A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering [QP363.3. T253 2006 f rb].

Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi, serupa dengan penerangan teori bagi peta otak, untuk visualisasi, klasifikasi dan pengklusteran data. In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving map, which is akin to the theoretical explanation of the brain map, for data visualisation, classification, and clustering is presented

A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering

Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving ma

Exploring Students’ Online Learning Interaction Behaviors and Experiences: A Case Study

This study chose an undergraduate course offered at a public university in Malaysia as the case to discover students’ unseen online interaction behaviors and experiences in order to obtain insights into ways to devise relevant online pedagogical approaches. The study employed the learning management system’s (LMS) analytics and the analysis of interactions within the social messaging app and virtual live classes to discover students’ online interaction behaviors, focusing mainly on student-content, student-instructor interactions, and student-student interactions. It also employed interviews and a survey to gain insights into students’ online learning experiences. The analysis and reflection of the derived online interaction behaviors and experiences reveal that students require conducive learning environments, regular check-ins on their progress and social-emotional well-being, and favor the learning flexibility afforded by asynchronous learning. It also provides insights into commendable pedagogical practices and reveals some considerations in virtual communication and virtual collaboration to improve students’ online interaction behaviors and experiences

Real-time object-based video segmentation using colour segmentation and connected component labeling

In this paper, we described two-scan connected component labeling (CCL) approach on a real-time colour video image segmentation. CCL approach is an act of region labeling and could provides opportunity to find feature of object and establish boundaries of objects which are the common properties needed by many object-based video segmentation applications. We tested the proposed technique in two experimental studies that simulates real-time object-based video segmentation. Our experiments results shown that the proposed technique could perform region labeling in a fast manner. Another advantage of the proposed technique is that it does not provide extra storage to store same label equivalence. This property gives advantage to avoid label equivalence redundancies that always happen in the CCL approac

Predicting Students’ Course Performance Based on Learners’ Characteristics via Fuzzy Modelling Approach

Frequent assessment allows instructors to ensure students have met the course learning objectives. Due to lack of instructor-student interaction, most of the assessment feedbacks and early interventions are not carried out in the large class size. This study is to proposes a new way of assessing student course performance using a fuzzy modeling approach. The typical steps in designing a fuzzy expert system include specifying the problem, determining linguistic variables, defining fuzzy sets as well as obtaining and constructing fuzzy rules is deployed. An educational expert is interviewed to define the relationship between the factors and student course performance. These steps help to determine the range of fuzzy sets and fuzzy rules in fuzzy reasoning. After the fuzzy assessing system has been built, it is used to compute the course performances of the students. The subject expert is asked to validate and verify system performance. Findings show that the developed system provides a faster and more effective way for instructors to assess the course performances of students in large class sizes.  However, in this study, the system is developed based on 150 historical student data and only a total of six factors related to course performance are considered. It is expected that considering more historical student data and adding more factors as the variables help to increase the accuracy of the system

Enhancing an instructional design model for virtual realitybased learning

In order to effectively utilize the capabilities of virtual reality (VR) in supporting the desired learning outcomes, careful consideration in the design of instruction for VR learning is crucial. In line with this concern, previous work proposed an instructional design model that prescribes instructional methods to guide the design of VR-based learning environments. This article provides a thorough elaboration on how formative research is employed to enhance the earlier model. The study has successfully generated five new hypothesized principles to enhance the robustness of the instructional design model through the formative research process. The newly derived hypothesized principles also provide insights into the design of various experimental studies for testing them in the effort to form a more comprehensive guide for the design of VR-based learning environments

Exploring Students' Online Learning Interaction Behaviors and Experiences : A Case Study

This study chose an undergraduate course offered at a public university in Malaysia as the case to discover students’ unseen online interaction behaviors and experiences in order to obtain insights into ways to devise relevant online pedagogical approaches. The study employed the learning management system’s (LMS) analytics and the analysis of interactions within the social messaging app and virtual live classes to discover students’ online interaction behaviors, focusing mainly on student-content, student-instructor interactions, and student-student interactions. It also employed interviews and a survey to gain insights into students’ online learning experiences. The analysis and reflection of the derived online interaction behaviors and experiences reveal that students require conducive learning environments, regular check-ins on their progress and social-emotional well-being, and favor the learning flexibility afforded by asynchronous learning. It also provides insights into commendable pedagogical practices and reveals some considerations in virtual communication and virtual collaboration to improve students’ online interaction behaviors and experiences

Design and Development of Scene Recognition and Classification Model Based on Human Pre-attentive Visual Attention

Recent works on scene classification still utilize the advantages of generic feature of Convolutional Neural Network while applying object-ontology technique that generates limited amount of object regions. Human can successfully recognize and classify scene effortlessly within short period of time. By utilizing this idea, we present a novel approach of scene classification model that built based on human pre-attentive visual attention. We firstly utilize saliency model to generate a set of high-quality regions that potentially contain salient objects. Then we apply a pre-trained Convolutional Neural Network model on these regions to extract deep features. Extracted features of every region are then concatenated to a final features vector and feed into one-vs-all linear Support Vector Machines. We evaluate our model on MIT Indoor 67 dataset. The result proved that saliency model used in this work is capable to generate high-quality informative salient regions that lead to good classification output. Our model achieves a better average accuracy rate than a standard approach that classifies as one whole image

Hybridization of Learning Vector Quantization (LVQ) and Adaptive Coordinates (AC) for data classification and visualization

Most of the artificial neural network (ANN) methods do not support data classification and visualization simultaneously. Some ANN methods such as learning vector quantization (LVQ), multi-layer perceptrons (MLP) and radial basis function (RBF) perform classification without any visualization. Excellent data visualization on the other hand has been prominently supported by various unsupervised methods such as self-organizing maps (SOM) and its recent variants of visualization induced SOM (ViSOM) and probabilistic regularized SOM (PRSOM). However, being unsupervised these methods do not optimize classification accuracy compared with the supervised classification methods such as LVQ. Thus, the scope of a novel supervised method is felt necessary to facilitate applications requiring good data visualization and intensive classification. LVQ demonstrates classification performance at least as high as other supervised ANN classifiers. Adaptive coordinate (AC) on the other hand, has demonstrated the ability of mirroring weight vectorspsila movements in N-dimensional input space to low dimensional output space to reveal the clustering tendency of data learned by SOM. This mirroring concept motivates this work to hybridize a modified AC with LVQ (LVQwihAC) to support data visualization and classification simultaneously. Empirical studies on benchmark data sets demonstrated that, the LVQwihAC method provides better classification accuracy than the unsupervised methods of SOM, ViSOM and PRSOM besides its promising data visualization with higher computational efficiency. The classification performance is also found at least as good as other supervised classifiers with additional data visualization abilities over them

AC-ViSOM: Hybridising the Modified Adaptive Coordinate (AC) and ViSOM for Data Visualization

ViSOM’s (Visualization induced SOM) final map can be seen as a smooth net embedded in the input space, where the distances among neurons are controlled by a regularization control parameter which is usually heuristically chosen on a trial and error basis. Empirical studies shown that ViSOM suffers from dead neuron problem, since a big number of neurons fall outside of the data region due to the regularization effect, even though the regularization control parameter is properly chosen. In this paper, a modified Adaptive Coordinate (AC) approach that is able to preserve data structure is hybridised with ViSOM is proposed. Experimental studies on benchmark datasets shown that the proposed method was able to eliminate the selection of regularization control parameter and minimizing the dead neuron for better data representation