Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,...
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Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.
The development of large language models (LLMs) has led to the proliferation of chatbot services like ChatGPT, Replika and Project December further contributing to technologically mediated grief. Called variously grie...
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Single-cell RNA sequencing (scRNA-seq) represents a paradigm shift in understanding the complexities of cellular functions and states at an individual level. Despite its transformative potential in revealing cellular ...
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The latest innovation in digital twin technology is called cognitive Digital Twins (CDT). The sophisticated and autonomous activities made possible by this technology have the potential to revolutionize manufacturing....
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This study deployed k-means clustering to formulate earthquake categories based on magnitude and consequence,using global earthquake data spanning from 1900 to *** on patterns within the historical data,numeric bounda...
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This study deployed k-means clustering to formulate earthquake categories based on magnitude and consequence,using global earthquake data spanning from 1900 to *** on patterns within the historical data,numeric boundaries were extracted to categorize the magnitude,deaths,injuries,and damage caused by earthquakes into low,medium,and high *** a future earthquake incident,the classification scheme can be utilized to assign earthquakes into appropriate categories by inputting the magnitude,number of fatalities and injuries,and monetary estimates of total *** resulting taxonomy provides a means of classifying future earthquake incidents,thereby guiding the allocation and deployment of disaster management resources in proportion to the specific characteristics of each ***,the scheme can serve as a reference tool for auditing the utilization of earthquake management resources.
In this study, two deep learning models for automatic tattoo detection were analyzed;a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer lea...
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Knowledge propagation is a necessity,both in academics and in the *** focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study *** practice of knowledge sharing in study groupsfind...
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Knowledge propagation is a necessity,both in academics and in the *** focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study *** practice of knowledge sharing in study groupsfinds relevance in conferences,workshops,and class ***-nately,there appears to be only few researches on empirical best practices and techniques on study groups formation,especially for achieving rapid knowledge *** work bridges this gap by presenting a workflow driven compu-tational algorithm for autonomous and unbiased formation of study *** system workflow consists of a chronology of stages,each made of distinct *** of the most important steps,subsumed within the algorithmic stage,are the algorithms that resolve the decisional problem of number of study groups to be formed,as well as the most effective permutation of the study group participants to form collaborative *** work contributes a number of new algorithmic concepts,such as autonomous and unbiased matching,exhaustive multiplication technique,twisted round-robin transversal,equilibrium summation,among *** concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’performances in an examination,rather than through any external *** part of practical demon-stration of this work,study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty(40)participants,and partially for study groups of 50,60 and 80 *** quantitative proof of this work was done through the technique called equilibrium summation,as well as the calculation of inter-study group Pearson Correlation Coefficients,which resulted in values higher than 0.9 in all *** life experimentation was carried out while teaching Object-Oriented Programming to forty(40)under-graduates between February and May *** result showed
In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed approach sidesteps the computational deman...
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This study investigates the feature extraction capabilities of two prominent convolutional neural network (CNN) architectures, Inception V3 and AlexNet, in the context of website visuals. With the growing need for aut...
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ISBN:
(纸本)9798331528799
This study investigates the feature extraction capabilities of two prominent convolutional neural network (CNN) architectures, Inception V3 and AlexNet, in the context of website visuals. With the growing need for automated visual data analysis on the web, effective feature extraction is critical for tasks such as image classification, object detection, and visual content analysis. Inception V3 and AlexNet were selected due to their distinct architectural approaches and widespread use in various computer vision applications. The research employed a controlled experimental design, utilizing a dataset of website visuals collected from platforms like Themeforest. Data preprocessing included image resizing, normalization, and augmentation techniques to enhance model robustness. Both models were fine-tuned on the specific dataset to adapt their pretrained weights from ImageNet to the unique characteristics of website visuals. Performance metrics were used to evaluate the models' effectiveness, including precision, recall, F1 score, Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). Results indicated that AlexNet outperformed Inception V3 in precision (0.7062 vs. 0.6860) and recall (0.5914 vs. 0.0506), highlighting its efficiency and suitability for more straightforward feature extraction tasks with lower computational costs. In contrast, Inception V3 exhibited superior potential in capturing complex visual patterns but struggled with computational efficiency, as evidenced by its significantly longer inference time (222.9552 seconds compared to 63.2978 seconds for AlexNet). The findings underscore the importance of model selection based on specific application requirements, such as prioritizing speed or depth of feature extraction. Recommendations for practitioners include leveraging AlexNet for resource-constrained environments and opting for Inception V3 when handling complex, multi-scale visual data. Future research could explore additional
The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as...
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