Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model's performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model's ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.

Transfer Learning Model in Deep Neural Network for COVID-19 Prediction from CT Images

Deep learning models are often used to process radi-ological images automatically and can accurately train networks' weights on appropriate datasets. One of the significant benefits of the network is that it is possible to use the weight of a pre-trained network for other applications by fine-tuning the current weight. The primary purpose of this work is to employ a pre-trained deep neural framework known as transfer learning to detect and diagnose COVID-19 in CT images automatically. This paper uses a popular deep neural model, ResNet152, as a neural transfer approach. The presented framework uses the weight obtained from the ImageNet dataset, fine-tuned by the dataset used in the work. The effectiveness of the suggested COVID-19 prediction system is evaluated experimentally and compared with DenseNet, another transfer learning model. The recommended ResNet152 transfer learning model exhibits improved performance and has a 99% accuracy when analogized with the DenseNet201 transfer learning model. © 2022 IEEE.

DenseNet201 Model for Robust Detection on Incorrect Use of Mask

In 2022, the COVID-19 pandemic is still occurring. One of the optimal prevention efforts is to wear a mask properly. Several previous studies have classified the use of masks incorrectly. However, the accuracy resulting from the classification process is not optimal. This research aims to use the transfer learning method to achieve optimal accuracy. In this research, we used three classes, namely without a mask, incorrect mask, and with a mask. The use of these three classes is expected to be more detailed in detecting violations of the use of masks on the face. The classification method used in this research uses transfer learning as feature extraction and Global Average Pooling and Dense layers as classification layers. The transfer learning models used in this research are MobileNetV2, InceptionV3, and DenseNet201. We evaluate the three models' accuracy and processing time when using video data. The experimental results show that the DenseNet201 model achieves an accuracy of 93%, but the processing time per video frame is 0.291 s. In contrast to the MobileNetV2 model, which produces an accuracy of 89% and the processing speed of each video frame is 0.106 s. This result is inversely proportional to accuracy and speed. The DenseNet201 model produces high accuracy but slow processing time, while the MobileNetV2 model is less accurate but has faster processing. This research can be applied in the crowd center to monitor health protocols in the use of masks in the hope of inhibiting the transmission of the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Transfer learning-based CNN diagnostic framework for diagnosis of COVID-19 from lung CT images

COVID-19 is an infectious disease that affects the respiratory system. To assist the physician in diagnosing lung disorders from chest CT images various systems have been developed and used. Detection of COVID-19 remains a challenging area of research. The objective of the work is to develop an inductive parameter-transfer learning-based approach for the prediction of COVID-19, pneumonia, from lung CT images. Our proposed approach is built on layer wise and convolution block-wise fine-tuning which designs the CNN architecture highly specific to lung CT image. We implemented the DenseNet201, InceptionV3, Xception, VGG19, and ResNet50 as baseline models. The network architectures are developed to learn feature representation of lung CT images. For the experimental analysis, five datasets are used. From the experimental results, it is inferred that the DenseNet201 model yields higher accuracy of 0.94 for Adam optimizer and 0.93 for the RMSprop optimizer compared to other models.

Transfer learning-based CNN diagnostic framework for diagnosis of COVID-19 from lung CT images

COVID-19 is an infectious disease that affects the respiratory system. To assist the physician in diagnosing lung disorders from chest CT images various systems have been developed and used. Detection of COVID-19 remains a challenging area of research. The objective of the work is to develop an inductive parameter-transfer learning-based approach for the prediction of COVID-19, pneumonia, from lung CT images. Our proposed approach is built on layer wise and convolution block-wise fine-tuning which designs the CNN architecture highly specific to lung CT image. We implemented the DenseNet201, InceptionV3, Xception, VGG19, and ResNet50 as baseline models. The network architectures are developed to learn feature representation of lung CT images. For the experimental analysis, five datasets are used. From the experimental results, it is inferred that the DenseNet201 model yields higher accuracy of 0.94 for Adam optimizer and 0.93 for the RMSprop optimizer compared to other models. © 2023 The Royal Photographic Society.

An Integrated Framework for COVID-19 Classification Based on Ensembles of Deep Features and Entropy Coded GLEO Feature Selection

COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach's effectiveness. © World Scientific Publishing Company.

Assessment of Chest X-Ray Image-Based COVID-19 Detection using Deep Transfer Learning Models

Wuhan is the city in China where COVID-19 was first discovered, and the disease quickly spread throughout the world, affecting over 215 million people. Vaccination has been tried to control the disease effects. Many data scientists contributed and analyzed the disease using chest X-Rays and Computed Tomography (CT) scans in order to control it. The data collected from Chest X-rays have been proven to be extremely effective for screening COVID-19 patients, particularly in terms of resolving overcapacity in emergency departments and urgent-care centers. Our proposed approach towards COVID-19 research contribution consists of four transfer learning models i.e., MobileNet, DenseNet201, InceptionNetV2 and NasNetMobile. Grayscale images of chest X-Rays that have been preprocessed are fed into these models as input data. The dataset used in the proposed framework is the COVID19 Radiography Database, which is available to all researchers on the Kaggle platform and contains four different types of chest X-ray images i.e., COVID-19, Pneumonia, Opacity and Normal. For multiclass classification that is MobileNet, DenseNet201, InceptionNetV3 and NasNetMobile the models showed an impressive accuracy of 91.26%, 90.38%, 89.27, and 87.74, while for binary class classification, the prediction capability of our used models is 97.03%, 96.78%, 95.18% and 95.40% respectively. © 2022 IEEE.

Computer-Aided Diagnosis of COVID-19 Radiography using deep transfer deep learning model TLDeNet

Computer-Aided Diagnosis (CAD) is applied in the medical analysis of X-ray images widely. Due to the COVID-19 pandemic, the speed of COVID-19 detection is slow, and the workforce is scarce. Therefore, we have an idea to use CAD to diagnose COVID-19 and effectively respond to the pandemic. Recent studies show that convolutional neural network (CNN) is an appropriate technique for medical image classification. However, CNN is more suitable for datasets with many images, such as ImageNet. Medical image classification relies on doctors to label medical images, so obtaining large-scale medical image data sets is a time-consuming, costly, and unrealistic task. The method of data augmentation for a limited medical dataset can be used to increase the number of images. However, this technology will produce many repeated images, which will easily lead to the overfitting problem of CNN. In the case of a limited number of radiological images, transfer learning is a practical and effective method which can help us overcome the overfitting problem of ordinary CNN by transferring the pre-Trained models on large datasets to our tasks. The proposed model is DenseNet based deep transfer learning model (TLDeNet) to identify the patients into three classes: COVID-19, Normal or Pneumonia. We then analyzed and assessed the performance of our model on COVID-19 X-ray testing images by performing extensive experiments. It is finally demonstrated that the proposed model is superior to other deep transfer learning models according to comparative analyses. The Grad-Cam method is finally applied to interpret the convolutional neural network, revealing that our proposed model focuses on the similar region of the X-ray images as doctors. © 2022 IEEE.

LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases.

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models.

One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders.

A NOVEL COMPARATIVE STUDY FOR AUTOMATIC THREE-CLASS AND FOUR-CLASS COVID-19 CLASSIFICATION ON X-RAY IMAGES USING DEEP LEARNING

The contagiousness rate of the COVID-19 virus, which was evaluated to have been transmitted from an animal to a human during the last months of 2019, is higher than the MERS-Cov and SARS-Cov viruses originating from the same family. The high rate of contagion has caused the COVID-19 virus to spread rapidly to all countries of the world. It is of great importance to be able to detect cases quickly in order to control the spread of the COVID-19 virus. Therefore, the development of systems that make automatic COVID-19 diagnoses using artificial intelligence approaches based on X-ray, CT scans, and ultrasound images are an urgent and indispensable requirement. In order to increase the number of X-ray images used within the study, a mixed data set was created by combining eight different data sets, thus maximizing the scope of the study. In the study, a total of 9,667 X-ray images were used, including 3,405 of COVID-19 samples, 2,780 of bacterial pneumonia samples, 1,493 of viral pneumonia samples and 1,989 of healthy samples. In this study, which aims to diagnose COVID-19 disease using X-ray images, automatic classification has been performed using two different classification structures: COVID-19 Pneumonia/Other Pneumonia/Healthy and COVID-19 Pneumonia/Bacterial Pneumonia/Viral Pneumonia/Healthy. Convolutional Neural Networks (CNNs), a successful deep learning method, were used as a classifier within the study. A total of seven CNN architectures were used: Mobilenetv2, Resnet101, Googlenet, Xception, Densenet201, Efficientnetb0, and Inceptionv3 architectures. The classification results were obtained from the original X-ray images, and the images were obtained by using Local Binary Pattern and Local Entropy. Then, new classification results were calculated from the obtained results using a pipeline algorithm. Detailed results were obtained to meet the scope of the study. According to the results of the experiments carried out, the three most successful CNN architectures for both three-class and four-class automatic classification were Densenet201, Xception, and Inceptionv3, respectively. In addition, it is understood that the pipeline algorithm used in the study is very useful for improving the results. The study results show that up to an improvement of 1.57% were achieved in some comparison parameters. © 2022, Malaysian Journal of Computer Science. All Rights Reserved.

A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images

Because Covid-19 spreads swiftly in the community, an automatic detection system is required to prevent Covid-19 from spreading among humans as a rapid diagnostic tool. In this paper, we propose to employ Convolution Neural Networks to detect coronavirus-infected patients using Computed Tomography and X-ray images. In addition, we look into the transfer learning of a deep CNN model, DenseNet201 for detecting infection from CT and X-ray scans. Grid Search optimization is utilized to select ideal values for hyperparameters, while image augmentation is employed to increase the model's capacity to generalize. We further modify DenseNet architecture to incorporate a depthwise separable convolution for detecting coronavirus-infected patients utilizing CT and Xray images. Interestingly, all of the proposed models scored greater than 94% accuracy, which is equivalent to or higher than the accuracy of earlier deep learning models. Further, we demonstrate that depthwise separable convolution reduces the training time and computation complexity.

Multiple Transfer Learning-AI design for Lung Disease-Pneumothorax and Tension Pneumothorax classification from Chest-X ray

The research paper discuss the Artificial Intelligence based Multiple Transfer Learning Mechanism in identification of lung diseases like pneumothorax, tension pneumothorax from a set of chest X-rays. Pneumothorax being a primary stage of many sorts of pulmonary diseases, it has now a days being noticed as an impact with COVID cases due to the insertion of the tubes into the lungs. The proper diagnosis of the various stages of Pneumothorax is thus essential in the current scenario. Identification of the patients with Pneumothrax with less diagnostic time is the highlight of this research work. The deep learning technology of AI has enlightened the research in the medical imaging field. The chest X-ray images are with the pre-processing analysis, normalised the images for a uniform image data processing. The advanced method of transfer learning is equipped with modifications in the various fully connected convolutional network layers. The modified transfer learning has been used with DenseNet and VGG 19. The convolutional neural networks with DenseNet201 and VGG19 utilized stochastic gradient decent optimization for parameter optimization. The data set with pneumothorax and tension pneumothorax along with the control set has been trained and validated. The training and validation of these network has proven results with 89% accuracy with VGG19 and 100% accuracy with Densenet. The evaluation of modified Multi-transfer learning algorithm is identified successfully with new random input chest X-ray with a less diagnostic time. © 2022 IEEE.

AI-driven deep and handcrafted features selection approach for Covid-19 and chest related diseases identification.

To identify various pneumonia types, a gap of 15% value is being created every five years. To fill this gap, accurate detection of chest disease is required in the healthcare department to avoid any serious issues in the future. Testing the affected lungs to detect a Coronavirus 2019 (COVID-19) using the same imaging modalities may detect some other chest diseases. This wrong diagnosis strongly needs a multidisciplinary approach to the right diagnosis of chest-related diseases. Only a few works till now are targeting pathological x-ray images. Many studies target only a single chest disease that is not enough to automate chest disease detection. Only a few studies regarding the observation of the COVID-19, but more cases are those where it can be misclassified as detecting techniques not providing any generic solution for all types of chest diseases. However, the existing studies can only detect if the person has COVID-19 or not. The proposed work significantly contributes to detecting COVID-19 and other chest diseases by providing useful analysis of chest-related diseases. One of our testing approaches achieves 90.22% accuracy for 15 types of chest disease with 100% correct classification of COVID-19. Though it analyzes the perfect detection as the accuracy level is high enough, but it would be an excellent decision to consider the proposed study until doctors can visually inspect the input images used by models that lead to its detection.

A Comparative Analysis of Deep Convolutional Models for COVID-19 Classification on Chest Radiography Images

Since it was firstly reported in China, COVID-19 becomes the cause of death for a large number of individuals and the spreading speed of the disease throughout the world is very high. Due to this, the World Health Organization declares it as a pandemic. Early detection of the COVID-19 has a high probability to cure it. Chest X-ray is the common and a lower cost modality of image for detecting COVID-19, this is because the disease affects the human lung. For processing and analyzing images for the purpose of detecting human disease, deep learning is the most promising technique. Inspired by the previous works, in this work we have taken radiography images to detect COVID-19 by using four pre-trained deep learning techniques, namely VGG19, DenseNet-201, DenseNet-169, and MobileNetV2, which have better performance in the earlier works. We have used a COVIDx dataset, which consists of 15,156 chest X-ray images divided into three classes. After preparing the dataset we have trained and tested the models separately and then we compare the performance of each model. As a result, DenseNet-169 performs better and scores an accuracy, AUC, and loss of 0.94, 0.99, and0.27 respectively. © 2022 IEEE.

Detection of COVID-19 and Pneumonia Using Deep Convolutional Neural Network

COVID-19 has created a panic all around the globe. It is a contagious disoriginated from Wuhan in December 2019 and spread quickly all over the world. The healthcare sector of the world is facing great challenges tackling COVID cases. One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases. In this article, we propose a deep Convolutional Neural Network (CNN) based approach to detect COVID+ (i.e., patients with COVID-19), pneumonia and normal cases, from the chest X-ray images. COVID-19 detection from chest X-ray is suitable considering all aspects in comparison to Reverse Transcription Polymerase Chain Reaction (RT-PCR) and Computed DenseNet121, DenseNet201 and InceptionResNetV2 have been adopted in this proposed work. They have been trained individually to make particular predictions. Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy, recall, F1-score and precision of 94.75%, 96%, 95% and 95% respectively. After careful comparison with results available in the literature, we have found to develop models with a higher reliability. All the studies were carried out using a publicly available chest X-ray (CXR) image data-set.

COVID-19 severity detection using machine learning techniques from CT-images.

COVID-19 has spread worldwide and the World Health Organization was forced to list it as a Public Health Emergency of International Concern. The disease has severely impacted most of the people because it affects the lung and causes severe breathing problems and lung infections. Differentiating other lung ailments from COVID-19 infection and determining the severity is a challenging process. Doctors can give vital life-saving services and support patients' lives only if the severity of their condition is determined. This work proposed a two-step approach for detecting the COVID-19 infection from the lung CT images and determining the severity of the patient's illness. To extract the features, pre-trained models are used, and by analyzing them, integrated the features from AlexNet, DenseNet-201, and ResNet-50. The COVID-19 detection is carried out by using an Artificial Neural Network(ANN) model. After the COVID-19 infection has been identified, severity detection is performed. For that, image features are combined with the clinical data and is classified as High, Moderate, Low with the help of Cubic Support Vector Machine(SVM). By considering three severity levels, patients with high risk can be given more attention. The method was tested on a publicly available dataset and obtained an accuracy of 92.0%, sensitivity of 96.0%, and an F1-Score of 91.44% for COVID-19 detection and got overall accuracy of 90.0% for COVID-19 severity detection for three classes.

Automatic Classification of COVID-19 from Chest X-Ray Image using Convolutional Neural Network

COVID-19 has become one of the most virulent, acute, and life-Threatening diseases in recent times. No clinically approved drug is available till now for its treatment. Therefore, early and swift detection is very essential for reducing overall mortality. The chest x-ray image is one of the possible alternative methods for detecting COVID-19. Researchers are exploring image processing techniques along with deep learning-based models like AlexNet, VGGNet, SqueezeNet, GoogleNet, etc.To detect COVID-19. This study aims to formulate, implement and investigate deep learning-based models and their probable hyperparameters tuning for obtaining the best results when identifying COVID-19 using chest x-ray images. To meet this objective, images from different publicly available databases were collected. In this paper, ResNet18, ResNet50V2, DenseNet121, DenseNet201, modified DenseNet201 and VGG16 were used to detect COVID-19. From the experimental results, modified DenseNet201 showed the best performance with 99.5% mean accuracy, 99.5% mean F1 score and 100% mean sensitivity in binary (COVID-19 and normal) classification and 98.33% mean accuracy, 98.34 mean F1 score, and 98.34% mean sensitivity (98% sensitivity for COVID-19) in 3-class (COVID-19, pneumonia, normal) classification. This may contribute to the process of designing and implementing a system that can detect COVID-19 automatically in the near future and enhance the quality of healthcare services. © 2021 IEEE.

Comparative Study of Transfer Learning techniques for Lung Disease prediction

Lung Disease is one of the most common healthcare issues in the entire world in all age groups of humans due to the cause of smoking, infection and contaminated air. Pneumonia, Coronavirus disease 2019 (COVID-19) and Tuberculosis are the common types of lung disease. To reduce the diagnosis of the Lung Disease, several research works have been done with the help of Artificial Intelligence based-technologies. Prediction and classification of Lung Disease are the common tasks using Chest X-ray images, CT scan images, and MRI images with the help of machine learning, deep learning, and transfer learning models. The main idea of this paper is to show the comparative analysis of ResNet-50, MobileNet-V2, VGG-19 and DenseNet201 CNN-based Transfer Learning techniques and suggesting which image classification algorithm is more recommended to predicting and classifying Lung Disease classification problem. Those four algorithms have been trained on the X-ray images and evaluated using performance metrics techniques. The results have been shown 94.64% accuracy obtained using ResNet50, 97.43% accuracy obtained using VGG19, 98.49% accuracy obtained using MobileNetV2, and 98.05% accuracy obtained using DenseNet201 pre-trained model. Based on the experimental results MobileNet model has been outperformed as compared to other models. Training time per epoch and ROC-AUC values of MobileNet was better than other models. Based on our studies, MobileNet transfer learning is recommended pre-trained model for Lung Disease prediction and classification problem. © 2021 IEEE.

Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection.

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time.