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An attempt on Twitter ‘likes’ grading strategy using pure linguistic feature engineering: A novel approach*
-Accepted at MDCWC2020 ( Springer ) - Workshop on Machine learning, Deep learning and Computational Intelligence for Wireless Communication
-Proceedings published in Lecture Notes in Electrical Engineering (LNEE) Springer publication
Clustering Text: A comparison between available text vectorization techniques*
-Accepted at ICSCSP - 2020 ( Springer ) - International Conference on Soft Computing and Signal Processing
-Proceedings published in Advances in Intelligent Systems and Computing (AISC) Springer publication
Fake News Detection: a comparison between available Deep Learning Techniques in Vector Space*
-Accepted at IEEE CICT 2020 - Conference on Information and Commpunication Technology
-Proceedings published in IEEE-Xplore
Hybrid Ensemble for Fake News Detection: An attempt*
-Accepted at SSIC 2021 ( Springer ) - Smart Systems, Innovations in Computing
-Proceedings published in Lecture Notes in Networks and Systems (LNNS) Springer publication
Clustring Text Using Attention*
-Accepted at IEEE 12th ICCCNT 2021 - International Conference on Computing Communication and Networking Technologies
-Proceedings published in IEEE-Xplore
CSRC ( Cyber Security Research Centre ) | Undergraduate Research Assistant, PEC University of Technology | [Aug 2019 - July 2020]
Research work broadly on Machine Learning, Natural Language Processing and Deep Learning
Explored techniques in the field of NLP and implemented them. Exploring and implementing various models in the domain of statistical Machine Learning and modern Deep Learning.
Collecting dataset for fake news on recent events like Kashmir article 370, Chennai Floods, Balakot Air Strike. Made scrappers, spiders to crawl data from Fact checking websites, Trusted News websites, Twitter, Facebook, and collected Lakhs of records.
Alongside Semester Project on Fake News Detection, Report#, Slides# and Code# for the Fake News Detection project are present on the right
'Some' knowledge/experience in
Social Networks
Conversational AI
NLU - Natural Language Understanding
Attention mechanisms
Transformers
Transfer Learning, Domain Adaptation
One Shot Learning
Siamese Network
Deep Learning
ANN - Artificial Neural Networks
CNN - Convolutional Neural Networks
RNN - Recurrent Neural Networks
LSTM
BERT
Classical ML
NLP - Natural Language Processing
GANs - Generative Adversarial Networks
Fine Tuning
Clustering
Autoencoders
Hyperparameter Tuning
MACHINE LEARNING excites me because MATHS, PHYSICS and VECTORS look good
CURIOSITY, IMAGINATION spark interest
POSSIBILITIES are endless
Attention is all you need ! , Long live the transformers
FEED-FORWARD NETWORKS WITH ATTENTION CAN SOLVE SOME LONG-TERM MEMORY PROBLEMS