In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.
Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining richer and deeper insights and getting an advantage over the competition. For this reason, big data implementations need to be analyzed and executed as accurately as possible. In this paper; Firstly, we will discuss what big data and how it is defined according to different sources; Secondly, what are the characteristics of big data and where should it be used; Thirdly, the architecture of big data is discussed along with the different models of Big data; Fourthly, what are some potential applications of big data and how will it make the job easier for the persisting machines and users; Finally, we will discuss the future of Big data.
Some human diseases are recognized through of each type of White Blood Cell (WBC) count, so detecting and classifying each type is important for human healthcare. The main aim of this paper is to propose a computer-aided WBCs utility analysis tool designed, developed, and evaluated to classify WBCs into five types namely neutrophils, eosinophils, lymphocytes, monocytes, and basophils. Using a computer-artificial model reduces resource and time consumption. Various pre-trained deep learning models have been used to extract features, including AlexNet, Visual Geometry Group (VGG), Residual Network (ResNet), which belong to different taxonomy types of deep learning architectures. Also, Binary Border Collie Optimization (BBCO) is introduced as an updated version of Border Collie Optimization (BCO) for feature reduction based on maximizing classification accuracy. The proposed computer aid diagnosis tool merges transfer deep learning ResNet101, BBCO feature reduction, and Support Vector Machine (SVM) classifier to form a hybrid model ResNet101-BBCO-SVM an accurate and fast model for classifying WBCs. As a result, the ResNet101-BBCO-SVM scores the best accuracy at 99.21%, compared to recent studies in the benchmark. The model showed that the addition of the BBCO algorithm increased the detection accuracy, and at the same time, decreased the classification time consumption. The effectiveness of the ResNet101-BBCO-SVM model has been demonstrated and beaten in reasonable ratios in recent literary studies and end-to-end transfer learning of pre-trained models.
With the 5G technology so close to its launch we will discuss is this technology really the future of the tech industry. It is supposed to be launched in the market by the end of 2020 and people are still unaware of what this actually is and how will it affect their lives and the industry of almost every sector. It basically is a wireless network architecture which is anticipated to replace an already present wireless network architecture which supposedly will have lower energy consumption, less maintenance cost and offers very high quality services. In this paper we will discuss what 5G technology is and how will it affect different aspects of life once it is released. How does this technology affect the life of a common man and how will it change the world for the good or worse? Is the world willing to accept such a massive change in its ways of working and is 5G a reliable replacement? All these questions are discussed in this paper on the base of detailed reviews, surveys and interviews carried out with notable spokespersons of different industries.
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