Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variabilit...
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To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches a...
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Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we pr...
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Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna actiondependent heuristic dynamic programming(Dyna-ADHDP)method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method's excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life.
Diabetes is a widespread chronic condition that impacts people all over the globe and requires a clear and timely diagnosis. Untreated diabetes leads to retinopathy, nephropathy, and damage to the nervous system. In t...
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Today for an organisation, data security is the most crucial topic. An organisation needs to protect its information against cyberattacks. Cryptography, DLT, and blockchain technology provide higher security for data ...
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The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of bi...
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In the current digital era, video surveillance has become a part of daily life. The person re-identification(re-ID) task involves choosing a person as a target in one camera feed and recognizing that target in footage...
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Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text cont...
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Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu
The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse ...
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Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different m...
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Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different methods and *** Learning(ML)techniques are used for analyz-ing the images andfinding out the location of the *** restriction of the ML is a dataset size,which is used for model *** problem has been overcome by using an augmentation method by generating larger datasets with multidimensional *** models are using only one augmentation tech-nique,which produces limited features of dataset and also lacks in the association of those data during DR detection,so multilevel augmentation is proposed for *** proposed method performs in two phases namely integrated aug-mentation model and dataset correlation(***).It eliminates overfit-ting problem by considering relevant *** method is used for solving the Diabetic Retinopathy problem with a thin vessel identification using the UNET *** based image segmentation achieves 98.3%accuracy when com-pared to RV-GAN and different UNET models with high detection rate.
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