Zobrazeno 1 - 10
of 7 261
pro vyhledávání: '"few shot learning"'
Publikováno v:
Cybersecurity, Vol 7, Iss 1, Pp 1-19 (2024)
Abstract Malware classification has been successful in utilizing machine learning methods. However, it is limited by the reliance on a large number of high-quality labeled datasets and the issue of overfitting. These limitations hinder the accurate c
Externí odkaz:
https://doaj.org/article/a734a570de5544f5a5eadd8ca9c010b6
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Tuberculous meningitis (TBM) is a fatal tuberculosis caused by a large number of Mycobacterium tuberculosis (M. tuberculosis) spread by blood flow, with a case fatality rate of more than 50%. It is one of the most serious complications of mi
Externí odkaz:
https://doaj.org/article/85a982be2f7c430da372486403affe77
Autor:
Sandeep Kumar, Amit Sharma, Vikrant Shokeen, Ahmad Taher Azar, Syed Umar Amin, Zafar Iqbal Khan
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Modern natural language processing (NLP) state-of-the-art (SoTA) deep learning (DL) models have hundreds of millions of parameters, making them extremely complex. Large datasets are required for training these models, and while pretraining h
Externí odkaz:
https://doaj.org/article/4b576478175e40a78d55f7f4e65df8df
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 853-864 (2025)
Semantic segmentation of aerial images is crucial yet resource-intensive. Inspired by human ability to learn rapidly, few-shot semantic segmentation offers a promising solution by utilizing limited labeled data for efficient model training and genera
Externí odkaz:
https://doaj.org/article/aaaca3a28fe34d469546abc6e7f38aef
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-19 (2024)
Abstract Nuclear receptors (NRs) play a crucial role as biological targets in drug discovery. However, determining which compounds can act as endocrine disruptors and modulate the function of NRs with a reduced amount of candidate drugs is a challeng
Externí odkaz:
https://doaj.org/article/c22421c89d3a4596ba148a105dceb718
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 9, Pp 2326-2336 (2024)
Relation extraction, as a key task in natural language processing, plays a significant role in deepening language understanding, constructing knowledge graphs, and optimizing information retrieval systems. However, traditional supervised learning met
Externí odkaz:
https://doaj.org/article/84f6e43c102c45628198bcbcbfb881df
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 6, Pp 8249-8272 (2024)
Abstract Most previous few-shot action recognition works tend to process video temporal and spatial features separately, resulting in insufficient extraction of comprehensive features. In this paper, a novel hybrid attentive prototypical network (HAP
Externí odkaz:
https://doaj.org/article/4e639100028647ec93ebf6ba19e839c1
Autor:
Zeqiang Wang, Yuqi Wang, Haiyang Zhang, Wei Wang, Jun Qi, Jianjun Chen, Nishanth Sastry, Jon Johnson, Suparna De
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Accurately assigning standardized diagnosis and procedure codes from clinical text is crucial for healthcare applications. However, this remains challenging due to the complexity of medical language. This paper proposes a novel model that in
Externí odkaz:
https://doaj.org/article/18377574a07448bb975a3ac975b1328e
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Plant diseases pose significant threats to agriculture, impacting both food safety and public health. Traditional plant disease detection systems are typically limited to recognizing disease categories included in the training dataset, rende
Externí odkaz:
https://doaj.org/article/fa7e765c0ffd4abcabce60319feb53c0
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 6, Pp 7577-7589 (2024)
Abstract Humans excel at learning and recognizing objects, swiftly adapting to new concepts with just a few samples. However, current studies in computer vision on few-shot learning have not yet achieved human performance in integrating prior knowled
Externí odkaz:
https://doaj.org/article/42784788559b4f15a4e4349d354504b0