“TwitterSpamDetector”: A Spam Detection Framework for Twitter

TL;DR: In this paper, a Sine Cosine Political Optimization-based Deep Residual Network (SC-Political ResNet) classifier is proposed for hashtag recommendation, which is designed by integrating the Sine cosine Algorithm (SCA) with the Political Optimizer (PO) algorithm.

TL;DR: In this article , the authors proposed a novel idea of virtual personal trainer applications that recognize user actions through videos, where the video data is processed using convolutional neural network and bidirectional long short-term memory network.

TL;DR: In this paper, an evolutionary multi-objective optimization algorithm (E-MOA) was proposed for the anti-spam filtering issue that discusses both e-mail classification requirements (FP and FN error rates) and e-email classification times (minimisation).

TL;DR: A hybrid method with the combination of Artificial Neural Networks with Fuzzy Decision Tree (ANN-FDT) and the proposed classifier classified the span and non-span tweets based on the labels to determine the twitter spam.

TL;DR: The Bayesian classifier is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption, and will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain.

TL;DR: The use of support vector machines in classifying e-mail as spam or nonspam is studied by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees, which found SVM's performed best when using binary features.

TL;DR: Sometimes a "friendly" email message tempts recipients to reveal more online than they otherwise would, playing right into the sender's hand.

TL;DR: This paper proposes to improve target-dependent Twitter sentiment classification by incorporating target- dependent features; and taking related tweets into consideration; and according to the experimental results, this approach greatly improves the performance of target- dependence sentiment classification.