliminating the false positives. A weighted non-linear affinity function is employed in the training of MTAIRS 1 and MTAIRS 2 to replace Euclidean distance in affinity measurement. The enhanced algorithms named, E-MTAIRS 1 and E-MTAIRS 2 are capable to reduce the false positives in the non-nodule classification while maintaining the accuracy in nodule detection. In order to further provide comparative analysis of pixel-based classification algorithms in lung nodules detection, a pixel-based evaluation method of Kullback Leibler (KL) divergence is proposed in this study. Based on the pixel-based quantitative analysis, MTAIRS 1 performs better in the elimination of false positives, while MTAIRS 2 in lung nodules detection. The average detection accuracy for both MTAIRS algorithms is 95%

An Improved Finite Cloud Method With Uniformly Distributed Clouds and Enhanced Boundary Conditions

Finite cloud method (FCM) employs the fixed kernel reproducing technique to construct the interpolation function and point collocation approach is adopted for the discretization. In this study, an improved FCM is proposed such that a node of interest is approximated with its nearest cloud. This feature enables a set of uniformly distributed clouds of various densities such that all the information in the problem domain is captured and stored in the clouds. Additionally, the instability of FCM near the boundaries is treated by having the boundary nodes also satisfy the governing differential equation. Besides, a splitting mechanism is suggested for the node refinement to improve the accuracy of solution. Parameters are introduced to control the density of clouds and the singularity of the moment matrices associated with the clouds. Thus, a more controllable numerical simulation is developed. Numerical examples are presented and the results have shown that the improved FCM produces a stable and better accuracy of solution

Pixel machine learning with clonal selection algorithm for lung nodules visualization

The early detection of lung nodules is critical to provide a better chance of survival from lung cancer. Since benign/malignant lung cancer may be caused by the growth of lung nodules, the diagnosis of an early detection of lung nodules is important. With rapidly development of advanced technology, detection of lung nodules becomes efficient by utilizing computer-aided detection (CAD) systems that can automatically detect and localize the nodules from computed tomography (CT) scans. CAD is fundamentally based on pattern recognition by extensive use of machine learning approaches which is highly interrelated to mathematical algorithms. In this study, a pixel machine learning algorithm which is developed by artificial immune system (AIS) based algorithm – Clonal Section Algorithm (CSA) is proposed for lung nodules visualization. By using pixel machine learning algorithm, several pre-processing procedures can be avoided to prevent the loss of information from image intensities. It is found that the proposed classification algorithm using original intensity values from CT scans is able to provide reasonable visualization results for lung nodules detection

Texture classification of lung computed tomography images

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Gaussian mixture model - Expectation maximization algorithm for brain images

Segmentation of human brain can be performed with the aid of mathematical algorithm as well as computer-based system to assist radiologists and medical related profession to monitor the condition of one's brain comprehensively. Due to the complex structure of the human brain, one cannot simply analyze them just by looking at the MRI images. This research examines the brain segmentation and the validation of the segmentation using ground truth data for seven subjects. The segmentation of brain regions such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) can be accomplished by using Gaussian Mixture Model (GMM) and Expectation-Maximization (EM) Algorithm. The results of segmentation are shown by the Gaussian distribution graph that indicates the volume of brain regions. The segmentation results are validated by the value of Dice index, Jaccard index, and positive predictive value (PPV). It is found that all seven subjects have high value for every index as the values ranging from more than 0.6 to almost approaching 1. For all subjects, the lowest percentage for Dice is 77.82% while the highest is 84.28%, the lowest percentage for Jaccard is 63.70% while the highest is 72.84%, and the lowest percentage for PPV is 94.44% while the highest is 98.75%. In conclusion, the index values for all subjects are acceptable and this means the segmentation by using GMM and EM Algorithm is accurate after going through the process of validation of segmentation

Automatic white matter lesion detection and segmentation on Magnetic Resonance Imaging: A review of past and current state-of-the-art

White matter lesion (WML) is an abnormal tissue occurring in white matter. It indicated the damage of the myelin sheath that used to surround the axon of a neurone. This resulting neurological and vascular disorder occur in the patient, also commonly developed in the healthy brain of elderly. Magnetic Resonance Imaging is a non-invasive medical equipment preferred choice by the clinician to diagnose and observed the injury of brain tissue. However, WML quantitative assessment and analyse on the large volume of MR imaging is a challenge. In this paper, we provide an intensive review of the past and recent WML delineation and detection methods. This review included visual scoring assessment, a common preprocessing step for WML segmentation, false positive elimination, and the latest automatic WML segmentation approaches will be presented

E-learning as a supplementary tool for enhanced students’ satisfaction

Information and communication technology (ICT) is used widely in educational industry for pedagogical activities including teaching approach, resources sharing, classroom communication and assessment. In Universiti Teknologi Malaysia (UTM), e-learning played a significant role as a supplementary tool for effective web-based learning. A study was conducted with the aim to determine what factors drive students' satisfaction in UTM e-learning. This paper present some of the results. A total of 194 samples were collected from undergraduate students in UTM using quantitative method. Purposive sampling technique used to select the participants of the study. The Statistical Package for the Social Sciences (SPSS) is utilized for data analysis. The findings showed that delivery method and content have a positive and significant relationship with students' satisfaction in using e-learning. However, system operations have no impact on the satisfaction of the students with UTM e-learning. In conclusion, the study proposed to provide an effective teaching model for general education schools

Students’ Satisfaction Using E-Learning as a Supplementary Tool

E-learning is useful to help students gaining digital and data literacy during their studies particularly in the era of Industrial Revolution 4.0 (IR 4.0). E-learning which is characterized by time and place flexibility should be utilized as a tool for self-learning. In Universiti Teknologi Malaysia (UTM), e-learning plays an important role as a supplementary tool for effective web-based learning. The purpose of this study is to examine what are the factors that drive students’ satisfaction in e-learning. A total of 194 samples were collected from undergraduate students in UTM using quantitative method. Purposive sampling technique was used to select the respondents. Statistical Package for the Social Sciences (SPSS) was utilized for data analysis. The findings showed that delivery method and con-tent have a positive and significant relationship with satisfaction of using e-learning. However, system operations has no impact on students’ satisfaction in e-learning. In conclusion, the finding of this study is expected to provide an effective teaching model for general education schools

Enhancing consumer repurchase intention towards Airbnb

Airbnb is the world largest accommodation platform, and it has expanded rapidly across the world since 2008. However, the growth rate of Airbnb is slower than the hotel industry in the hospitality market. In Malaysia, Airbnb is also facing strong competition with the hotel industry in the hospitality market. The average hotel occupancy level is roughly 65%, but the occupancy rate of Airbnb in Malaysia is much lower than the hotel industry. It is important for marketers to focus on factors that can lead to repurchase intention of Airbnb. Thus, the purpose of this study is to examine the factors that affect the user's adoption of the Airbnb website and guest's satisfaction with the Airbnb stay, as well as Airbnb's repurchase intention. A total of 143 samples were collected using quantitative method through Google Form. Purposive sampling technique was employed to select respondents who have stayed with Airbnb before. Structural equation modelling (SEM) was employed for data analysis. The findings indicated that perceived ease of use (PEOU) and perceived usefulness (PU) of the Airbnb website have positive and significant effects on consumer attitudes toward the Airbnb website. In contrast, the trust of the Airbnb website has no effect on consumer attitudes. Besides, amenities in the Airbnb property and host-guest relationship have a positive and significant effect on consumer satisfaction. Additionally, consumers' attitudes toward the Airbnb website and consumers' satisfaction also have a positive and significant effect on their repurchase intention on Airbnb. This study is expected to contribute to Airbnb website developer, Airbnb hosts, and Airbnb marketing team regarding the factors influencing consumer attitudes and satisfaction, which ultimately lead to repurchase intention