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Parameter identifiability of a deep feedforward ReLU neural network
The possibility for one to recover the parameters—weights and biases—of a neural network thanks to the knowledge of its function on a subset of the...
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High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network
In radar systems, target tracking errors are mainly from motion models and nonlinear measurements. When we evaluate a tracking algorithm, its...
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A feedforward neural network framework for approximating the solutions to nonlinear ordinary differential equations
In this paper, we propose a method to approximate the solutions to nonlinear ordinary differential equations (ODE) using a deep learning feedforward...
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Grafting constructive algorithm in feedforward neural network learning
Constructive algorithm provides a gradually building mechanism by increasing nodes from zero. By this means, the neural network can independently and...
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Deep-efficient-guard: securing wireless ad hoc networks via graph neural network
This study presents a new intrusion detection system (IDS) for Wireless Ad hoc Networks, leveraging graph neural networks (GNN). Overcoming the...
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FCNet: a deep neural network based on multi-channel feature cascading for image denoising
A lot of current work based on convolutional neural networks (CNNs) has fetched good visual results on AWGN (additive white Gaussian noise) removal....
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Sparse Deep Neural Network Based Directional Modulation Design
When receivers change their locations, the transmitter array needs to change its weight coefficient to steer the mainbeam pointing to the...
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Image category classification using 12-Layer deep convolutional neural network
In comparison to human vision, it’s hard for systems to understand images and figure them out on their own. In the modern world, image processing is...
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Identification of glaucoma in fundus images utilizing gray wolf optimization with deep convolutional neural network-based resnet50 model
The main objective of this research is to explore and predict the potentiality of the image analysis model for early detection and diagnosis of...
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Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
AbstractA smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving...
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Architecture Search for Deep Neural Network
Deep learning has become a popularly used tool in large amount of applications. Given its ability to explore the input and output relationship, deep...
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Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model
Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM)...
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Fully Distributed Deep Neural Network: F2D2N
Recent advances in Artificial Intelligence (AI) have accelerated the adoption of AI at a pace never seen before. Large Language Models (LLM) trained...
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Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey
Over the past few years, larger and deeper neural network models, particularly convolutional neural networks (CNNs), have consistently advanced...
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A review on big data based on deep neural network approaches
Big data analytics has become a significant trend for many businesses as a result of the daily acquisition of enormous volumes of data. This...
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Formal Models of Neural Network and Deep Learning
Having been widely used in NLP in recent years, neural networks and deep learning have gradually become the mainstream technology in NLP research....
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An optimized profound memory-affiliated de-noising of aerial images through deep neural network for disaster management
De-noising is an effective mechanism for removing the aberration present in the image and has been exploited in diverse fields. In this proposed...
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Climate change characteristics and population health impact factors using deep neural network and hyperautomation mechanism
This work investigates the impact of climate change characteristics on population health and employs the deep neural network (DNN) and...
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NNV 2.0: The Neural Network Verification Tool
This manuscript presents the updated version of the Neural Network Verification (NNV) tool. NNV is a formal verification software tool for deep...
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NILRNN: A Neocortex-Inspired Locally Recurrent Neural Network for Unsupervised Feature Learning in Sequential Data
Unsupervised feature learning refers to the problem of learning useful feature extraction functions from unlabeled data. Despite the great success of...
