A Review of Current Perspective and Propensity in Reinforcement Learning (RL) in an Orderly Manner

Pandey, Shweta; Agarwal, Rohit; Bhardwaj,; Singh, Sanjay Kumar; Perwej, Yusuf; Singh, Niraj Kumar

doi:10.32628/cseit2390147

Cited by 6 publications

(2 citation statements)

References 52 publications

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“…An algorithm was presented by Xiao et al to identify groups of phony accounts on social media before they do harm or interact with real people [25]. It uses the kmeans clustering method to group the accounts into groups, identifies cluster level properties [26], scores the accounts in each group, and assigns labels to the groups based on the group's average score. In order to identify false accounts in social networks, a model built on the similarity between the user's buddy networks is suggested in [27].…”

Section: Related Workmentioning

confidence: 99%

Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

et al. 2023

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Currently, almost everyone spends more time on online social media platforms engaging with and exchanging information with people from all over the world, from children to adults. Our lives are greatly influenced by social media sites like Twitter, Facebook, Instagram, and LinkedIn. The social network is evolving into a well-liked platform for connecting with individuals across the globe. Social media platforms exist as a result of the enormous connectivity and information sharing that the internet has made possible. Social media's rising popularity has had both beneficial and detrimental consequences on society. However, it also has to deal with the issue of bogus profiles. False profiles are often constructed by humans, bots, or cyborgs and are used for phishing, propagating rumors, data breaches, and identity theft. Thus, we are emphasizing in this post the significance of setting up a system that can identify false profiles on social media networks. To illustrate the suggested concept of machine learning-based false news identification, we used the Twitter dataset for phony profile detection. The suggested model involves pre-processing to improve the dataset's quality and minimize its dimensions by modifying its contents and features. To forecast the bogus profiles, the widely used machine learning algorithms are used.

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Section: Related Workmentioning

confidence: 99%

Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

et al. 2023

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“…One of the fundamental aspects of machine learning is its ability to generalize from the data it is trained on to make predictions on new, unseen data (Omar et al, 2023;Dou et al, 2023). Common types of machine learning include supervised learning, unsupervised learning, and reinforcement learning (Pandey et al, 2023;Menon et al, 2023). Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning deals with finding patterns in unlabeled data (Rani et al, 2023).…”

Section: A Definition Of Machine Learning and Data Miningmentioning

confidence: 99%

Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

Ananna,

Nowreen,

Al Jahwari

et al. 2023

IJEMD-CSAI

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The fascination with understanding student academic performance has drawn widespread attention from various stakeholders, including parents, policymakers, and businesses. The 'Students Performance in Exams' dataset, available on platforms like Kaggle, stands as a treasure trove. It extends beyond test scores, encompassing diverse student attributes like ethnicity, gender, parental education, test preparation, and even lunch type. In our tech-driven age, predicting academic success has become a compelling pursuit. This study aims to delve deep into this dataset, utilizing data mining methods and robust classification algorithms like Logistic Regression and Random Forest in a Jupyter Notebook environment. Rigorous model training, testing, and fine-tuning strive for the utmost predictive accuracy. Data cleaning and preprocessing play a crucial role in establishing a reliable dataset for accurate predictions. Beyond numbers, the project emphasizes data visualization's impact, transforming raw data into comprehensible insights for effective communication. The Logistic Regression Model exhibits an impressive 87.6% accuracy, highlighting its potential in predicting academic performance. Moreover, the Random Forest Model excels with a remarkable 100% accuracy in forecasting student grades, showcasing its effectiveness in this domain.

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A Potent Technique for Identifying Fake Accounts on Social Platforms

Kajal,

Uttam Kumar Singh,

Dr. Nikhat Akhtar

et al. 2023

IJSRCSEIT

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In this generation, online social media networks are rapidly growing in popularity and becoming more and more integrated into people's daily lives. These networks are used by users to exchange movies, read news articles, market products, and more. It has been simpler to add new friends and stay in touch with them and their updates. These online social networks have been the subject of research to see how they affect people. A significant amount of a user's data may attract attackers as these networks continue to develop, and these attackers may subsequently exchange incorrect information and disseminate dangerous falsehoods. Some fraudulent accounts are used to spread false information and further political agendas, for example. Finding a fraudulent account is important. Furthermore, these social networking platforms are increasingly being used by attackers to disseminate a vast amount of fake information. As a result, based on the categorization algorithms, researchers have started to investigate efficient strategies for spotting these sorts of actions and bogus accounts. In this study, various machine learning algorithms are investigated to successfully identify a phony account. To address this issue, several machine learning algorithms are utilized in conjunction with pre-processing methods to identify bogus accounts. The identification of bogus accounts uses the classification abilities of the algorithms Nave Bayes, Artificial Neural Network, Bagged Decision Tree, Radial Basis Function (RBF), Support Vector Machines, and Random Tree. The best features are used to compare the proposed model to other benchmark techniques on the dataset. The suggested Artificial Neural Network strategy outperforms the prior employed strategies to identify phony user accounts on major online social platforms, with a precision of 98.90%, when machine learning techniques are also compared.

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A Review of Current Perspective and Propensity in Reinforcement Learning (RL) in an Orderly Manner

Cited by 6 publications

References 52 publications

Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

A Potent Technique for Identifying Fake Accounts on Social Platforms

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