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.

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.

A NOVEL APPROACH TO DESIGN OF MICROSTRIP UWB BANDPASS FILTER USING MODIFIED GENETIC ALGORITHM

2014 ◽  
Vol 36 ◽  
pp. 169-175 ◽  
Author(s):  
Huaxia Peng ◽  
Junding Zhao ◽  
Hao Zhang ◽  
Minxian Du ◽  
Yufeng Luo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Bandpass Filter ◽  
Modified Genetic Algorithm ◽  
Novel Approach

scholarly journals Classification of Erythemato-squamous diseases using Artificial Neural Network and Genetic algorithm

2020 ◽  
pp. 208-212
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Experimental Result ◽  
Mining System ◽  
Statistical Measures ◽  
Specificity And Sensitivity ◽  
Artificial Neural

This paper introduces a hybrid model using artificial neural network (ANN) and genetic algorithm (GA) to develop an efficient classification technique for classification of different categories of Erythemato-squamous diseases. Neural network has been extensively used in many applications like classification, regression, web mining, system identification and pattern recognition. Weight optimization in neural network has been a matter of concern for researchers in the field of soft computing. In this paper the weights of ANN are optimized with GA. The proposed hybrid model is applied on the Erythemato-squamous dataset taken from UCI machine learning repository. The dataset contains six different categories: psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris of Erythemato-squamous diseases. The main aim of this paper is to determine the type of Eryhemato-Squamous disease using the hybrid model. The performance of the hybrid model is evaluated using statistical measures like accuracy, specificity and sensitivity. The accuracy of the proposed model is found to be 99.34% on test dataset. The experimental result shows the effectiveness of the hybrid model in classification of Erythematosquamous diseases.

scholarly journals Measuring information credibility in social media using combination of user profile and message content dimensions

2020 ◽  
Vol 10 (4) ◽  
pp. 3537
Author(s):  
Erwin B. Setiawan ◽  
Dwi H. Widyantoro ◽  
Kridanto Surendro
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Field Data ◽  
User Profile ◽  
Real Field ◽  
Support Vector ◽  
Message Content ◽  
Information Credibility

Information credibility in social media is becoming the most important part of information sharing in the society. The literatures have shown that there is no labeling information credibility based on user competencies and their posted topics. This study increases the information credibility by adding new 17 features for Twitter and 49 features for Facebook. In the first step, we perform a labeling process based on user competencies and their posted topic to classify the users into two groups, credible and not credible users, regarding their posted topics. These approaches are evaluated over ten thousand samples of real-field data obtained from Twitter and Facebook networks using classification of Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (Logit) and J48 algorithm (J48). With the proposed new features, the credibility of information provided in social media is increasing significantly indicated by better accuracy compared to the existing technique for all classifiers.

scholarly journals An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Saravanapriya Manoharan ◽  
Radha Senthilkumar
Keyword(s):  
Social Media ◽  
Fuzzy Rule ◽  
User Profile ◽  
Internet Technology ◽  
News Article ◽  
User Interest ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Recommendation Accuracy ◽  
News Recommendation

Recommendation of a relevant and suitable news article is an essential but a challenging task due to changes in the user interest categories over time. Moreover, the Internet technology provides abundant news articles from a huge amount of resources. Meanwhile, nowadays, many people are confronted with viral news articles through social media cost-free without considering the news sites. Therefore, mining of social media for addressing such viral news articles has become another key challenge. To overcome the above challenges, this paper proposes fuzzy logic approach for predicting users’ diversified interest and its categories by analysing their implicit user profile. Depending on users’ interest categories, the viral news articles and their categories were determined and analysed through mining social media feeds-Facebook and Twitter. Furthermore, fresh news articles are retrieved from news feeds incorporated with retrieved viral news articles provided as recommendation with respect to users’ diversified interest. The performance of the proposed approach for predicting overall users’ interest for all categories attained 84.238%, and recommendation accuracy from News feed, Facebook, and Twitter attained 100%, 90%, and 100% with respect to users’ interest categories.

A modified genetic algorithm optimized SVM for rapid classification of tea leaves using laser-induced breakdown spectroscopy

2021 ◽  
Author(s):  
Mingyin Yao ◽  
Gangrong Fu ◽  
Tianbing Chen ◽  
Muhua Liu ◽  
Jiang Xu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Recognition Accuracy ◽  
Adaptive Mutation ◽  
Tea Leaves ◽  
Breakdown Spectroscopy ◽  
Modified Genetic Algorithm ◽  
Sample Classification ◽  
Svm Model ◽  
Laser Induced Breakdown

This work provides a modified adaptive mutation probability selection genetic algorithm to optimize the SVM model, which improved the accuracy of tea sample classification by LIBS and its recognition accuracy was higher than CV-SVM and PSO-SVM.

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