This document summarizes a research paper that studied the detection of cyberbullying using machine learning and deep learning techniques. Specifically, it used a hybrid model combining KNN, SVM and Random Forest algorithms (stacking algorithm) and a Convolutional Neural Network (CNN) on a Twitter dataset. The stacking algorithm achieved an accuracy of X% while the CNN achieved a higher accuracy of Y%. A comparison of the two models found that CNN produced a more precise prediction of cyberbullying. The document also reviewed related work on cyberbullying detection using content-based, user-based and network-based approaches with machine learning algorithms like SVM, Naive Bayes and deep learning methods like CNN.
A MACHINE LEARNING ENSEMBLE MODEL FOR THE DETECTION OF CYBERBULLYINGijaia
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
Categorize balanced dataset for troll detectionvivatechijri
As we know cyber bullying is increasing day by day and Cyber troll is one of the cyber-aggressive actions that is not much different from cyberbullying in online abuse so that the victims feel uncomfortable. One of the most used social media platforms in which cyber trolling frequently happens is Twitter. Basically, it is found that during an investigation of cyberbullying cases a lot of information gathered is false which aims to give discomfort, hatred and waste lots of time. So, it is necessary to classify between cyberbullying tweets and normal tweets on twitter. There has already been research on classification of cyberbullying tweets and normal tweets using the Support vector machine (SVM) algorithm. But the drawback of the system is that it only gives 63.83% of accuracy. Firstly, we can improve the accuracy of the system by using the Recurrent Neural Network (RNN) And Secondly, for balancing the dataset we will be using Synthetic Minority Over-sampling Technique (SMOTE). We believe that using these techniques we will be able to increase the accuracy of the previous proposed.
Fake accounts detection system based on bidirectional gated recurrent unit n...IJECEIAES
Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the global vectors (GloVe) word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature.
Integrated approach to detect spam in social media networks using hybrid feat...IJECEIAES
Online social networking sites are becoming more popular amongst Internet users. The Internet users spend some amount of time on popular social networking sites like Facebook, Twitter and LinkedIn etc. Online social networks are considered to be much useful tool to the society used by Internet lovers to communicate and transmit information. These social networking platforms are useful to share information, opinions and ideas, make new friends, and create new friend groups. Social networking sites provide large amount of technical information to the users. This large amount of information in social networking sites attracts cyber criminals to misuse these sites information. These users create their own accounts and spread vulnerable information to the genuine users. This information may be advertising some product, send some malicious links etc to disturb the natural users on social sites. Spammer detection is a major problem now days in social networking sites. Previous spam detection techniques use different set of features to classify spam and non spam users. In this paper we proposed a hybrid approach which uses content based and user based features for identification of spam on Twitter network. In this hybrid approach we used decision tree induction algorithm and Bayesian network algorithm to construct a classification model. We have analysed the proposed technique on twitter dataset. Our analysis shows that our proposed methodology is better than some other existing techniques.
A MACHINE LEARNING ENSEMBLE MODEL FOR THE DETECTION OF CYBERBULLYINGijaia
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
Categorize balanced dataset for troll detectionvivatechijri
As we know cyber bullying is increasing day by day and Cyber troll is one of the cyber-aggressive actions that is not much different from cyberbullying in online abuse so that the victims feel uncomfortable. One of the most used social media platforms in which cyber trolling frequently happens is Twitter. Basically, it is found that during an investigation of cyberbullying cases a lot of information gathered is false which aims to give discomfort, hatred and waste lots of time. So, it is necessary to classify between cyberbullying tweets and normal tweets on twitter. There has already been research on classification of cyberbullying tweets and normal tweets using the Support vector machine (SVM) algorithm. But the drawback of the system is that it only gives 63.83% of accuracy. Firstly, we can improve the accuracy of the system by using the Recurrent Neural Network (RNN) And Secondly, for balancing the dataset we will be using Synthetic Minority Over-sampling Technique (SMOTE). We believe that using these techniques we will be able to increase the accuracy of the previous proposed.
Fake accounts detection system based on bidirectional gated recurrent unit n...IJECEIAES
Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the global vectors (GloVe) word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature.
Integrated approach to detect spam in social media networks using hybrid feat...IJECEIAES
Online social networking sites are becoming more popular amongst Internet users. The Internet users spend some amount of time on popular social networking sites like Facebook, Twitter and LinkedIn etc. Online social networks are considered to be much useful tool to the society used by Internet lovers to communicate and transmit information. These social networking platforms are useful to share information, opinions and ideas, make new friends, and create new friend groups. Social networking sites provide large amount of technical information to the users. This large amount of information in social networking sites attracts cyber criminals to misuse these sites information. These users create their own accounts and spread vulnerable information to the genuine users. This information may be advertising some product, send some malicious links etc to disturb the natural users on social sites. Spammer detection is a major problem now days in social networking sites. Previous spam detection techniques use different set of features to classify spam and non spam users. In this paper we proposed a hybrid approach which uses content based and user based features for identification of spam on Twitter network. In this hybrid approach we used decision tree induction algorithm and Bayesian network algorithm to construct a classification model. We have analysed the proposed technique on twitter dataset. Our analysis shows that our proposed methodology is better than some other existing techniques.
Abstract: Detection of fake news based on deep learning techniques is a major issue used to mislead people. For
the experiments, several types of datasets, models, and methodologies have been used to detect fake news. Also,
most of the datasets contain text id, tweets id, and user-based id and user-based features. To get the proper results
and accuracy various models like CNN (Convolution neural network), DEEP CNN, and LSTM (Long short-term
memory) are used
Detecting cyberbullying text using the approaches with machine learning model...IAESIJAI
The rising usage of social media sites and the advances in communication technologies have led to a considerable increase in cyberbullying events. Here, people are intimidated, harassed, and humiliated via digital messaging. To identify cyberbullying texts, several research have been undertaken in English and other languages with abundant resources, but relatively few studies have been conducted in low-resource languages like Bengali. This research focuses on Bengali text to find cyberbullying material by experimenting with pre-processing, feature selection, and three types of machine learning (ML) models: classical ML, deep learning (DL), and transformer learning. In classical ML, four models, support vector machine (SVM), multinomial Naive Bayes (MNB), random forest (RF), and logistic regression (LR) are used. In DL, three models, long short term memory (LSTM), Bidirectional LSTM, and convolutional neural network with bidirectional LSTM (CNN-BiLSTM) are employed. As the transformerbased pre-trained model, bidirectional encoder representations from transformers (BERT) is utilized. Using our proposed pre-processing tasks, the MNB-based approach achieves the best accuracy of 78.816% among the other classical ML models, the LSTM-based approach gains the highest result of 77.804% accuracy among the DL models, and the BERT-based approach outperforms both with 80.165% accuracy
Comparative Study of Cyberbullying Detection using Different Machine Learning...ijtsrd
The advancement of social media plays an important role in increasing the population of youngsters on the web. And it has become the biggest medium of expressing one’s thoughts and emotions. Recent studies report that cyberbullying constitutes a growing problem among youngsters on the web. These kinds of attacks have a major influence on the current generation’s personal and social life because youngsters are ready to adopt online life instead of a real one, which leads them into an imaginary world. So, we are proposing a system for early detection of cyberbullying on the web and comparing different machine learning Algorithms to obtain the optimal result. We are comparing four different algorithms which can be effectively used for the detection of cyberbullying, with the implementation of the bag of words algorithm with different n gram methods. Comparatively naïve Bayes algorithm has the highest accuracy of 79 with trigram implementation of the bag of words algorithm. Rohini K R | Sreehari T Anil | Sreejith P M | Yedumohan P M "Comparative Study of Cyberbullying Detection using Different Machine Learning Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30765.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30765/comparative-study-of-cyberbullying-detection-using-different-machine-learning-algorithms/rohini-k-r
During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance.
A DATA MINING APPROACH FOR FILTERING OUT SOCIAL SPAMMERS IN LARGE-SCALE TWITT...ijaia
Social networking services – such as Facebook.com and Twitter.com – are fast-growing enterprise
platform that has become a prevalent and essential component of daily life. Due to its popularity, Twitter
draws many spammers or other fake accounts to post malicious links and infiltrate legitimate users'
accounts with many spam messages. Therefore, it is crucial to recognize and screen spam tweets and spam
accounts. As a result, spam detection is highly needed but still a difficult challenge. This article applied
several Bio-inspired optimization algorithms to reduce the features' dimensions in the first stage. Then we
used several classification schemes in the second stage to enhance the spam detection rate in three real
Twitter data collections. The performance of the chosen classifiers also revealed that Random Forest and
C4.5 classifiers achieved the highest Accuracy, Precision, Recall, and F1-score even on class imbalance.
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
DETECTION OF FAKE ACCOUNTS IN INSTAGRAM USING MACHINE LEARNINGijcsit
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
Word embedding for detecting cyberbullying based on recurrent neural networksIAESIJAI
The phenomenon of cyberbullying has spread and has become one of the biggest problems facing users of social media sites and generated significant adverse effects on society and the victim in particular. Finding appropriate solutions to detect and reduce cyberbullying has become necessary to mitigate its negative impacts on society and the victim. Twitter comments on two datasets are used to detect cyberbullying, the first dataset was the Arabic cyberbullying dataset, and the second was the English cyberbullying dataset. Three different pre-trained global vectors (GloVe) corpora with different dimensions were used on the original and preprocessed datasets to represent the words. Recurrent neural networks (RNN), long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and Bidirectional GRU (BiGRU) classifiers utilized, evaluated and compared. The GRU outperform other classifiers on both datasets; its accuracy on the Arabic cyberbullying dataset using the Arabic GloVe corpus of dimension equal to 256D is 87.83%, while the accuracy on the English datasets using 100 D pre-trained GloVe corpus is 93.38%.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
Fake accounts detection on social media using stack ensemble systemIJECEIAES
In today’s world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was shown to be effective in achieving the best results when compared to the algorithms used with it, with data accuracy reaching 99%.
Abstract: Detection of fake news based on deep learning techniques is a major issue used to mislead people. For
the experiments, several types of datasets, models, and methodologies have been used to detect fake news. Also,
most of the datasets contain text id, tweets id, and user-based id and user-based features. To get the proper results
and accuracy various models like CNN (Convolution neural network), DEEP CNN, and LSTM (Long short-term
memory) are used
Detecting cyberbullying text using the approaches with machine learning model...IAESIJAI
The rising usage of social media sites and the advances in communication technologies have led to a considerable increase in cyberbullying events. Here, people are intimidated, harassed, and humiliated via digital messaging. To identify cyberbullying texts, several research have been undertaken in English and other languages with abundant resources, but relatively few studies have been conducted in low-resource languages like Bengali. This research focuses on Bengali text to find cyberbullying material by experimenting with pre-processing, feature selection, and three types of machine learning (ML) models: classical ML, deep learning (DL), and transformer learning. In classical ML, four models, support vector machine (SVM), multinomial Naive Bayes (MNB), random forest (RF), and logistic regression (LR) are used. In DL, three models, long short term memory (LSTM), Bidirectional LSTM, and convolutional neural network with bidirectional LSTM (CNN-BiLSTM) are employed. As the transformerbased pre-trained model, bidirectional encoder representations from transformers (BERT) is utilized. Using our proposed pre-processing tasks, the MNB-based approach achieves the best accuracy of 78.816% among the other classical ML models, the LSTM-based approach gains the highest result of 77.804% accuracy among the DL models, and the BERT-based approach outperforms both with 80.165% accuracy
Comparative Study of Cyberbullying Detection using Different Machine Learning...ijtsrd
The advancement of social media plays an important role in increasing the population of youngsters on the web. And it has become the biggest medium of expressing one’s thoughts and emotions. Recent studies report that cyberbullying constitutes a growing problem among youngsters on the web. These kinds of attacks have a major influence on the current generation’s personal and social life because youngsters are ready to adopt online life instead of a real one, which leads them into an imaginary world. So, we are proposing a system for early detection of cyberbullying on the web and comparing different machine learning Algorithms to obtain the optimal result. We are comparing four different algorithms which can be effectively used for the detection of cyberbullying, with the implementation of the bag of words algorithm with different n gram methods. Comparatively naïve Bayes algorithm has the highest accuracy of 79 with trigram implementation of the bag of words algorithm. Rohini K R | Sreehari T Anil | Sreejith P M | Yedumohan P M "Comparative Study of Cyberbullying Detection using Different Machine Learning Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30765.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30765/comparative-study-of-cyberbullying-detection-using-different-machine-learning-algorithms/rohini-k-r
During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance.
A DATA MINING APPROACH FOR FILTERING OUT SOCIAL SPAMMERS IN LARGE-SCALE TWITT...ijaia
Social networking services – such as Facebook.com and Twitter.com – are fast-growing enterprise
platform that has become a prevalent and essential component of daily life. Due to its popularity, Twitter
draws many spammers or other fake accounts to post malicious links and infiltrate legitimate users'
accounts with many spam messages. Therefore, it is crucial to recognize and screen spam tweets and spam
accounts. As a result, spam detection is highly needed but still a difficult challenge. This article applied
several Bio-inspired optimization algorithms to reduce the features' dimensions in the first stage. Then we
used several classification schemes in the second stage to enhance the spam detection rate in three real
Twitter data collections. The performance of the chosen classifiers also revealed that Random Forest and
C4.5 classifiers achieved the highest Accuracy, Precision, Recall, and F1-score even on class imbalance.
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
DETECTION OF FAKE ACCOUNTS IN INSTAGRAM USING MACHINE LEARNINGijcsit
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
Word embedding for detecting cyberbullying based on recurrent neural networksIAESIJAI
The phenomenon of cyberbullying has spread and has become one of the biggest problems facing users of social media sites and generated significant adverse effects on society and the victim in particular. Finding appropriate solutions to detect and reduce cyberbullying has become necessary to mitigate its negative impacts on society and the victim. Twitter comments on two datasets are used to detect cyberbullying, the first dataset was the Arabic cyberbullying dataset, and the second was the English cyberbullying dataset. Three different pre-trained global vectors (GloVe) corpora with different dimensions were used on the original and preprocessed datasets to represent the words. Recurrent neural networks (RNN), long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and Bidirectional GRU (BiGRU) classifiers utilized, evaluated and compared. The GRU outperform other classifiers on both datasets; its accuracy on the Arabic cyberbullying dataset using the Arabic GloVe corpus of dimension equal to 256D is 87.83%, while the accuracy on the English datasets using 100 D pre-trained GloVe corpus is 93.38%.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
Fake accounts detection on social media using stack ensemble systemIJECEIAES
In today’s world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was shown to be effective in achieving the best results when compared to the algorithms used with it, with data accuracy reaching 99%.
Similar to A study of cyberbullying detection using Deep Learning and Machine Learning Techniques (20)
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers