scholarly journals Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 83 ◽  
Author(s):  
Giannis Haralabopoulos ◽  
Ioannis Anagnostopoulos ◽  
Derek McAuley
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Affective Computing ◽  
Binary Classification ◽  
User Generated Content ◽  
Emotional Information ◽  
Multiple Classifiers ◽  
Different Levels

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .

Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Vikas Khullar ◽  
Karuna Salgotra ◽  
Harjit Pal Singh ◽  
Davinder Pal Sharma
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Binary Classification ◽  
Mr Images

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

scholarly journals Application of neuron networks of deep learning for exposures editing of digital phonograms

2020 ◽  
Vol 65 (4) ◽  
pp. 506-512
Author(s):  
V. I. Solovyov ◽  
O. V. Rybalskiy ◽  
V. V. Zhuravel ◽  
V. K. Zheleznyak
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Maximum Size ◽  
Maximum Efficiency ◽  
Correct Classification ◽  
Digital Audio ◽  
Neuron Networks ◽  
Effective System ◽  
Further Development

Possibility of creation of effective system, which is intended for exposure of tracks of editing in digital phonograms and is built on the basis of neuron networks of the deep learning, is experimentally proven. Sense of experiment consisted in research of ability of the systems on the basis of such networks to expose pauses with tracks of editing. The experimental array of data is created in a voice editor from phonograms written on the different apparatus of the digital audio recording (at frequency of discretisation 44,1 kHz). A preselection of pauses was produced from it, having duration from 100 мs to a few seconds. From 1000 selected pauses the array of fragments of pauses is formed in the automatic (computer) mode, from which the arrays of fragments of pauses of different duration are generated by a dimension about 100 000. For forming of array of fragments of pauses with editing, the chosen pauses were divided into casual character parts in arbitrary correlation. Afterwards, the new pauses were created from it with the fixed place of editing. The general array of all fragments of pauses was broken into training and test arrays. The maximum efficiency, achieved on a test array in the process of educating, was determined. In general case this efficiency is determined by the maximum size of probability of correct classification of fragments with editing and fragments without editing. Scientifically reasonable methodology of exposure of signs of editing in digital phonograms is offered on the basis of neuron networks of the deep learning. The conducted experiments showed that the construction of the effective system is possible for the exposure of such tracks. Further development of methodology must be directed to find the ways to increase the probability of correct binary classification of investigated pauses.

scholarly journals Interactivity features of Online Newspapers: from a facsimile model to a multimedia model one. Interactivity in online journals.

2017 ◽  
Vol 20 (2) ◽  
Author(s):  
María-José Baños-Moreno ◽  
Juan-Antonio Pastor-Sánchez ◽  
Rodrigo Martínez-Béjar
Keyword(s):  
User Generated Content ◽  
Multimedia Model ◽  
Stages Of Development ◽  
Online Newspapers ◽  
Online Journals ◽  
Media Firms ◽  
Measuring Tool ◽  
Different Levels

The purpose of this article is to know the current interactivity options in online newspapers. To do this, we analyze the concept of (structural) interactivity and establish a measuring tool based on some previous methodologies. This model includes a classification of newspapers into stages of development. In this way, we provide a tool, which allows media firms to know the degree of interactivity in newspapers, assessing what dimensions and parameters are being incorporated. It was applied to a sample of 21 online newspapers. Results show poor implementation of participation and customization options. There is a niche to be covered by sections of user-generated content and closer collaboration journalists-citizens. It will require a real adaptation to the new possibilities of interaction with readers at different levels of involvement and participation. The majority of newspapers are classified in a digital stage and some of them are close to the multimedia phase.

Deep learning based affective computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saurabh Kumar
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Transfer Learning ◽  
Affective Computing ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Learning Models ◽  
Text And Image ◽  
Content Type

PurposeDecision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.Design/methodology/approachThe present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.FindingsThe result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.Originality/valueThe study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.

scholarly journals Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

2020 ◽  
Vol 7 ◽  
Author(s):  
Kenneth Thomsen ◽  
Anja Liljedahl Christensen ◽  
Lars Iversen ◽  
Hans Bredsted Lomholt ◽  
Ole Winther
Keyword(s):  
Deep Learning ◽  
Skin Diseases ◽  
Binary Classification ◽  
Multiple Lesion ◽  
Lesion Skin

scholarly journals Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying

2019 ◽  
Vol 9 (13) ◽  
pp. 2760 ◽  
Author(s):  
Khai Tran ◽  
Thi Phan
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Model ◽  
Sentiment Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Deep Learning Model

Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains.

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