the proceedings contain 286 papers. the topics discussed include: video transmission using wireless optical communication in underwater;unveiling the dark patterns in e-commerce web sites;brain cancer detection using ...
ISBN:
(纸本)9798331530013
the proceedings contain 286 papers. the topics discussed include: video transmission using wireless optical communication in underwater;unveiling the dark patterns in e-commerce web sites;brain cancer detection using a fusion of convolutional neural networks and random forests in deep vision systems;advancements and applications in object detection and patternrecognition;machinelearning strategies for customer churn prediction in competitive enterprises;a review on facial expression recognition models, datasets, and future direction of research;advanced gradient boosting techniques for predicting obesity risk: a comprehensive machinelearning approach;and reviewing facial insight - a smart approach to identity unveiling through active facial patches and multi-task cascaded convolutional networks.
the technology of named entity recognition has been gradually refined. However, current research is primarily focused on non-nested named entity recognition, with less attention devoted to nested named entity recognit...
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Withthe rapid development of artificial intelligence technology, machinelearning and deep learning are widely applied in various fields, showing great potential and value. this article is aimed to explore the integr...
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Given the rapid onset, swift evolution, and short lifespan of severe convective precipitation events, accurately forecasting these within a two-hour window presents significant challenges. In the context of meteorolog...
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this paper predicts whether a customer will apply for a bank loan based on the customer persona created by machinelearning. the data generated in the loan ac-tivities of a foreign commercial bank are pre-processed an...
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the objective of the current work was to investigate LULC utilizing a Supervised classification method based on the MLC algorithm. Using images from Land sat 5 and 8, the quinquennial LULC for the previous 20 years (f...
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the objective of the current work was to investigate LULC utilizing a Supervised classification method based on the MLC algorithm. Using images from Land sat 5 and 8, the quinquennial LULC for the previous 20 years (from 2000 to 2020) in the Hyderabad district of Telangana was determined. the LULC pattern in this area has been altered by recent significant development, which compelled the completion of this study using GIS (Geographical Information System) tools and RS (Remote Sensing) data. the rise in the built-up area (which expanded by 33%) contrasts withthe change in the LULC every five years, vegetation (which reduced by 24%), and water body (which increased by 1%) all differed dramatically. As a result, it is determined that a possibility exists.
A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. the generator can produce real images or remove noise by learningth...
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In addressing the inherent challenge of imbalanced data distribution within long-tailed classification problems and the limitations of traditional classification models in fully extracting intricate information from i...
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Withthe digital transformation of highway construction, a considerable amount of structured, semi-structured, and unstructured data resources have been accumulated, exerting an extremely high application value in the...
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this study examines the emotional engagement of 8 adult EFL learners divided into two groups receiving either immediate or delayed corrective feedback. Utilizing deep learning-based facial recognition technology, the ...
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ISBN:
(纸本)9798400718083
this study examines the emotional engagement of 8 adult EFL learners divided into two groups receiving either immediate or delayed corrective feedback. Utilizing deep learning-based facial recognition technology, the study quantifies the learners' emotional responses and assesses their emotional engagement through affective computing models. Findings suggest that learners receiving immediate feedback showed higher emotional engagement than those in the delayed feedback group, despite significant emotional variability among individuals. the application of machinelearning technology in data analysis offers a detailed and objective insight into the emotional states of EFL learners, surpassing traditional assessment methods.
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