Jui Hung Chang

CPRS: A cloud-based program recommendation system for digital TV platforms

Traditional electronic program guides (EPGs) cannot be used to find popular TV programs. A personalized digital video broadcasting-terrestrial (DVB-T) digital TV program recommendation system is ideal for providing TV program suggestions based on statistics results obtained from analyzing large-scale data. The frequency and duration of the programs that users have watched are collected and weighted by data mining techniques. A large dataset produces results that best represent a viewers preferences of TV programs in a specific area. To process such a massive amount of viewer preference data, the bottleneck of scalability and computing power must be removed. In this paper, an architecture for a TV program recommendation system based on cloud computing and a map-reduce framework, the map-reduce version of k-means and the k-nearest neighbor (kNN) algorithm, is introduced and applied. The proposed architecture provides a scalable and powerful backend to support the demand of large-scale data processing for a program recommendation system.

3PRS: a personalized popular program recommendation system for digital TV for P2P social networks

Digital TV channels require users to spend more time to choose their favorite TV programs. Electronic Program Guides (EPG) cannot be used to find popular TV programs. Hence, this paper proposes a personalized Digital Video Broadcasting — Terrestrial(DVB-T) Digital TV program recommendation system for P2P social networks. From the DVB-T signal, we obtain EPG of TV programs. The frequency and duration of the programs that users have watched are used to extract programs that users are interested in. The information is collected and weighted by Information Retrieval (IR). The program information is then clustered by k-means. Clusters of users are also grouped by k-means to find cluster relationships. In each group, we decide the most popular program in the group according to the program weight of the channel. When a new user begins to watch the TV program, the K-Nearest Neighbor (kNN) classification method is used to determine the user’s predicted cluster label. Then, our system recommends popular programs in the predicted cluster and similar clusters.

A cloud-based intelligent TV program recommendation system

In recent years, cloud computing technology has matured significantly, as has the development of digital TV services. This, therefore, has led to an increased demand for improved quality TV services. In this paper, cloud computing technology is used to build a program recommendation system for digital TV programs, and the Hadoop Fair Scheduler is utilized to improve processing performance. Historical data of watched TV programs are collected through an electronic program guide, and then processed using K-means clustering, term frequency/inverse document frequency and k-nearest neighbor algorithms, to obtain clusters of audience groups and to find popular TV programs for each cluster. The proposed system can process massive amounts of user data in real-time, and can easily be scaled up.

A fair scheduler using cloud computing for digital TV program recommendation system

With hundreds of TV channels, a good TV program recommendation system can save time. Hadoop fair scheduler cloud computing is designed to make information processing and filtering effective and scalable. In cloud computing, computers are connected over a network and perform computation simultaneously; more computation power can be obtained by adding more computer nodes. In the present study, cloud computing is used to build a TV program recommendation system. A fair scheduler cloud structure is applied to improve the system performance. For program recommendation, the K-means recursive clustering algorithm is used for user clustering, the term frequency/inverse document frequency algorithm is applied for finding related popular programs, and k-nearest neighbor is used to recommend programs. Most TV program recommendation systems focus on providing a personal recommendation system. The proposed system also considers user groups and the program watching preferences of the majority. The proposed fair scheduler cloud-based architecture is scalable; a massive amount of information can be processed in real-time to obtain program recommendation results that can represent almost all users.

Satisfaction analysis for agricultural worker digital course learning platform

In the era of knowledge economy, learning has become increasingly important, it is more and more convenient to acquire the information in a fast way and E-learning has become increasingly popular. Under this trend, the traditional teaching method in the classroom will not be the only choice any longer and the application of E-learning system will be the important trend in the future. In recent years, related governmental departments also vigorously promote the E-learning system. Some special courses enable learners to firstly possess the prior knowledge of agriculture and then the relevant agricultural knowledge is digitalized; lastly, the professional teachers will be equipped for teaching and a system platform for high-efficient learning will be provided through E-learning. The learning effectiveness desired by this research through the establishment of an agricultural E-learning platform is shown as below: (1) apply E-learning platform to increase learning variety, further enhance learners’ knowledge-ability and learning willingness, break through the territory restriction in learning and facilitate the learning; (2) apply satisfaction survey and assessment mechanism to understand the learning effectiveness of E-learning platform. This research established the agricultural E-learning platform and used students’ satisfaction with information contents and course contents for E-learning system obtained through the satisfaction survey to present the positive affirmation. Therefore, it can be known that E-learning is greatly helpful to improve agricultural workers’ learning.

Analysis of Correlation Between Secondary PM2.5 and Factory Pollution Sources by Using ANN and the Correlation Coefficient

Industry 4.0 is gaining more attention from the public, and thus the correlation between factories and nearby environmental pollution sources is a subject worth in-depth research. Among environmental issues, Particulate Matter2.5 (PM2.5) has received considerable attention in recent years from academic units and governments, and one of the secondary PM2.5 sources is the complex chemical reaction of exhaust gases emitted from factories and ammonia (NH3), with NH3 mostly coming from stock farming. Therefore, the correlation between stock farming data and pollutionsources emitted from factories can be examined by using an artificial neural network (ANN). The first target of this study is to investigate the correlation of factory air pollution source data and stock farming data nearby air monitoring stations to the annual mean PM2.5 concentration of nearby air monitoring stations. Second, the study uses Tensorflow to build an ANN model to analyze whether the industrial and stock farming data have an effect on the PM2.5 concentration. Weather data are taken in this experiment to learn about the correlation. The experimental results show that the Spearman’s correlation coefficient of the factory emitted air pollution data and stock farming data nearby air monitoring stations for the annual mean PM2.5 concentration is 0.6 to 0.9, representing positive correlation. The ANN experiment shows the annual mean PM2.5 concentration classification model with industrial data plus stock farming data plus weather data, in which the ANN classification accuracy is 0.75 as validated by mean square error (MSE) methods. Compared with the ANN classification model only with weather data, the MSE classification accuracy is 1.5. According to the two experiments, the industrial factor and stock farming factor are items that may influence the PM2.5 concentration change.

A Study of the Correlation Between Livestock Data Analysis and the Concentration of PM2.5 - Using the Cloud Computing Platform

The subject of air pollution is paid increasing attention to in recent years. NH3 has significant effect on PM2.5, especially due to animal excrements and chemical fertilizer. Some PM2.5 monitoring data in Taiwan show that the concentration in south central Taiwan is apparently on the high side, and south central Taiwan has dense livestock and poultry. Therefore, this paper combines the livestock and poultry data opened by Taiwan Council of Agriculture (COA) with the PM2.5 data obtained by air monitoring stations opened by Taiwan Environmental Protection Administration (EPA), and uses SpatialHadoop to build a Cloud platform to analyze the correlation between Taiwan’s livestock data and the concentration of PM2.5. The analysis results show that the annual mean concentration of PM2.5 of the air monitoring station in the livestock and poultry dense region is higher than that in other regions by 33%.

Construction of Efficient Cloud-Based Digital Course Learning Platform for Agricultural Worker

The trend of e-learning has become increasingly important. E-learning technically applies the advantages of features of the network, including no limitation to place and time, highly interactive, community impacts, and data analysis. Governments, educational institutions, companies and non-profit organizations have quickly established an eco-system to replace the traditional forms of teaching. How to use the ever-changing technology to enhance the manpower quality of government agencies and to help nurture talent is the objective of this study. Therefore, this study proposed a construction of efficient cloud architecture based digital course learning platform for agricultural workers. Through the proposed real-time streaming technology in this study, we can learn the network streaming traffic when videos are played. Furthermore, by employing the cloud architecture, the proposed method can optimize the efficiency of server resources via dynamic allocation and will contribute to a high-quality digital learning effect when users learn digital courses online.