Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset

Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: An empirical investigation

As the years progress there has been rapid growth in Blended Learning (BL) adoption, but only few research focused on adoption issues related to learners, academic staffs and management. Thus, research is needed to guide universities in strategically examining learners, academic staffs and management adoption of BL. Accordingly, this study develops a model to facilitate university policy makers in their decision making to assess students learning and academic staffs teaching outcome. Furthermore, this study explores on the factors that influence BL adoption in universities, through an empirical study from the perspectives of learners, academic staffs, and management. In particular, it examines the current BL practice adoption effectiveness in universities. Based on extensive review of prior studies, survey questionnaires was designed and distributed to convenience samples of 87 students, academic staffs, and management in 3 Malaysia universities to validate the developed model. Next, Partial Least Square-Structural Equation Modeling (PLS-SEM) was employed to analyze the survey data. Findings reveal that supportive factors, attitude, learning mode, satisfaction, course management, and ease of use positively predict the perception of learners and academic staffs’ to adopt BL. Similarly, findings suggest that the perception of management towards BL adoption is positively determined by the strategy, structure, and support factors. Moreover, findings reveal that the impact of BL on learners’ effectiveness is positively predicted by achievement, engagement, involvement, retention, and cognitive outcome. Additionally, findings suggest that the impact BL on academic staffs’ effectiveness is significantly influence by delivery, performance, evaluation, motivation. Theoretical implications from this study contribute to enhance teaching quality by enriching course management, improving learning content, and facilitate management policies towards effective BL adoption

A managerial perspective on institutions' administration readiness to diffuse blended learning in higher education: concept and evidence

There has been rapid development in Blended Learning (BL) diffusion and prior studies mainly focused on issues related to students and lecturers in improving teaching and learning outcomes, but very few studies focused on institution?s readiness and diffusion issues. Thus, there is need for institutional-based research to guide universities, colleges, and polytechnics to strategically diffuse BL. Accordingly, this study develops a model to investigate the variables and associated factors that influence institutions' administration readiness to diffuse BL initiatives based on Diffusion of Innovation (DoI) theory and institutional BL adoption framework that comprises of mature implementation stage of BL. Quantitative research approach was employed and data was collected using online survey questionnaire from 223 e-learning administrators/managers in Malaysia universities, colleges, and polytechnics. Next, Partial Least Square-Structural Equation Modeling (PLS-SEM) technique was employed for data analysis. Results indicate that institutional structure, resource support, technology infrastructure, management strategies, and ethical considerations are key variables that positively predict administration readiness to diffuse BL initiatives in higher education. Additional results from Importance Performance Map Analysis (IPMA) in PLS-SEM suggest that institutional structure has the strongest effect on administrators? readiness to diffuse BL and is also the most important variable that influences BL diffusion in institutions. Theoretically, findings from this study provide insights on how institutions? administration perception and acceptance of BL approach can be enhanced. Practically, the developed model can be employed as a readiness tool to assess institutions current state in implementing BL environment and further provides a road map for future improvement

A managerial perspective on institutions' administration readiness to diffuse blended learning in higher education: concept and evidence

There has been rapid development in Blended Learning (BL) diffusion and prior studies mainly focused on issues related to students and lecturers in improving teaching and learning outcomes, but very few studies focused on institution’s readiness and diffusion issues. Thus, there is need for institutional-based research to guide universities, colleges, and polytechnics to strategically diffuse BL. Accordingly, this study develops a model to investigate the variables and associated factors that influence institutions' administration readiness to diffuse BL initiatives based on Diffusion of Innovation (DoI) theory and institutional BL adoption framework that comprises of mature implementation stage of BL. Quantitative research approach was employed and data was collected using online survey questionnaire from 223 e-learning administrators/managers in Malaysia universities, colleges, and polytechnics. Next, Partial Least Square-Structural Equation Modeling (PLS-SEM) technique was employed for data analysis. Results indicate that institutional structure, resource support, technology infrastructure, management strategies, and ethical considerations are key variables that positively predict administration readiness to diffuse BL initiatives in higher education. Additional results from Importance Performance Map Analysis (IPMA) in PLS-SEM suggest that institutional structure has the strongest effect on administrators’ readiness to diffuse BL and is also the most important variable that influences BL diffusion in institutions. Theoretically, findings from this study provide insights on how institutions’ administration perception and acceptance of BL approach can be enhanced. Practically, the developed model can be employed as a readiness tool to assess institutions current state in implementing BL environment and further provides a road map for future improvement

Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision

Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: An empirical investigation

As the years progress there has been rapid growth in Blended Learning (BL) adoption, but only few research focused on adoption issues related to learners, academic staffs and management. Thus, research is needed to guide universities in strategically examining learners, academic staffs and management adoption of BL. Accordingly, this study develops a model to facilitate university policy makers in their decision making to assess students learning and academic staffs teaching outcome. Furthermore, this study explores on the factors that influence BL adoption in universities, through an empirical study from the perspectives of learners, academic staffs, and management. In particular, it examines the current BL practice adoption effectiveness in universities. Based on extensive review of prior studies, survey questionnaires was designed and distributed to convenience samples of 87 students, academic staffs, and management in 3 Malaysia universities to validate the developed model. Next, Partial Least Square-Structural Equation Modeling (PLS-SEM) was employed to analyze the survey data. Findings reveal that supportive factors, attitude, learning mode, satisfaction, course management, and ease of use positively predict the perception of learners and academic staffs’ to adopt BL. Similarly, findings suggest that the perception of management towards BL adoption is positively determined by the strategy, structure, and support factors. Moreover, findings reveal that the impact of BL on learners’ effectiveness is positively predicted by achievement, engagement, involvement, retention, and cognitive outcome. Additionally, findings suggest that the impact BL on academic staffs’ effectiveness is significantly influence by delivery, performance, evaluation, motivation. Theoretical implications from this study contribute to enhance teaching quality by enriching course management, improving learning content, and facilitate management policies towards effective BL adoption

Fusion iris and periocular recognitions in non-cooperative environment

The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset

An integrative framework to investigate the impact of blended learning adoption in higher education: A theoretical perspective

The importance of Blended Learning (BL) in institutions is momentarily increasing at all educational levels and particularly for higher education. However, relatively little research addresses students, lecturers, and administrators' readiness towards BL adoption. Although, such study would support higher education to strategically assess current state and future direction of BL. Therefore, this study develops an integrative framework based on the Organisation for Economic Co-operation and Development (OECD) framework, Hexagonal E-Learning Assessment Model (HELAM), and Khan octagonal framework to investigate the impact of BL towards measuring students, lecturers and administrator's readiness and further explore on the intensity of implementation of BL impact in higher education. Survey data was collected from 87 samples from 3 Malaysia public universities. Findings from this study provide understanding of BL initiatives, and offers insights to universities on improving teaching and learning effectiveness. Besides, our findings will be valuable to improve the impact of BL implementation

Predictors of blended learning deployment in institutions of higher learning: Theory of planned behavior perspective

Purpose: Blended learning (BL) has been increasing in popularity and demand and has developed as a common practice in institutions of higher learning. Therefore, this study develops a model to evaluate the critical predictors that determine students' acceptance and deployment of BL in institutions of higher education based on the theory of planned behavior (TPB). Design/methodology/approach: The empirical analysis entails data collected from 1,811 responses from an online survey questionnaire from students in Malaysian universities, colleges and polytechnics. Partial least square–structural equation modeling (PLS–SEM) was employed for data analysis. Findings: The results reveal that the attitude, subjective norm, perceived behavioral control and self-efficacy were found to influence students' intention to accept BL. Moreover, results suggest that the intention of students to accept BL approach is significantly influenced by actual BL deployment. Research limitations/implications: Data were collected from students in universities, colleges and polytechnics only. Besides, this research is one of the limited studies that explored BL deployment in a Malaysian perspective. Practical implications: Findings from this research not only add scientific evidence to BL literature but also provide a better understanding of the predictors that may motivate or discourage learners to deploy BL in institutions of higher learning. Social implications: Respectively, findings from this study aid students to acquire and apply knowledge on how to effectively improve BL initiatives in learning activities. Originality/value: This study is one of the fewer studies that investigate students' behavioral intentions toward BL deployment in Malaysia. Additionally, this study contributes to the understanding of the predictors that influence students' intention to accept and deploy BL in their respective institutions