Deep Neural Network Mapping and Performance Analysis on Tiled RRAM Architecture

2020 ◽  
Author(s):  
Xinxin Wang ◽  
Qiwen Wang ◽  
Fan-Hsuan Meng ◽  
Seung Hwan Lee ◽  
Wei D Lu
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Deep Neural Network ◽  
And Performance ◽  
Network Mapping

scholarly journals Neuron Blockchain Algorithm for Legal Problems in Inheritance of Legacy

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1595
Author(s):  
Seong-Kyu Kim ◽  
Jun-Ho Huh
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Non Fiction ◽  
Legal Problems ◽  
Artificial Neurons ◽  
The Will ◽  
Artificial Neural ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Artificial Network

This paper discusses the worldwide trend of aging as the lifespan of humans increases. Nonetheless, most people do not write wills, which results in many legal problems after their death. There are many reasons for this including the problem of the validity of their heritage possibly not being legally certified. Wills can be divided into two categories, i.e., testimony and documents. A lawyer in the middle should notarize them, however, instead of providing these notarized services, we propose more transparent algorithms, blockchain shading, and smart country functions. Architectures are designed based on a neural network, the blockchain deep neural network (DNN), and deep neural network-based units are built with a necessary artificial neural network (ANN) base. A heritage inherited blockchain architecture is designed to communicate between nodes based on the minimum distance algorithm and multichannel protocol. In addition, neurons refer to the nerve cells that make up the nervous system of an organism, and artificial neurons are an abstraction of the functions of dendrite, soma, and axon that constitute the neurons of an organism. Similar to the neurons in organisms, artificial neural algorithms such as the depth-first search (DFS) algorithm are expressed in pseudocode. In addition, all blockchain nodes are equipped with verified nodes. A research model is proposed for an artificial network blockchain that is needed for this purpose. The experimental environment builds the server and network environments based on deep neural networks that require verification. Weights are also set for the required verification and performance. This paper verifies the blockchain algorithm equipped with this non-fiction preprocessor function. We also study the blockchain neuron engine that can safely construct a block node for a suicide blockchain. After empirical testing of the will system with artificial intelligence and blockchain, the values are close to 2 and 10 and the distribution is good. The blockchain node also tested 50 nodes more than 150 times, and we concluded that it was suitable for actual testing by completing a demonstration test with 4500 TPS.

scholarly journals Result and Performance Analysis of Rainfall Prediction System Based on Deep Neural Network

2020 ◽  
pp. 633-638
Author(s):  
Akshay Rajendra Naik ◽  
A. V. Deorankar ◽  
P. B. Ambhore
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Rainfall Prediction ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
And Performance

Rainfall prediction is useful for all people for decision making in all fields, such as out door gamming, farming, traveling, and factory and for other activities. We studied various methods for rainfall prediction such as machine learning and neural networks. There is various machine learning algorithms are used in previous existing methods such as naïve byes, support vector machines, random forest, decision trees, and ensemble learning methods. We used deep neural network for rainfall prediction, and for optimization of deep neural network Adam optimizer is used for setting modal parameters, as a result our method gives better results as compare to other machine learning methods.

scholarly journals Breast Cancer Prediction based on Deep Neural Network Model Implemented AWS Machine Learning Platform

2020 ◽  
Vol 9 (2) ◽  
pp. 868-873
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Breast Tissue ◽  
Model Accuracy ◽  
Cancer Prediction ◽  
Learning Platform ◽  
Public Dataset ◽  
And Performance

Breast cancer in women is one of the most dangerous cancers leading to death in women by developing breast tissue. In this work, the application of the Deep Neural Network (DNN) model is implemented on AWS machine learning platform, besides, a comparison with other ML techniques includes XGBoost and Random Forest on a public dataset. Breast cancer prediction based on DNN model with Hyperparameter tuning has the best results of the plot of model accuracy for the training and validation sets and performance evaluation metrics to test the model.

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