Apricot detection is a prerequisite for counting and harvesting tasks. Existing algorithms face challenges in adapting to the impacts of complex environmental factors such as lighting variations, shadows, dense foliag...
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Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of ...
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The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment p...
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The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome *** by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical ***,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a *** primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and *** proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer *** models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and *** was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation *** instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model *** 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the
This paper introduces an advanced road damage detection algorithm that effectively addresses the shortcomings of existing models, including limited detection performance and large parameter sizes, by utilizing the YOL...
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Diabetes retinopathy (DR) is one of the complications of diabetes. Early diagnosis of retinopathy is helpful to avoid vision loss or blindness. The difficulty of this task lies in the significant differences in the si...
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The Traveling Salesman Problem (TSP) seeks the shortest closed tour that visits each city once and returns to the starting city. This problem is NP-hard, so it is not easy to solve using conventional methods. The grey...
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Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving ...
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Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.
In the electronic manufacturing industry, accurate detection of PCB defects is crucial as it directly impacts product quality and reliability. The primary challenges in PCB defect detection include missed detections a...
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Identifying drug–target interactions (DTIs) is a critical step in both drug repositioning. The labor-intensive, time-consuming, and costly nature of classic DTI laboratory studies makes it imperative to create effici...
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Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojournin...
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