Smart Agricultural Robot with Real-Time Data Analysis Using IBM Watson Cloud Platform

2021 ◽  
pp. 415-427
Author(s):  
Prathyusha Thatipelli ◽  
R. Sujatha
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Cloud Platform ◽  
Time Data ◽  
Real Time Data ◽  
Ibm Watson ◽  
Agricultural Robot

scholarly journals Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Sivadi Sivadi ◽  
Moorthy Moorthy ◽  
Vijender Solanki
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Gaussian Mixture Model ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Cloud Platform ◽  
Time Data ◽  
Huge Amount ◽  
Real Time Data

Introduction: The article is the product of the research “Due to the increase in popularity of Internet of Things (IoT), a huge amount of sensor data is being generated from various smart city applications”, developed at Pondicherry University in the year 2019. Problem:To acquire and analyze the huge amount of sensor-generated data effectively is a significant problem when processing the data. Objective:  To propose a novel framework for IoT sensor data analysis using machine learning based improved Gaussian Mixture Model (GMM) by acquired real-time data.  Methodology:In this paper, the clustering based GMM models are used to find the density patterns on a daily or weekly basis for user requirements. The ThingSpeak cloud platform used for performing analysis and visualizations. Results:An analysis has been performed on the proposed mechanism implemented on real-time traffic data with Accuracy, Precision, Recall, and F-Score as measures. Conclusions:The results indicate that the proposed mechanism is efficient when compared with the state-of-the-art schemes. Originality:Applying GMM and ThingSpeak Cloud platform to perform analysis on IoT real-time data is the first approach to find traffic density patterns on busy roads. Restrictions:There is a need to develop the application for mobile users to find the optimal traffic routes based on density patterns. The authors could not concentrate on the security aspect for finding density patterns.

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

Implementation of DDS Cloud Platform for Real-time Data Acquisition of Sensors

2021 ◽  
Author(s):  
Min-Huang Ho ◽  
Huang-Chu Yen ◽  
Ming-Yi Lai ◽  
Yung-Tien Liu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Cloud Platform ◽  
Time Data ◽  
Real Time Data

scholarly journals Real Time Data Analysis With the ATLAS Trigger at the LHC in Run-2

2020 ◽  
Vol 67 (9) ◽  
pp. 2128-2135
Author(s):  
Pierre-Hugues Beauchemin
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Time Data ◽  
Real Time 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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