Predictive Analytics of COVID-19 Pandemic: Statistical Modelling Perspective

Kumar, Sachin; M, Vergin Raja SAROBIN; L, Jani ANBARASI

doi:10.48048/wjst.2021.15583

Cited by 8 publications

(4 citation statements)

References 18 publications

(18 reference statements)

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“…To expedite the diagnostic process and provide accurate predictions, a Convolutional Neural Network (CNN) approach has been developed. CNN technology, already proven effective in various domains such as healthcare [1,2] and intelligent automation [3], is leveraged in this study. By harnessing its capabilities, the CNN model is applied to diagnose diabetic retinopathy from eye images and classify them based on severity accurately.…”

Section: A Proliferative Diabetic Retinopathymentioning

confidence: 99%

Detection of Diabetic Retinopathy using Convolutional Neural Networks

et al. 2024

IJFMR

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Diagnosing diabetic retinopathy (DR) from colour fundus images is a challenging and time-consuming task, requiring experienced clinicians to detect numerous small features and interpret a complex grading system. In our paper, we suggest using a Convolutional Neural Network (CNN) approach to automate DR diagnosis and accurately classify its severity from digital fundus images. Our developed CNN architecture, combined with data augmentation techniques, is capable of identifying intricate features crucial for classification, such as micro-aneurysms, exudates, and haemorrhages on the retina, enabling automatic diagnosis without manual intervention. We trained this network using a high-performance graphics processing unit (GPU) on the publicly available Kaggle dataset and achieved impressive results, especially for high-level classification tasks. In our experiments, utilizing a dataset comprising 3600 images, our proposed CNN attained an accuracy of 87% when validated against 500 additional images. These results demonstrate the effectiveness of our CNN approach in automating DR diagnosis with high accuracy.

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Section: A Proliferative Diabetic Retinopathymentioning

confidence: 99%

Detection of Diabetic Retinopathy using Convolutional Neural Networks

et al. 2024

IJFMR

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“…Deep learning utilizes parallel processing to accelerate learning. Deep learning models are easy to use; researchers can apply deep learning to many applications, such as COVID-19 Outbreak Forecasts, to estimate the morbidity and severity of COVID-19 in Russia, Brazil, India, and the United States by comparing deep learning models with other models [28]. Alternatively, research on improved agricultural efficiency in plant leaf disease recognition using deep learning [29].…”

Section: Deep Learning Classifiersmentioning

confidence: 99%

A combination of machine learning based natural language processing with technical analysis for stock trading

Meesad

Boonmatham

2023

IJEECS

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Stock price analysis appropriately is a challenging area of research as many factors directly affect stock prices. As a result, so not easy to analyze to identify stock trading signals appropriately. The proposed approach builds a framework for classifying stock trading signals by combining natural language processing with technical analysis. The dataset implemented focuses on corporate news and stock indicators from 01-01-2019 to 31-12-2021 from the eight corporates of the Thai Industry Group Index and Sector Index. Two traditional machine learning models, multilayer perceptron (MLP) and support vector machine (SVM), and four deep learning models, Bidirectional GRU (BiGRU), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and long short-term memory (LSTM) used for comparison purposes. The training model classifies daily trading signals into three classes: buy, sell, and hold-after that, the model’s efficiency evaluates by measuring accuracy, precision, recall, and F1-score. For the results, classification average efficiency in all models showed that the BiGRU model obtained higher average accuracy (0.93), precision (0.93), recall (0.93), and F1-score (0.92) than other models. Therefore, the BiGRU model was appropriate for our experiment and was applied to determine daily trading signals for analyzing investment returns.

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“…For the purpose of speeding up the process and precise predictions CNN approach is developed. CNN has already been applied for effective predictions in various fields like healthcare [1,2] and intelligent automation [3].By understanding its strength, CNN is applied in this work to diagnose diabetic retinopathy from eye images and classify them accurately based on the severity. This system shall diagnose diabetic retinopathy automatically without user intervention.…”

Section: Proliferative Diabetic Retinopathymentioning

confidence: 99%

Diabetic Retinopathy Classification Using CNN and Hybrid Deep Convolutional Neural Networks

Yasashvini

Panjanathan

et al. 2022

Symmetry

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Diabetic Retinopathy (DR) is an eye condition that mainly affects individuals who have diabetes and is one of the important causes of blindness in adults. As the infection progresses, it may lead to permanent loss of vision. Diagnosing diabetic retinopathy manually with the help of an ophthalmologist has been a tedious and a very laborious procedure. This paper not only focuses on diabetic retinopathy detection but also on the analysis of different DR stages, which is performed with the help of Deep Learning (DL) and transfer learning algorithms. CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet are used on a huge dataset with around 3662 train images to automatically detect which stage DR has progressed. Five DR stages, which are 0 (No DR), 1 (Mild DR), 2 (Moderate), 3 (Severe) and 4 (Proliferative DR) are processed in the proposed work. The patient’s eye images are fed as input to the model. The proposed deep learning architectures like CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet 2.1 are used to extract the features of the eye for effective classification. The models achieved an accuracy of 96.22%, 93.18% and 75.61% respectively. The paper concludes with a comparative study of the CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet architectures that highlights hybrid CNN with DenseNet as the perfect deep learning classification model for automated DR detection.

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Predictive Analytics of COVID-19 Pandemic: Statistical Modelling Perspective

Cited by 8 publications

References 18 publications

Detection of Diabetic Retinopathy using Convolutional Neural Networks

Detection of Diabetic Retinopathy using Convolutional Neural Networks

A combination of machine learning based natural language processing with technical analysis for stock trading

Diabetic Retinopathy Classification Using CNN and Hybrid Deep Convolutional Neural Networks

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