Results 71 to 80 of about 7,753,315 (190)
seabass-detection/seabass-detection v.01-alpha
, 2019 <p>Example code and images for paper Using Machine Vision to Estimate Fish Length from Images using Regional Convolutional Neural Networks in journal Methods in Ecology and Evolution.</p ...
seabass-detection
core +1 more source
Applied Sciences, 2023 A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for ...
Eiman Alothali +2 more
doaj +1 more source
Bot generation and detection for sensor time series data using LSTM
, 2022 Στην εποχή του IoT, οι συσκευές παράγουν τεράστιες και συνεχείς ροές πληροφοριών. Διερευνώντας τέτοιες ροές δεδομένων για νέα γεγονότα, προβλέποντας μελλοντικές εμπειρίες και αποφασίζοντας για δυνατότητες ελέγχου, χρησιμοποιούνται προγράμματα που ...
Ανδριάνης, Αθανάσιος
core
Bot detection using machine learning algorithms on social media platforms
, 2020 Using bots in social media is a significant concern for information validity and authenticity. Currently, there are several solutions for bots detection. However, the accuracy of the detection still needs improvement.
Ali, Rasha S. +7 more
core +1 more source
Bot detection on twitter [PDF]
, 2019 Twitter is one of the most popular social media platforms with 319 million monthly active users who publish 500 million tweets per day. With this popularity, spam accounts are also emerging for phishing on Twitter or spreading malicious software ...
Coskun, Aysun +2 more
core
TwiBot-22: Towards Graph-Based Twitter Bot Detection
, 2023 Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
Bai, Yuyang +21 more
core
Evaluation of social bot detection models
, 2022 Social bots are employed to automatically perform online social network activities; thereby, they can also be utilized in spreading misinformation and malware.
MÜCAHİD KUTLU +5 more
core +1 more source
BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning
Computers, Materials & Continua, 2023 The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet. Bot detection using machine learning (ML) with flow-based features has been extensively studied
Khlood Shinan, Khalid Alsubhi, M. Ashraf
semanticscholar +1 more source
CALEB: A Conditional Adversarial Learning Framework to Enhance Bot Detection [PDF]
, 2022 The high growth of Online Social Networks (OSNs) over the last few years has allowed automated accounts, known as social bots, to gain ground. As highlighted by other researchers, most of these bots have malicious purposes and tend to mimic human ...
Vakali, Athena +2 more
core +1 more source
BotFP: FingerPrints Clustering for Bot Detection
, 2020 International audienceEfficient bot detection is a crucial security matter and has been widely explored in the past years. Recent approaches supplant flow-based detection techniques and exploit graph-based features, incurring however in scalability ...
Blaise, Agathe +7 more
core +1 more source