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Author

Deepika Kumar

Other affiliations: GD Goenka University
Bio: Deepika Kumar is an academic researcher from Bharati Vidyapeeth's College of Engineering. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 5, co-authored 16 publications receiving 103 citations. Previous affiliations of Deepika Kumar include GD Goenka University.

Papers
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Journal ArticleDOI
01 Jun 2021
TL;DR: The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz. random forest, logistic regression, and Naive Bayes for the classification.
Abstract: Diabetes is a dreadful disease identified by escalated levels of glucose in the blood Machine learning algorithms help in identification and prediction of diabetes at an early stage The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz random forest, logistic regression, and Naive Bayes for the classification Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as AdaBoost, Logistic Regression,Support Vector machine, Random forest, Naive Bayes, Bagging, GradientBoost, XGBoost, CatBoost by taking accuracy, precision, recall, F1-score as the evaluation criteria The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 7904%, 7348%, 7145% and 806% respectively on the PIMA diabetes dataset Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset The proposed ensemble soft voting classifier has given 9702% accuracy on the breast cancer dataset

141 citations

Journal ArticleDOI
15 Feb 2020-Sensors
TL;DR: A combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed and detect pneumonia from chest X-ray (CXR) images with test accuracy of 95.33% and 95.90%, respectively.
Abstract: An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.

89 citations

Journal ArticleDOI
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.
Abstract: Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells’ images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. 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.

63 citations

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a model for fake news classification based on news titles, following the content-based classification approach, which uses a BERT model with its outputs connected to an LSTM layer.
Abstract: Fake News has been a concern all over the world and social media has only amplified this phenomenon. Fake News has been affecting the world on a large scale as these are targeted to sway the decisions of the crowd in a particular direction. Since manually verifying the legitimacy of news is very hard and costly, there has been a great interest of researchers in this field. Different approaches to identifying fake news were examined, such as content-based classification, social context-based classification, image-based classification, sentiment-based classification, and hybrid context-based classification. This paper aims to propose a model for fake news classification based on news titles, following the content-based classification approach. The model uses a BERT model with its outputs connected to an LSTM layer. Training and evaluation of the model were done on the FakeNewsNet dataset which contains two sub-datasets, PolitiFact and GossipCop. A comparison of the model with base classification models has been done. A vanilla BERT model has also been trained on the dataset under similar constraints as the proposed model has to evaluate the impact same using an LSTM layer. The results obtained showed a 2.50% and 1.10% increase in accuracy on PolitiFact and GossipCop datasets respectively over the vanilla pre-trained BERT model.

36 citations

Journal ArticleDOI
13 Jul 2018
TL;DR: This paper demonstrates how even with minimal text pre-processing, the fine-tuned BERT model is robust enough to perform significantly well on the downstream task of classification of news articles.
Abstract: With the ever-increasing rate of information dissemination and absorption, “Fake News” has become a real menace. People these days often fall prey to fake news that is in line with their perception. Checking the authenticity of news articles manually is a time-consuming and laborious task, thus, giving rise to the requirement for automated computational tools that can provide insights about degree of fake ness for news articles. In this paper, a Natural Language Processing (NLP) based mechanism is proposed to combat this challenge of classifying news articles as either fake or real. Transfer learning on the Bidirectional Encoder Representations from Transformers (BERT) language model has been applied for this task. This paper demonstrates how even with minimal text pre-processing, the fine-tuned BERT model is robust enough to perform significantly well on the downstream task of classification of news articles. In addition, LSTM and Gradient Boosted Tree models have been built to perform the task and comparative results are provided for all three models. Fine-tuned BERT model could achieve an accuracy of 97.021% on NewsFN data and is able to outperform the other two models by approximately eight percent.

28 citations


Cited by
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Journal ArticleDOI
TL;DR: A new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification and a comparison with existing techniques shows the proposed design yields comparable accuracy.
Abstract: Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.

160 citations

Journal ArticleDOI
01 Jun 2021
TL;DR: The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz. random forest, logistic regression, and Naive Bayes for the classification.
Abstract: Diabetes is a dreadful disease identified by escalated levels of glucose in the blood Machine learning algorithms help in identification and prediction of diabetes at an early stage The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz random forest, logistic regression, and Naive Bayes for the classification Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as AdaBoost, Logistic Regression,Support Vector machine, Random forest, Naive Bayes, Bagging, GradientBoost, XGBoost, CatBoost by taking accuracy, precision, recall, F1-score as the evaluation criteria The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 7904%, 7348%, 7145% and 806% respectively on the PIMA diabetes dataset Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset The proposed ensemble soft voting classifier has given 9702% accuracy on the breast cancer dataset

141 citations

Journal ArticleDOI
TL;DR: The study highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep learning techniques.

135 citations

Journal ArticleDOI
TL;DR: In this article, a review of deep learning on chest X-ray images is presented, focusing on image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation.

121 citations