Performance Analysis of Initialization Algorithms of Deep Neural Network Based Coordinated Beamforming System for mmWave

2021 ◽  
Author(s):  
Dibaloke Chanda ◽  
Ali-Emam-Al-Badi ◽  
A K M Nazrul Islam
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Deep Neural Network ◽  
Coordinated Beamforming

Importance-Performance Analysis of Product Attributes Using Explainable Deep Neural Network From Online Reviews

2020 ◽  
Author(s):  
Junegak Joung ◽  
Harrison M. Kim
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Online Reviews ◽  
Product Attributes ◽  
Neural Network Architecture ◽  
Single Neural Network

Abstract Importance-performance analysis (IPA) is a technique used to understand customer satisfaction and improve the quality of product attributes. This study proposes an explainable deep-neural-network-based method to carry out IPA of product attributes from online reviews for product design. Previous works used shallow neural network (SNN)-based methods to estimate importance values, but it was unclear whether the SNN is an optimal neural network architecture. The estimated importance has high variability by a single neural network from a training set that is randomly selected. However, the proposed method provides importance values with a lower variance by improving the importance estimation of each product attribute in the IPA. The proposed method first identifies the product attributes and estimates their performance. Then, it infers the importance values by combining explanations of the input features from multiple optimal neural networks. A case study on smartphones is used herein to demonstrate the proposed method.

scholarly journals Deep-CAPTCHA: A Deep Learning Based CAPTCHA Solver for Vulnerability Assessment

2020 ◽  
Author(s):  
Zahra Nouri ◽  
Mahdi Rezaei
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Performance Analysis ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Numerical Test ◽  
High Rate ◽  
Human Intelligence ◽  
Artificial Agents ◽  
Research Gaps

CAPTCHA is a human-centred test to distinguish a human operator from bots, attacking programs, or other computerised agents that tries to imitate human intelligence. In this research, we investigate a way to crack visual CAPTCHA tests by an automated deep learning based solution. The goal of this research is to investigate the weaknesses and vulnerabilities of the CAPTCHA generator systems; hence, developing more robust CAPTCHAs, without taking the risks of manual try and fail efforts. We develop a Convolutional Neural Network called Deep-CAPTCHA to achieve this goal. The proposed platform is able to investigate both numerical and alphanumerical CAPTCHAs. To train and develop an efficient model, we have generated a dataset of 500,000 CAPTCHAs to train our model. In this paper, we present our customised deep neural network model, we review the research gaps, the existing challenges, and the solutions to cope with the issues. Our network's cracking accuracy leads to a high rate of 98.94% and 98.31% for the numerical and the alpha-numerical test datasets, respectively. That means more works is required to develop robust CAPTCHAs, to be non-crackable against automated artificial agents. As the outcome of this research, we identify some efficient techniques to improve the security of the CAPTCHAs, based on the performance analysis conducted on the Deep-CAPTCHA model.

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