Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Iyatomi*, Hitoshi"'
Publikováno v:
Computers and Electronics in Agriculture, Volume 222, July 2024, 109021
The development of practical and robust automated diagnostic systems for identifying plant pests is crucial for efficient agricultural production. In this paper, we first investigate three key research questions (RQs) that have not been addressed thu
Externí odkaz:
http://arxiv.org/abs/2407.18000
Recently, object detection methods (OD; e.g., YOLO-based models) have been widely utilized in plant disease diagnosis. These methods demonstrate robustness to distance variations and excel at detecting small lesions compared to classification methods
Externí odkaz:
http://arxiv.org/abs/2407.17906
Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail distribution, featuring numer
Externí odkaz:
http://arxiv.org/abs/2401.11431
Autor:
Cap, Quan Huu, Fukuda, Atsushi, Kagiwada, Satoshi, Uga, Hiroyuki, Iwasaki, Nobusuke, Iyatomi, Hitoshi
With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based systems (e.g., EfficientNet-based), such as the ability to detect disease
Externí odkaz:
http://arxiv.org/abs/2309.01903
Medical images are extremely valuable for supporting medical diagnoses. However, in practice, low-quality (LQ) medical images, such as images that are hazy/blurry, have uneven illumination, or are out of focus, among others, are often obtained during
Externí odkaz:
http://arxiv.org/abs/2304.01864
We propose a simple yet effective image captioning framework that can determine the quality of an image and notify the user of the reasons for any flaws in the image. Our framework first determines the quality of images and then generates captions us
Externí odkaz:
http://arxiv.org/abs/2211.09427
Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimensionality reduction is necessary for CBIR using machine learning techni
Externí odkaz:
http://arxiv.org/abs/2210.00506
Publikováno v:
in IEEE Access, vol. 10, pp. 120023-120034, 2022
There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combi
Externí odkaz:
http://arxiv.org/abs/2209.03126
Publikováno v:
Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM'22), October 17--21, 2022, Atlanta, GA, USA
It is often difficult to correctly infer a writer's emotion from text exchanged online, and differences in recognition between writers and readers can be problematic. In this paper, we propose a new framework for detecting sentences that create diffe
Externí odkaz:
http://arxiv.org/abs/2208.14244
Publikováno v:
Appl. Sci. 2022, 12(7), 3594
Discontinuing ad creatives at an appropriate time is one of the most important ad operations that can have a significant impact on sales. Such operational support for ineffective ads has been less explored than that for effective ads. After pre-analy
Externí odkaz:
http://arxiv.org/abs/2204.11588