These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
106 related articles for article (PubMed ID: 37362691)
1. SMS sentiment classification using an evolutionary optimization based fuzzy recurrent neural network. Srinivasarao U; Sharaff A Multimed Tools Appl; 2023 Apr; ():1-32. PubMed ID: 37362691 [TBL] [Abstract][Full Text] [Related]
2. Enhancing Spam Message Classification and Detection Using Transformer-Based Embedding and Ensemble Learning. Ghourabi A; Alohaly M Sensors (Basel); 2023 Apr; 23(8):. PubMed ID: 37112202 [TBL] [Abstract][Full Text] [Related]
3. Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques. Rodrigues AP; Fernandes R; A A; B A; Shetty A; K A; Lakshmanna K; Shafi RM Comput Intell Neurosci; 2022; 2022():5211949. PubMed ID: 35463239 [TBL] [Abstract][Full Text] [Related]
4. Application of interval type-2 fuzzy logic and type-1 fuzzy logic-based approaches to social networks for spam detection with combined feature capabilities. Atacak İ; Çıtlak O; Doğru İA PeerJ Comput Sci; 2023; 9():e1316. PubMed ID: 37346510 [TBL] [Abstract][Full Text] [Related]
5. Evading obscure communication from spam emails. Rafat KF; Xin Q; Javed AR; Jalil Z; Ahmad RZ Math Biosci Eng; 2022 Jan; 19(2):1926-1943. PubMed ID: 35135236 [TBL] [Abstract][Full Text] [Related]
6. A Hybrid Model with New Word Weighting for Fast Filtering Spam Short Texts. Xia T; Chen X; Wang J; Qiu F Sensors (Basel); 2023 Nov; 23(21):. PubMed ID: 37960672 [TBL] [Abstract][Full Text] [Related]
7. Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique. Rayan A Comput Intell Neurosci; 2022; 2022():2500772. PubMed ID: 35983156 [TBL] [Abstract][Full Text] [Related]
8. A systematic literature review on spam content detection and classification. Kaddoura S; Chandrasekaran G; Elena Popescu D; Duraisamy JH PeerJ Comput Sci; 2022; 8():e830. PubMed ID: 35174265 [TBL] [Abstract][Full Text] [Related]
9. DSmishSMS-A System to Detect Smishing SMS. Mishra S; Soni D Neural Comput Appl; 2023; 35(7):4975-4992. PubMed ID: 34341626 [TBL] [Abstract][Full Text] [Related]
10. Dataset of Arabic spam and ham tweets. Kaddoura S; Henno S Data Brief; 2024 Feb; 52():109904. PubMed ID: 38093848 [TBL] [Abstract][Full Text] [Related]
11. Efficient information theoretic strategies for classifier combination, feature extraction and performance evaluation in improving false positives and false negatives for spam e-mail filtering. Zorkadis V; Karras DA; Panayotou M Neural Netw; 2005; 18(5-6):799-807. PubMed ID: 16111865 [TBL] [Abstract][Full Text] [Related]
12. Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text. Shaaban MA; Hassan YF; Guirguis SK Complex Intell Systems; 2022; 8(6):4897-4909. PubMed ID: 35496326 [TBL] [Abstract][Full Text] [Related]
13. An intelligent identification and classification system for malicious uniform resource locators (URLs). Abu Al-Haija Q; Al-Fayoumi M Neural Comput Appl; 2023 Apr; ():1-17. PubMed ID: 37362563 [TBL] [Abstract][Full Text] [Related]
14. SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning. Shinde A; Shahra EQ; Basurra S; Saeed F; AlSewari AA; Jabbar WA Sensors (Basel); 2024 Sep; 24(18):. PubMed ID: 39338829 [TBL] [Abstract][Full Text] [Related]
16. Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning. Janjua SH; Siddiqui GF; Sindhu MA; Rashid U PeerJ Comput Sci; 2021; 7():e433. PubMed ID: 33954232 [TBL] [Abstract][Full Text] [Related]
17. Machine learning for email spam filtering: review, approaches and open research problems. Dada EG; Bassi JS; Chiroma H; Abdulhamid SM; Adetunmbi AO; Ajibuwa OE Heliyon; 2019 Jun; 5(6):e01802. PubMed ID: 31211254 [TBL] [Abstract][Full Text] [Related]
18. Toward automatic evaluation of medical abstracts: The current value of sentiment analysis and machine learning for classification of the importance of PubMed abstracts of randomized trials for stroke. Fischer I; Steiger HJ J Stroke Cerebrovasc Dis; 2020 Sep; 29(9):105042. PubMed ID: 32807454 [TBL] [Abstract][Full Text] [Related]
19. A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain Text. Liu N; Zhao J Comput Intell Neurosci; 2022; 2022():8726621. PubMed ID: 35795761 [TBL] [Abstract][Full Text] [Related]
20. A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach. Jian Y; Huang D; Yan J; Lu K; Huang Y; Wen T; Zeng T; Zhong S; Xie Q Sensors (Basel); 2017 Jun; 17(6):. PubMed ID: 28629202 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]