Proposed object-based e-learning framework embracing cloud computing

E-learning is continually evolved in the adaptation of emerging technologies and pedagogies, so does the development of learning objects. Object-based learning approach has been widely adopted in e-learning.The principle concern in the conventional e-learning models is the inflexibility of the e-learning content to assist the use and management of the learning sources which are highly distributed.Rigidity of e-learning content limits its reusability and shareability.Possessing the ability to deliver strong computing power and secure data storage as services, Cloud computing is a promising computing model to promote innovative changes and add notable values to e-learning landscape.The flexibility and on-demand access to a centralized shared pool of computing resources provided by Cloud computing enables high re-usability and share-ability, hence overcomes the principle concern in e-learning due to the rigidity of e-learning content.This paper describes the design of an object-based e-learning framework embracing Cloud computing.The proposed object-based e learning framework can be used to form a new education domain that shares the Cloud characteristics of elasticity, flexibility, efficiency and reliability.Principal to the framework design is the development of Cloud-based e-learning objects where learners have the flexibility to access, personalize and deploy them in e-learning environment.Cloud-based e-learning objects are highly adaptable, reusable and easy changeable, hence allowing them to be used dynamically with greater customizability and flexibility in e-learning.Adapting Cloud computing into object-based e-learning is believed to be able to create a breakthrough in future dynamic -e-learning development

Building A Fuzzy Expert System For Flood Prevention

The objective of this study is to find an alternative solution for flooding problem and to implement it. To do so, a detailed analysis of the current strategies dealing with flooding problem was first performed. Then, the causes and effects of a flood process were studied. From the detailed understanding , an alternative solution was proposed

A New Big Data Processing Framework for the Online Roadshow

The Online Roadshow, a new type of web application, is a digital marketing approach that aims to maximize contactless business engagement. It leverages web computing to conduct interactive game sessions via the internet. As a result, massive amounts of personal data are generated during the engagement process between the audience and the Online Roadshow (e.g., gameplay data and clickstream information). The high volume of data collected is valuable for more effective market segmentation in strategic business planning through data-driven processes such as web personalization and trend evaluation. However, the data storage and processing techniques used in conventional data analytic approaches are typically overloaded in such a computing environment. Hence, this paper proposed a new big data processing framework to improve the processing, handling, and storing of these large amounts of data. The proposed framework aims to provide a better dual�mode solution for processing the generated data for the Online Roadshow engagement process in both historical and real-time scenarios. Multiple functional modules, such as the Application Controller, the Message Broker, the Data Processing Module, and the Data Storage Module, were reformulated to provide a more efficient solution that matches the new needs of the Online Roadshow data analytics procedures. Some tests were conducted to compare the performance of the proposed frameworks against existing similar frameworks and verify the performance of the proposed framework in fulfilling the data processing requirements of the Online Roadshow. The experimental results evidenced multiple advantages of the proposed framework for Online Roadshow compared to similar existing big data processing frameworks

Online Roadshow: A New Model for the Next-Generation Digital Marketing

This paper proposed an Interactive Online Roadshow Model that comprises three components which are Roadshow Core, Delivery Agent and Digital Interface. The model is developed in action to the impact of physical engagement difficulties during COVID-19, the global pandemic that forced individuals to keep social distance between each other. Thus, it aims to lower the business impact from movement control order that significantly reduces the level of engagement towards the public physically while enhancing stability, flexibility and usability by using digital devices. The proposed model complies essential criteria of physical roadshow in the Roadshow Core component as the fundamental elements of a roadshow. By leveraging the VARK Learning Model, the proposed framework urges a higher degree of engagement for better understanding and deeper impression to overcome the inefficient of non-face-to-face interaction in an Online Roadshow with dynamic advertising and interactive content delivered by Delivery Agent. The Digital Interface in the proposed model allows participants without physical contact, eliminating them from constraints of specific geographical location and time, alternatively lays the foundation for any Online Roadshow implementations. This launches the next generation marketing era that leverages the rising capability of the Internet and big data waves

Integrated Live-Feed Big Data Processing Framework on Campaign Advertising System

Big Data Analysis : Turn chunks of raw data into human readable data towards specific demands. Batch processing framework : Hadoop MapReduce Stream processing framework : Apache Spark Distributed storage : Hadoop HDFS (Hadoop Distributed File System) Campaign Advertising System • An web application that inter-connected to a Centralised campaign management system . • Option to choose personal preference to participate in campaign (e.g mini games) that soon use to analysis the pattern of the data. • Page click (click-stream) is being recorded for further processing

Review on Generic Components of the MMORPGs

The massively multiplayer online role-playing games (MMORPGs) have grown to be an interesting emergence since 1980s and rapidly mature into an essential cultural element of the civilization today. The MMORPGs are cross-generational with players coming from kids to adults into the age of 70s. They are cross-cultural with players coming from different societies, different languages and different cultural traditions. They are also cross-genres with titles ranging from science fiction fantasy, mythology, sword and sorcery, crime fiction, romance to serious professional practices drawing inspiration from comics, novels, occults etc. The extraordinarily good reception of these games prompted for this study on what makes these games so addictive, influential and even corruptive. More interestingly, given that all these games come with complicated structures, the study attempts to discover the factors that make these difficult knowledge transfer process so appealing to the gamers that they are able to scale up the steep learning curve to finally completing these difficult games successfully, and loving it

Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster

Raspberry Pi (Pi) is a versatile general-purpose embedded computing device that can be used for both machine learning (ML) and deep learning (DL) inference applications such as face detection. This study trials the use of a Pi Spark cluster for distributed inference in TensorFlow. Specifically, it investigates the performance difference between a 2-node Pi 4B Spark cluster and other systems, including a single Pi 4B and a mid-end desktop computer. Enhancements for the Pi 4B were studied and compared against the Spark cluster to identify the more effective method in increasing the Pi 4B’s DL performance. Three experiments involving DL inference, which in turn involve image classification and face detection tasks, were carried out. Results showed that enhancing the Pi 4B was faster than using a cluster as there was no significant performance difference between using the cluster and a single Pi 4B. The difference between the mid-end computer and a single Pi 4B was between 6 and 15 times in the experiments. In the meantime, enhancing the Pi 4B is the more effective approach for increasing the DL performance, and more work needs to be done for scalable distributed DL inference to eventuate

Telehealth Using PoseNet-Based System for In-Home Rehabilitation

The increasing cost of healthcare services is accelerating the development of the telehealth system to fulfill the necessity of delivering an efficient and cost-effective remote healthcare services. Moreover, the ageing of the global population and the disruption of the COVID-19 pandemic are creating a rapid rise of demand for healthcare services. This includes those who are in need of remote monitoring for chronic conditions through rehabilitation exercises. Therefore, this paper presents a telehealth system using PoseNet for in-home rehabilitation, with built-in statistical computation for doctors to analyze the patient’s recovery status. This system enables patients to perform rehabilitation exercises at home using an ordinary webcam. The PoseNet skeleton-tracking method is applied to detect and track the patients’ angular movements for both elbows and knees. By using this system, the measurement of the elbow and knee joint angles can be calculated and recorded while patients are performing rehabilitation exercises in front of the laptop webcam. After the patients complete their rehabilitation exercises, the skeleton results of four body parts will be generated. Based on the same actions performed by patients on selected days, the doctors can examine and evaluate the deviation rate of patients’ angular movements between different days to determine the recovery rate

Clustering Algorithms Analysis Based on Arcade Game Player Behavior

The purpose of this study is to investigate the feasibility of using different clustering algorithms in grouping arcade game data for player behavior profiling. Using 3 clustering algorithms namely K-Means, Hierarchical Agglomerative Clustering, and DBSCAN, recorded game data for 6 games were clustered and the performance of each clustering algorithm was measured and compared. K-Means was shown to produce the highest quality and well formed clusters among all other algorithms used, and it also scored the highest on two of the evaluation metrics used. This study definitely answered the question regarding the utilization of different clustering algorithm with the use of arcade game data. Further studies are needed in order to generalize the idea of player profiling on games as a whole, with no regards in genres

Experimental Study on Predictive Modeling in the Gamification Marketing Application

Nowadays, many companies are using the gamification approach to promote their products in digital marketing. A gamification marketing approach can only be more effective if the companies can understand their customers’ behaviors through their navigation patterns. Once their behaviors are known by the companies, the appropriate enhancements can be made to improve the marketing strategy. This paper aims to analyze the navigation patterns of the customers based on customer engagement metrics of the time spent on page and visit frequency of each page. Based on these engagement metrics, the action sequences of customers are generated and then evaluated. A sequence model is created to predict the subsequent actions of the customers and to determine the likelihood of using the gamification marketing application. The sequence algorithms, Markov model, and Recurrent Neural Network (RNN) are applied to the sequence models to analyze the customer’s navigation pattern when they are accessing the gamification marketing application