Ahmad Fadhil Yusof

Theoretical model for continuance use of mobile phone wellness application

Mobile wellness application is widely used for assisting self-monitoring practice to monitor users daily food intake and physical activities. Although these mostly free downloadable mobile application is easy to use and covers many aspects of wellness routines, there is no proof of prolonged use. Previous research reported that user will stop using the application and turned back into their old attitude of food consumptions. The purpose of this study is to examine the factors that influence the continuance intention to adopt a mobile phone wellness application. Review of Information System Continuance Model in the areas such as mobile health, mobile phone wellness application, social network and web 2.0, were done to examine the existing factors. From the critical review, two external factors namely Social Norm and Perceive Interactivity is believed to have the ability to explain the social perspective behavior and also the effect of perceiving interactivity towards prolong usage of wellness mobile application. These findings contribute to the development of the Mobile Phones Wellness Application Continuance Use theoretical model.

The Implementation of Gamification in Massive Open Online Courses (MOOC) Platform

The development of Massive Open Online Course (MOOC) platform has opened more opportunities for students around the world to learn and many education institutions have taken up this initiative in their effort to widen target students scope. However, MOOC faces a huge challenge whereby its students’ engagement rate is decreasing. Implementing gamification component in MOOC is said to be beneficial in engaging students, hence providing a solution to MOOC main challenge. Therefore, the purpose of this study is to present the implementation of gamification specifically in MOOC platform. This ongoing research features review of MOOC and gamification, mapping of gamification elements in MOOC, as well as design of the prototype.

A conceptual model for flipped classroom: Influence on continuance use intention

Flipped (inverted) classroom is an evolving method of active learning in todays world. The concept has reversed the teaching method away from the traditional face-to-face where teachers are regarded as sage on the stage to what is now perceived as guide on the side. The model contributions include the chances for student to learn at his/her own pace, just as it help hasten up the completion of course work amongst others. Notwithstanding these benefits, students in flipped classroom faced the challenge of engagement arising factors such as boringness, bulky note, lack of well formulated video lecture notes etc. Consequently, the research proposed a hybrid model for the purpose of ensuring students continuance use intention of flipped classroom. Meanwhile the results of the survey conducted shows that perceived ease of use, perceived usefulness, teaching method and task-technology fit have significant influence on behavioral attitude as well as continue intention to use flipped classroom by the students. Thus the conclusion that information system (IS) theory has the capacity to support teaching and learning.

Adaptive memory-based single distribution resampling for particle filter

AbstractThe restrictions that are related to using single distribution resampling for some specific computing devices’ memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices’ memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices’ physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle’s particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

Adaptive Memory Size Based Fuzzy Control for Mobile Pedestrian Navigation

The range of memory specifications of mobile pedestrian navigation systems poses difficulties for the developer (in terms of increased time and effort) when it comes to developing a resampling algorithm for mobile pedestrian navigation devices. Thus, a new resampling algorithm is required with a flexible capacity that would cater for a range of computing device memory specifications. This paper develops a new single distribution resampling algorithm, the Adaptive Memory Size-based Fuzzy Control (AMSFC), that integrates traditional resampling and traditional variation resampling in one architecture. The algorithm switches the resampling algorithm on the basis of the memory of the particular mobile pedestrian navigation, thus making it easier for the developer to develop a particle filter without having to consider the memory utilisation of mobile pedestrian navigation devices during different particle filter development processes. At the beginning of the operational process, the AMSFC selector is used to select a suitable resampling algorithm (for example, systematic resampling or rounding copy resampling) based on the physical memory of current computing devices. If systematic resampling is selected, the resampling algorithm samples each particle for each j cycle, while if the rounding copy resampling algorithm is selected, the resampling samples more than one particle of each j cycle. This demonstrates that the proposed method (AMSFC) can switch resampling algorithms to meet the differing physical memory requirements. The authors aim to extend this work in future by implementing their proposed method in a number of different emerging applications (in example, medical applications and real time locator systems).

A Proposal of Location Aware Shopping Assistance Using Memory-Based Resampling

The range of memory specifications of location aware shopping assistance poses difficulties for the developer (in terms of increased time and effort) when it comes to developing a resampling algorithm for mobile devices. Thus, a new resampling algorithm is required with a flexible capacity that would cater for a range of computing device memory devices specifications. This paper develops a memory based resampling in standard particle filter. The memory resampling is capable to read memory specifications of mobile devices before determines the most suitable resampling functions. The authors aim to extend this work in future by implementing their proposed method in a number of different emerging applications (in example, medical applications and real time locator systems).

Adaptive Resampling for Emergency Rescue Location: An Initial Concept

The different of memory specification mobile devices or smart phone make it hard for developer to establish a resampling in emergency rescue location for specific smart phone. It took much time for developer to develop it. In this paper, we will introduce a good solution for developer to develop a resampling algorithm for different mobile devices or smart phone. The proposed resampling can adapt memory specification of mobile device in order to determine which suitable resampling operation or function for specific mobile device. As overall, the paper just present a concept that can be used as a guideline to develop a flexible resampling.