Over the course of the last decade, the unmanned aerial vehicle (UAV) research community has received a significant amount of attention. Emergency response operations, such as those that follow a natural disaster, are one of the civil applications that could benefit from the use of UAVs in disaster and crisis management. In the event of a catastrophic event, it would be extremely beneficial for both victims and first responders to have access to a UAV network that is capable of deploying independently and offering communication services. However, when working with complicated situations, one of the most difficult things is coming up with exploratory paths for the networks involved. A crisis and disaster management system using a swarm optimization algorithm (SOA) is proposed to assist in disaster and crisis management. In this system, the UAV search and rescue team follows the strategy called the delay tolerant network, which has the ability to explore. The proposed approach is able to find the global maximum in the search space without ever settling for a suboptimal solution. This work has two primary objectives: the first is to investigate a potential disaster zone, and the second is to direct the UAV to a number of victim groups that were found during the investigation phase. For the purpose of performing a characterization, performance metrics such as delay, throughput, performance rate, and path loss have been analyzed. The results show the superiority of the performance over the existing work.
The COVID-19 pandemic continues to impact both the international economy and individual lives. A fast and accurate diagnosis of COVID-19 is required to limit the spread of this disease and reduce the number of infections and deaths. However, a time consuming biological test, Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR), is used to diagnose COVID-19. Furthermore, sometimes the test produces ambiguous results, especially when samples are taken in the early stages of the disease. As a potential solution, machine learning algorithms could help enhance the process of detecting COVID-19 cases. In this paper, we have provided a study that compares the stand-alone CNN model and hybrid machine learning models in their ability to detect COVID-19 from chest X-Ray images. We presented four models to classify such kinds of images into COVID-19 and normal. Visual Geometry Group (VGG-16) is the architecture used to develop the stand-alone CNN model. This hybrid model consists of two parts: the VGG-16 as a features extractor, and a conventional machine learning algorithm, such as support-vector-machines (SVM), Random-Forests (RF), and Extreme-Gradient-Boosting (XGBoost), as a classifier. Even though several studies have investigated this topic, the dataset used in this study is considered one of the largest because we have combined five existing datasets. The results illustrate that there is no noticeable improvement in the performance when hybrid models are used as an alternative to the stand-alone CNN model. VGG-16 and (VGG16+SVM) models provide the best performance with a 99.82% model accuracy and 100% model sensitivity. In general, all the four presented models are reliable, and the lowest accuracy obtained among them is 98.73%.
Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.
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