End users’ computing environment has been consistently changing in recent years due to the major advancements of Information Technology. This work aimed to measure the level of users’ satisfaction and provide feedbacks for continuous improvement of a course offered in an academic institution. End users here were the students enrolled for the course and the faculty members who offered the same and also acted as an assessor for the assessments. All assessments were scheduled and conducted online. This study was conducted to focus on two different aspects: Measuring User satisfaction and investigating information systems measures to improve usability using nature inspired computing. For user satisfaction analysis, the study employed the Multicriteria Satisfaction Analysis. The course considered was a problem solving and programming course offered to the fresh students enrolled in the first year of the undergraduate degree program in the academic institution during 2015-2016. To identify the factors for improved usability, PSO was employed in this work.

No abstract

In this paper, an unsupervised segmentation methodology is proposed for remotely sensed images by using Fractional Differential (FD) based texture analysis model and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Essentially, image segmentation is used to assign unique class labels to different regions of an image. In this work, it is transformed into texture segmentation by signifying each class label as a unique texture class. The FD based texture analysis model is suggested for texture feature extraction from images and ISODATA is used for segmentation. The proposed methodology was first implemented on artificial target images and then on remote sensing images from Google Earth. The results of the proposed methodology are compared with those of the other texture analysis methods such as LBP (Local Binary Pattern) and NBP (Neighbors based Binary Pattern) by visual inspection as well as using classification measures derived from confusion matrix. It is justified that the proposed methodology outperforms LBP and NBP methods.

Presently, Learning Style Detection (LSD) has acquired a greater interest in the adaptive learning environment of any academic system. The existing methods of learning environment have facility such as content management and learner data analysis. The learning style detection based on learner's capability, assessment based on mental processing skill and knowledge improvement has not been addressed completely in these systems. Hence, this research works mainly emphasize on creating a reinforcement model for adaptive learning environment based on the Cognitive Skill (CS) of the learners. The model approaches the issues in threefolds; the first is to detect the Learning Style (LS) based on the cognitive skills of a learner dynamically. The second focus is on mapping cognitive skill, Bloom's taxonomy with the Learning Object (LO). The third focus is to create a reinforcement model to keep track and provide feedback on the knowledge competency level improvement. Ó 2016 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

No abstract