Brain tumors pose a significant health concern globally, with their detection and diagnosis being crucial for timely intervention and treatment planning. These abnormal growths can develop within the brain or originat...
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Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependenc...
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In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven t...
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In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.
Online reviews significantly influence decision-making in many aspects of *** integrity of internet evaluations is crucial for both consumers and *** concern necessitates the development of effective fake review detec...
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Online reviews significantly influence decision-making in many aspects of *** integrity of internet evaluations is crucial for both consumers and *** concern necessitates the development of effective fake review detection *** goal of this study is to identify fraudulent text reviews.A comparison is made on shill reviews *** reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion *** analyze textual features accessible in internet reviews by merging sentiment mining approaches with ***,the research improves fake review screening by using various transformer models such as Bidirectional Encoder Representation from Transformers(BERT),Robustly Optimized BERT(Roberta),XLNET(Transformer-XL)and XLM-Roberta(Cross-lingual Language model–Roberta).This proposed research extracts and classifies features from product reviews to increase the effectiveness of review *** evidenced by the investigation,the application of transformer models improves the performance of spam review filtering when related to existing machine learning and deep learning models.
This research article introduces a novel approach to text-independent speaker recognition by integrating Mel-Frequency Cepstral Coefficients (MFCC) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks, with noi...
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This paper put forward an embedded scheme to execute image watermarking in light of the discrete wavelet transform (DWT), singular value decomposition (SVD) and Charge System Search (CSS) method. In the proposed schem...
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Agriculture plays a vital role in providing food to a growing world population. However, plant diseases and pests result in 50% reductions in crop yields, which exacerbates poverty and threatens a sustainable food sys...
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Earthquakes have the potential to cause catastrophic structural and economic damage. This research explores the application of machine learning for earthquake prediction using LANL (Los Alamos National Laboratory) dat...
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Face recognition is a fast-growing technology that is widely used in forensics such as criminal identification, secure access, and prison *** contrasts from other classification issues in that there are normally a mor...
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The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and *** proposed Virtual Cloud Storage Archi-...
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The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and *** proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and confidentiality,as well as *** provide confidentiality and availability,thefile to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct *** the encoded chunks ensured thefile integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk ***file could be retrieved by performing the opera-tions in *** the regenerating code,the model could regenerate the missing and corrupted chunks from the *** proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage *** results analysis show that the proposed model has been tested with storage space and response time for storage and *** VCSA model consumes 1.5x(150%)storage *** was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisfies the CIA *** response time VCSA model was tested with different sizedfiles starting from 2 to 16 *** response time for storing and retrieving a 2 MBfile is 4.96 and 3.77 s respectively,and for a 16 MBfile,the response times are 11.06 s for storage and 5.6 s for retrieval.
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