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Nikita Jain
Researcher at Bharati Vidyapeeth's College of Engineering
Publications - 49
Citations - 623
Nikita Jain is an academic researcher from Bharati Vidyapeeth's College of Engineering. The author has contributed to research in topics: Medicine & Deep learning. The author has an hindex of 8, co-authored 39 publications receiving 215 citations. Previous affiliations of Nikita Jain include University of Mumbai & Indraprastha Institute of Information Technology.
Papers
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Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images
TL;DR: A mathematical model P F S E C TL based on transfer learning is used in which a CNN architecture, VGG-16 trained on ImageNet dataset is used as a feature extractor for the classification task.
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Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks
Deepika Kumar,Nikita Jain,Aayush Khurana,Sweta Mittal,Suresh Chandra Satapathy,Roman Senkerik,Jude Hemanth +6 more
TL;DR: This study indicates that the DCNN model’s performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset, Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.
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An Emotion Care Model using Multimodal Textual Analysis on COVID-19.
Vedika Gupta,Nikita Jain,Piyush Katariya,Adarsh Kumar,Senthilkumar Mohan,Ali Ahmadian,Ali Ahmadian,Massimiliano Ferrara +7 more
TL;DR: In this paper, a novel emotion care scheme has been proposed in this paper to analyze multimodal textual data contained in real-time tweets related to COVID-19, where 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) over multiple categories such as nature, lockdown, health, education, market, and politics were analyzed.
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Prediction modelling of COVID using machine learning methods from B-cell dataset.
Nikita Jain,Srishti Jhunthra,Harshit Garg,Vedika Gupta,Senthilkumar Mohan,Ali Ahmadian,Ali Ahmadian,Soheil Salahshour,Massimiliano Ferrara +8 more
TL;DR: In this article, the authors used various machine learning models, such as SVM, Naive Bayes, K-nearest neighbors, AdaBoost, Gradient boosting, XGBoost, Random Forest, ensembles, and neural networks, to predict SARS-CoV and SARS CoV-2.
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HashJacker- Detection and Analysis of Hashtag Hijacking on Twitter
TL;DR: A tool is proposed: HashJacker which detects and analyses hijacking of Hashtag tweets and discusses best practises to circumvent wrecked Hash tag tweets.