3,063 research outputs found
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning models with multilayer
processing architecture is showing better performance as compared to the
shallow or traditional classification models. Deep ensemble learning models
combine the advantages of both the deep learning models as well as the ensemble
learning such that the final model has better generalization performance. This
paper reviews the state-of-art deep ensemble models and hence serves as an
extensive summary for the researchers. The ensemble models are broadly
categorised into ensemble models like bagging, boosting and stacking, negative
correlation based deep ensemble models, explicit/implicit ensembles,
homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised,
semi-supervised, reinforcement learning and online/incremental, multilabel
based deep ensemble models. Application of deep ensemble models in different
domains is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining
Educational Data Mining (EDM) has emerged as a vital field of research, which
harnesses the power of computational techniques to analyze educational data.
With the increasing complexity and diversity of educational data, Deep Learning
techniques have shown significant advantages in addressing the challenges
associated with analyzing and modeling this data. This survey aims to
systematically review the state-of-the-art in EDM with Deep Learning. We begin
by providing a brief introduction to EDM and Deep Learning, highlighting their
relevance in the context of modern education. Next, we present a detailed
review of Deep Learning techniques applied in four typical educational
scenarios, including knowledge tracing, undesirable student detecting,
performance prediction, and personalized recommendation. Furthermore, a
comprehensive overview of public datasets and processing tools for EDM is
provided. Finally, we point out emerging trends and future directions in this
research area.Comment: 21 pages, 5 figure
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU
Deep learning (DL) has emerged as a powerful subset of machine learning (ML)
and artificial intelligence (AI), outperforming traditional ML methods,
especially in handling unstructured and large datasets. Its impact spans across
various domains, including speech recognition, healthcare, autonomous vehicles,
cybersecurity, predictive analytics, and more. However, the complexity and
dynamic nature of real-world problems present challenges in designing effective
deep learning models. Consequently, several deep learning models have been
developed to address different problems and applications. In this article, we
conduct a comprehensive survey of various deep learning models, including
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs),
Generative Models, Deep Reinforcement Learning (DRL), and Deep Transfer
Learning. We examine the structure, applications, benefits, and limitations of
each model. Furthermore, we perform an analysis using three publicly available
datasets: IMDB, ARAS, and Fruit-360. We compare the performance of six renowned
deep learning models: CNN, Simple RNN, Long Short-Term Memory (LSTM),
Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU.Comment: 16 pages, 29 figure
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