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Large scale performance analysis of distributed deep learning frameworks for convolutional neural networks
Continuously increasing data volumes from multiple sources, such as simulation and experimental measurements, demand efficient algorithms for an...
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Distributed Training of Deep Neural Networks: Convergence and Case Study
Deep neural network training on a single machine has become increasingly difficult due to a lack of computational power. Fortunately, distributed...
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Distributed source DOA estimation based on deep learning networks
With space electromagnetic environments becoming increasingly complex, the direction of arrival (DOA) estimation based on the point source model can...
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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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E-SDNN: encoder-stacked deep neural networks for DDOS attack detection
The increasing reliance on internet-based services has heightened the vulnerability of network infrastructure to cyberattacks, particularly...
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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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A Novel Distributed Process Monitoring Framework of VAE-Enhanced with Deep Neural Network
Intelligent manufacturing process needs to adopt distributed monitoring scenario due to its massive, high-dimensional and complex data. Distributed...
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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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AdaInNet: an adaptive inference engine for distributed deep neural networks offloading in IoT-FOG applications based on reinforcement learning
The increasing expansion of Internet-of-Things (IoT) in the world requires Big Data analytic infrastructures to produce valuable knowledge in IoT...
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Dependent Task Scheduling Using Parallel Deep Neural Networks in Mobile Edge Computing
Conventional detection techniques aimed at intelligent devices rely primarily on deep learning algorithms, which, despite their high precision, are...
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ODRNN: optimized deep recurrent neural networks for automatic detection of leukaemia
Leukaemia image classification involves using machine learning, and often deep learning, techniques to automatically analyse medical images and...
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Deep-Learning Based Detection for Cyber-Attacks in IoT Networks: A Distributed Attack Detection Framework
The widespread use of smart devices and the numerous security weaknesses of networks has dramatically increased the number of cyber-attacks in the...
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Deep neural networks for rank-consistent ordinal regression based on conditional probabilities
In recent times, deep neural networks achieved outstanding predictive performance on various classification and pattern recognition tasks. However,...
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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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Quantitative Gaussian approximation of randomly initialized deep neural networks
Given any deep fully connected neural network, initialized with random Gaussian parameters, we bound from above the quadratic Wasserstein distance...
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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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UDL: a cloud task scheduling framework based on multiple deep neural networks
Cloud task scheduling and resource allocation (TSRA) constitute a core issue in cloud computing. Batch submission is a common user task deployment...
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Instance segmentation on distributed deep learning big data cluster
Distributed deep learning is a promising approach for training and deploying large and complex deep learning models. This paper presents a...
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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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Exploring the distributed learning on federated learning and cluster computing via convolutional neural networks
Distributed learning has led to the development of federated learning and cluster computing; however, the two methods are very different. Therefore,...
