Recent developments in Web technologies have transformed Web users from passive consumers to active creators of digital content. As users see the Web as a means to enable dialogical exchange, debating, and commenting ...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challen...
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Existing in-memory graph storage systems that rely on DRAM have scalability issues because of the limited capacity and volatile nature of DRAM. The emerging persistent memory (PMEM) offers us a chance to solve these i...
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Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a ...
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Background & Need: The early detection of thoracic diseases and COVID-19 (coronavirus disease) significantly limits propagation and increases therapeutic outcomes. This article focuses on swiftly distinguishi...
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Background & Need: The early detection of thoracic diseases and COVID-19 (coronavirus disease) significantly limits propagation and increases therapeutic outcomes. This article focuses on swiftly distinguishing COVID-19 patients with 10 chronic thoracic illnesses from healthy examples. The death rates of COVID-19-confirmed patients are rising due to chronic thoracic illnesses. Method: To identify thoracic illnesses (Consolidation, Tuberculosis, Edema, Fibrosis, Hernia, Mass, Nodule, Plural-thickening, Pneumonia, Healthy) from X-ray images with COVID-19, we provide an ensemble-feature-fusion (FFT) deep learning (DL) model. 14,400 chest X-ray images (CXRI) of COVID-19 and other thoracic illnesses were obtained from five public sources and applied UNet-based data augmentation. High-quality images were intended to be provided under the CXR standard. To provide model parameters and feature extractors, four deep convolutional neural networks (CNNs) with a proprietary CapsNet as the backbone were employed. To generate the ensemble-fusion classifiers, we suggested five additional USweA (Unified Stacking weighted Averaging)-based comparative ensemble models as an alternative to depending solely on the findings of the single base model. Additionally, USweA enhanced the models' performance and reduced the base error-rate. USweA models were knowledgeable of the principles of multiple DL evaluations on distinct labels. Results: The results demonstrated that the feature-fusion strategy performed better than the standalone DL models in terms of overall classification effectiveness. According to study results, Thoracic-Net significantly improves COVID-19 context recognition for thoracic infections. It achieves superior results to existing CNNs, with a 99.75% accuracy, 97.89% precision, 98.69% recall, 98.27% F1-score, shallow 28 CXR zero-one loss, 99.27% ROC-AUC-score, 1.45% error rate, 0.9838 MCC, (0.98001, 0.99076) 95% CI, and 5.708 s to test individual CXR. This suggested USweA m
In the growing information retrieval (IR) world, selecting suitable keywords and generating queries is important for effective retrieval. Modern database applications need a sophisticated interface for automatically u...
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Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taki...
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Significant progress has been made in image inpainting methods in recent ***,they are incapable of producing inpainting results with reasonable structures,rich detail,and sharpness at the same *** this paper,we propos...
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Significant progress has been made in image inpainting methods in recent ***,they are incapable of producing inpainting results with reasonable structures,rich detail,and sharpness at the same *** this paper,we propose the Pyramid-VAE-GAN network for image inpainting to address this *** network is built on a variational autoencoder(VAE)backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of *** prior assists in reconstructing reasonable structures when *** also adopt a pyramid structure in our model to maintain rich detail in low-level latent *** avoid the usual incompatibility of requiring both reasonable structures and rich detail,we propose a novel cross-layer latent variable transfer *** transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed *** further use adversarial training to select the most reasonable results and to improve the sharpness of the *** experimental results on multiple datasets demonstrate the superiority of our *** code is available at https://***/thy960112/Pyramid-VAE-GAN.
With the increasing popularity of smart portable electronic gadgets, voice-based online person verification systems have become prevalent. However, these systems are susceptible to attacks where illegitimate individua...
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With the increasing popularity of smart portable electronic gadgets, voice-based online person verification systems have become prevalent. However, these systems are susceptible to attacks where illegitimate individuals exploit the recorded voices of legitimate users, leading to false confirmations—spoofing attacks. To overcome this limitation, this article presents an innovative solution by combining speech and online handwritten signatures to mitigate the risks associated with spoofing attacks in voice-based authentication systems because a person has to be present in front of the system to produce an online handwritten signature. To accomplish this objective, this work proposes a novel bidirectional Legendre memory unit (BLMU), a type of recurrent neural network (RNN), for person authentication (verification) and recognition. The Legendre memory unit (LMU) is an innovative memory cell for RNNs that efficiently retains temporal/non-temporal sequential information over a long period with minimal resources. It achieves information orthogonalization by solving coupled ordinary differential equations (ODEs) and leveraging Legendre polynomials, ensuring effective data representation. The proposed framework for person authentication and recognition comprises seven convolution layers, four BLMU layers, two dense layers, and one output layer. The performance of the proposed BLMU-based deep learning framework has been evaluated on a self-generated/private dataset of combined feature matrix of voice signals and online handwritten signatures in the Devanagari script. To assess performance, experiments have also been conducted using various RNN architectures, such as LSTM, BLSTM, and ordinary differential equation recurrent neural network (ODE-RNN), to have a performance comparison with the proposed BLMU-based deep learning (DL) framework. The results demonstrate the superiority of the proposed BLMU-based DL framework in enhancing the accuracy of person verification systems,
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