In today’s digital world,millions of individuals are linked to one another via the Internet and social *** opens up new avenues for information exchange with *** analysis(SA)has gotten a lot of attention during the l...
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In today’s digital world,millions of individuals are linked to one another via the Internet and social *** opens up new avenues for information exchange with *** analysis(SA)has gotten a lot of attention during the last *** analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as *** also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral *** addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was *** contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.
Microbolometers using a-Si0.15Ge0.85O0.0236 thin films were fabricated using radio frequency magnetron sputtering and lift of technique. When a 200 nm-thick and 40 × 40 μm2 Si0.15Ge0.85O0.0236 pixels on top of t...
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Traffic forecasting is the foundation for intelligent transportation systems. Spatiotemporal graph neural networks have demonstrated state-of-the-art performance in traffic forecasting. However, these methods do not e...
Traffic forecasting is the foundation for intelligent transportation systems. Spatiotemporal graph neural networks have demonstrated state-of-the-art performance in traffic forecasting. However, these methods do not explicitly model some of the natural characteristics in traffic data, such as the multiscale structure that encompasses spatial and temporal variations at different levels of granularity or scale. To that end, we propose a Wavelet-Inspired Graph Convolutional Recurrent Network (WavGCRN) which combines multiscale analysis (MSA)-based method with Deep Learning (DL)-based method. In WavGCRN, the traffic data is decomposed into time-frequency components with Discrete Wavelet Transformation (DWT), constructing a multi-stream input structure; then Graph Convolutional Recurrent networks (GCRNs) are employed as encoders for each stream, extracting spatiotemporal features in different scales; and finally the learnable Inversed DWT and GCRN are combined as the decoder, fusing the information from all streams for traffic metrics reconstruction and prediction. Furthermore, road-network-informed graphs and data-driven graph learning are combined to accurately capture spatial correlation. The proposed method can offer well-defined interpretability, powerful learning capability, and competitive forecasting performance on real-world traffic data sets.
We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured,...
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Particle swarm optimization (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. PSO performance depends on the use of a suitable contro...
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Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy...
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We develop error-control based time integration algorithms for compressible fluid dynam-ics(CFD)applications and show that they are efficient and robust in both the accuracy-limited and stability-limited *** on discon...
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We develop error-control based time integration algorithms for compressible fluid dynam-ics(CFD)applications and show that they are efficient and robust in both the accuracy-limited and stability-limited *** on discontinuous spectral element semidis-cretizations,we design new controllers for existing methods and for some new embedded Runge-Kutta *** demonstrate the importance of choosing adequate controller parameters and provide a means to obtain these in *** compare a wide range of error-control-based methods,along with the common approach in which step size con-trol is based on the Courant-Friedrichs-Lewy(CFL)*** optimized methods give improved performance and naturally adopt a step size close to the maximum stable CFL number at loose tolerances,while additionally providing control of the temporal error at tighter *** numerical examples include challenging industrial CFD applications.
In this article we consider the filtering problem associated to partially observed diffusions, with observations following a marked point process. In the model, the data form a point process with observation times tha...
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In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field ...
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Understanding the neural underpinnings of dyslexia is an open and fundamental question in developmental neuroscience. A widely agreed causal risk factor for dyslexia is phonological deficit (PD). However, the causal r...
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