scholarly journals Attackdet: Combining web data parsing and real-time analysis with machine learning

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Zeydin Pala ◽  
Musa Şana
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Time Analysis ◽  
Web Data ◽  
Real Time Analysis

PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform

2019 ◽  
Vol 23 (1) ◽  
pp. 59-65 ◽  
Author(s):  
Jacob R. Sutton ◽  
Ruhi Mahajan ◽  
Oguz Akbilgic ◽  
Rishikesan Kamaleswaran
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Real Time ◽  
Time Analysis ◽  
Real Time Analysis

Mobile Cloud-Based Framework for Health Monitoring with Real-Time Analysis Using Machine Learning Algorithms

2021 ◽  
pp. 173-183
Author(s):  
Suman Mohanty ◽  
Ravi Anand ◽  
Ambarish Dutta ◽  
Venktesh Kumar ◽  
Utsav Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Health Monitoring ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mobile Cloud ◽  
Time Analysis ◽  
Real Time Analysis

scholarly journals 34: Real-time analysis of data using machine learning model significantly improves prediction of unplanned cesarean deliveries

2020 ◽  
Vol 222 (1) ◽  
pp. S29
Author(s):  
Yishai Sompolinsky ◽  
Joshua Guedalia ◽  
Amihai Rottenstreich ◽  
Michal Novoselsky Persky ◽  
Gabriel levin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Model ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Cesarean Deliveries ◽  
Machine Learning Model

scholarly journals Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning

2019 ◽  
Vol 3 (11) ◽  
pp. 930-942 ◽  
Author(s):  
Fabrice de Chaumont ◽  
Elodie Ey ◽  
Nicolas Torquet ◽  
Thibault Lagache ◽  
Stéphane Dallongeville ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Time Analysis ◽  
Depth Sensing ◽  
Real Time Analysis

scholarly journals COVID-19 Prognosis and Mortality Risk Predictions from Symptoms: A Cloud-Based Smartphone Application

BioMed ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 114-125
Author(s):  
Ocean Monjur ◽  
Rahat Bin Preo ◽  
Abdullah Bin Shams ◽  
Md. Mohsin Sarker Raihan ◽  
Fariha Fairoz
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Mortality Risk ◽  
Smartphone Application ◽  
Time Analysis ◽  
The Real ◽  
Real Time Analysis ◽  
Efficient Management ◽  
Machine Learning Model ◽  
Critical Patients

The coronavirus pandemic overwhelmed many countries and their healthcare systems. Shortage of testing kits and Intensive-Care-Unit (ICU) beds for critical patients have become a norm in most developing countries. This has prompted the need to rapidly identify the COVID-19 patients to stop the spread of the virus and also to find critical patients. The latter is imperative for determining the state of critically ill patients as quickly as possible. This will lower the number of deaths from the infection. In this paper, we propose a cloud-based smartphone application for the early prognosis of COVID-19 infected patients and also predict their mortality risk using their symptoms. Moreover, we heuristically identified the most important symptoms necessary for making such predictions. We have successfully reduced the number of features by almost half for the prognosis and by more than a third for forecasting the mortality risk, compared to the contemporary studies. The application makes the real-time analysis using machine learning models, designed and stored in the cloud. Our machine learning model demonstrates an accuracy, precision, recall, and F1 score of 97.72%, 100%, 95.55%, and 97.70%, respectively, in identifying the COVID-19 infected patients and with an accuracy, precision, recall, and F1 score of 90.83%, 88.47%, 92.94%, and 90.65%, respectively, in forecasting the mortality risk from the infection. The real-time cloud-based approach yields faster responses, which is critical in the time of pandemic for mitigating the infection spread and aiding in the efficient management of the limited ICU resources.

An Interactive Minicomputer Program for the Indexing and Simulation of Electron Diffraction Patterns

1976 ◽  
Vol 34 ◽  
pp. 542-543
Author(s):  
R.P. Goehner ◽  
W.T. Hatfield ◽  
Prakash Rao
Keyword(s):  
Electron Diffraction ◽  
Real Time ◽  
Pattern Analysis ◽  
Flow Diagram ◽  
Hard Copy ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Transmission Electron ◽  
One Step ◽  
Diffraction Patterns

Computer programs are now available in various laboratories for the indexing and simulation of transmission electron diffraction patterns. Although these programs address themselves to the solution of various aspects of the indexing and simulation process, the ultimate goal is to perform real time diffraction pattern analysis directly off of the imaging screen of the transmission electron microscope. The program to be described in this paper represents one step prior to real time analysis. It involves the combination of two programs, described in an earlier paper(l), into a single program for use on an interactive basis with a minicomputer. In our case, the minicomputer is an INTERDATA 70 equipped with a Tektronix 4010-1 graphical display terminal and hard copy unit.A simplified flow diagram of the combined program, written in Fortran IV, is shown in Figure 1. It consists of two programs INDEX and TEDP which index and simulate electron diffraction patterns respectively. The user has the option of choosing either the indexing or simulating aspects of the combined program.

scholarly journals Real-Time Analysis of Animal Feeding Behavior With a Low-Calculation-Power CPU

2020 ◽  
Vol 67 (4) ◽  
pp. 1197-1205 ◽  
Author(s):  
Yuki Totani ◽  
Susumu Kotani ◽  
Kei Odai ◽  
Etsuro Ito ◽  
Manabu Sakakibara
Keyword(s):  
Feeding Behavior ◽  
Real Time ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Animal Feeding
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