DKNN and HPFS: An Efficacious Deep Learning Approach with Fuzzy Sets for Social Network Hostility

[1] T. Ahmed, S. Ivan, M. Kabir, H. Mahmud, K. Hasan, Performance analysis of transformer-based architectures
and their ensembles to detect trait-based cyberbullying, Social Network Analysis and Mining, 12(1) (2022), 99.
https://doi.org/10.1007/s13278-022-00934-4

[2] A. Akhter, U. K. Acharjee, M. A. Talukder, M. M. Islam, M. A. Uddin, A robust hybrid machine learning model
for detecting Bengali cyberbullying in social media, Natural Language Processing Journal, 4 (2023), 100027. https:
//doi.org/10.1016/j.nlp.2023.100027

[3] A. Al-Marghilani, Artificial intelligence-enabled cyberbullying-free online social networks in smart cities, International
Journal of Computational Intelligence Systems, 15(1) (2022), 9. https://doi.org/10.1007/
s44196-022-00063-y

[4] A. Almomani, K. Nahar, M. Alauthman, M. A. Al-Betar, Q. Yaseen, B. B. Gupta, Image cyberbullying detection
and recognition using transfer deep machine learning, International Journal of Cognitive Computing in Engineering,
5 (2024), 14-26. https://doi.org/10.1016/j.ijcce.2023.11.002

[5] A. F. Alqahtani, M. Ilyas, An ensemble-based multi-classification machine learning classifiers approach to detect
multiple classes of cyberbullying, Machine Learning and Knowledge Extraction, 6(1) (2024), 156-170. https://doi.
org/10.3390/make6010009

[6] M. Alzaqebah, G. M. Jaradat, D. Nassan, R. Alnasser, M. K. Alsmadi, I. Almarashdeh, S. Alkhushayni, Cyberbullying
detection framework for short and imbalanced Arabic datasets, Journal of King Saud University-Computer and
Information Sciences, 35(8) (2023), 101652. https://doi.org/10.1016/j.jksuci.2023.101652

[7] K. I. Arce-Ruelas, O. Alvarez-Xochihua, L. Pellegrin, L. Cardoza-Avenda˜no, J. ´A. Gonz´alez-Fraga, Automatic cyberbullying detection: A Mexican case in high school and higher education students, IEEE Latin America Transactions,
20(5) (2022), 770-779. https://doi.org/10.1109/TLA.2022.9693561

[8] C. E. Gomez, M. O. Sztainberg, R. E. Trana, Curating cyberbullying datasets: A human-AI collaborative approach,
International Journal of Bullying Prevention, 4(1) (2022), 35-46. https://doi.org/10.1007/s42380-021-00114-6

[9] C. Iwendi, G. Srivastava, S. Khan, P. K. R. Maddikunta, Cyberbullying detection solutions based on deep learning
architectures, Multimedia Systems, 29(3) (2023), 1839-1852. https://doi.org/10.1007/s00530-020-00701-5

[10] A. Kumar, N. Sachdeva, A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social
media, World Wide Web, 25(4) (2022), 1537-1550. https://doi.org/10.1007/s11280-021-00920-4

[11] A. Kumar, N. Sachdeva, Multi-input integrative learning using deep neural networks and transfer learning for
cyberbullying detection in real-time code-mix data, Multimedia Systems, 28(6) (2022), 2027-2041. https://doi.
org/10.1007/s00530-020-00672-7

[12] A. Kumar, N. Sachdeva, Multimodal cyberbullying detection using capsule network with dynamic routing and
deep convolutional neural network, Multimedia Systems, 28(6) (2022), 2043-2052. https://doi.org/10.1007/
s00530-020-00747-5

[13] K. Maity, A. Kumar, S. Saha, A multitask multimodal framework for sentiment and emotion-aided cyberbullying
detection, IEEE Internet Computing, 26(4) (2022), 68-78. https://doi.org/10.1109/MIC.2022.3158583

[14] A. Mangaonkar, R. Pawar, N. S. Chowdhury, R. R. Raje, Enhancing collaborative detection of cyberbullying
behaviour in Twitter data, Cluster Computing, 25(2) (2022), 1263-1277. https://doi.org/10.1007/
s10586-021-03483-1

[15] B. A. H. Murshed, J. Abawajy, S. Mallappa, M. A. N. Saif, H. D. E. Al-Ariki, DEA-RNN: A hybrid deep learning
approach for cyberbullying detection in Twitter social media platform, IEEE Access, 10 (2022), 25857-25871. https:
//doi.org/10.1109/ACCESS.2022.3153675

[16] B. A. H. Murshed, Suresha, J. Abawajy, M. A. N. Saif, H. M. Abdulwahab, F. A. Ghanem, FAEO-ECNN:
Cyberbullying detection in social media platforms using topic modelling and deep learning, Multimedia Tools and
Applications, 82(30) (2023), 46611-46650. https://doi.org/10.1007/s11042-023-15372-3

[17] V. L. Paruchuri, P. Rajesh, CyberNet: A hybrid deep CNN with N-gram feature selection for cyberbullying detection
in online social networks, Evolutionary Intelligence, 16(6) (2023), 1935-1949. https://doi.org/10.1007/
s12065-022-00774-3

[18] S. Paul, S. Saha, CyberBERT: BERT for cyberbullying identification: BERT for cyberbullying identification, Multimedia
Systems, 28(6) (2022), 1897-1904. https://doi.org/10.1007/s00530-020-00710-4

[19] S. Paul, S. Saha, M. Hasanuzzaman, Identification of cyberbullying: A deep learning based multimodal approach,
Multimedia Tools and Applications, 1 (2022), 1-20. https://doi.org/10.1007/s11042-020-09631-w

[20] P. K. Roy, F. U. Mali, Cyberbullying detection using deep transfer learning, Complex and Intelligent Systems, 8(6)
(2022), 5449-5467. https://doi.org/10.1007/s40747-022-00772-z

[21] R. Suhas Bharadwaj, S. Kuzhalvaimozhi, N. Vedavathi, A novel multimodal hybrid classifier-based cyberbullying
detection for social media platforms, In Proceedings of the Computational Methods in Systems and Software, 1(2022), 689-699. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21438-7_57

[22] F. Wu, B. Gao, X. Pan, Z. Su, Y. Ji, S. Liu, Z. Liu, FACapsnet: A fusion capsule network with congruent attention
for cyberbullying detection, Neurocomputing, 542 (2023), 126253. https://doi.org/10.1016/j.neucom.2023.
126253