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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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Network properties determine neural network performance
Machine learning influences numerous aspects of modern society, empowers new technologies, from Alphago to ChatGPT, and increasingly materializes in...
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An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction
Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts...
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An integrated fuzzy neural supervision and attention-based graph neural network for improving network clustering
In recent years, graph neural network (GNN) and auto-encoding (AE) have been widely utilized in multiple data mining problems. These architectures...
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Explainable generalized additive neural networks with independent neural network training
Neural Networks are one of the most popular methods nowadays given their high performance on diverse tasks, such as computer vision, anomaly...
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Analytical Calculation of Weights Convolutional Neural Network
AbstractIn this paper proposes an algorithm for the analytical calculation of convolutional neural networks without using neural network training...
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Hybrid Hopfield Neural Network
Hopfield and Tank have shown that a neural network can find solutions for complex optimization problems, although it can be trapped in a local...
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The cross-sectional stock return predictions via quantum neural network and tensor network
In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically,...
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Partitioning multi-layer edge network for neural network collaborative computing
There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device...
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Mutual Information-Based Neural Network Distillation for Improving Photonic Neural Network Training
AbstractPhotonic neural networks are among the most promising recently proposed neuromorphic solutions for providing fast and energy efficient Deep...
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Resource constrained neural network training
Modern applications of neural-network-based AI solutions tend to move from datacenter backends to low-power edge devices. Environmental,...
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Heterogeneous wireless network selection using feed forward double hierarchy linguistic neural network
Network selection in heterogeneous wireless networks (HWNs) is a complex issue that requires a thorough understanding of service features and user...
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Hardware design and the fairness of a neural network
Ensuring the fairness of neural networks is crucial when applying deep learning techniques to critical applications such as medical diagnosis and...
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Anterior cruciate ligament tear detection based on convolutional neural network and generative adversarial neural network
Knee ligament tear injury is frequent in many volleyball, football, basketball, and cricket players. In the past, various deep learning-based ACL...
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An integrated simplicial neural network with neuro-fuzzy network for graph embedding
In recent years, graph neural network (GNN) has become the main stream for most of recent researches due to its powers in dealing with complex graph...
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Network intrusion detection based on variational quantum convolution neural network
With the rapid development of quantum machine learning (QML), quantum convolutional neural networks (QCNN) have been proposed and shown advantages in...
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Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give...
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Hybrid Neural Network for Classification of Mammography Images
Abstract —An important step in solving the problem of classification and segmentation of 2D images is the extraction of local geometric features....
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HRNN: Hypergraph Recurrent Neural Network for Network Intrusion Detection
In intrusion detection systems, deep learning has demonstrated its capability to effectively mine flow representations, significantly enhancing the...
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A neural network accelerated optimization method for FPGA
A neural network accelerated optimization method for FPGA hardware platform is proposed. The method realizes the optimized deployment of neural...
