Multi‐texture features and optimized DeepNet for COVID‐19 detection using chest x‐ray images

COVID-19 prediction is of great importance to build stronger government prevention and control of the global pandemic. This pandemic has a devastating impact on people and their lives. Many industries are suffering and are struggling to overcome unexpected pandemic challenges. Therefore, it is extremely important to develop up to date and practicable models for time series prediction of COVID-19 that would give promising results. In this paper, we build a model for predicting daily confirmed n w cases for the time series data of Europe by applying the Extreme Gradient Boosting (XGBoost) algorithm and using two performance parameters such as RMSE and MAPE to evaluate the effect of model fitting. Since machine learning methods (ML) have shown favorable results recently, among the tested models, XGBoost has shown to give the best results among the other models tested. Another advantage of the model is that we can use the XGBoost model to determine the robustness of the predictive model by adjusting parameter features. Our results are in line with the expectations of the performance of the model based on the available data and the variability of the data. By analyzing important features and updating the prediction for the new cases of the pandemic, the governments all around the globe could manage the situation much more effortlessly and with greater impact.

Section: Model Fitting and Resultsmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

A novel metaheuristic prediction approach for COVID-19 cases using XGBoost algorithm

Gopatoti¹

2023

“…The goal of this is to improve the image quality of CXR scans, as well as to detect and diagnose medical conditions at an affordable cost. In [16,17], authors implemented segmentation and classification based approaches to detect COVID-19 from the chest X-ray images. In today's world, methods using artificial intelligence such as machine learning [18] and deep learning [19] are beginning to show prospective outcomes in the analysis of medical pictures.…”

Section: Literature Surveymentioning

confidence: 99%

“…is the most significant factor that has to be taken into account when applying reduction to high-dimensional data. The primary goal of classifiers is to strike a balance between the quality of the algorithm and the amount of the data [16]. This is necessary in order to accomplish the goal of lowering the dimensionality of the data.…”

Section: Sae Feature Extractionmentioning

confidence: 99%

Deep learning framework for classification of COVID-19 from Chest X-rays

Gopatoti¹

2023

The Coronavirus Disease 2019 (COVID-19) has emerged as the most pressing health concern in recent years. In addition, a wide variety of severe acute respiratory syndrome-related COVID-19 strains are now in the process of developing (SARS). Therefore, the identification of all of these variations utilising a Real-time polymerase chain reaction (RT-PCR) test is a challenging endeavour that takes a significant amount of time. Due of the intricate structure of the chest x-ray (CXR) picture, traditional approaches were unable to correctly categorise the COVID-19 at an early stage. As a result, the primary emphasis of this paper is placed on the use of an artificial intelligence strategy that is based on deep learning convolutional neural networks (DLCNN) for the classification of COVID-19 illness. In the beginning, the hybrid features are extracted from the CXR dataset by using stacked auto ensemble (SAE), a technique that retrieved features by partitioning data into various subsets. This approach was used. After that, a DLCNN model is trained with these characteristics in order to classify COVID-19 for each test CXR picture. According to the findings of the simulations, the suggested classification led to superior results in terms of both subjectivity and objectivity in comparison to both traditional machine learning and deep learning approaches.

“…The risk of abuse of drugs is determined from first usage by the consumer, inevitably, before completion. In [18][19][20], authors presented many DL networks to detect deceases.Adverse events (AE), which are one of the main triggers of death and illness, are a big problem for health care [21]. For AE cases, there had been a recorded rise of 222% between 2006 and 2014 [13], according to the Food and Drug Administration (FDA).…”

Section: Literature Surveymentioning

confidence: 99%

Artificial intelligence-based Medicine Recognition System using faster Recurrent Convolutional Neural Networks

Kumar¹,

Jaiswal²,

Jena³

2023

Recently, chronic patients are taking multiple medications incorrectly and taking the wrong medications due to similarity of drugs.It is possible that taking the improper medication can result in hazardous interactions with other medications or they will counteract the intended benefits of the medications, resulting in extra severe repercussions such as acute complications. The conventional methods are failed to provide the maximum efficiency. Therefore, this article is focused on implementation of faster recurrent convolutional neural networks (FR-CNN), which is capable of extracting the features from images. FR-CNN mainly used to analyze the patterns of the medicines and extracts the deep features. Further, classification of medicines is carried out by comparing with ground truth labels. The simulation results shows that the proposed system resulted in superior performance as compared to state of art approaches.