Hybrid Deep Learning Models. The model leverages the strengths of both the Transformer … Hybrid

The model leverages the strengths of both the Transformer … Hybrid models, which may fuse deep learning techniques with traditional machine learning approaches, strive to amalgamate the representational power of neural networks with … This collection aims to highlight innovative research at the intersection of deep learning, fuzzy logic, and evolutionary computation, with a particular focus on hybrid neural-fuzzy models and … Consequently, the ensemble FC-AE-RES-CNN model predictions demonstrated a spatiotemporal distribution of surface NO 2 with higher fidelity. The model begins by utilizing a … Ship trajectory prediction plays a vital role in situation awareness and maritime safety monitoring systems. Results reveal that the proposed … In this paper, we propose a hybrid deep learning model for day-ahead wind power interval forecasting. 5) … A Hybrid Deep Learning and Machine Learning Model for Multi-Class Lung Disease Detection in Medical Imaging Mustafa Abdul Salam1* Amr Abdellatif2 Marwa Abdallah2 Deep learning represents a promising approach for developing efficient drought monitoring models. 39 introduces a hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) … Hybrid Models: Typically, these models integrate multiple machine learning (ML) or deep learning (DL) techniques, incorporating strategies such as data decomposition, feature … The proposed model has been optimized and tested using two different heart disease datasets. Deep learning models have been intensively … In this study, two hybrid residual deep learning models coupled with physical knowledge are proposed for improving daily transpiration (Ec) estimation… The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various mar… Finally, to evaluate the effectiveness of the proposed model, experiments were conducted on four widely utilized HAR datasets. In recent years, deep learning algorithms have rapidly revolutionized artificial intelligence, particularly machine learning, enabling researchers and practitioners to extend previously hand-crafted feature extr… This collection aims to highlight innovative research at the intersection of deep learning, fuzzy logic, and evolutionary computation, with a particular focus on hybrid neural-fuzzy models and … This review presents a comprehensive exploration of hybrid and ensemble deep learning models within Natural Language Processing (NLP), shedding light on their … The diverse collection of datasets further supports the robustness of the approach across different domains. Both fine particulate matter (PM2. The … A rolling horizon planning strategy is adopted to continuously update the production and maintenance plans based on new data obtained through sensors. Convolutional neural network variant, AlexNet and DenseNet are used for initial feature … A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting Xinyu Wang , Wenping Ma Show … The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an … To address these challenges, this review article proposes three advanced conceptual models leveraging hybrid deep learning … State of charge (SoC) estimation is critical for the safe and efficient operation of electric vehicles (EVs). Moreover, the proposed innovative architecture … Deep learning models acquire the capability to learn several representation and abstraction levels across a large scale because they … PDF | Predicting crop yield is a complex task since it depends on multiple factors. This work proposes a hybrid multi-layer … This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple … This paper provides a comprehensive review of ensemble and hybrid methods that integrate deep learning with traditional statistical and … This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The study highlights the potential of hybrid data-driven models in enhancing the accuracy of flood forecasting, offering insights for reducing uncertainty in flood prediction and … The Hybrid models combine deep learning architectures. Currently, it dominates the imaging field—in particular, im… The main contributions include: (1) A multi-channel hybrid deep learning model (1DCNN-Att-BiLSTM) that merges a one-dimensional convolutional neural network, a … PDF | The advances in deep learning (DL) models have proven to achieve outstanding results in text classification tasks. The paper proposes a hybrid framework … Here I am combining best of the both worlds, one is traditional Machine Learning and Deep Learning to create a hybrid model which classifies Tomato Leaf Diseases. 1038/s41598-023-50505-6 License CC BY 4. This success is … Nous voudrions effectuer une description ici mais le site que vous consultez ne nous en laisse pas la possibilité. When two DL architectures are connected by a bridge network, or when DL is … This paper proposes a novel hybrid model for sentiment analysis. … Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines … 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. DL models based on single DL architecture are termed solo deep learning (SDL) models. . In this study, a hybrid deep learning model, combining convolutional neural … This review presents a comprehensive exploration of hybrid and ensemble deep learning models within Natural Language Processing (NLP), shedding light on their … These models exhibited precision, sensitivity and specificity. When two or more deep learning architectures are combined over multiple sensory modalities, the result is called a multimodal hybrid deep learning model. Since this research … Artificial intelligence (AI) has served humanity in many applications since its inception. 0 This paper starts with some characteristics of deep learning models to provide a quick view of deep learning. Overall, the proposed hybrid deep learning model demonstrates an … In this article, we introduce a novel hybrid deep-learning model designed to enhance the accuracy of deepfake video detection using a Transfer Learning approach. Moreover, we have … Time series forecasting is crucial in various domains, ranging from finance and economics to weather prediction and supply chain management. The proposed HDRaNN combines a … The study uses three openly accessible brain MRI datasets to compare the effectiveness of different pre-trained models as deep feature extractors, various machine … This paper proposes a hybrid deep learning framework for predicting monthly rainfall in Latakia, Syria, using historic data from 1993 to 2023 and utilizing three deep learning … Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Most models are influenced b… In this study, a novel hybrid deep learning model is proposed for multi-step wind speed forecasting, which simultaneously captures pairwise dependencies and temporal … PDF | Hybrid Learning Systems: Integrating Traditional Machine Learning with Deep learning Techniques. Unlike … This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, … To well optimize the deep learning CNNs’ parameters, a novel optimization strategy using the artificial Bee Colony (ABC) algorithm is adopted and used to test its capability with … Different from the existing deep learning models for traffic flow prediction, we propose a novel hybrid model integrating CNN and Bi-LSTM networks which is able to capture both spatial … Additionally, a visual analysis of actual and predicted rainfall data is conducted to identify the most proficient forecasting model for each region. Conclusions The hybrid approach combines the strengths of deep … The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially … This research presents innovative hybrid deep learning models for solar power prediction, methodically assessing the effectiveness of various combinations of CNN, LSTM, … In machine learning, models are typically either discriminative or generative. Many of the DL models are employed in a variety of applications, like convolutional neural network (CNN) [64] that was … To address the above highlighted issues, this paper presents a hybrid deep learning (CNN-GRU) model for the automatic detection of BC … With further consideration of the recent advancements of deep learning models that are mostly hybrid based, it is therefore deemed necessary to have a review paper that mainly … Furthermore, to estimate the price of Bitcoin, we first merged two deep learning algorithms, LSTM and attention mechanisms, and then … This study developed a hybrid model for predicting dissolved oxygen (DO) using real-time sensor data for thirteen parameters. The proposed ensemble is compared … Detection and classification of brain tumor using hybrid deep learning models December 2023 Scientific Reports 13 (1) DOI: 10. Currently, the mainstream ship trajectory… This paper investigates how deep learning, specifically hybrid models, can enhance time series forecasting and address the shortcomings of traditional approaches. Although many models have been developed so far in … Traffic flow prediction is one of the basic, key problems with developing an intelligent transportation system since accurate and timely … Deep learning has been shown to increase prediction accuracy and capture complicated dependencies in time series forecasting [7]. In light of the comprehensive … These documents have been extracted from Google Scholar and many of them have been indexed on the Web of Science. These proposed models can be … In order to detect fraudulent faces, This paper introduces a pioneering hybrid deep learning model, which merges the capabilities of Generative Adversarial Networks (GANs) and … In this paper, a novel hybrid LSM framework is proposed based on four heterogeneous ensemble learning (HEL) methods with three single DL models: deep belief … In this work, a hybrid model is proposed for depression detection using deep learning algorithms, which mainly combines textual features and audio features of patient's … A novel Hybrid Deep random Neural network (HDRaNN) is proposed by Huma et al. This novel hybrid model … Firstly, swarm intelligence optimization algorithms often optimize a single deep learning model (such as LSTM), but combining them with hybrid deep learning models may … However, in datasets with numerous abrupt changes, increasing the temporal interval proves beneficial. Accurate RUL estimation plays a crucial role … The main goal of this paper is to develop a hybrid deep learning model that takes advantage of the capabilities of GANs and CapsNets. Traditional statistical … Accurate short-time traffic flow prediction has gained gradually increasing importance for traffic plan and management with the deployment of intelligent transportation systems … Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Unlike the existing … Recommendation systems based on Deep Learning have recently led to significant progress in different application domains. Currently, the mainstream ship trajectory… This study proposes a hybrid deep learning model combining convolutional neural networks (CNN) and long short-term memory … The methodology for traffic prediction has evolved significantly over the past decades from simple statistical models to recent complex integration of different deep learning … Due to AD’s complex etiology and pathogenesis, an effective and medically practical solution is a challenging task. Additionally, the model’s performance was … Presented a hybrid deep learning models for extracting features from emotional data. | Find, read and cite all the research you need on ResearchGate The methodology for traffic prediction has evolved significantly over the past decades from simple statistical models to recent complex integration of different deep learning … Towards a generalized hybrid deep learning model with optimized hyperparameters for malicious traffic detection in the Industrial Internet of Things Bilal Babayigit a , Mohammed … Addressing this research gap, we propose FusionNet-Remote, a novel hybrid deep learning ensemble model that integrates CNNs with Random Forests (RF) to enhance … The paper by Zhou et al. [14] to serve as a security model for an Industrial IoT. Next, the configurations and characteristics of hybrid deep … Numerous recent studies have attempted to create efficient mechanical trading systems through the use of machine learning approaches for stock price e… Deep learning hybrid models for effective supply chain risk management: mitigating uncertainty while enhancing demand prediction Nisrine Rezki These stand-alone 1D-CNN and LSTM models are fed with exogenous and endogenous datasets for GHI forecasting. In this paper, we developed and evaluated two novel … To further enhance the recognition accuracy of automatic modulation recognition, improve communication efficiency, strengthen … The Diebold-Mariano test results also reveal that the proposed model performed better in terms of prediction performance. In this study, we have developed different hybrid … As people focus more on environmental protection, air quality prediction plays an increasingly important role in reducing pollution hazards. Since there … Ship trajectory prediction plays a vital role in situation awareness and maritime safety monitoring systems. In comparison to state of the arts’ models such as Hybrid AlexNet SVM and DCNN LSTM our proposed models … Motivated by recent advancements in deep learning methods and their satisfactory performance in the energy sector, a hybrid deep learning model combining wavelet packet … The hybrid deep learning model with average pooling layers, along with SVM-linear and neural networks, both achieved an accuracy of 92%. Discriminative models often attain higher predictive accuracy, while the … We developed several hybrid deep learning-based crop yield prediction models and investigated their performance on public datasets … The proposed hybrid BiLSTM-ANN model beats all the implemented models with the most noteworthy accuracy score of 93% for both validation & testing. ezgl0
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