Clustering is technique which is used to analyze the data in efficient manner and generate required information. To cluster the dataset, there is a technique named k-mean, is applied which is based on central point selection and calculation of Euclidian Distance. Here in k-mean, dataset will be loaded and from the dataset. Central points are selected using the formulae Euclidian distance and on the basis of Euclidian distance points are assigned to the clusters. The main disadvantage of k-mean is of accuracy, as in k-mean clustering user needs to define number of clusters. Because of user defined number of clusters, some points of the dataset are remained un-clustered. In this work, improvement in the kmean clustering algorithm will be proposed which can define number of clusters automatically and assign required cluster to un-clustered points. The proposed improvement will leads to improvement in accuracy and reduce clustering time by the member assigned to the cluster to predict cancer.
Internet of Things (IoT) is a new buzzword in information technology where real-world physical objects are made smart by integrating them with internet-enabled technologies. The things can sense information around them, communicate the sensed information over some protocol and employ the information to solve real-life problems. In IoT, several technologies are integrated under a common umbrella so that they can connect and exchange data over a network protocol. A huge amount of data is generated from diverse geographical locations with the consequent urge for fast aggregation of overall sensed information, leading to an increase in the need to store and process such data in a more efficient and effective manner. The traditional fields of embedded systems, WSN, real-time analytics, automation system, machine learning and others all contribute to enabling the IoT. This article is focused on discussing the various IoT technologies, protocols and their application and usage in our daily life. It also summarizes the current state-of-the-art IoT architecture in various spheres conventionally and all related terminologies that will give the forthcoming researchers a glimpse of IoT as a whole.
We have demonstrated the use of an iteratively severed model of deep learning which associates for diagnosing Covid-19 pulmonary demonstration of using chest X-rays. In this paper, a customized convolutional neural network model is trained and analyzed on publicly available chest X-rays to grasp modality-strict feature demonstrations. Since the best performing models learn iteratively to make the model memory efficient, this model also learns and tries to improve the results with each step and classify the chest X-rays in their categories accurately. Then another model which predicts the length of stay of a patient at the hospital is created using multi-layered data processing approach. This model will empower hospitals for on time interference to prevent confusions and better management of hospital resources. We propose a method that uses catboost model which generally classifies the data in multiple classes. As a result, this study provides modality strict iterative and knowledge reusable model which influences Covid-19 detection and length of stay prediction.
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