In the contemporary era, social media platforms amass an extensive volume of
social data contributed by their users. In order to promptly grasp the opinions
and emotional inclinations of individuals regarding a product or event, it
becomes imperative to perform sentiment analysis on the user-generated content.
Microblog comments often encompass both lengthy and concise text entries,
presenting a complex scenario. This complexity is particularly pronounced in
extensive textual content due to its rich content and intricate word
interrelations compared to shorter text entries. Sentiment analysis of public
opinion shared on social networking websites such as Facebook or Twitter has
evolved and found diverse applications. However, several challenges remain to
be tackled in this field. The hybrid methodologies have emerged as promising
models for mitigating sentiment analysis errors, particularly when dealing with
progressively intricate training data. In this article, to investigate the
hesitancy of COVID-19 vaccination, we propose eight different hybrid deep
learning models for sentiment classification with an aim of improving overall
accuracy of the model. The sentiment prediction is achieved using embedding,
deep learning model and grid search algorithm on Twitter COVID-19 dataset.
According to the study, public sentiment towards COVID-19 immunization appears
to be improving with time, as evidenced by the gradual decline in vaccine
reluctance. Through extensive evaluation, proposed model reported an increased
accuracy of 98.86%, outperforming other models. Specifically, the combination
of BERT, CNN and GS yield the highest accuracy, while the combination of GloVe,
BiLSTM, CNN and GS follows closely behind with an accuracy of 98.17%. In
addition, increase in accuracy in the range of 2.11% to 14.46% is reported by
the proposed model in comparisons with existing works.

通过使用嵌入、深度学习模型和网格搜索算法对 Twitter COVID-19 数据集进行情感分类，本研究提出了八种不同的混合深度学习模型，旨在提高模型的整体准确性，研究表明，COVID-19 疫苗接种的公众情绪随时间逐渐改善，所提出的模型在广泛的评估中报告了 98.86% 的增加准确性，超过其他模型。

基于网格搜索方法的混合深度学习模型的 COVID-19 Twitter 情绪分类

COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning  Model Based on Grid Search Methodology

My research investigates the use of cutting-edge hybrid deep learning models
to accurately differentiate between AI-generated text and human writing. I
applied a robust methodology, utilising a carefully selected dataset comprising
AI and human texts from various sources, each tagged with instructions.
Advanced natural language processing techniques facilitated the analysis of
textual features. Combining sophisticated neural networks, the custom model
enabled it to detect nuanced differences between AI and human content.

利用切割边缘的混合深度学习模型，通过精心选择的包含 AI 和人类文本的数据集，在各种来源的指导下进行准确区分，结合先进的自然语言处理技术进行特征分析，使得该定制模型能够检测 AI 和人类内容之间微妙的差异。

评估混合深度学习模型在区分人工智能生成文本上的功效

Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing  AI-Generated Text

Numerous deep learning architectures have been developed to accommodate the
diversity of time series datasets across different domains. In this article, we
survey common encoder and decoder designs used in both one-step-ahead and
multi-horizon time series forecasting -- describing how temporal information is
incorporated into predictions by each model. Next, we highlight recent
developments in hybrid deep learning models, which combine well-studied
statistical models with neural network components to improve pure methods in
either category. Lastly, we outline some ways in which deep learning can also
facilitate decision support with time series data.

本文概述了深度学习建模在时序数据领域的应用，介绍了单步和多步预测中通用的编码器和解码器的设计，分析了每一种方法如何加入时间信息以提高预测结果。接下来，介绍了深度学习和统计模型的混合模型在时间序列预测中的最新进展，最后讨论了深度学习如何为决策支持提供便利。