scholarly journals User Targeted Offline Advertising using Recognition Based Demographics and Queue Scheduling

2020 ◽  
Vol 9 (3) ◽  
pp. 2751-2757
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Scheduling Algorithm ◽  
Research Paper ◽  
Priority Queues ◽  
Fixed Amount ◽  
Advertising Industry ◽  
Random Fashion ◽  
Queue Scheduling

Offline advertisements are static in nature. Advertising companies use billboards for advertising. These billboards display advertisements in a random fashion depending on the investment made by the advertiser. Advertisers pay a fixed amount of money for displaying their advertisements and not on the basis of relevant viewership. The technology proposed in the paper ensures that this disparity is handled wherein offline advertisements are targeted to the relevant audience. The technology has been named TARP which is an abbreviation for Target. Advertise. Revolutionise. Promote. TARP uses built in cameras on offline advertising platforms such as billboards & TV Screens in malls, restaurants, metro & airports to target advertisements based on gender, age and other relevant demographics. The technology is a boon for the advertising industry and benefits both advertisers and viewers. It displays what viewers want to see and who the advertisers want to reach out to. Convolutional neural networks are used to generate demographics of viewing population. Centroids of the viewing population are maintained for each billboard. Advertisements search for the most relevant billboard for display. Display of advertisements is monitored by a queue scheduling algorithm. The research paper proposes an algorithm to generate demographics, search most relevant billboard for each advertisement as well as generate priority queues.

scholarly journals A Result Analysis of Mask Detection based Notification System

2021 ◽  
Vol 9 (VI) ◽  
pp. 15-17
Author(s):  
Gaurav Kumar D. K. Singh
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Face Mask ◽  
Research Paper ◽  
Public Places ◽  
Notification System ◽  
Full Face ◽  
Result Analysis

Face mask detection system will be the best option for preventing covid-19 spread at public places. Those models are mainly required for ensuring safety and hygiene in a public premises .The research paper consist of full face scan using a pre-trained model such that all the facial characters can be imprinted on the pixel basis by the pre-trained model that takes input from the camera associated with the program. The whole of the program is based on convolutional neural networks which extract features and associate them in the form of neurons

scholarly journals Analysis on Deep Learning methods for ECG based Cardiovascular Disease prediction

2020 ◽  
Vol 21 (1) ◽  
pp. 127-136
Author(s):  
S Kusuma ◽  
J Divya Udayan
Keyword(s):  
Neural Networks ◽  
Cardiovascular Disease ◽  
Deep Learning ◽  
Detailed Analysis ◽  
Convolutional Neural Networks ◽  
Risk Evaluation ◽  
Research Paper ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Learning Methods

The cardiovascular related diseases can however be controlled through earlierdetection as well as risk evaluation and prediction. In this paper the applicationof deep learning methods for CVD diagnosis using ECG is addressed.A detailed Analysis of related articles has been conducted. The results indicatethat convolutional neural networks (CNN) are the most widely used deeplearning technique in the CVD diagnosis. This research paper looks into theadvantages of deep learning approaches that can be brought by developing aframework that can enhance prediction of heart related diseases using ECG.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline
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