PSU Knowledge Bank Community:http://kb.psu.ac.th:80/psukb/handle/2553/8942024-03-28T08:23:22Z2024-03-28T08:23:22Zระบบแนะนำตามบริบทสำหรับธุรกิจวัสดุก่อสร้างและอาคารสุทธิรัตน์ เกลี้ยงเกลาhttp://kb.psu.ac.th:80/psukb/handle/2016/193122024-01-23T09:23:46Z2022-01-01T00:00:00ZTitle: ระบบแนะนำตามบริบทสำหรับธุรกิจวัสดุก่อสร้างและอาคาร
Authors: สุทธิรัตน์ เกลี้ยงเกลา
Abstract: Nowadays, the recommendation system is one of the most important supported technologies to e-commerce that aims for recommending the products or services to be purchased, to increase sales. In this work, the focus on the recommendation system for the building materials business. Building materials business is a business that sales construction related materials and equipment, such as, structural goods, tools supplies, etc. For customers who come to buy products will builder professionally or customers who want to improve their homes. Products recommendation system in this business will recommend products that can be used in profession. Generally, system recommends products that are like the ones purchased but regardless of context or profession of the customer. In this paper, we propose a context awareness data modeling to specialize the recommendation system aiming for the building materials business.
Description: วิศวกรรมศาสตรมหาบัณฑิต (วิศวกรรมคอมพิวเตอร์), 25652022-01-01T00:00:00ZFeature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional ApproachesKhin Sandar Kyawhttp://kb.psu.ac.th:80/psukb/handle/2016/191182023-12-04T02:24:46Z2020-01-01T00:00:00ZTitle: Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches
Authors: Khin Sandar Kyaw
Abstract: Nowadays, the culture for accessing news around the world is changed from paper to electronic format and the rate of publication for newspapers and magazines on website are increased dramatically. Meanwhile, text feature selection for the automatic document classification (ADC) is becoming a big challenge because of the unstructured nature of text feature, which is called “multi-dimension feature problem”. On the other hand, various powerful schemes dealing with text feature selection are being developed continuously nowadays, but there still exists a research gap for “optimization of feature selection problem (OFSP)”, which can be looked for the global optimal features. Meanwhile, the capacity of meta-heuristic intelligence for knowledge discovery process (KDP) is also become the critical role to overcome NP-hard problem of OFSP by providing effective performance and efficient computation time. Therefore, the idea of meta-heuristic based approach for optimization of feature selection is proposed in this research to search the global optimal features for ADC.
In this thesis, case study of meta-heuristic intelligence and traditional approaches for feature selection optimization process in document classification is observed. It includes eleven meta-heuristic algorithms such as Ant Colony search, Artificial Bee Colony search, Bat search, Cuckoo search, Evolutionary search, Elephant search, Firefly search, Flower search, Genetic search, Rhinoceros search, and Wolf search, for searching the optimal feature subset for document classification. Then, the results of proposed model are compared with three traditional search algorithms like Best First search (BFS), Greedy Stepwise (GS), and Ranker search (RS). In addition, the framework of data mining is applied. It involves data preprocessing, feature engineering, building learning model and evaluating the performance of proposed meta-heuristic intelligence-based feature selection using various performance and computation complexity evaluation schemes. In data processing, tokenization, stop-words handling, stemming and lemmatizing, and normalization are applied. In feature engineering process, n-gram TF-IDF feature extraction is used for implementing feature vector and both filter and wrapper approach are applied for observing different cases. In addition, three different classifiers like J48, Naïve Bayes, and Support Vector Machine, are used for building the document classification model. According to the results, the proposed system can reduce the number of selected features dramatically that can deteriorate learning model performance. In addition, the selected global subset features can yield better performance than traditional search according to single objective function of proposed model.
Description: Doctor of Philosophy (Computer Engineering), 20202020-01-01T00:00:00ZCustomer Feedback Analysis Applying the RFC Model Hybrid Sentiment Extraction and Contrast DictionaryMyint Zawhttp://kb.psu.ac.th:80/psukb/handle/2016/191142023-11-24T07:12:02Z2019-01-01T00:00:00ZTitle: Customer Feedback Analysis Applying the RFC Model Hybrid Sentiment Extraction and Contrast Dictionary
Authors: Myint Zaw
Abstract: In recent years, the marketers have been moving to sale and trade their products in social markets due to the growth of Internet technologies. This allows the consumers to generate, share, criticize, and feedback on their interested products. The customer feedbacks come from many sources and the data become massive. The so- called Social Market Big Data (SMBD) involves many types of customers' expressions which are valuable to extract their opinions and satisfactions to support the marketers to make better decisions. However, the characteristics of SMBD are represented in unstructured formats which usually are only human readable. Manual information extraction is time consuming and labor-intensive. In this study, a new algorithm multi- level sentiment analysis to extract the customer opinions from feedbacks, called contrast dictionary is proposed. It can perform better than two well-known algorithms, the SentiStrength and Word-count, especially on negative feedbacks. Furthermore, a new hybrid approach to extract sentiments which improves the performance of sentiment extraction algorithms, called the aspect-based sentiment information extraction is also proposed.
Moreover, the marketers require more comprehensive information on customer perspectives concerning the products and services comparing with the others. Previously, there had been no studies to grade the products based on the comparison of extracted information from SMBD. In relation to that, the RFM (Recency, Frequency, and Monetary) model is a measurement technique to compare market information, especially in traditional market analytics. This research also proposes a new approach by modifying the RFM model to classify the products from SMBD, called the RFC (Recency, Frequency, and Credit) model. The model focuses on the social market information and product categorization based on the customer satisfactions from customer feedbacks.
The performance of the contrast dictionary has been validated with well- known sentiment information extraction algorithms, SentiStrength and Word-count, tested on Yelp and Amazon review polarity datasets. The proposed algorithm yields the 76.09% accuracy on the Yelp dataset, comparing with the 73.79% accuracy of SentiStrength and the 69.38% accuracy of Word-count. Also, it yields the 72.58% accuracy on the Amazon dataset that is more correct than the SentiStrength and Word- count those yield the 69.68% and 67.35% accuracies respectively. Furthermore, the proposed hybrid sentiment approach can improve the accuracy about 6.12% on the training dataset and 11.67% on the testing dataset. The RFC model produces new knowledges from customer feedbacks on products to be applied on a decision support and recommendation system in marketing management.
Description: Thesis (Ph.D., Computer Engineering)--Prince of Songkla University, 20212019-01-01T00:00:00Zกลไกซอฟต์แวร์แคชสำหรับวิดีโอสตรีมมิ่งโดยใช้ดัชนีความนิยมเตาฟีก หลำสุบhttp://kb.psu.ac.th:80/psukb/handle/2016/191132023-11-24T07:01:06Z2019-01-01T00:00:00ZTitle: กลไกซอฟต์แวร์แคชสำหรับวิดีโอสตรีมมิ่งโดยใช้ดัชนีความนิยม
Authors: เตาฟีก หลำสุบ
Abstract: The present, there are growing demands for video streaming which are likely to increase as the users can easily access the Internet. Video file data is usually large and continuously sorted. It requires a lot of resources to process and communicate data between clients and the server. Cache is used to increase the efficiency and to reduce the time to read data from the storage which has a slow reading rate. This thesis introduces a cache system that exploits the video file popularity in the cache replacement policy in order to increase the efficiency of the cache system that serves video files via HTTP streaming, and also add the popularity attenuation process that decrease the popularity as the time passes by. It helps increase the cache efficiency by increasing the cache hit rate, reducing the latency of data retrieval from the backend storage, and as a result for reducing
the process time for transmit from the HTTP server to the client.
Description: วิทยานิพนธ์ (วศ.ม. (วิศวกรรมคอมพิวเตอร์))--มหาวิทยาลัยสงขลานครินทร์, 25622019-01-01T00:00:00Z