scholarly journals REGULATING WATER SUPPLY AND WATER CUTBACKS IN AGRICULTURE USING IOT BASED SMART IRRIGATION SYSTEM

2021 ◽  
Vol 9 (1) ◽  
pp. 629-633
Author(s):  
Prabhas Kumar Gupta, Dr. Nagendra Tripathi
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Irrigation System ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Underground Water ◽  
Contributing Factors ◽  
Time Data ◽  
Cloud Data ◽  
Real Time Data

Involvement of Machine Learning, Real-time Data Analysis and IOT are critically contributing factors in contemporary technical scenarios. Utilization of these three technologies can play a major role in the success of farming thereby modernizing the irrigation system. This paper is focused on the Smart Irrigation System which draws a lot from real-time data analysis, IOT and Machine Leaning. It also presents a study of a system that processes real time data and takes decision about to what extent the field needs to be irrigated. In this way water is saved, its misuse regulated and can be restored for future use if required. Here we rely on cloud data and some other agri-factors which help in decision making.  The Smart Irrigation System discussed here shall also regulate the use of underground water my incorporating IOT and weather forecast. The system will also contribute to effective irrigation taking in view the contemporary weather conditions and the requirement of water in the crop.

scholarly journals Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks

2018 ◽  
Vol 19 (S18) ◽  
Author(s):  
Ahmed Sanaullah ◽  
Chen Yang ◽  
Yuri Alexeev ◽  
Kazutomo Yoshii ◽  
Martin C. Herbordt
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Real Time ◽  
Medical Diagnosis ◽  
Time Data ◽  
Real Time Data

scholarly journals How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

2019 ◽  
Vol 100 (4) ◽  
pp. 605-619 ◽  
Author(s):  
A. J. Illingworth ◽  
D. Cimini ◽  
A. Haefele ◽  
M. Haeffelin ◽  
M. Hervo ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Positive Impact ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Time Data ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Wind Lidar ◽  
Microwave Radiometers

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

2020 ◽  
Vol 223 (3) ◽  
pp. 437.e1-437.e15
Author(s):  
Joshua Guedalia ◽  
Michal Lipschuetz ◽  
Michal Novoselsky-Persky ◽  
Sarah M. Cohen ◽  
Amihai Rottenstreich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Learning Model ◽  
Time Data ◽  
Machine Learning Model ◽  
Vaginal Deliveries ◽  
Real Time Data
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