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1دورية أكاديمية
الوصف: In this paper, three types of domain adaptation which are defined as image-level domain adaptation, interdomain adaptation, and intradomain adaptation are efficiently combined to construct a high efficiency framework for semantic segmentation. The proposed domain adaptation platform can achieve a high reduction of time-consuming to generate exhausted supervised data in the real world using photorealistic images. The proposed framework achieved a mean Intersection-over-Union (mIoU) of 45.0%. Furthermore, by combining the proposed method with intradomain adaptation, the improvement of 1.2% mIoU is achieved compared to previous work.
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2دورية أكاديمية
المؤلفون: Liu, Jin-Xian, Leu, Jenq-Shiou, Holst, Stefan
المساهمون: The Kyushu Institute of Technology—National Taiwan University of Science and Technology Joint Research Program
المصدر: PeerJ Computer Science ; volume 9, page e1403 ; ISSN 2376-5992
الوصف: Investor sentiment plays a crucial role in the stock market, and in recent years, numerous studies have aimed to predict future stock prices by analyzing market sentiment obtained from social media or news. This study investigates the use of investor sentiment from social media, with a focus on Stocktwits, a social media platform for investors. However, using investor sentiment on Stocktwits to predict stock price movements may be challenging due to a lack of user-initiated sentiment data and the limitations of existing sentiment analyzers, which may inaccurately classify neutral comments. To overcome these challenges, this study proposes an alternative approach using FinBERT, a pre-trained language model specifically designed to analyze the sentiment of financial text. This study proposes an ensemble support vector machine for improving the accuracy of stock price movement predictions. Then, it predicts the future movement of SPDR S&P 500 Index Exchange Traded Funds using the rolling window approach to prevent look-ahead bias. Through comparing various techniques for generating sentiment, our results show that using the FinBERT model for sentiment analysis yields the best results, with an F1-score that is 4–5% higher than other techniques. Additionally, the proposed ensemble support vector machine improves the accuracy of stock price movement predictions when compared to the original support vector machine in a series of experiments.
الإتاحة: https://doi.org/10.7717/peerj-cs.1403Test
https://peerj.com/articles/cs-1403.pdfTest
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3دورية أكاديمية
المؤلفون: Faisal, Muhamad, Leu, Jenq-Shiou, Darmawan, Jeremie T.
المصدر: IEEE Access ; volume 11, page 62281-62291 ; ISSN 2169-3536
الإتاحة: https://doi.org/10.1109/access.2023.3286935Test
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10154257.pdf?arnumber=10154257Test -
4دورية أكاديمية
المؤلفون: Rufaida, Syahidah Izza, Putra, Tryan Aditya, Leu, Jenq-Shiou, Song, Tian, Katayama, Takafumi
المساهمون: Taiwan University of Science and Technology (TAIWAN TECH) and Tokushima University (TU) Joint Research Program
المصدر: IEEE Access ; volume 11, page 79838-79850 ; ISSN 2169-3536
مصطلحات موضوعية: General Engineering, General Materials Science, General Computer Science, Electrical and Electronic Engineering
الإتاحة: https://doi.org/10.1109/access.2023.3299819Test
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10196449.pdf?arnumber=10196449Test -
5دورية أكاديمية
المصدر: Agronomy Journal; May2024, Vol. 116 Issue 3, p826-838, 13p
مستخلص: The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single‐CNN model, DFNet relies on a double‐pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. Core Ideas: Propose a novel CNN model with multiple automated feature extractors.Apply feature fusion scheme to DFNet.Evaluate the proposed models for corn and coffee leaf disease detection.DFNet improves the performance in plant leaf disease detection. [ABSTRACT FROM AUTHOR]
: Copyright of Agronomy Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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6دورية أكاديمية
المؤلفون: Darmawan, Jeremie Theddy, Sigalingging, Xanno Kharis, Faisal, Muhamad, Leu, Jenq-Shiou, Ratnasari, Nanda Rizqia Pradana
المصدر: The Visual Computer ; ISSN 0178-2789 1432-2315
مصطلحات موضوعية: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Software
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7دورية أكاديمية
المصدر: Applied Soft Computing ; volume 155, page 111480 ; ISSN 1568-4946
مصطلحات موضوعية: Software
الإتاحة: https://doi.org/10.1016/j.asoc.2024.111480Test
https://api.elsevier.com/content/article/PII:S1568494624002540?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1568494624002540?httpAccept=text/plainTest -
8دورية أكاديمية
المؤلفون: Putro, Nur Achmad Sulistyo, Avian, Cries, Prakosa, Setya Widyawan, Mahali, Muhammad Izzuddin, Leu, Jenq-Shiou
المصدر: Biomedical Signal Processing and Control ; volume 87, page 105447 ; ISSN 1746-8094
الإتاحة: https://doi.org/10.1016/j.bspc.2023.105447Test
https://api.elsevier.com/content/article/PII:S1746809423008807?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1746809423008807?httpAccept=text/plainTest -
9دورية أكاديمية
المؤلفون: Hasanah, Uswatun, Avian, Cries, Darmawan, Jeremie Theddy, Bachroin, Nabil, Faisal, Muhamad, Prakosa, Setya Widyawan, Leu, Jenq-Shiou, Tsai, Chia-Ti
المصدر: International Journal of Cardiovascular Imaging; Apr2024, Vol. 40 Issue 4, p709-722, 14p
مستخلص: The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagnose multiple diseases that occurred in a single X-Ray image since multiple levels of features are generated in the images, and create different features as in single label detection. Various works were developed to address this challenge with proposed deep learning architectures to improve classification performance and enrich diagnosis results with multi-probability disease detection. The objective is to create an accurate result and a faster inference system to support a quick diagnosis in the medical system. To contribute to this state-of-the-art, we designed a fusion architecture, CheXNet and Feature Pyramid Network (FPN), to classify and discriminate multiple thoracic diseases from chest X-Rays. This concept enables the model to extract while creating a pyramid of feature maps with different spatial resolutions that capture low-level and high-level semantic information to encounter multiple features. The model's effectiveness is evaluated using the NIH ChestXray14 dataset, with the Area Under Curve (AUC) and accuracy metrics used to compare the results against other cutting-edge approaches. The overall results demonstrate that our method outperforms other approaches and has become promising for multilabel disease classification in chest X-Rays, with potential applications in clinical practice. The result demonstrated that we achieved an average AUC of 0.846 and an accuracy of 0.914. Further, our proposed architecture diagnoses images in 0.013 s, faster than the latest approaches. [ABSTRACT FROM AUTHOR]
: Copyright of International Journal of Cardiovascular Imaging is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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10دورية أكاديمية
المؤلفون: Potortì, Francesco, Park, Sangjoon, Crivello, Antonino, Palumbo, Filippo, GIROLAMI, MICHELE, Barsocchi, Paolo, Lee, Soyeon, Torres-Sospedra, Joaquín, Jimenez Ruiz, Antonio Ramon, Perez-Navarro, Antoni, Mendoza-Silva, Germán Martín, Seco, Fernando, ortiz, miguel, PERUL, Johan, Renaudin, Valerie, Kang, Hyunwoong, Park, Soyoung, Lee, Jae Hong, Park, Chan Gook, Ha, Jisu, Han, JaeSeung, Park, Changjun, Kim, Keunhye, Lee, Yonghyun, Gye, Seunghun, lee, keumryeol, Kim, Eunjee, Choi, Jeongsik, choi, Yang-Seok, Talwar, Shilpa, Cho, Seong Yun, Ben-Moshe, Boaz, Scherbakov, Alex, Antsfeld, Leonid, Sansano-Sansano, Emilio, Chidlovskii, Boris, Kronenwett, Nikolai, Prophet, Silvia, Landay, Yael, Marbel, Revital, Zheng, Lingxiang, Peng, Ao, Lin, Zhichao, Wu, Bang, Ma, Chengqi, Poslad, Stefan, Selviah, David, Wu, Wei, Ma, Zixiang, Zhang, Wenchao, Wei, Dongyan, Yuan, Hong, Jiang, Jun-Bang, Huang, Shao-Yung, Liu, Jing-Wen, Su, Kuan-Wu, Leu, Jenq-Shiou, Nishiguchi, Kazuki, BOUSSELHAM, Walid, Uchiyama, Hideaki, Thomas, Diego, Shimada, Atsushi, Taniguchi, Rin-ichiro, Cortés Puschel, Vicente, Lungenstrass, Tomás, Ashraf, Imran, Lee, Chanseok, Ali, Muhammad Usman, Im, Yeongjun, Kim, Gunzung, Eom, Jeongsook, Hur, Soojung, Park, Yongwan, Opiela, Miroslav, Moreira, Adriano, Nicolau, Maria João, Pendão, Cristiano, Silva, Ivo, Meneses, Filipe, Costa, António, Trogh, Jens, Plets, David, Chien, Ying-Ren, Chang, Tzu-Yu, Fang, Shih-Hau, Tsao, Yu
مصطلحات موضوعية: indoor localisation, indoor navigation, competition, benchmarking, smartphone-based positioning, foot-mounted pedestrian dead reckoning, Wi-Fi fingerprinting, magnetic field, camera-based positioning, inertial-based positioning, sensor fusion, Kalman filter, particle filter, stakeholders, atmospheric measurements, particle measurements, real-time systems, wireless fidelity, sensor systems
الوصف: IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.
وصف الملف: application/pdf
العلاقة: Development of human enhancement fire helmet and fire suppression support system; Basic Science Research Program; ICT Research and Development Program of MSIP/IITP (Development of Precise Positioning Technology for the Enhancement of Pedestrian Position/Spatial Cognition and Sports Competition Analysis); MICROCEBUS; REPNIN PLUS; TECHNOFUSION(III)CM; Development of wireless communication tracking-based location information system in disaster scene for fire-fighters and person who requested rescue; Strategic Priority Research Program; IEEE Access, Vol. 8 (2020); F. Potortì et al., "The IPIN 2019 Indoor Localisation Competition—Description and Results," in IEEE Access, vol. 8, pp. 206674-206718, 2020, doi:10.1109/ACCESS.2020.3037221.; http://hdl.handle.net/10234/200929Test
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