The Deep Learning Random Neural Network with a Management Cluster

2017 ◽  
pp. 185-195 ◽  
Author(s):  
Will Serrano ◽  
Erol Gelenbe
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Neural Network

Review on Machine and Deep Learning Applications for Cyber Security

2020 ◽  
pp. 42-63 ◽  
Author(s):  
Thangavel M. ◽  
Abiramie Shree T. G. R. ◽  
Priyadharshini P. ◽  
Saranya T.
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cyber Security ◽  
Traditional Approach ◽  
Support Vector ◽  
Data Generation ◽  
Cyber Threats ◽  
Random Neural Network ◽  
Types Of Information ◽  
Core Functionality

In today's world, everyone is generating a large amount of data on their own. With this amount of data generation, there is a change of security compromise of our data. This leads us to extend the security needs beyond the traditional approach which emerges the field of cyber security. Cyber security's core functionality is to protect all types of information, which includes hardware and software from cyber threats. The number of threats and attacks is increasing each year with a high difference between them. Machine learning and deep learning applications can be done to this attack, reducing the complexity to solve the problem and helping us to recover very easily. The algorithms used by both approaches are support vector machine (SVM), Bayesian algorithm, deep belief network (DBN), and deep random neural network (Deep RNN). These techniques provide better results than that of the traditional approach. The companies which use this approach in the real time scenarios are also covered in this chapter.

RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING

2019 ◽  
pp. 1-31 ◽  
Author(s):  
Yonghua Yin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mathematical Model ◽  
Deep Learning ◽  
Energy Expenditure ◽  
Great Success ◽  
Integrate And Fire ◽  
Random Neural Network ◽  
Network Methods ◽  
Spiking Network

The random neural network (RNN) is a mathematical model for an “integrate and fire” spiking network that closely resembles the stochastic behavior of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.

scholarly journals HRNN4F: HYBRID DEEP RANDOM NEURAL NETWORK FOR MULTI-CHANNEL FALL ACTIVITY DETECTION

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ahsen Tahir ◽  
Jawad Ahmad ◽  
Gordon Morison ◽  
Hadi Larijani ◽  
Ryan M. Gibson ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Health Care Services ◽  
Fall Detection ◽  
Sensor Data ◽  
Health Concern ◽  
Learning Models ◽  
Sensor Position ◽  
Random Neural Network ◽  
High Level

Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron.

The Random Neural Network with Deep Learning Clusters in Smart Search

2020 ◽  
Vol 396 ◽  
pp. 394-405 ◽  
Author(s):  
Will Serrano ◽  
Erol Gelenbe ◽  
Yonghua Yin
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Neural Network

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

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