[HTML][HTML] A systematic study on reinforcement learning based applications
K Sivamayil, E Rajasekar, B Aljafari, S Nikolovski… - Energies, 2023 - mdpi.com
We have analyzed 127 publications for this review paper, which discuss applications of
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
[HTML][HTML] Machine learning-based social media bot detection: A comprehensive literature review
In today's digitalized era, Online Social Networking platforms are growing to be a vital aspect
of each individual's daily life. The availability of the vast amount of information and their …
of each individual's daily life. The availability of the vast amount of information and their …
Application of natural language processing and machine learning boosted with swarm intelligence for spam email filtering
Spam represents a genuine irritation for email users, since it often disturbs them during their
work or free time. Machine learning approaches are commonly utilized as the engine of …
work or free time. Machine learning approaches are commonly utilized as the engine of …
[HTML][HTML] An unsupervised method for social network spammer detection based on user information interests
Online Social Networks (OSNs) are a popular platform for communication and collaboration.
Spammers are highly active in OSNs. Uncovering spammers has become one of the most …
Spammers are highly active in OSNs. Uncovering spammers has become one of the most …
Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced data
Cybercriminals use social media platforms to disseminate spam, misleading facts, fake
news, and malicious links. Blocking such deceptive social media spam is essential …
news, and malicious links. Blocking such deceptive social media spam is essential …
“Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice
Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset
O Abayomi‐Alli, S Misra… - … : Practice and Experience, 2022 - Wiley Online Library
SMS, one of the most popular and fast‐growing GSM value‐added services worldwide, has
attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant …
attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant …
An intelligent system for multi-topic social spam detection in microblogging
The communication revolution has perpetually reshaped the means through which people
send and receive information. Social media is an important pillar of this revolution and has …
send and receive information. Social media is an important pillar of this revolution and has …
[HTML][HTML] Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text
The increase in people's use of mobile messaging services has led to the spread of social
engineering attacks like phishing, considering that spam text is one of the main factors in the …
engineering attacks like phishing, considering that spam text is one of the main factors in the …
SybilFlyover: Heterogeneous graph-based fake account detection model on social networks
S Li, J Yang, G Liang, T Li, K Zhao - Knowledge-Based Systems, 2022 - Elsevier
Organized social robot accounts can launch Sybil attacks on online social networks (OSNs)
for various malicious purposes, thus significantly affecting the user experience of online …
for various malicious purposes, thus significantly affecting the user experience of online …