This paper investigates the rate outage constrained (ROC) energy efficiency (EE) under multiple-input single-output (MISO) interference channels, where only channel distribution information (CDI) is available at base ...
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This study aims to develop a clothing recommendation application for users who possess a large number of clothes but have limited time due to their intense work tempo. This application aims to assist them in using the...
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Lattice structures with excellent physical properties have attracted great research *** this paper,a novel volume parametric modeling method based on the skeleton model is proposed for the construction of threedimensi...
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Lattice structures with excellent physical properties have attracted great research *** this paper,a novel volume parametric modeling method based on the skeleton model is proposed for the construction of threedimensional lattice *** skeleton model is divided into three types of *** the corresponding algorithms are utilized to construct diverse types of volume parametric *** unit-cell is assembled with distinct nodes according to the geometric *** final lattice structure is created by the periodic arrangement of *** different types of volume parametric lattice structures are constructed to prove the stability and applicability of the proposed *** quality is assessed in terms of the value of the Jacobian ***,the volume parametric lattice structures are tested with the isogeometric analysis to verify the feasibility of integration of modeling and simulation.
Zero-Shot Learning (ZSL) is a technique that transfers knowledge from seen classes to unseen classes by establishing cross-modal mapping relationships. However, traditional ZSL methods heavily rely on a large number o...
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ISBN:
(纸本)9781643684369
Zero-Shot Learning (ZSL) is a technique that transfers knowledge from seen classes to unseen classes by establishing cross-modal mapping relationships. However, traditional ZSL methods heavily rely on a large number of expensive labeled data, which may not be readily available in practical applications. In practical applications there is often a lack of labels, and the approach implies that the lack of effective supervised information in the transfer process of seen classes can lead to 'negative causality' problem between different modalities. Therefore, we propose an unsupervised counterfactual approach to solve the above problem. Therefore, we propose an unsupervised counterfactual approach to solve the above problem. In this paper, we propose an unsupervised learning model and use a Counterfactual Causal Inference framework to cross-modal mapping relationship adjustment (CMRA). Specifically, we aim to regard images as cause and Wikipedia text as effect form a causal relationship diagram. First, it uses multiple attributes attention to learn the visual semantic attributes of images and the corresponding Wikipedia text description words to form cross-modal alignment. Then, we combine contrastive learning and stop-gradient techniques to create a novel cross-modal mapping relationship. Finally, we conducted an investigation to assess the consistency of the multiple attribute attention in the distribution of visual semantic attributes before and after image transformation. To tackle this issue, we implemented a deactivation strategy specifically designed to eliminate the multiple attribute attention of visual semantic attributes that displayed noticeable distribution gaps at different stages. This approach also involves eliminating the mapping relationships between their corresponding Wikipedia text description words. This model evaluates the classification accuracy in AWA, CUB, APY, SUN. The experimental results show that the algorithm outperforms the state-of-the-ar
This paper proposes a multi-domain sample classification method based on Baidu API's general object recognition function. we used three datasets in the experiment,including CIFAR-10, CIFAR-100, and Mini-ImageNet. ...
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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%)
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can si...
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Urban road network form and commercial layout play an important role in urban development, the study of the relationship between the two can provide support for urban commercial functional area layout, road planning a...
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Accurate cell counting in biomedical images is a fundamental yet challenging task for disease diagnosis. The early manual cell counting methods are mainly based on detection and regression, which are time-consuming an...
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The complexity of modern power systems makes predicting and mitigating cascading outages challenging. Quantifying their impact requires extensive simulations, which are computationally expensive and impractical for re...
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