Empirical experience and observations have shown us when powerful and highly tunable classifiers such as maximum entropy classifiers, boosting and SVMs are applied to language processing tasks, it is possible to achie...
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This paper investigates stacking and voting methods for combining strong classifiers like boosting, SVM, and TBL, on the named-entity recognition task. We demonstrate several effective approaches, culminating in a mod...
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In recent years, artificial intelligence (AI) has been developed vigorously, and a great number of AI autonomous applications have been proposed. However, how to decrease computations and shorten training time with hi...
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Cloud computing has become an essential part of the computational world, offering a variety of server capabilities as scalable virtualized services. Big data centers that deliver cloud computing services contain thous...
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Researchers have attempted to measure the success of crowdfunding campaigns using a variety of determinants,such as the descriptions of the crowdfunding campaigns,the amount of funding goals,and crowdfunding project *...
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Researchers have attempted to measure the success of crowdfunding campaigns using a variety of determinants,such as the descriptions of the crowdfunding campaigns,the amount of funding goals,and crowdfunding project *** many successful determinants have been reported in the literature,it remains unclear whether the cover photo and the text in the title and description could be combined in a fusion classifier to better predict the crowdfunding campaign’s *** this work,we focus on the performance of the crowdfunding campaigns on GoFundMe across a wide variety of funding *** analyze the attributes available at the launch of the campaign and identify attributes that are important for each category of the ***,we develop a fusion classifier based on the random forest that significantly improves the prediction result,thus suggesting effective ways to make a campaign successful.
Sign language includes the motion of the arms and hands to communicate with people with hearing *** models have been available in the literature for sign language detection and classification for enhanced *** the late...
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Sign language includes the motion of the arms and hands to communicate with people with hearing *** models have been available in the literature for sign language detection and classification for enhanced *** the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural *** paper introduces an Arabic Sign language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)*** presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language *** presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)*** gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language ***,the DHO algorithm is utilized for parameter optimization of the MLP *** experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct *** comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.
In this paper, we apply Constrained Maximum a Posteriori Linear Regression (CMAPLR) transformation on Universal Background Model (UBM) when characterizing each speaker with a supervector. We incorporate the covariance...
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Recently, RNN-Transducers have achieved remarkable results on various automatic speech recognition tasks. However, lattice-free sequence discriminative training methods, which obtain superior performance in hybrid mod...
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We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modeled and learned explicitly. Most contem...
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ASR can be improved by multi-task learning (MTL) with domain enhancing or domain adversarial training, which are two opposite objectives with the aim to increase/decrease domain variance towards domain-aware/agnostic ...
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