The escalating rates of municipal waste generation in urban areas worldwide present a critical challenge for effective waste management. This paper examines the complexities surrounding municipal waste management, emp...
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Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-based methods have been developed to ta...
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Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs perf...
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Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. ...
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With the largest population of the world and one of the highest enrolments in higher education, India needs efficient and effective means to educate its learners. India started focusing on open and digital education i...
作者：
Elhashash, MostafaQin, RongjunGeospatial Data Analytics Lab
Department of Civil Environment and Geodetic Engineering Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio state University Columbus United States
Many practical systems for image-based surface reconstruction employ a stereo/multi-stereo paradigm, due to its ability to scale for large scenes and its ease of implementation for out-of-core operations. In this proc...
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This paper considers the problem of online clustering with bandit feedback. A set of arms (or items) can be partitioned into various groups that are unknown. Within each group, the observations associated to each of t...
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This paper considers the problem of online clustering with bandit feedback. A set of arms (or items) can be partitioned into various groups that are unknown. Within each group, the observations associated to each of the arms follow the same distribution with the same mean vector. At each time step, the agent queries or pulls an arm and obtains an independent observation from the distribution it is associated to. Subsequent pulls depend on previous ones as well as the previously obtained samples. The agent's task is to uncover the underlying partition of the arms with the least number of arm pulls and with a probability of error not exceeding a prescribed constant δ. The problem proposed finds numerous applications from clustering of variants of viruses to online market segmentation. We present an instance-dependent information-theoretic lower bound on the expected sample complexity for this task, and design a computationally efficient and asymptotically optimal algorithm, namely Bandit Online Clustering (BOC). The algorithm includes a novel stopping rule for adaptive sequential testing that circumvents the need to exactly solve any NP-hard weighted clustering problem as its subroutines. We show through extensive simulations on synthetic and real-world datasets that BOC's performance matches the lower bound asymptotically, and significantly outperforms a non-adaptive baseline algorithm.
Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain functio...
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Federated analysis can help perform large-scale analyses using neuroimaging datasets across various research groups overcoming the limitations of institutional data-sharing policies, privacy or regulatory concerns as ...
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One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin L...
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ISBN:
(数字)9798350369175
ISBN:
(纸本)9798350369182
One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL.
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