Fuzzy and genetic algorithm based approach for classification of personality traits oriented social media images

2021 ◽  
pp. 108024
Author(s):  
Kunal Biswas ◽  
Palaiahnakote Shivakumara ◽  
Umapada Pal ◽  
Tapabrata Chakraborti ◽  
Tong Lu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
Personality Traits ◽  
Media Images

Optimizing Social Media Data Using Genetic Algorithm

2019 ◽  
pp. 126-153
Author(s):  
Sourav Das ◽  
Anup Kumar Kolya ◽  
Dipankar Das
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
Feature Selection ◽  
Feature Reduction ◽  
Social Media Data ◽  
Training Time ◽  
Redundant Feature ◽  
Media Data ◽  
And Training

Twitter-based research for sentiment analysis is popular for quite some time now. This is used to represent documents in a corpus usually. This increases the time of classification and also increases space complexity. It is hence very natural to say that non-redundant feature reduction of the input space for a classifier will improve the generalization property of a classifier. In this approach, the researchers have tried to do feature selection using Genetic Algorithm (GA) which will reduce the set of features into a smaller subset. The researchers have also tried to put forward an approach using Genetic Algorithm to reduce the modelling complexity and training time of classification algorithm for 10k Twitter data based on GST. They aim to improve the accuracy of the classification that the researchers have obtained in a preface work to this work and achieved an accuracy of 87% through this work. Hence the Genetic Algorithm will do the feature selection to reduce the complexity of the classifier and give us a better accuracy of the classification of the tweet.

scholarly journals Optimized MLCNN for Personalized News Recommendation Based on Social Media Harnessing Using Modified Genetic Algorithm

2021 ◽  
Author(s):  
Saravanapriya Manoharan ◽  
radha senthilkumar ◽  
Saktheewaran J ◽  
Kannan A
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
User Profile ◽  
Experimental Result ◽  
Breaking News ◽  
Modified Genetic Algorithm ◽  
Novel Approach ◽  
News Recommendation ◽  
Computational Resources

Abstract Classification of label-specific users’ diversified interests is the most formidable task in personalized news recommendation systems (PNRS). To bring personalization to PNRS, many remarkable features have to be considered from their user profile to classify their interest. In this paper, 13, 346 features are considered per user to classify their interest for 15 labels using Multi-label Convolution Neural Network (MLCNN). The efficiency of MLCNN highly depends on its architecture through the tuning of its hyper parameters. Generally, researchers have manually designed a constant CNN architecture for each label and every label and verified the effectiveness, but this leads to additional complexity as well as large computational resources were consumed. Moreover, Designing the structure for all 15 labels leads to an increase in network structure exponentially with an increase in labels. Hence, in this paper, MLCNN architectures are optimized by implementing a novel approach Modified Genetic Algorithm (MGA) with the help of introducing four novel crossover operators to strengthen CNN performance for users interest classification. Further, for the recommendation process, the label-specific news articles were clustered from social media Facebook and Twitter feeds, and then most popular news articles were determined along with label-specific breaking news articles rendered from news feeds concerning users’ interest. The experimental result precisely proves that the proposed approach MGA attained an accuracy of 89.64%, 90.56%, 90.41%, and 91.79% for classifying users label specific interest and label-wise recommendation accuracy attained 93.3%, 90%, 90% from Twitter, Facebook, and also from Newsfeed respectively.

Classification of senior tourists according to personality traits

2021 ◽  
Vol 26 (5) ◽  
pp. 539-556
Author(s):  
Felix Elvis Otoo ◽  
Seongseop (Sam) Kim ◽  
Jerome Agrusa ◽  
Joseph Lema
Keyword(s):  
Personality Traits

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

scholarly journals A Novel Co-Training-Based Approach for the Classification of Mental Illnesses Using Social Media Posts

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166165-166172
Author(s):  
Subhan Tariq ◽  
Nadeem Akhtar ◽  
Humaira Afzal ◽  
Shahzad Khalid ◽  
Muhammad Rafiq Mufti ◽  
...  
Keyword(s):  
Social Media ◽  
Mental Illnesses

scholarly journals Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 248
Author(s):  
Simone Leonardi ◽  
Giuseppe Rizzo ◽  
Maurizio Morisio
Keyword(s):  
Social Media ◽  
Network Architecture ◽  
Diffusion Mechanism ◽  
Automated Classification ◽  
Fake News ◽  
Neural Network Architecture ◽  
User Classification ◽  
The Social ◽  
Network Topologies

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

scholarly journals Classification of SSVEP-based BCIs using Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamideh Soltani ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Keivan Maghooli
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Bayesian Method ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Effective Dimension ◽  
Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

Sign in / Sign up

Export Citation Format

Share Document