A deep learning approach to integrate convolutional neural networks in speaker recognition

2020 ◽  
Vol 23 (3) ◽  
pp. 615-623 ◽  
Author(s):  
Soufiane Hourri ◽  
Nikola S. Nikolov ◽  
Jamal Kharroubi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Speaker Recognition ◽  
Learning Approach

A deep learning approach for brain tumour detection system using convolutional neural networks

2021 ◽  
Vol 11 (5/6) ◽  
pp. 514
Author(s):  
T. Kalaiselvi ◽  
S.T. Padmapriya ◽  
P. Sriramakrishnan ◽  
K. Somasundaram
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumour ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Learning Approach ◽  
Tumour Detection

scholarly journals PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing

2020 ◽  
Vol 12 (7) ◽  
pp. 1092
Author(s):  
David Browne ◽  
Michael Giering ◽  
Steven Prestwich
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Recognition Task ◽  
Scene Recognition ◽  
Training Data ◽  
Learning Approach ◽  
Scene Classification

Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems. We use transfer learning to compensate for the lack of data, and data augmentation to tackle varying scale and orientation. To reduce network size, we use a novel unsupervised learning approach based on k-means clustering, applied to all parts of the network: most network reduction methods use computationally expensive supervised learning methods, and apply only to the convolutional or fully connected layers, but not both. In experiments, we set new standards in classification accuracy on four remote-sensing and two scene-recognition image datasets.

scholarly journals Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 59069-59080 ◽  
Author(s):  
Peng Jiang ◽  
Yuehan Chen ◽  
Bin Liu ◽  
Dongjian He ◽  
Chunquan Liang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Learning Approach ◽  
Real Time Detection

scholarly journals Newspaper Article Classification using Machine Learning Techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 872-877
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Training Time ◽  
Time Prediction ◽  
Learning Techniques ◽  
Prediction Time ◽  
Newspaper Articles

Newspaper articles offer us insights on several news. They can be one of many categories like sports, politics, Science and Technology etc. Text classification is a need of the day as large uncategorized data is the problem everywhere. Through this study, We intend to compare several algorithms along with data preprocessing approaches to classify the newspaper articles into their respective categories. Convolutional Neural Networks(CNN) is a deep learning approach which is currently a strong competitor to other classification algorithms like SVM, Naive Bayes and KNN. We hence intend to implement Convolutional Neural Networks - a deep learning approach to classify our newspaper articles, develop an understanding of all the algorithms implemented and compare their results. We also attempt to compare the training time, prediction time and accuracies of all the algorithms.

Deep Learning Approach Based on Convolutional Neural Networks for Image Processing Applications

2017 ◽  
Author(s):  
João Sauer ◽  
Marco Boaretto ◽  
Edson Gnatkovski Gruska ◽  
arthur canciglieri ◽  
Gabriel Herman Bernardim Andrade ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Approach

scholarly journals A deep learning approach to identify blepharoptosis by convolutional neural networks

2021 ◽  
Vol 148 ◽  
pp. 104402
Author(s):  
Ju-Yi Hung ◽  
Chandrashan Perera ◽  
Ke-Wei Chen ◽  
David Myung ◽  
Hsu-Kuang Chiu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Approach

Traditional Classifiers vs. Deep Learning for Cyberbullying Detection

2018 ◽  
pp. 133-149
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Search Algorithm ◽  
Learning Approach ◽  
Brute Force ◽  
Brute Force Search ◽  
Novel Method ◽  
Objective View ◽  
Cyberbullying Detection

In this chapter, the authors present their approach to cyberbullying detection with the use of various traditional classifiers, including a deep learning approach. Research has tackled the problem of cyberbullying detection during recent years. However, due to complexity of language used in cyberbullying, the results obtained with traditional classifiers has remained only mildly satisfying. In this chapter, the authors apply a number of traditional classifiers, used also in previous research, to obtain an objective view on to what extent each of them is suitable to the task. They also propose a novel method to automatic cyberbullying detection based on convolutional neural networks and increased feature density. The experiments performed on actual cyberbullying data showed a major advantage of the presented approach to all previous methods, including the two best performing methods so far based on SO-PMI-IR and brute-force search algorithm, presented in previous two chapters.

scholarly journals Sentiment Analysis about Smart Phones Using Twitter Corpus by Deep Learning Approach

2019 ◽  
Vol 8 (S2) ◽  
pp. 39-45
Author(s):  
R. Pavithra ◽  
A. R. Mohamed Shanavas
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Deep Learning ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Social Networking Sites ◽  
Smart Phones ◽  
Learning Approach ◽  
Human Beings

Micro blogging websites are nothing but social media website to which user makes quick and frequent posts. Twitter is one of the well-known micro blog sites which offer the space for person which can read and put up messages that are 148 characters in duration. Twitter messages also are referred to as Tweets. And will use these tweets as raw facts. Then use a way that automatically extracts tweets into advantageous, bad or neutral sentiments. By the usage of the sentiment evaluation the consumer can recognize the feedback about the product or services before make a purchase. The organization can use sentiment evaluation to know the opinion of clients about their products, so can examine customer pleasure and in line with that they could improve their product. Now-a-days social networking sites are at the growth, so massive amount of data is generated. Millions of human beings are sharing their views each day on micro blogging sites, since it includes short and simple expressions. In this thesis, able to discuss approximately a paradigm to extract the sentiment from a famous micro running a blog carrier, Twitter, wherein customers submit their opinions for the whole thing. And can use the deep mastering algorithm to categories the twitters which incorporates Convolutional Neural Networks. The experimental end result is presented to demonstrate the use and effectiveness of the proposed system.

scholarly journals Forecasting SARS-CoV-2 in the peruvian regions: a deep learning approach using temporal convolutional neural networks

2021 ◽  
Vol 8 (1) ◽  
pp. 12-26
Author(s):  
Luis Aguilar I. ◽  
Miguel Ibáñez-Reluz ◽  
Juan C. Z. Aguilar ◽  
Elí W. Zavaleta-Aguilar ◽  
L. Antonio Aguilar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Approach

scholarly journals A Deep Learning Approach to Detect Pornography Videos in Educational Repositories

2020 ◽  
Author(s):  
Pedro V. A. de Freitas ◽  
Antonio J. G. Busson ◽  
Álan L. V. Guedes ◽  
Sérgio Colcher
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Automated Detection ◽  
Learning Approach ◽  
Multimodal Approach ◽  
Deep Convolutional Neural Networks ◽  
High Level

A large number of videos are uploaded on educational platforms every minute. Those platforms are responsible for any sensitive media uploaded by their users. An automated detection system to identify pornographic content could assist human workers by pre-selecting suspicious videos. In this paper, we propose a multimodal approach to adult content detection. We use two Deep Convolutional Neural Networks to extract high-level features from both image and audio sources of a video. Then, we concatenate those features and evaluate the performance of classifiers on a set of mixed educational and pornographic videos. We achieve an F1-score of 95.67% on the educational and adult videos set and an F1-score of 94% on our test subset for the pornographic class.

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