Recent prevailing works on graph machine learning typically follow a similar methodology that involves designing advanced variants of graph neural networks (GNNs) to maintain the superior performance of GNNs on differ...
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Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treat...
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Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati
The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily...
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The CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sen...
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Developmental dyslexia is a learning disorder that typically begins in early childhood, making it difficult for affected individuals to read, write, and spell, even when they possess average or above-average intellige...
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ISBN:
(纸本)9798331523923
Developmental dyslexia is a learning disorder that typically begins in early childhood, making it difficult for affected individuals to read, write, and spell, even when they possess average or above-average intelligence. Children with dyslexia often experience negative emotions such as low selfesteem, frustration, and anger, highlighting the importance of early detection and intervention. Various methods have been proposed for detecting dyslexia, including reading and writing tests, eye tracking, facial image analysis, and advanced imaging techniques such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG). However, one key challenge in dyslexia detection is the reliance on small, unbalanced datasets, which can lead to poor generalization in models. This review suggests that deep learning techniques, particularly those utilizing advanced data augmentation methods, could address this issue by enhancing the model's ability to learn from limited data, improving both accuracy and robustness in real-world applications. The review focuses on the use of Machine Learning (ML) and Deep Learning (DL) for the automatic detection of dyslexia, aiming to improve diagnostic accuracy and accessibility. It explores various ML approaches, such as decision trees and support vector machines, as well as DL architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which analyze text, speech, and eye-tracking patterns. The review provides a comparison of feature extraction methods, classification techniques, and performance metrics, with an emphasis on data enhancement and interpretability. Additionally, it addresses challenges such as dataset variability, ethical concerns, and the practical application of these methods in real-world settings. The goal of this review is to provide an overview of the most current methods for dyslexia detection and propose innovative ways to incorporate ML and DL into personalized learning plans an
This paper presents our approach to the MGT Detection Task 1, which focuses on detecting AI-generated content. The objective of this task is to classify texts as either machine-generated or human-written. We participa...
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With the rapid development and application of distributed systems and Ethernet technology, higher requirements have been put forward for the clock synchronization of the global network. The IEEE1588 protocol is able t...
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In this paper, Investigated the characteristics of fire targets, the flames and smoke targets in natural light and nighttime ambient infrared thermal imaging environment, and incorporate an improved SE attention mecha...
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In the field of NLP, Large Language Models (LLMs) have markedly enhanced performance across a variety of tasks. However, the comprehensive evaluation of LLMs remains an inevitable challenge for the community. Recently...
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This study presents an innovative approach to managing traffic congestion through an intelligent system that combines advanced congestion detection with adaptive signal control. The system leverages artificial intelli...
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