Personalized authentication procedure for restricted web service access in mobile phones

Postpartum haemorrhage is the prime source of parental fatality. Postpartum haemorrhage occurs extra blood loss after delivery. If the blood loss occurs more than 500ml, it may create a problem in blood pressure where the women has to go through a lot of pain. Presently, the complete cure are still under research and there is no result which helps to dwindle the endanger of postpartum haemorrhage. Intent process is so supportive in classifying the danger circumstances and current automation is used. The methodology used here is Deep Learning technique which will be easier to conclude the postpartum haemorrhage in the previous phase. There is an certain stage of postpartum haemorrhage where it can control the blood loss and save the women by permitting higher level treatments. Haemorrhage is one of the major factor responsible for maternal death. Haemorrhage may occur before, during or after delivery of placenta. Based on the amount of blood flow, postpartum haemorrhage will be classified in to two types such as primary and secondary pph. To manage all these problems, methods are handled based on respective situation.

Section: Related Workmentioning

confidence: 99%

Predictive Analysis of Postpartum Haemorrhage Using Deep Learning Technique

Ruphitha¹,

Kumar²

2021

“…The author uses Internet of things with Arduino and Raspberry pi to execute the project. In [7] the author has used two machine learning algorithms one is multi target regression model and another one is recurrent neural network model .using these two models they have predicted rainfall possibilities, temperature and humidity [8][9][10][11][12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

IOT Based Weather Reporting System Using Arduino and Node MCU

Sree.R¹,

A²,

Manasvinii³

et al. 2021

Weather is an ever changing phenomenon. It can change in within seconds or milliseconds. Weather was quite difficult to monitor in the days when technology was still primitive. But now it is easy as there are various sensors and computing techniques to not only monitor weather but predict it with high accuracy as well. To simplify the detection process IOT (Internet of things) is employed in the project. As IOT is used Arduino is added to the project along with other sensor to monitor the weather. The data from the sensor is uploaded to an open source web-server. The open server can be used both to get real time data as well as download the data for the prediction process. Prediction is done using machine learning algorithms and training the detected datasets.

“…They present correlation results between the proposed strategy and agent AI based recognition calculations. [10][11][12][13]…”

Section: Introductionmentioning

confidence: 99%

Detection of Malicious Web Applications Using Machine Learning Algorithm

Sivagurunathan

SivakumarP

Somasundaram

2021

The emerging development of the internet everyone using web applications for their products or services. The large number of web applications created day by day. Since the demand is very high for web development, developers are creating an application not in a secure manner and hosting without the testing process. Web clients regularly store and oversee basic data that draws in cybercriminals who exploitation the web weaknesses for their benefits. Pernicious website pages are coming to pass undermining issue over the web on account of the reputation and their capacity to impact. Recognizing and examining them is exorbitant due to their characteristics and complexities. The complexities of assaults are expanding step by step in light of the fact that the assailants are utilizing mixed methodologies of different existing assaulting procedures. Using this opportunityattacker used their malicious script in their web application. Attacker, theft user’s data, or redirect to malicious websites. In this project, we are proposing detection methods, to prevent the users from approaching the malicious web application. Using a Machine learning algorithm, extract the feature of the web application that is URL features and static features of the network. From the trained model of date set, using the Random forest algorithm detect the malicious web application.