Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (9): 668-678.doi: 10.23940/ijpe.22.09.p8.668678
Mansi Mahendrua and Sanjay Kumar Dubeyb,*
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*E-mail address: Mansi Mahendru and Sanjay Kumar Dubey. Portable Learning Approach towards Capturing Social Intimidating Activities using Big Data and Deep Learning Technologies [J]. Int J Performability Eng, 2022, 18(9): 668-678.
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1. Van Hee, C., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., Verhoeven, B., De Pauw, G., Daelemans, W., and Hoste, V. Automatic Detection of Cyberbullying in Social Media Text. 2. Singh, V.K., Ghosh, S., and Jose, C. Toward Multimodal Cyberbullying Detection. In 3. Van Bruwaene, D., Huang, Q., and Inkpen, D. A Multi-Platform Dataset for Detecting Cyberbullying in Social Media. 4. Mahendru, M. and Dubey, S.K.Performance Analysis of Various Classifiers for Social Intimidating Activities Detection. In 5. Cheng L., Li J., Silva Y.N., Hall D.L., andLiu H.Xbully: Cyberbullying Detection within a Multi-Modal Context. In 6. Aluru S.S., Mathew B., Saha P., andMukherjee A.Deep Learning Models for Multilingual Hate Speech Detection.arXiv preprint arXiv:2004.06465, 2020. 7. Mossie, Z. and Wang, J.H.Vulnerable Community Identification using Hate Speech Detection on Social Media. 8. Florio K., Basile V., Polignano M., Basile P., andPatti V.Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media. 9. Rajamanickam S., Mishra P., Yannakoudakis H., andShutova E.Joint Modelling of Emotion and Abusive Language Detection.arXiv preprint arXiv:2005.14028, 2020. 10. Samghabadi N.S., Patwa P., Pykl S., Mukherjee P., Das A., andSolorio T.Aggression and Misogyny Detection using BERT: A Multi-Task Approach. In 11. Kumar, A. and Sachdeva, N.Multimodal Cyberbullying Detection using Capsule Network with Dynamic Routing and Deep Convolutional Neural Network. 12. Adikara P.P., Adinugroho S., andInsani S.Detection of Cyber Harassment (Cyberbullying) on Instagram using Naïve Bayes Classifier with Bag of Words and Lexicon based Features. In 13. Kumari K., Singh J.P., Dwivedi Y.K., andRana N.P.Towards Cyberbullying-Free Social Media in Smart Cities: A Unified Multi-Modal Approach. 14. Agrawal, S. and Awekar, A.Deep Learning for Detecting Cyberbullying across Multiple Social Media Platforms. In 15. Kumari, K. and Singh, J.P.Identification of Cyberbullying on Multi‐Modal Social Media Posts using Genetic Algorithm. 16. Rezvani N., Beheshti A., andTabebordbar A.Linking Textual and Contextual Features for Intelligent Cyberbullying Detection in Social Media. In 17. Zinovyeva E., Härdle W.K., andLessmann S.Antisocial Online Behavior Detection using Deep Learning. 18. Mohaouchane H., Mourhir A., andNikolov N.S.Detecting Offensive Language on Arabic Social Media using Deep Learning. In 19. Das A., Wahi J.S., andLi S.Detecting Hate Speech in Multi-Modal Memes.arXiv preprint arXiv:2012.14891, 2020. 20. Madukwe K., Gao X., andXue B.In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets. In 21. Ousidhoum N., Lin Z., Zhang H., Song Y., andYeung D.Y.Multilingual and Multi-Aspect Hate Speech Analysis.arXiv preprint arXiv:1908.11049, 2019. 22. Gomez R., Gibert J., Gomez L., andKaratzas D.Exploring Hate Speech Detection in Multimodal Publications. In 23. Kiela D., Firooz H., Mohan A., Goswami V., Singh A., Ringshia P., andTestuggine D.The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes. 24. Hosseinmardi H., Mattson S.A., Ibn Rafiq, R., Han, R., Lv, Q., and Mishra, S. Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network. In 25. Hosseinmardi H., Rafiq R.I., Han R., Lv Q., andMishra S.Prediction of Cyberbullying Incidents in a Media-based Social Network. In 26. Rafiq R.I., Hosseinmardi H., Mattson S.A., Han R., Lv Q., andMishra S.Analysis and Detection of Labeled Cyberbullying Instances in Vine, a Video-based Social Network. 27. Rafiq R.I., Hosseinmardi H., Han R., Lv Q., Mishra S., andMattson S.A.Careful What You Share in Six Seconds: Detecting Cyberbullying Instances in Vine. In 28. Adnan, K. and Akbar, R.An Analytical Study of Information Extraction from Unstructured and Multidimensional Big Data. 29. Mayer, R. and Jacobsen, H.A.Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques, and Tools. 30. Sergeev, A. and Del Balso, M. Horovod: Fast and Easy Distributed Deep Learning in Tensorflow.arXiv preprint arXiv:1802.05799, 2018. 31. Zhang Q., Yang L.T., Chen Z., andLi P.A Survey on Deep Learning for Big Data. 32. Dai J.J., Wang Y., Qiu X., Ding D., Zhang Y., Wang Y., Jia X., Zhang C.L., Wan Y., Li Z., andWang J.Bigdl: A Distributed Deep Learning Framework for Big Data. In 33. Kumar A., Sangwan S.R., andNayyar A.Multimedia Social Big Data: Mining. In 34. Paulin H., Milton R.S., andJanakiRaman, S. Efficient Pre Processing of Audio and Video Signal Dataset for Building an Efficient Automatic Speech Recognition System. 35. Chaki, J. and Dey, N.A Beginner’s Guide to Image Preprocessing Techniques. CRC Press, 2018. 36. Effrosynidis D., Symeonidis S., andArampatzis A.A Comparison of Pre-processing Techniques for Twitter Sentiment Analysis. In 37. Zečević P., Slater C.T., Jurić M., Connolly A.J., Lončarić S., Bellm E.C., Golkhou V.Z., andSuberlak K.Axs: A Framework for Fast Astronomical Data Processing based on Apache Spark. 38. Sontakke, M.D. and Kulkarni, M.S.Different Types of Noises in Images and Noise Removing Technique. 39. Singh H.Advanced Image Processing using Opencv. In 40. Hamilton M., Raghunathan S., Annavajhala A., Kirsanov D., Leon E., Barzilay E., Matiach I., Davison J., Busch M., Oprescu M., andSur R.Flexible and Scalable Deep Learning with MMLSpark. In 41. Ahuja R., Chug A., Kohli S., Gupta S., andAhuja P.The Impact of Features Extraction on the Sentiment Analysis. 42. Fortuna, P. and Nunes, S.A Survey on Automatic Detection of Hate Speech in Text. 43. Salminen J., Hopf M., Chowdhury S.A., Jung S.G., Almerekhi H., andJansen B.J.Developing an Online Hate Classifier for Multiple Social Media Platforms. 44. Liang, H., Sun, X., Sun, Y., and Gao, Y. Text Feature Extraction based on Deep Learning: A Review. 45. He C., Chen S., Huang S., Zhang J., andSong X.Using Convolutional Neural Network with BERT for Intent Determination. In 46. Jogin M., Madhulika M.S., Divya G.D., Meghana R.K., andApoorva S.Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning. In 47. Safaya A., Abdullatif M., andYuret D.BERT-CNN for Offensive Speech Identification in Social Media. In 48. Zhang J., Li Y., Tian J., andLi T.LSTM-CNN Hybrid Model for Text Classification. In 49. Rhanoui M., Mikram M., Yousfi S., andBarzali S.A CNN-BiLSTM Model for Document-Level Sentiment Analysis. 50. Mahendru M., Dubey S.K., andGaur D.Deep Convolutional Sequence Approach Towards Real-Time Intelligent Optical Scanning. 51. Vishwamitra N., Hu H., Luo F., andCheng L.Towards Understanding and Detecting Cyberbullying in Real-World Images. In2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021. 52. Khan A., Sohail A., Zahoora U., andQureshi A.S.A Survey of the Recent Architectures of Deep Convolutional Neural Networks. 53. Tammina S.Transfer Learning using Vgg-16 with Deep Convolutional Neural Network for Classifying Images. 54. Savoiu, A. and Wong, J.Recognizing facial expressions using deep learning.Recognizing Facial Expressions Using Deep Learning, 2017. 55. Alashhab S., Gallego A.J., andLozano M.Á.Hand Gesture Detection with Convolutional Neural Networks. In 56. Deng J., Dong W., Socher R., Li L.J., Li K., andFei-Fei, L. Imagenet: A Large-Scale Hierarchical Image Database. In 57. Simonyan, K. and Zisserman, A.Very Deep Convolutional Networks for Large-Scale Image Recognition.arXiv preprint arXiv:1409.1556, 2014. 58. Gadicha A.B., Sarode M.V., andThakare V.M.Empirical Approach Towards Video Analysis using Shot Frontier Detection and Key-Frame Mining. In 59. Kumar, A. and Sachdeva, N.Multi-Input Integrative Learning using Deep Neural Networks and Transfer Learning for Cyberbullying Detection in Real-Time Code-Mix Data. 60. Banga M., Bansal A., andSingh A.Proposed Intelligent Software System for Early Fault Detection. |
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