Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, ...
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Device-to-Device (D2D) relaying helps improve the coverage range and throughput of the millimeter Wave (mmWave) networks. This work focuses on improving the uplink throughput in a single-cell mmWave-based Internet-of-...
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Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. Dur...
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Resolvers are widely employed as position sensors to detect both rotational and linear displacement. Among the various types, wound rotor resolvers offer superior accuracy in absolute measurements, especially under me...
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In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial los...
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In understanding brain functioning by Electroencephalography (EEG), it is essential to be able to not only identify more active brain areas but also understand connectivity among different areas. The functional and ef...
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Plant diseases significantly threaten global food security and economic stability by reducing crop yields, increasing production costs, and exacerbating food shortages. Early and precise detection of plant diseases is...
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Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) sign...
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In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to ***,it makes sense to learn more information(knowledge)from a small numbe...
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In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to ***,it makes sense to learn more information(knowledge)from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training *** this paper,using Pixel-Level Labeled Images forMulti-Task Learning(PLDMLT),we focus on grading the severity of fundus images for Diabetic Retinopathy(DR).This is because,for the segmentation task,there is a finely labeled mask,while the severity grading task is without classification *** this end,we propose a two-stage multi-label learning weakly supervised algorithm,which generates initial classification pseudo labels in the first stage and visualizes heat maps at all levels of severity using Grad-Cam to further provide medical interpretability for the classification task.A multitask model framework with U-net as the baseline is proposed in the second stage.A label update network is designed to alleviate the gradient balance between the classification and segmentation *** experimental results show that our PLDMLTmethod significantly outperforms other stateof-the-art methods in DR segmentation on two public datasets,achieving up to 98.897%segmentation *** addition,our method achieves comparable competitiveness with single-task fully supervised learning in the DR severity grading task.
Understanding the learner’s requirements and status is important for recommending relevant and appropriate learning materials to the learner in personalized learning. For this purpose, the learning recommendatio...
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