Instructor Authoring Tool for Creating E-Learning Content Using Learning Objects

This thesis illustrates the conception of creating e-learning content. It is based on a research study with the aim to create a framework that can assist instructors to create e-learning content. This research presents the wage and approaches of XML and LCMS Component Technologies in the creation of IAT functional model and the development of IAT. XML can simplify the courseware authoring and structuring process. As a neutral meta-language, it can also separate course contents from course presentation. However, LCMS Component technology is a multi-user environment where instructors can create, store, reuse, manage, and deliver digital learning content from a central object repository. The use of XML and LCMS Component Technologies help a lot in adding the features of easy-to-use, easy-to-maintain, and flexibility to assist instructor to create e-learning content using IAT. This research employs courseware development research method, which aims to create IAT functional model and also develop IAT prototype system in order to help instructor create e-learning content. IAT allows instructors with non-programming skills to create e-learning content easily by using a template to customize their own course content

Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

This thesis addresses the problem of automatic delineation and recognition of the images of Harumanis mangoes acquired in the natural environment. Harumanis is one of the main export produce in Perlis as it is very popular because of its deliciousness, sweetness and aromatic fragrance. In the agricultural industry, the fundamental factor for consistent marketing of the fruit is its quality. The quality of Harumanis is based on the shape and size of the fruits. The ability to efficiently and consistently manufacture high-quality products, and to ensure correct delineation and recognition processes, are the basis for success in the highly competitive fruit industry. Computer vision is a technology that imitates effects of human vision by electronically perceiving and understanding an object in the image. In fact, computer vision is gaining more attention in image-processing applications especially in the agricultural area. The technology involves several stages relating to image acquisition, pre-processing, segmentation, feature extraction and classification. The aim of this research is to assess of the Harumanis fruit quality in natural images. This reserarch adapted a methodology of computer vision and algorithms that exploit image segmentation, feature extraction and fuzzy classification to guide the research activities. In general, image segmentation isolates an object from the images, feature extraction creates features for classification phase while object classification categorizes objects into the correct groups. However, segmentation is challenging for images that are acquired in the natural environment as non-uniform illumination, noisy background, and external appearance are the critical issues that must be addressed. Based on previous researches, most existing segmentation methods focused on a specific environment. Therefore, this research has developed an improved edge detection and contour segmentation algorithm that is able to correctly segment various objects from both indoor and outdoor images. This improved algorithm, known as the edge-template Contour Delineation (etCD), is based on the fusion of edge detection with corner-template detection and dynamic thresholding to produce enhanced edge map. Then, two morphological operators that are embedded with condition inversion and dynamic threshold is used to produce robust and accurate contour objects. Next, contour-tracing and ellipse-tracking are employed to provide precise object boundaries. From each successful contour segmentation, four basic morphological features are extracted to create the Harumanis data set. Feature extraction gathers higher-level information of the fruit from segmentation images. Feature extraction and selection reduces the number of features. In this research, the shape and size feature were extracted using aspect ratio of selected morphological features. The shape and size are measured to estimate the maturity stages and grade levels of the Harumanis. Due to the inherent and uncertain variability of the Harumanis features, fuzzy learning algorithm has been designed to classify these fruits similar to the ability of human experts. Therefore, this research has designed fuzzy learning algorithm that is able to classify fruits based on their shape and size features using Harumanis dataset. This learning algorithm represents an automatic generation of membership functions and rules from the data. Experimental results show that the developed methods and model are able to classify the Harumanis quality with accuracy of 79% using fuzzy classification based on shape and size

Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

This thesis addresses the problem of automatic delineation and recognition of the images of Harumanis mangoes acquired in the natural environment. Harumanis is one of the main export produce in Pedis as it is very popular because of its deliciousness, sweetness and aromatic fragrance. In the agricultural industry, the fundamental factor for consistent marketing of the fruit is its quality. The quality of Harumanis is based on the shape and size of the fruits. The ability to efficiently and consistently manufacture high-quality products, and to ensure correct delineation and recognition processes, are the basis for success in the highly competitive fruit industry. Computer vision is a technology that imitates effects of human vision by electronically perceiving and understanding an object in the image. In fact, computer vision is gaining more attention in image-processing applications especially in the agricultural area. The technology involves several stages relating to image acquisition, pre-processing, segmentation, feature extraction and classification. The aim of this research is to assess of the Harumanis fruit quality in natural images. This research adapted a methodology of computer vision and algorithms that exploit image segmentation, feature extraction and fuzzy classification to guide the research activities. In general, image segmentation isolates an object from the images, feature extraction creates features for classification phase while object classification categorizes objects into the correct groups

Managing behavioral academic self-esteem using FuzzyXteem

Behavioral academic self-esteem (BASE) has been used with children of preschool, elementary, and junior high school classes, both individually and in groups. In this study, BASE is used to estimate the factor structures and determine the levels of academic self-esteem of the student. The current practice of the existing system using BASE scale may be scored by hand or by computer based on the rigid crisp values to represent rating number one through five. Since BASE requires the ability for estimating the factor structure and also the ability to explain how the conclusion is derived, therefore artificial intelligent techniques that are required to perform BASE must be able to perform estimation and provide reasoning. For this purpose, fuzzy logic and expert system have been integrated in a web-based environment to demonstrate the use of hybrid system on BASE factor structure and levels of academic self-esteem. For each BASE factor, the sub score is provided based on the classifications of academic self-esteem and their respective ranges. In FuzzyXteem, users in particular teachers, counselors, or parent are allowed to measure students' self-esteem at early age using real time computation. FuzzyXteem facilitates user by automatically evaluating BASE factors and helps the user diagnoses their students' levels of academic self-esteem in 3 ratings: low, moderate and high. It is also able to provide explanation and describe how the conclusion can be derived. The system has been successfully tested by the counselors and conforms to the BASE factor rating scale and sub-scores. FuzzyXteem can be used as an aid to decision making in improving a person's self esteem, and indirectly increases an Taniza Tajuddin, MSc; research fields: fuzzy logic, expert system, neural network, web based programming. Kamaruzaman Jusoff, Ph.D.; research field: forest engineering survey. Fadzilah Siraj, associate professor; research fields: neural network, case based reasoning, fuzzy logic, data mining and mobile computing. Khairul Adilah Ahmad, MSc; research fields: XML, data management. Samsiah Bidin, MSc; research fields: education and motivation, test. individual for productivity. The same system functions can be applied to business organization for managing and improving the organizations performance

Managing behavioral academic self-esteem using FuzzyXteem

Behavioral academic self-esteem (BASE) has been used with children of preschool,elementary, and junior high school classes, both individually and in groups.In this study, BASE is used to estimate the factor structures and determine the levels of academic self-esteem of the student.The current practice of the existing system using BASE scale may be scored by hand or by computer based on the rigid crisp values to represent rating number one through five.Since BASE requires the ability for estimating the factor structure and also the ability to explain how the conclusion is derived, therefore artificial intelligent techniques that are required to perform BASE mustbe able to perform estimation and provide reasoning.For this purpose, fuzzy logic and expert system have been integrated in a web-based environment to demonstrate the use of hybrid system on BASE factor structure and levels of academic self-esteem.For each BASE factor, the sub score is provided based on the classifications of academic self-esteem and their respective ranges.In FuzzyXteem, users in particular teachers, counselors,or parent are allowed to measure students’ self-esteem at early age using real time computation. FuzzyXteem facilitates user by automatically evaluating BASE factors and helps the user diagnoses their students’ levels of academic self-esteem in 3 ratings: low, moderate and high. It is also able to provide explanation and describe how the conclusion can be derived.The system has been successfully tested by the counselors and conforms to the BASE factor rating scale and sub-scores. FuzzyXteem can be used as an aid to decision making in improving a person’s self esteem, and indirectly increases an individual for productivity. The same system functions can be applied to business organization for managing and improving the organizations performance

The fusion of edge detection and mathematical morphology algorithm for shape boundary recognition

Edge detection is important in image analysis to form the shape of an object.Edge is the boundary between different textures, which helps with object segmentation and recognition.Currently, several edge detection techniques are able to identify objects but are unable to localize the shape of an object. To address this problem, this paper proposes a fusion of selected edge detection algorithms with mathematical morphology to enhance the ability to detect the object shape boundary. Edge detection algorithm is used to simplify image data by minimizing the amount of pixel to be processed, whereas the mathematical morphology is used for smoothing effects and localizing the object shape using mathematical theory sets.The discussion section focuses on the improved edge map and boundary morphology (EmaBm) algorithm as a new technique for shape boundary recognition.A comparative analysis of various edge detection algorithms is presented.It reveals that the LoG’s edge detection embedded in EmaBM algorithm performs better than the other edge detection algorithms for fruit shape boundary recognition. Implementation of the proposed method shows that it is robust and applicable for various kind of fruit images and is more accurate than the existing edge detection algorithms

The use of ICT in public and private institutions of higher learning, Malaysia

This study examines the extent of ICT utilization among the members of Faculty A of four public higher learning institutions (IPTA) and seven private higher learning institutions (IPTS) in Northern Malaysia. Its focus is on a) to investigate the extent of ICT resources provided by universities authorities, b) focus on types and extent of ICT usage in daily activities, c) to explore the ICT proficiencies level and d) to investigate the level of ICT integration in teaching activities. A total of 76 responses out of 77 from IPTA and only 105 out of 108 responses of IPTS are usable for further analysis in this study. Findings indicate that in the IPTA, though the facilities provided are not as plenty as in IPTS, the level of usage is quite encouraging. While in the IPTS, the levels of ICT usage among the educators are still not satisfactorily. Results also indicated that usage frequencies are more prone on informative in nature, besides integrating computer technology. Furthermore, the study also indicates that there were considerable differences in the use of ICT by educators in their perceived proficiencies and integrating computer technology. This study could be improved by expanding the total sampling population to all faculties in both universities. Methods of analysis could also be varied beyond the descriptive analysis done. Factors that could hinder the level of ICT usage by the educators could also be studied

EDUX: EDucation with an aUthoring tool using XML

EDUX (EDucation with an aUthoring tool using XML) is an e-learning system using the intelligent hypertexts language; Extensible Markup Language (XML). EDUX enable students to follow courses at their own pace, based on their ability. EDUX is designed as an adaptable e-learning environment, which can meet the needs of the individual student. It has the intelligent agent features where for each and every student the system will generate different type of layout or presentation, based on their understanding on the subject or course. The learning process comprises of two main components: an instructor component and a student component. Instructors will be provided with a tool that has functionality for the creation of course. Students will also have a tool with three integrated functionalities for the collaborative curriculum. EDUX acts as a portal with customize web page for the instructors and students