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75 results on '"Seo, Yeong-Seok"'

1. Hybrid Convolutional Network Fusion: Enhanced Medical Image Classification with Dual-Pathway Learning from Raw and Enhanced Visual Features

Author

Musaev, Javokhir, Anorboev, Abdulaziz, Anorboeva, Sarvinoz, Seo, Yeong-Seok, Nguyen, Ngoc Thanh, Hwang, Dosam, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Franczyk, Bogdan, editor, Ludwig, André, editor, Núñez, Manuel, editor, Treur, Jan, editor, Vossen, Gottfried, editor, and Kozierkiewicz, Adrianna, editor

Published
2024
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2. Enhancing Classification of Parasite Microscopy Images Through Image Edge-Accentuating Preprocessing

Author

Anorboev, Abdulaziz, Musaev, Javokhir, Anorboeva, Sarvinoz, Seo, Yeong-Seok, Nguyen, Ngoc Thanh, Hong, Jeongkyu, Hwang, Dosam, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Chbeir, Richard, editor, Manolopoulos, Yannis, editor, Fujita, Hamido, editor, Hong, Tzung-Pei, editor, Nguyen, Le Minh, editor, and Wojtkiewicz, Krystian, editor

Published
2024
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4. Classifying Chicken-Made Food Images Using Enhanced MobilNetV2

Author

Anorboev, Abdulaziz, Musaev, Javokhir, Anorboeva, Sarvinoz, Hong, Jeongkyu, Nguyen, Ngoc Thanh, Seo, Yeong-Seok, Hwang, Dosam, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Botzheim, János, editor, Gulyás, László, editor, Nunez, Manuel, editor, Treur, Jan, editor, Vossen, Gottfried, editor, and Kozierkiewicz, Adrianna, editor

Published
2023
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5. Deep-Learning- and GCN-Based Aspect-Level Sentiment Analysis Methods on Balanced and Unbalanced Datasets

Author

Phan, Huyen Trang, Nguyen, Ngoc Thanh, Seo, Yeong-Seok, Hwang, Dosam, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Boonsang, Siridech, editor, Fujita, Hamido, editor, Hnatkowska, Bogumiła, editor, Hong, Tzung-Pei, editor, Pasupa, Kitsuchart, editor, and Selamat, Ali, editor

Published
2023
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8. Recommendation Systems for the Healthcare Domain: A Comprehensive Survey of Evaluation Datasets.

Author

Pham, Dinh Tai, Seo, Yeong-Seok, and Phan, Huyen Trang

Subjects
RECOMMENDER systems, INFORMATION storage & retrieval systems, RESEARCH personnel, ADVICE, MEDICAL care
Abstract

Healthcare Recommendation Systems (HRSs) primarily aim to offer advice, recommendations, or suggestions related to human healthcare. Similar to other information systems, datasets affect HRSs' efficiency. The larger datasets can make the information more diverse and complete. Therefore, the recommendation systems can be more accurate and deliver better performance. In addition, several recent studies have revealed that to enhance the accuracy of systems, there should be a switch from a model-centered method to data-centricity. Therefore, several datasets have been introduced over the past few years to assess the effectiveness of HRSs. However, building new datasets and selecting suitable datasets to evaluate HRSs is an issue. Another problem is that there are presently no articles surveying the datasets that have been published to offer the requirements that a dataset uses to judge the HRSs needs to have. These are the reasons that motivated us to conduct this research. To address these problems, we systematically focus on a comprehensive survey of the datasets used to evaluate HRSs published in recent years. Some of the primary contributions of this study are as follows: (i) Surveying 34 datasets commonly used in methods for building and developing HRSs. (ii) Extracting 10 common characteristics and comparing datasets with those 10 common characteristics. (iii) Providing the 13 requirements regarding a dataset used in HRSs (iv) Comparing surveyed datasets and state-of-the-art HRSs based on 13 constructed requirements (v) Discussing the five main challenges and set out some directions for building new datasets in the future. The results of this research analysis are valuable to many new researchers in choosing or constructing suitable datasets to evaluate how they build their HRSs for the present and future. [ABSTRACT FROM AUTHOR]

Published
2024
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9. A Location-Based Solution for Social Network Service and Android Marketing Using Augmented Reality

Author

Huh, Jun-Ho, Seo, Yeong-Seok, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Park, James J., editor, Fong, Simon James, editor, Pan, Yi, editor, and Sung, Yunsick, editor

Published
2021
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10. Toward a GUI-Based Comprehension of Software Architecture

Author

Seo, Yeong-Seok, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Park, James J., editor, Yang, Laurence T., editor, Jeong, Young-Sik, editor, and Hao, Fei, editor

Published
2020
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11. Software Test Effort Estimation Based on Source Code Change History and Defect Information

Author

Won, Jongse, Seo, Yeong-Seok, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Park, James J., editor, Park, Doo-Soon, editor, Jeong, Young-Sik, editor, and Pan, Yi, editor

Published
2020
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12. Assessment of False Identity by Variability in Operating Condition for Memristor Write Time-Based Device Fingerprints

Author

Nguyen, Ha-Phuong, Nguyen, The-Nghia, Nguyen, Nhat-An, Park, SungHyun, Seo, Yeong-Seok, Hwang, Dosam, Shin, Donghwa, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Hoang, Bao Hung, editor, Huynh, Cong Phap, editor, Hwang, Dosam, editor, Trawiński, Bogdan, editor, and Vossen, Gottfried, editor

Published
2020
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13. A Study of an Automated Software Effort Measurement Method

Author

Seo, Yeong-Seok, Jang, Hyun-Soo, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Park, Jong Hyuk, editor, Shen, Hong, editor, Sung, Yunsick, editor, and Tian, Hui, editor

Published
2019
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22. M2SA: a novel dataset for multi-level and multi-domain sentiment analysis.

Author

Phan, Huyen Trang, Nguyen, Ngoc Thanh, Hwang, Dosam, and Seo, Yeong-Seok

Subjects
SENTIMENT analysis
Abstract

People have more channels to express their opinions and feelings about events, products, and celebrities because of the development of social networks. They are becoming rich data sources, gaining attention for many practical applications and in the field of research. Sentiment analysis (SA) is one of the most common uses of this data source. Of the currently available SA datasets, most are only suitable for use in SA corresponding to a specific level, such as document, sentence, or aspect levels. This renders it difficult to develop practical systems that require a combination of sentiment analyzes at all three levels. Additionally, the previous datasets included opinions on only a single domain, although many people often mention multiple domains when expressing their views. This study introduces a new dataset called multi-level and multi-domain (M2SA) for SA. Each sample in M2SA contains a short text with at least two sentences and two aspects with different domains and sentiment polarities. The release of the M2SA dataset will contribute to the promotion of research in the field of SA, primarily by promoting the development and improvement of methods for multi-level SA or multi-aspect, multi-domain SA. The M2SA dataset was tested using state-of-the-art SA methods and was compared with other standard datasets. The results demonstrate that the M2SA dataset is better than the previous datasets in supporting to improve of the performance of SA methods. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Improving response capability of chatbot using twitter

Author

Jeong, Seong-Soo and Seo, Yeong-Seok

Abstract

One of the most familiar services to smartphone users is mobile messenger. At present, such messenger services are rapidly developing into chatbots, including links with commercial platforms to pursue customer service. A chatbot is not a person but a virtual conversation partner or a conversational robot that can respond to questions through a chatting interface according to predefined response rules in a widely used messaging platform. A chatbot usually has a knowledge database to have conversations with users and provides smart responses by applying various algorithms. However, because it mostly only uses limited dialogue information pre-stored in the knowledge database, the chatbot cannot provide flexible answers when it receives an input question that does not exist in the knowledge database. Therefore, this study proposes a novel technique that can provide accurate and flexible answers to the users of chatbot services by expanding the knowledge database through the use of Twitter, a popular social networking service. To this end, a keyword matching-based answer retrieval technique was studied to provide appropriate answers in terms of context based on a method that can collect the data of dialogues between users from Twitter. To verify the effectiveness of the proposed technique, a comparative analysis with three commercial chatbots, currently used by many people, was performed. Ten evaluators were recruited and the response appropriateness of each chatbot was evaluated with a variety of question sets. As a result, it was concluded that the proposed technique provided the most appropriate answers in terms of context.

Published
2024
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