scholarly journals The Random Neural Network with a Genetic Algorithm and Deep Learning Clusters in Fintech: Smart Investment

2018 ◽  
pp. 297-310 ◽  
Author(s):  
Will Serrano
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Deep Learning ◽  
Random Neural Network

scholarly journals Evolutionary design of molecules based on deep learning and a genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngchun Kwon ◽  
Seokho Kang ◽  
Youn-Suk Choi ◽  
Inkoo Kim
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Deep Learning ◽  
Design Method ◽  
Network Models ◽  
Molecular Structures ◽  
Evolutionary Design ◽  
Neural Network Models ◽  
Practical Challenge ◽  
Significant Attention

AbstractEvolutionary design has gained significant attention as a useful tool to accelerate the design process by automatically modifying molecular structures to obtain molecules with the target properties. However, its methodology presents a practical challenge—devising a way in which to rapidly evolve molecules while maintaining their chemical validity. In this study, we address this limitation by developing an evolutionary design method. The method employs deep learning models to extract the inherent knowledge from a database of materials and is used to effectively guide the evolutionary design. In the proposed method, the Morgan fingerprint vectors of seed molecules are evolved using the techniques of mutation and crossover within the genetic algorithm. Then, a recurrent neural network is used to reconstruct the final fingerprints into actual molecular structures while maintaining their chemical validity. The use of deep neural network models to predict the properties of these molecules enabled more versatile and efficient molecular evaluations to be conducted by using the proposed method repeatedly. Four design tasks were performed to modify the light-absorbing wavelengths of organic molecules from the PubChem library.

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.

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