scholarly journals IOT Based ECG Monitoring and Analysis System with Machine Learning

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Prajwal Khupat ◽  
Usha Barapatre ◽  
Nikita Khadatkar ◽  
Sneha Badge ◽  
Pooja Meharkar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ecg Monitoring ◽  
Analysis System

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

scholarly journals A Study on the Psychological Analysis System Using Machine Learning

2018 ◽  
Vol 7 (3.33) ◽  
pp. 128
Author(s):  
Ki Young Lee ◽  
Kyu Ho Kim ◽  
Jeong Jin Kang ◽  
Sung Jai Choi ◽  
Yong Soon Im ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Smart Phone ◽  
Principal Component ◽  
Digital Data ◽  
Expression Recognition ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Psychological Analysis ◽  
Analysis System

Real-time facial expression recognition and analysis technology is recently drawing attention in areas of computer vision, computer graphics, and HCI. Recognition of user’s emotion on the basis of video and voice is drawing particular interest. The technology may help managers of households or hospitals. In the present study, video and voice were converted into digital data through MATLAB by using PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis), KNN(K Nearest Neighbor) algorithms to analyze emotions through machine learning. The manager of the psychological analysis counseling system may understand a user’s emotion in an smart phone environment. This system of the present study may help the manager to have a smooth conversation or develop a smooth relationship with a user on the basis of the provided psychological analysis results. 

scholarly journals Object detection and used car price predicting analysis system (ucpas) using machine learning technique

2021 ◽  
Vol 5 (S2) ◽  
Author(s):  
Anu Yadav ◽  
Ela Kumar ◽  
Piyush Kumar Yadav
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Research Area ◽  
Machine Learning Technique ◽  
Car Detection ◽  
Machine Learning Model ◽  
Implementation Techniques ◽  
Learning Technique ◽  
Used Car ◽  
Analysis System

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of  car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self.

scholarly journals Automatic Evaluation of Strephenopodia Based on Plantar Pressure and Machine Learning: A Pilot Study

2021 ◽  
Author(s):  
Jinjin Nong ◽  
Zikang Zhou ◽  
Xiaoming Xian ◽  
Guowei Huang ◽  
Peiwen Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gait Analysis ◽  
Plantar Pressure ◽  
Muscle Tension ◽  
Walking Ability ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Study Results ◽  
Gait Features ◽  
Analysis System

Abstract Purpose Stroke patients often suffer from strephenopodia because of high muscle tension or muscle spasms, which seriously affect their walking ability and rehabilitation. During the treatment of strephenopodia, there are practical demands for convenient, automatic, and quantitative assessments of the angle of strephenopodia. However, existing strephenopodia detection methods, including traditional clinical gait analysis, gait video analysis and plantar pressure systems, suffer from object obstruction or require complex setups. In this paper, we proposed a novel methodology for automatically predicting the angles of strephenopodia based on a gait analysis system using machine learning methods.Methods Plantar pressure distribution data from thirty healthy participants were recorded during walking on the Zebris FDM-THM instrumented treadmill and were processed to generate 15 gait features. The right ankle angles on the coronal plane were measured by the Vicon system to provide a detailed description and explanation of strephenopodia walking. Three machine learning methods were implemented to build stochastic function mapping from gait features to strephenopodia angles.Results This study showed good reliability and precision prediction of the angle of strephenopodia [determination coefficient (R2)\(\ge\)0.80]. Gaussian process regression (GPR) exhibited the best regression performance [R2 = 0.93, mean root-mean-square error (RMSE) = 0.67].Conclusion The study results showed that this strephenopodia-detection method is not only convenient to implement but also has high accuracy and outperforms previous reports. Measurements derived from the gait analysis system are proper estimators of the angle of strephenopodia and should be considered to improve diagnosis and assessment of the stroke population.

scholarly journals Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3

2021 ◽  
Vol 251 ◽  
pp. 02009
Author(s):  
Ioan-Mihail Stan ◽  
Siarhei Padolski ◽  
Christopher Jon Lee ◽  
Keyword(s):  
Machine Learning ◽  
Data Visualization ◽  
Hadron Collider ◽  
Machine Learning Algorithms ◽  
Main Role ◽  
Scientific Experiments ◽  
Self Service ◽  
Analysis System ◽  
Learning Analysis ◽  
Computing Infrastructure

A large scientific computing infrastructure must offer versatility to host any kind of experiment that can lead to innovative ideas. The ATLAS experiment offers wide access possibilities to perform intelligent algorithms and analyze the massive amount of data produced in the Large Hadron Collider at CERN. The BigPanDA monitoring is a component of the PanDA (Production ANd Distributed Analysis) system, and its main role is to monitor the entire lifecycle of a job/task running in the ATLAS Distributed Computing infrastructure. Because many scientific experiments now rely upon Machine Learning algorithms, the BigPanDA community desires to expand the platform’s capabilities and fill the gap between Machine Learning processing and data visualization. In this regard, BigPanDA partially adopts the cloud-native paradigm and entrusts the data presentation to MLFlow services running on Openshift OKD. Thus, BigPanDA interacts with the OKD API and instructs the containers orchestrator how to locate and expose the results of the Machine Learning analysis. The proposed architecture also introduces various DevOps-specific patterns, including continuous integration for MLFlow middleware configuration and continuous deployment pipelines that implement rolling upgrades. The Machine Learning data visualization services operate on demand and run for a limited time, thus optimizing the resource consumption.

scholarly journals Data mining methods of healthy indoor climate coefficients for comfortable well-being

2018 ◽  
Vol 29 (3) ◽  
pp. 7-12
Author(s):  
Grit Behrens ◽  
Klaus Schlender ◽  
Florian Fehring
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Indoor Air ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Indoor Climate ◽  
Measurement And Analysis ◽  
Scientific Project ◽  
Mining Methods ◽  
Analysis System

Abstract This article provides information about a currently developed measurement and analysis system ‘Smart Monitoring’, which is used on scientific project in terms of healthy indoor air coefficients, as well as the processing of the collected data for machine learning algorithms. The target is to reduce CO2 emissions caused by wrong ventilation habits in building sector after renovation process in older buildings.

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