Vegetation monitoring is a task that requires much time and human effort, but by using an unmanned aerial vehicle with a system that can store captured data digitally, the task can be more manageable and efficient. Past research has shown many formulas were developed by researchers to capture vegetation data in varying conditions and equipment. This paper discusses an experiment conducted to test three of those formulas using visible band data images. The formulas are the visible atmospherically resistant index, the green leaf index, and the visible atmospherically resistant indices green. The objective of this paper is to report and discuss our findings from experiments conducted using each formula as well as to compare the accuracy of these formulas.

MATEC Web of Conferences, open access proceedings in Materials science, Engineering and Chemistry

User interface design plays an important role in web design. Designing a user interface that satisfies the emotional needs of the users is essential as the user interface plays a crucial role in generating remarkable user experiences for websites. A poorly designed user interface leads to bad user interaction while raising the users’ arousal and a displeasing user experience with a website elicits dissatisfaction emotion where consecutively results in avoidance and prevents revisit to the website. A standard emotional based user interface web design would enhance user experience which will lead to success of a website. Kansei engineering which is a popular technique in transforming users’ feelings into product design is used in this research. This study tries to convert the feelings and emotions of users into design elements with the use of Kansei engineering technology in order to design an appropriate higher learning institution website by identifying a standard web design that cultivates emotional engagement. A standard user interface web design guideline for the development of the higher learning institutions’ website has been proposed in this research.

This study attempts to illustrate the phases of designing a flipped learning mobile application. It is worth noting that changes in students’ learning behavior should be met by changes in the classroom – particularly on the way a course should be delivered. Studies have shown that students who learn using the flipped learning method are less likely to fail as opposed to their counterparts in the traditional classroom setting. The rising importance and popularity of flipped learning necessitates the development of a mobile application that assists both students to learn and allow instructors to manage their course via their mobile devices, almost anywhere and anytime. The software development life cycle (SDLC) is divided into four distinct phases: 1) Preliminary study, 2) content design, 3) System design and development, and 4) System evaluation. The effectiveness of the application is tested using electroencephalography (EEG). The findings suggest effectiveness of the mobile application falls within the acceptable range. Improvements for the flipped learning mobile application is also presented.

The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data. Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions. The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy. To address the issues, the logical permutation serves as an alternative mechanism that can enhance the probability of the 2 Satisfiability logical rule becoming true by utilizing the definitive finite arrangement of attributes. This work aims to examine and analyze the significant effect of logical permutation on the performance of data extraction ability of the logic mining approach incorporated with the recurrent discrete Hopfield Neural Network. Based on the theory, the effect of permutation and associate memories in recurrent Hopfield Neural Network will potentially improve the accuracy of the existing logic mining approach. To validate the impact of the logical permutation on the retrieval phase of the logic mining model, the proposed work is experimentally tested on a different class of the benchmark real datasets ranging from the multivariate and time-series datasets. The experimental results show the significant improvement in the proposed logical permutation-based logic mining according to the domains such as compatibility, accuracy, and competitiveness as opposed to the plethora of standard 2 Satisfiability Reverse Analysis methods.