Computational approaches can speed up the drug discovery process by predicting drug-target affinity, otherwise it is time-consuming. In this study, we developed a convolutional neural network (CNN)-based model named S...
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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.
As a low-value solid waste fuel,asphalt rock is prone to slagging even under fluidized bed *** purpose of this study is to improve the slagging characteristics of asphalt rock by adding the mineral additives CaCO_(3),...
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As a low-value solid waste fuel,asphalt rock is prone to slagging even under fluidized bed *** purpose of this study is to improve the slagging characteristics of asphalt rock by adding the mineral additives CaCO_(3),MgO,and *** results showed that the K,Al,Ca salts in asphalt rock ash will evolve at different temperatures and exist mainly as K_(2)SO_(4),KAlSiO_(4),Al_(2)O_(3)·SiO_(2),Al_(2)O_(3),CaSO_(4),and CaSiO_(3).The CaSO_(4) formed from sulfur oxides and calcium-containing compounds is the main factor in asphalt rock slagging and can be facilitated by CaSiO_(3) with a small amount of CaCO_(3).The MgO can form MgCa(SiO_(3))_(2) with a high melting point and helps raise the ash fusion *** addition,the Kaolin will promote the formation of low-temperature eutectics,resulting in a slight decrease in ash fusion *** optimization,it was found that with the addition of 9.0%MgO+9.0%Kaolin+2.0%CaCO_(3)(in weight),the slagging ratio and pressure difference of asphalt rock under fluidized bed conditions decreased from 6.5% to 4.2% and from 6.0 Pa to 4.0 Pa,*** combining simulation and experimental methods,it has been shown that appropriate mineral additives of CaCO_(3),MgO,and Kaolin can effectively improve the slagging characteristics of asphalt rock.
As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive...
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As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In ...
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Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the appli...
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Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.
This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines...
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Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most ...
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Spatial modulation Orthogonal Frequency Division Multiplexing (SM OFDM) is a newly developed transmission technique that has been proposed as an alternative for multiple input multiple output (MIMO)-OFDM transmission....
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Multi-exposure image fusion (MEF) involves combining images captured at different exposure levels to create a single, well-exposed fused image. MEF has a wide range of applications, including low light, low contrast, ...
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