Results 261 to 270 of about 811,653 (307)

Correction: Multicriteria Optimization of Language Models for Heart Failure With Preserved Ejection Fraction Symptom Detection in Spanish Electronic Health Records: Comparative Modeling Study. [PDF]

J Med Internet Res
Mata J, Pachón V, Manovel A, Maña MJ, de la Villa M.   +4 more
europepmc   +1 more source

In-context adaptation of VLMs for few-shot cell detection in optical microscopy. [PDF]

Front Artif Intell
Ganguly S, Biswas A, Rade J, Hasib MHH, Masud N, Singla N, Dash A, Bhattacharjee U, Sarkar S, Balu A, Sarkar A, Krishnamurthy A.   +11 more
europepmc   +1 more source

Novel goose parvovirus as a major risk factor for red skin and bristle feather syndrome in meat ducks: Epidemiological associations with duck circovirus and reovirus. [PDF]

Poult Sci
Sun Z, Wang X, Shi H, Zhu H, Liu X, Yang T, Song Q, Li Z, Liu C.   +8 more
europepmc   +1 more source

Enhancing grape disease detection: A comparative analysis of hybrid CNN-LSTM and CNN methods. [PDF]

MethodsX
Mulik V, Patil V.
europepmc   +1 more source

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Sentiment Detection in Economics Texts

2020
Deriving intelligence from text is important as it can provide valuable information on how events influence public opinion. In this work, a classification task was done in order to obtain the sentiment behind the polarity of an economic text using machine learning and deep learning methods. We analyzed the text for keywords that can be categorized into
Olumide E. Ojo, Alexander F. Gelbukh, Hiram Calvo, Olaronke Oluwayemisi Adebanji, Grigori Sidorov   +4 more
openaire   +1 more source

Detecting Sarcasm in Text

2019
Sarcasm is a nuanced form of speech extensively employed in various online platforms such as social networks, micro-blogs etc. and sarcasm detection refers to predicting whether the text is sarcastic or not. Detecting sarcasm in text is among the major issues facing sentiment analysis.
Sakshi Thakur, Sarbjeet Singh, Makhan Singh   +2 more
openaire   +1 more source

Fast and effective text detection

2008 15th IEEE International Conference on Image Processing, 2008
Text in images and videos is a significant cue for visual content understanding and retrieval. In this paper, we present a fast and effective approach to locate text lines even under complex background. First, our algorithm uses the stroke filter to calculate the stroke maps in horizontal, vertical, left-diagonal, right-diagonal directions.
Xiaojun Li, Weiqiang Wang 0001, Shuqiang Jiang, Qingming Huang, Wen Gao 0001   +4 more
openaire   +1 more source