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A deep feature fusion network using residual channel shuffled attention for cassava leaf disease detection
Cassava is a significant source of carbohydrates for tropical populations. However, diseases caused by agents such as bacteria, viruses, fungi, and...
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Harris Hawks optimization based hybrid deep learning model for efficient network slicing in 5G network
Newly devised fifth-generation (5G) and sixth-generation (6G) networks next-generation networks are extremely secure, low latency, dependable, and...
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Deep neural network techniques for monaural speech enhancement and separation: state of the art analysis
Deep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved...
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Physics informed neural network for dynamic stress prediction
Structural failures are often caused by catastrophic events such as earthquakes and winds. As a result, it is crucial to predict dynamic stress...
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PETLFC: Parallel ensemble transfer learning based framework for COVID-19 differentiation and prediction using deep convolutional neural network models
Despite a worldwide research involvement in the global COVID-19 pandemic, the research community is still struggling to develop reliable and faster...
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Deep embeddings and Graph Neural Networks: using context to improve domain-independent predictions
Graph neural networks (GNNs) are deep learning architectures that apply graph convolutions through message-passing processes between nodes,...
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MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs
In this paper, we present MambaNet: a hybrid neural network for predicting the outcomes of Basketball games. Contrary to other studies, which focus...
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An AI-enabled feedback-feedforward approach to promoting online collaborative learning
Online collaborative learning has been broadly applied in higher education. However, learners face many challenges in collaborating with one another...
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Neural Network
Originally conceived as far back as the 1940s, neural networks have received a lot of attention in recent years due to frequently astounding results....
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Computational characteristics of feedforward neural networks for solving a stiff differential equation
Feedforward neural networks offer a possible approach for solving differential equations. However, the reliability and accuracy of the approximation...
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A novel neural network training framework with data assimilation
In recent years, the prosperity of deep learning has revolutionized the Artificial Neural Networks. However, the dependence of gradients and the...
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Physics-informed neural network for bending and free vibration analysis of three-dimensional functionally graded porous beam resting on elastic foundation
This study investigates the application of physics-informed neural networks (PINN) for bending and free vibration analysis of three-dimensional...
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Neural Networks and Deep Learning
At the heart of most of today’s “flagship” or “hyped-up” applications of Artificial Intelligence is Deep Learning, and, specifically the use and...
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A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters
Model extraction is a major threat for embedded deep neural network models that leverages an extended attack surface. Indeed, by physically accessing...
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Fake news detection using recurrent neural network based on bidirectional LSTM and GloVe
In the world of technology, the electronic and technical development of the fields of communication and the internet has increased, which has caused...
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Explainability for Deep Learning Models
Deep learning models are becoming the backbone of artificial intelligence implementations. At the same time, it is super important to build the...
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Mispronunciation detection and diagnosis using deep neural networks: a systematic review
The increased need for foreign language learning, along with advances in speech technology have heightened interest in computer-assisted...
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A dimensionality reduction approach for convolutional neural networks
The focus of this work is on the application of classical Model Order Reduction techniques, such as Active Subspaces and Proper Orthogonal...
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Trs-net tropical revolving storm disasters analysis and classification based on multispectral images using 2-d deep convolutional neural network
Damage assessment is one of the most important factors in estimating loss after hurricane. The assessment's findings are very important to identify...
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Optimization of artificial neural network structure and hyperparameters in hybrid model by genetic algorithm: iOS–android application for breast cancer diagnosis/prediction
Breast cancer is a common disease that can result in death among women. Cancer research is important because early detection of cancer facilitates...
