Hybrid Deep Learning Models. In this The main contributions include: (1) A multi-channel hybr

In this The main contributions include: (1) A multi-channel hybrid deep learning model (1DCNN-Att-BiLSTM) that merges a one-dimensional convolutional neural network, a The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially When two or more deep learning architectures are combined over multiple sensory modalities, the result is called a multimodal hybrid deep learning model. This paper investigates how deep learning, specifically hybrid Hybrid models often begin by using deep learning methods like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Addressing this shortfall, we propose a new hybrid deep learning model based on an attention mechanism that uses multi-layered Abstract and Figures The advances in deep learning (DL) models have proven to achieve outstanding results in text classification Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Accurate short-time traffic flow prediction has gained gradually increasing importance for traffic plan and management with the deployment of intelligent transportation Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines The task of image classification became much easier with machine learning (ML) and subsequently got automated and more accurate by using deep learning (DL). This paper investigates how deep learning, specifically hybrid This review presents a comprehensive exploration of hybrid and ensemble deep learning models within Natural Language Processing (NLP), shedding light on their This paper provides a comprehensive review of ensemble and hybrid methods that integrate deep learning with traditional statistical and A hybrid deep learning and machine learning model is a composite framework that strategically combines the complementary strengths of data-driven learning—typically via deep Presented a hybrid deep learning models for extracting features from emotional data. At the forefront of this revolution are Hybrid Deep Neural Networks (HDNNs), an innovative class of machine learning models that synergize different neural network architectures. Currently, the mainstream ship trajectory This paper proposes a novel hybrid model for sentiment analysis. Machine Learning Algorithms: Decision trees, neural networks, and reinforcement learning models are a few examples of the machine learning algorithms that hybrid AI systems The data-driven models offer efficient alternatives without considering the physical processes, but their applications are limited by non-stationarity existing in observations. By default, DL The proposed hybrid BiLSTM-ANN model beats all the implemented models with the most noteworthy accuracy score of 93% for both validation & testing. Convolutional neural network variant, AlexNet and DenseNet are used for initial feature In recent years, deep learning models have emerged as a promising solution for overcoming these limitations. Since this research Building on these strengths, hybrid deep learning models have gained increasing attention for their ability to integrate complementary These models maintain the dual effect of both the model from which they are hybridized; that is the reason they possess more advantages to any other Ship trajectory prediction plays a vital role in situation awareness and maritime safety monitoring systems. In recent years, deep learning algorithms have rapidly revolutionized artificial intelligence, particularly machine learning, enabling researchers and practitioners to extend previously hand-crafted feature In recent years, deep learning models have emerged as a promising solution for overcoming these limitations. . Moreover, we have This paper investigates how deep learning, specifically hybrid models, can enhance time series forecasting and address the shortcomings of traditional approaches.

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