Single-cell based random neural network for deep learning

Yin, Yilong; Gelenbe, Erol

doi:10.1109/ijcnn.2017.7965840

Cited by 20 publications

(33 citation statements)

References 31 publications

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“…• Reinforcement learning [7][8][9] takes quick and specific local decisions; • Deep learning clusters [10][11][12] enable identity and memory; • Deep learning management clusters [13][14][15][16] make final strategic decisions.…”

Section: Research Proposalmentioning

confidence: 99%

“…Deep learning with random neural networks is described by Gelenbe and Yin [10][11][12]. This model is based on the generalized queuing networks with triggered customer movement (G-networks) where customers or tasks are either ''positive'' or ''negative'' and customers or tasks can be moved from queues or leave the network.…”

Section: The Random Neural Network With Multiple Clustersmentioning

confidence: 99%

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Genetic and deep learning clusters based on neural networks for management decision structures

Serrano

2019

Neural Comput & Applic

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Judgments are taken in a structured way; both human and business management decisions involve a hierarchical process that requires a level of compromise between risk, cost, reward, experience and knowledge. This article proposes a management decision structure that emulates the human brain approach based on genetic and deep learning cluster algorithms and the random neural network. Reinforcement learning takes quick and specific local decisions, deep learning clusters enables identity and memory, and deep learning management clusters make final strategic decisions. The presented genetic algorithm transmits the learned information to future generations in the network weights rather than the neurons. Because the subject's information, a combination of memory, identity and decision data, is never lost but transmitted, the genetic algorithm provides immortality. The management decision structure has been applied and validated in a smart investment Fintech application: an intelligent banker that makes buy and sell asset decisions with an associated market and risk that entirely transmits itself to a future generation. Results are rewarding; the management decision structure with genetics and machine learning based on the random neural network algorithm that emulates the human brain and biology transmits information to future generations and learns autonomously, gradually and continuously while adapting to the environment. Keywords Genetic learning Á Deep learning clusters Á Reinforcement learning Á Random neural network Á Fintech Á Smart investment & Will Serrano

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Section: Research Proposalmentioning

confidence: 99%

Section: The Random Neural Network With Multiple Clustersmentioning

confidence: 99%

Genetic and deep learning clusters based on neural networks for management decision structures

Serrano

2019

Neural Comput & Applic

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“…This section describes the use of random neural networks (RNN) [17,18] developed for deep learning recently [13][14][15][16] to detect network attacks, which can be viewed as a binary classification problem. First, we show how to construct training datasets from captured packets.…”

Section: Network-attack Detection With Random Neural Networkmentioning

confidence: 99%

Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments

Brun

Yin

Gelenbe

et al. 2018

Communications in Computer and Information Science

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Abstract.In this paper, we analyze the network attacks that can be launched against IoT gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We also present the principles and design of a deep learning-based approach using dense random neural networks (RNN) for the online detection of network attacks. Empirical validation results on packet captures in which attacks were inserted show that the Dense RNN correctly detects attacks.

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“…THE RANDOM NEURAL NETWORK (RNN) The Random Neural Network (RNN) [43]- [45] has a powerful property of approximating continuous and bounded real-valued functions [46]. This property serves as the foundation for RNN based learning algorithms, both for recurrent (containing feedback) and feedforward networks [47], and for Deep Learning [48], [49].…”

Section: Introductionmentioning

confidence: 99%

Accurate, Energy-Efficient Classification With Spiking Random Neural Network

Hussain

Bassyouni

Gelenbe

2019

Prob. Eng. Inf. Sci.

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Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running more bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network (RNN), a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of a spiking neural network. This is demonstrated on a number of real-world classification datasets.

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Single-cell based random neural network for deep learning

Cited by 20 publications

References 31 publications

Genetic and deep learning clusters based on neural networks for management decision structures

Genetic and deep learning clusters based on neural networks for management decision structures

Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments

Accurate, Energy-Efficient Classification With Spiking Random Neural Network

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