Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in s...
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Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.
Cloud-based Deep learning of models has been widely applied in intelligent healthcare management to deliver improved diagnostics and analytics for advanced patient outcomes. The aim of this research is to assess the c...
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Machine learning (ML) has developed at a superlative rate, accompanying requests spanning various fields. This research investigates the experience of strength data, exceptionally the request of machine learning (ML) ...
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The management of plastic waste in our ecosystem represents a significant environmental issue, necessitating effective sorting and categorization to facilitate efficient recycling practices. This study introduces a me...
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Based on a comparative analysis of the Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)networks,we optimize the structure of the GRU network and propose a new modulation recognition method based on feature ex...
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Based on a comparative analysis of the Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)networks,we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning ***-order cumulant,Signal-to-Noise Ratio(SNR),instantaneous feature,and the cyclic spectrum of signals are extracted firstly,and then input into the Convolutional Neural Network(CNN)and the parallel network of GRU for *** modulation modes of communication signals are recognized *** results show that the proposed method can achieve high recognition rate at low SNR.
Early time classification algorithms aim to label a stream of features without processing the full input stream, while maintaining accuracy comparable to that achieved by applying the classifier to the entire input. I...
Early time classification algorithms aim to label a stream of features without processing the full input stream, while maintaining accuracy comparable to that achieved by applying the classifier to the entire input. In this paper, we introduce a statistical framework that can be applied to any sequential classifier, formulating a calibrated stopping rule. This data-driven rule attains finite-sample, distribution-free control of the accuracy gap between full and early-time classification. We start by presenting a novel method that builds on the Learn-then-Test calibration framework to control this gap marginally, on average over i.i.d. instances. As this algorithm tends to yield an excessively high accuracy gap for early halt times, our main contribution is the proposal of a framework that controls a stronger notion of error, where the accuracy gap is controlled conditionally on the accumulated halt times. Numerical experiments demonstrate the effectiveness, applicability, and usefulness of our method. We show that our proposed early stopping mechanism reduces up to 94% of timesteps used for classification while achieving rigorous accuracy gap control. Copyright 2024 by the author(s)
Calculated parameters(soil layer resistivity,soil layer thickness,and the number of soil layers)of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement ...
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Calculated parameters(soil layer resistivity,soil layer thickness,and the number of soil layers)of horizontally layered soil are usually obtained based on the measured apparent resistivity under different measurement distances,which are significant for the design,operation,and maintenance of grounding *** existing calculation methods of soil parameters are just trying to make the calculation results approach the measurement data,ignoring the relationship among measurement data,the calculated soil parameters,and grounding parameters,which would increase the workload of the *** better balance the distance range of the measurement data and the influence of the calculated horizontally layered soil on grounding parameters,this paper systematically studies the relationship among measured apparent soil resistivity,calculated horizontally layered soil parameters,and grounding *** basic theories of apparent resistivity measurement,soil parameter calculation,and grounding parameter calculation are given,the influence of soil layer thickness on the measured apparent resistivity is studied,and the influence of the calculated soil parameters on the grounding resistance of different grounding models is *** on different scales of grounding grids,the results give a corresponding reference distance range of measured apparent soil ***,this paper can help decrease the workload of soil resistivity measurement during grounding parameters analysis,which has far-reaching engineering significance.
Over a quarter of the global population suffers from ocular diseases and millions of people die every year due to them. It takes a lot of time and effort to diagnose such diseases and so over 2/3rd of the people suffe...
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In actor-critic framework for fully decentralized multi-agent reinforcement learning (MARL), one of the key components is the MARL policy evaluation (PE) problem, where a set of N agents work cooperatively to evaluate...
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As the global population ages, neurocognitive disorders like the Alzheimer's disease which is a form of dementia, and several other forms of dementia are becoming more commonplace. These conditions significantly i...
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