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Neural Networks and Deep Learning A Textbook
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory...
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A new deep neural network for forecasting: Deep dendritic artificial neural network
Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural...
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Fake news detection using dual BERT deep neural networks
Fake news is a growing challenge for social networks and media. Detection of fake news always has been a problem for many years, but the evolution of...
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TSDNN: tube sorting with deep neural networks for surveillance video synopsis
High-quality cameras collect a large amount of surveillance video that can be labor-consuming for security guards to browse and analyze. One way to...
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Boosting deep neural networks with geometrical prior knowledge: a survey
Deep neural networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However,...
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Graph neural networks for deep portfolio optimization
There is extensive literature dating back to the Markowitz model on portfolio optimization. Recently, with the introduction of deep models in...
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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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Randomnet: clustering time series using untrained deep neural networks
Neural networks are widely used in machine learning and data mining. Typically, these networks need to be trained, implying the adjustment of weights...
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Deep neural networks for explainable feature extraction in orchid identification
Automated image-based plant identification systems are black-boxes, failing to provide an explanation of a classification. Such explanations are seen...
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Classification of Brain Tumors: A Comparative Approach of Shallow and Deep Neural Networks
Brain tumors can be generated anywhere in the brain, with an extensive size range and morphology that makes it challenging to identify and classify....
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A survey of uncertainty in deep neural networks
Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to...
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Deep neural networks watermark via universal deep hiding and metric learning
With the rising costs of model training, it is urgent to safeguard the intellectual property of deep neural networks. To achieve this, researchers...
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Prescribed attractivity region selection for recurrent neural networks based on deep reinforcement learning
Recurrent neural networks’ (RNNs’) outputs are the same when network states converge to the same saturation region. Strong external inputs can cause...
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Adversarial robustness improvement for deep neural networks
Deep neural networks (DNNs) are key components for the implementation of autonomy in systems that operate in highly complex and unpredictable...
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Android applications classification with deep neural networks
Currently, Android is the most widely used mobile operating system globally. This platform has become a target for malware activities due to its...
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Deep Learning, Neural Networks
Deep neural network learning capitalizes on translations of basic biological constructs, such as single neuronal cells, brain regions, and cognitive...
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Geometric deep learning and equivariant neural networks
We survey the mathematical foundations of geometric deep learning, focusing on group equivariant and gauge equivariant neural networks. We develop...
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Deep Neural Networks
Neocognitron is a neural network with the configuration shown in Fig. 5.1. It is a hierarchical multi-layered neural network by Kunihiko Fukushima...
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Emergency COVID-19 detection from chest X-rays using deep neural networks and ensemble learning
While several papers have explored the application of deep learning for COVID-19 detection in chest X-ray images, the consideration of images taken...
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Dental caries diagnosis using neural networks and deep learning: a systematic review
Dental caries is one of the oral health problems and the most common chronic infectious disease of childhood, and neural networks and artificial...
