Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Rauf, Hafiz Tayyab; Lali, M. Ikram Ullah; Khan, Muhammad Attique; Kadry, Seifedine; Alolaiyan, Hanan; Razaq, Abdul; Irfan, Rizwana

doi:10.1007/s00779-020-01494-0

Cited by 76 publications

(47 citation statements)

References 37 publications

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“…Compared with other types of cancer, there is less study on class imbalance methods for lung cancer. Few research es als o classified the Lung nodules [83,84], chest-related diseases [85,86], identification of thoracic diseases [87], forecasting of COVID-19 [84,88,89].…”

Section: Application Of Class Imbalance M Ethods To Cancer Datasetsmentioning

confidence: 99%

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

et al. 2021

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Medical datasets are usually imbalanced, where negative cases severely outnumber p osit iv e cases. Therefore, it is essential to deal with this data skew problem when training machine learning algorithms. This study uses two representative lung cancer datasets, PLCO an d NLST, wit h imb alan ce ratios (the proportion of samples in the majority class to those in the minority class) of 24.7 and 25.0, respectively, to predict lung cancer incidence. This research uses the performance o f 23 clas s imb alan ce methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) to identify the best imbalance techniques suitable for medical datasets. Resampling includes ten under-sampling methods (RUS, Etc.), seven over-sampling methods (SMOTE, Etc.), an d t wo integrated sampling methods (SMOTEENN, SMOTE-Tomek). Hybrid systems include (Balanced Bagging, Etc.). The results show that class imbalance learning can improve the classification abilit y o f t h e mo d el. Compared with other imbalanced techniques, under-sampling techniques have the highest standard deviation (SD), and over-sampling techniques have the lowest SD. Over-sampling is a stable met h od, an d the AUC in the model is generally higher than in other ways. Using ROS, the random forest p erforms t h e best predictive ability and is more suitable for the lung cancer datasets used in this study.

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Section: Application Of Class Imbalance M Ethods To Cancer Datasetsmentioning

confidence: 99%

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

et al. 2021

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“…Deep learning is an important artificial intelligence tool, which plays a crucial role in solving many complex computer vision problems [ 5 , 6 ]. Deep learning models, specifically convolutional neural networks (CNNs), are used extensively for various image classification problems.…”

Section: Introductionmentioning

confidence: 99%

Pneumonia detection in chest X-ray images using an ensemble of deep learning models

et al. 2021

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Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121. A weighted average ensemble technique was adopted, wherein the weights assigned to the base learners were determined using a novel approach. The scores of four standard evaluation metrics, precision, recall, f1-score, and the area under the curve, are fused to form the weight vector, which in studies in the literature was frequently set experimentally, a method that is prone to error. The proposed approach was evaluated on two publicly available pneumonia X-ray datasets, provided by Kermany et al. and the Radiological Society of North America (RSNA), respectively, using a five-fold cross-validation scheme. The proposed method achieved accuracy rates of 98.81% and 86.85% and sensitivity rates of 98.80% and 87.02% on the Kermany and RSNA datasets, respectively. The results were superior to those of state-of-the-art methods and our method performed better than the widely used ensemble techniques. Statistical analyses on the datasets using McNemar’s and ANOVA tests showed the robustness of the approach. The codes for the proposed work are available at https://github.com/Rohit-Kundu/Ensemble-Pneumonia-Detection.

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“…Evidence that this new strain may be passed from person to person has prompted the World Health Organization to elevate the epidemic to stage 5, which is a strong indication that an epidemic is imminent, and the use of association, correspondence and agreed moderate measures is shortlived.Therefore, this spread was increased to stage 6, which proves that the global epidemic continues. U.S. Department of Disease Control and Prevention(CDC) estimates that AI can help in the screening, tracking and predicting the current and future patients [5], [6]. Early detection and diagnosis are primary examples of AI usage in pandemics especially upon the availability of annotated datasets.…”

Section: B Recent Pandemics -Sars-cov Mers-cov and Swine Flumentioning

confidence: 99%

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

et al. 2021

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Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the nonappearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties.

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Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Cited by 76 publications

References 37 publications

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

Pneumonia detection in chest X-ray images using an ensemble of deep learning models

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

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