Introduction and Aims The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India. Material and methods A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases. Results Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way. Conclusion These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered).
Forecasting is a technique commonly used in the study of time series to forecast a variable response for a specified period of time, such as monthly earnings, stock performance, or unemployment figures. Forecasting is historical data behavior to determine the direction of future trends. Therefore, many machine learning algorithms are used in the past few years. In this study, a summary of an extreme learning machine with MapReduce technique (ELM_MapReduce) is presented. This technique is based on the concept of processing large amount of historical data and application of extreme learning machine to achieve fast learning speed. As stock market data is large set of historical data that need time to process, MapReduce method is used to handle such limitations. The technique shows the advantages and disadvantages of using MapReduce method in ELM and can be used in different areas of research.
The wireless sensor network is a large number of tiny nodes installed in insecure environment for monitoring, gathering and transferring data and are prone to security threats for its limited resources. In order to transmit the data and to protect from different attacks in the network, security is maintained. To achieve confidentiality, authenticity and authorization of data which secure the data from different attacks cryptographic algorithm were used. The number of keys used in the cryptographic algorithm determines the security of the data. Cryptographic algorithms are broadly classified into two types symmetric cryptography and asymmetric cryptography. In the symmetric key cryptographic algorithm, a secret key is shared in the network and in asymmetric key cryptographic algorithm two keys are used for data security. In wireless sensor network, symmetric key cryptography required more storage to store the key among all the nodes of the network and in asymmetric key cryptography more computation time is require for the data encryption and decryption. To avoid memory and computation overhead we proposed a hybrid cryptosystem to handle the security in the wireless sensor network. Initially shared key is exchanged among nodes using ECC which is a public key algorithm. Data is encrypted and decrypted using RC4 symmetric key algorithm. Various performance measures such as time taken for encryption and decryption process and memory needed for storing cipher text data. The proposed model shows faster encryption of data and takes less memory for key storage as compared to the traditional approach.
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