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
Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively un...
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Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.
This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) a...
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Aim: Recent advances in Artificial Intelligence (AI) and the addition of Deep Learning (DL) have made it possible to analyse both real-time and historical data from the Internet of Things (IoT). Recently, IoT technolo...
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Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redunda...
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Recently, Rumor Spreading over Online Social Media is found as one of the serious issue, which causes severe damage to society, organization and individuals. To control the rumor spread, rumor detection is found as on...
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According to WHO data, Chronic Low Back Pain (CLBP) is one of the leading causes of disability. Exercise plays a significant role in CLBP rehabilitation. Proper ordering of exercise is further important for the rehabi...
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In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high st...
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In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational *** this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and ***,the CT images are captured and transmitted to the fog node through IoT *** the fog node,the image gets modified into a convenient and efficient format for further *** encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating ***,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning *** different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature *** softmax of heterogeneous base classifiers assists to make individual *** a meta-classifier,the ensemble approach is employed to obtain final optimal *** predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the *** proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class *** experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.
INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECT...
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Data is always the source of all information and must be more secured at all cost. Cryptography helps in preventing data from outer malicious attacks while it is transferred through the network. In this we use both Tr...
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