In this study, state-of the art segmentation methods were tested for use in biomedical CTA images to precisely highlight aorta instances. A set of YOLO architectures was trained on a newly annotated dataset of 120 tra...
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Image inpainting aims to restore a realistic image from a damaged or incomplete version. Although Transformer-based methods have achieved impressive results by modeling long-range dependencies, the inherent quadratic ...
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Rapid stabilization of general stochastic quantum systems is investigated based on the rapid stability of stochastic differential *** introduce a Lyapunov-LaSalle-like theorem for a class of nonlinear stochastic syste...
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Rapid stabilization of general stochastic quantum systems is investigated based on the rapid stability of stochastic differential *** introduce a Lyapunov-LaSalle-like theorem for a class of nonlinear stochastic systems first,based on which a unified framework of rapidly stabilizing stochastic quantum systems is *** to the proposed unified framework,we design the switching state feedback controls to achieve the rapid stabilization of singlequbit systems,two-qubit systems,and N-qubit *** the unified framework,the state space is divided into two state subspaces,and the target state is located in one state subspace,while the other system equilibria are located in the other state *** the designed state feedback controls,the system state can only transit through the boundary between the two state subspaces no more than two times,and the target state is globally asymptotically stable in *** particular,the system state can converge exponentially in(all or part of)the state subspace where the target state is ***,the effectiveness and rapidity of the designed state feedback controls are shown in numerical simulations by stabilizing GHZ states for a three-qubit system.
In industrial production, visual defect inspection is crucial for ensuring product quality. However, due to difficulties in sample collection during defect inspection, there is often a scarcity of diverse defective sa...
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New technology File System (NTFS) is a file system being used by Windows Operating Systems since 1993. NTFS is a file system used by the Windows Operating Systems for storing, cataloguing and discovering/finding files...
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Air quality prediction is a complex system engineering. How to fully consider the impact of meteorological, spatial and temporal factors on air quality is the core problem. To address this central conundrum, in an ela...
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Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, h...
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ISBN:
(纸本)9783031770777
Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)
This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick ...
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Action segmentation in untrimmed videos is essential for comprehensive video understanding. Despite significant progress in unsupervised methods, capturing both long-range dependencies and short-duration actions simul...
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The steady rise in devices based on the Android operating system has ignited malware developers to invest their energy in launching various attacks targeting these devices. These attacks have caused pandemonium in var...
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