In a world of electronic data and communication, there is an urgent need for safe, fast, and automatic review on documents. With this in mind, this project is meant to develop an all-encompassing system that can accur...
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Multi-classification of pulmonary diseases poses a significant challenge, particularly when diseases share similar radiological presentations like lung cancer, pneumonia, and COVID-19. While chest CT scan images are e...
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ISBN:
(数字)9798350387063
ISBN:
(纸本)9798350387070
Multi-classification of pulmonary diseases poses a significant challenge, particularly when diseases share similar radiological presentations like lung cancer, pneumonia, and COVID-19. While chest CT scan images are essential for diagnosis, manual interpretation by radiologists is time-consuming and difficult, even for experts. deeplearning algorithms offer promise in this domain, demonstrating high accuracy in classifying these diseases from CT images. This review examines recent advances in using deeplearning for multiclassification of pulmonary diseases. It discusses various preprocessing techniques, deeplearning architectures to enhance model performance. Additionally, a comparative analysis of different deeplearning models on publicly available CT datasets is presented. The findings suggest that deeplearning models could serve as valuable tools for multiclassification of pulmonary diseases, potentially improving diagnostic accuracy and efficiency. Nonetheless, further research is required to develop and validate these models on large, diverse datasets and ensure their robustness in real-world clinical settings.
In Japan, the number of social infrastructure facilities, such as bridges and tunnels, which were constructed during a period of high economic growth and have been in existence for 50 years, has been increasing, and i...
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Traffic surveillance is a key factor in ITS whereby accurate and real-time object detection assures improvement of road safety and traffic management. This paper advances a deep-learning-based perspective that combine...
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ISBN:
(数字)9798331544607
ISBN:
(纸本)9798331544614
Traffic surveillance is a key factor in ITS whereby accurate and real-time object detection assures improvement of road safety and traffic management. This paper advances a deep-learning-based perspective that combines imageprocessing techniques with convolutional neural networks (CNNs) to maximize the object detection accuracy over the traffic camera feed. The filtering, edge detection, and feature extraction of images for data preprocessing enhance the model performance. This setup provides high inference speed with reliable detection of vehicles via YOLO (You Only Look Once) and Faster R-CNN. The obtained experimental results from our evaluation show great enhancement in the detection accuracy which places the model in a position for real-time application in traffic surveillance. The study also investigates the computational efficiency and real-life boundaries for implementing this strategy, providing an exhaustive account of the suitability of the proposed method in larger-scale applications in ITS.
This study investigates the use of deeplearning methods to improve imageprocessing for electronic document management. A critical convergence of cutting-edge technology, deeplearning-assisted imageprocessing for e...
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ISBN:
(数字)9798331527624
ISBN:
(纸本)9798331527631
This study investigates the use of deeplearning methods to improve imageprocessing for electronic document management. A critical convergence of cutting-edge technology, deeplearning-assisted imageprocessing for enhanced electronic document management has the potential to completely transform how electronic documents are handled, processed, and maintained. The capacity to effectively extract, organise, and use data from a variety of document sources has become essential for businesses in a time when digital information is growing at an exponential rate. The purpose of the study is to determine how well deeplearning models perform when applied to tasks like information extraction, document categorization, optical character recognition (OCR), and layout analysis. The research illustrates the resilience, scalability, and adaptability of deeplearning-assisted document processing processes through practical testing and assessment. There includes discussion of the practical ramifications for information management procedures, covering factors like data security, privacy, and compliance with regulations. In addition to highlighting the revolutionary potential of deeplearning technologies to revolutionise document handling procedures, the study proposes directions for future research to meet new possibilities and difficulties in the field.
With the continuous progress of modern society, people's lives have undergone great changes, and the rich material life has improved people's quality of life, and also extended the overall life expectancy of h...
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In this work, we experimentally generated Laguerre Gaussian (LG) and its multiplexed form (Mux-LG) in the 1610 nm regime of the optical communication band employing InAs/InP quantum dash laser diode. Later, we investi...
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In this work, we experimentally generated Laguerre Gaussian (LG) and its multiplexed form (Mux-LG) in the 1610 nm regime of the optical communication band employing InAs/InP quantum dash laser diode. Later, we investigated the detection of these spatial light modes encoding schemes under smoke channel conditions employing convolutional neural network (CNN) and uNET deeplearning algorithms in conjunction with multiple received orbital angular momentum (OAM) modes images as input for the first time. We studied OAM modes classification and visibility estimation and reported identification accuracies of > 92% and > 96%, respectively, with uNET even for a challenging visibility range of 0-50 m. In general, exploiting the similarity of temporally successive images resulted in better performance of deeplearning networks than just a single input image. Lastly, we propose a simple yet powerful imageprocessing technique as a pre-processing stage for the received mode patterns for visibility estimation via deeplearning regression and showed an improvement of similar to 4 m in root mean square error (RMSE).
Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The...
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Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deeplearning-based automated detection and advanced imageprocessing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method's effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation.
With the implementation of the development program of our country's transportation country, the traffic construction has welcomed a booming development, the passenger transportation and freight transportation incr...
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