Browsing by Author "Omar, Hani"

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A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles
(2016-01-01)
Omar, Hani
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Van Hai Hoang
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Liu, Duen-Ren
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.
Intelligent Power Resource Allocation by Context-Based Usage Mining
(2015)
Liao, Yu-Shan
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Liao, Hsiu-Yu
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Liu, Duen-Ren
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Fan, Wen-Ting
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Omar, Hani
The smart meter is an emerging appliance which can be used to collect and monitor the power consumption data on electrical devices. Users can monitor these devices in order to adjust their operations for energy saving results. To avoid wasting power, it is important to set a power usage constraint to effectively monitor and control users\' power consumption within a restricted power usage range. However, existing researches on smart-metering systems do not address this issue. In this study, a novel Intelligent Energy Saving (IES) system is proposed to effectively monitor and utilize the power usages of devices under the power consumption constraint. The IES system conducts context-based mining to discover device usage patterns and provide intelligent analysis of context-based usage behaviors, effective monitoring and utilization of power usages of devices for intelligent energy saving.
Recommending blog articles based on popular event trend analysis
(2015-06-01)
Liu, Duen-Ren
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Omar, Hani
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Liou, Chuen-He
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Chi, Huai-Chun
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Hsu, Cheng-Ho
Web 2.0 has become a popular social media on the Internet due to the fast evolution of Internet technologies, as well as increasing resources and users. Among the applications of Web 2.0, blogospheres are a new Internet social media for users to express their preferences and personal feelings. Most of the people tend to receive the newest information and articles related to popular issues. However, with the rapidly increasing number of active writers and viewers, it is hard for people to discover useful information that is beneficial or interesting to them. Accordingly, it is necessary to develop a recommendation approach that takes the emerging or popular events into consideration. In this work, we propose a novel event-based recommendation approach, which combines the event trend analysis and personal preference to recommend blog articles of popular events that suit user interests. We analyze blog articles to identify popular events, and then derive the popularity degrees of events based on blog-based popularity trend analysis and Google Insights-based popularity trend analysis. Our approach derives users\' personalized preferences on target articles of popular events by considering user interests (article-push records) and the predicted popularity degree of the events. Our recommendation methods improve recommendation accuracy by enhancing content-based filtering (CBF) and item-based collaborative filtering (ICF) with the event-based preference analysis. Our experiment result demonstrates that the proposed approach can effectively recommend users\' desired blog articles with respect to event popularity and personal interests. (C) 2015 Elsevier Inc. All rights reserved.