Snapshot-based offloading for machine learning web app

2017 ◽  
Author(s):  
InChang Jeong ◽  
Hyuk-Jin Jeong ◽  
Soo-Mook Moon
Keyword(s):  
Machine Learning ◽  
Web App

scholarly journals Data-Driven Intelligent Application for Youtube Video Popularity Analysis using Machine Learning and Statistics

2021 ◽  
Author(s):  
Wenxi Gao ◽  
Ishmael Rico ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Web Application ◽  
Data Driven ◽  
Accuracy Evaluation ◽  
Video Content ◽  
The Public ◽  
Left Behind ◽  
The World ◽  
Web App

People now prefer to follow trends. Since the time is moving, people can only keep themselves from being left behind if they keep up with the pace of time. There are a lot of websites for people to explore the world, but websites for those who show the public something new are uncommon. This paper proposes an web application to help YouTuber with recommending trending video content because they sometimes have trouble in thinking of the video topic. Our method to solve the problem is basically in four steps: YouTube scraping, data processing, prediction by SVM and the webpage. Users input their thoughts on our web app and computer will scrap the trending page of YouTube and process the data to do prediction. We did some experiments by using different data, and got the accuracy evaluation of our method. The results show that our method is feasible so people can use it to get their own recommendation.

scholarly journals COVID 19 DISEASE PREDICTION VIA MACHINE LEARNING

2021 ◽  
Author(s):  
Dhrumil Gala
Keyword(s):  
Machine Learning ◽  
Virus Disease ◽  
Disease Outbreak ◽  
Great Accuracy ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Learning Techniques ◽  
Physical Attributes ◽  
Web App ◽  
True Colors

<p>This research focuses primarily on a new worldwide problem: the continuing corona virus disease outbreak [COVID-19]. The disease originated from China and slowly it got spread to different places in the world and started showing its true colors .Reportedly it was known to have caused from bats. While some symptoms are severe, for a big portion of the population, symptoms are minor, and as a result, people may be unaware that they are infected with COVID-19 and hence fail to visit and be diagnosed by a doctor[1].Thus this paper mainly focuses on detecting whether a person is suffering from this disease or not. I have made a web app which will take in your current physical attributes such as your body temperature and other physical attributes and the web app will predict whether you are suffering from this disease or not. I have used machine learning techniques such as linear regression to predict whether the person suffers from the disease or not. This web app has a great accuracy and it will predict the outcome with precision and thus is a very helpful app.<b></b></p>

scholarly journals THYROID DETECTION USING MACHINE LEARNING

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Chandan R ◽  
Chetan Vasan ◽  
Chethan MS ◽  
Devikarani H S
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Blood Test ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Disease Prediction ◽  
Thyroid Disorders ◽  
Nearest Neighbours ◽  
Web App

The Thyroid gland is a vascular gland and one of the most important organs of a human body. This gland secretes two hormones which help in controlling the metabolism of the body. The two types of Thyroid disorders are Hyperthyroidism and Hypothyroidism. When this disorder occurs in the body, they release certain type of hormones into the body which imbalances the body’s metabolism. Thyroid related Blood test is used to detect this disease but it is often blurred and noise will be present. Data cleansing methods were used to make the data primitive enough for the analytics to show the risk of patients getting this disease. Machine Learning plays a very deciding role in the disease prediction. Machine Learning algorithms, SVM - support vector machine, decision tree, logistic regression, KNN - K-nearest neighbours, ANNArtificial Neural Network are used to predict the patient’s risk of getting thyroid disease. Web app is created to get data from users to predict the type of disease.

scholarly journals PuBliCiTy: Python Bioimage Computing Toolkit

2021 ◽  
Author(s):  
Marcelo Cicconet
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Image Annotation ◽  
Model Development ◽  
Web Interface ◽  
Biological Images ◽  
Link Type ◽  
Machine Learning Model ◽  
Code Base ◽  
Web App

AbstractThe Python Bioimage Computing Toolkit (PuBliCiTy) is an evolving set of functions, scripts, and classes, written primarily in Python, to facilitate the analysis of biological images, of two or more dimensions, from electron or light microscopes. While the early development was guided by the goal of replacing an existing internal code-base with Python code, the effort later came to include novel tools, specially in the areas of machine learning infrastructure and model development. The toolkit is built on top of the so-called python data science stack, which includes numpy, scipy, scikit-image, scikit-learn, and pandas. It also contains some deep learning models, written in TensorFlow and PyTorch, and a web-app for image annotation, which uses Flask as the web framework. The main features of the toolkit are: (1) simplifying the interface of some routinely used functions from underlying libraries; (2) providing helpful tools for the analysis of large images; (3) providing a web interface for image annotation, which can be used remotely and on tablets with pencils; (4) providing machine learning model implementations that are easy to read, train, and deploy – written in a way that minimizes complexity for users without a computer science or software development background. The source code is released under an MIT-like license at github.com/hms-idac/PuBliCiTy. Details, tutorials, and up-to-date documentation can be found at the project’s page as well.Project pagegithub.com/hms-idac/PuBliCiTy

scholarly journals COVID 19 DISEASE PREDICTION VIA MACHINE LEARNING

2021 ◽  
Author(s):  
Dhrumil Gala
Keyword(s):  
Machine Learning ◽  
Virus Disease ◽  
Disease Outbreak ◽  
Great Accuracy ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Learning Techniques ◽  
Physical Attributes ◽  
Web App ◽  
True Colors

<p>This research focuses primarily on a new worldwide problem: the continuing corona virus disease outbreak [COVID-19]. The disease originated from China and slowly it got spread to different places in the world and started showing its true colors .Reportedly it was known to have caused from bats. While some symptoms are severe, for a big portion of the population, symptoms are minor, and as a result, people may be unaware that they are infected with COVID-19 and hence fail to visit and be diagnosed by a doctor[1].Thus this paper mainly focuses on detecting whether a person is suffering from this disease or not. I have made a web app which will take in your current physical attributes such as your body temperature and other physical attributes and the web app will predict whether you are suffering from this disease or not. I have used machine learning techniques such as linear regression to predict whether the person suffers from the disease or not. This web app has a great accuracy and it will predict the outcome with precision and thus is a very helpful app.<b></b></p>

scholarly journals Tourism web app with Aspect Based Sentiment Classification Framework for Tourist Review

2021 ◽  
Vol 9 (11) ◽  
pp. 1452-1455
Author(s):  
Prof S. S. Khartad
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
High Accuracy ◽  
Sentiment Classification ◽  
Sources Of Information ◽  
Tourist Destinations ◽  
Classification Framework ◽  
Classification Tasks ◽  
Web App ◽  
Real World Datasets

Abstract: According to studies, current tourism recommendation systems make false recommendations that do not live up to tourist expectations. Among The majority of these systems are inefficient, which is one of the main causes of the problem. A recommendation system that incorporates user feedback element.Tourist reviews are sources of information for travellers interested in learning more about tourist destinations. Regrettably, some reviews are irrelevant, resulting in noisy statistics. Sentiment categorization algorithms based on aspects have showed potential in reducing noise. We proposed a framework for sentiment classification based on aspects that can not only detect aspects quickly but also execute classification tasks with high accuracy. The framework has been deployed to assists travellers in finding the best restaurant or lodging in a city, and its performance has been evaluated with outstanding results using real-world datasets. Keywords: Pre-processing, Classifier algorithm, Feature extraction NLP, Tourism Strategy,Machine Learning, Tourist Reviews, Aspect Based Sentiment Analysis etc.

scholarly journals Disease and Adverse Drug Reaction Prediction using Machine Learning

2021 ◽  
Vol 69 (1) ◽  
Author(s):  
Reshma Mathai ◽  
Ardra K John ◽  
Anima M M ◽  
Athulya James ◽  
Lakshmi K S
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Adverse Drug Reactions ◽  
Disease Risk ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Prediction Risk ◽  
Android App ◽  
Web App ◽  
Project Location

The aim of the project is to use machine learning techniques for disease prediction, risk prediction and prediction of adverse drug reactions. The project is divided into two modules, an android app and a web app. The android app is to predict possible diseases based on the symptoms the person is showing. Along with that the reviews of common drugs from online healthcare forums such as medications.com are extracted and tf-idf is used to find out the possible adverse drug reactions the drugs may have. The web app does disease risk prediction based on phenotypic details and lab reports. As an addition to the project, location based medical help and health tips are also implemented.

scholarly journals Machine learning–augmented objective functional testing in the degenerative spine: quantifying impairment using patient-specific five-repetition sit-to-stand assessment

2021 ◽  
Vol 51 (5) ◽  
pp. E8
Author(s):  
Victor E. Staartjes ◽  
Anita M. Klukowska ◽  
Moira Vieli ◽  
Christiaan H. B. van Niftrik ◽  
Martin N. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Healthy Volunteers ◽  
Functional Impairment ◽  
Normative Data ◽  
Patient Specific ◽  
Depression Symptoms ◽  
Testing Strategy ◽  
Sit To Stand ◽  
Web App ◽  
Objective Functional

OBJECTIVE What is considered “abnormal” in clinical testing is typically defined by simple thresholds derived from normative data. For instance, when testing using the five-repetition sit-to-stand (5R-STS) test, the upper limit of normal (ULN) from a population of spine-healthy volunteers (10.5 seconds) is used to identify objective functional impairment (OFI), but this fails to consider different properties of individuals (e.g., taller and shorter, older and younger). Therefore, the authors developed a personalized testing strategy to quantify patient-specific OFI using machine learning. METHODS Patients with disc herniation, spinal stenosis, spondylolisthesis, or discogenic chronic low-back pain and a population of spine-healthy volunteers, from two prospective studies, were included. A machine learning model was trained on normative data to predict personalized “expected” test times and their confidence intervals and ULNs (99th percentiles) based on simple demographics. OFI was defined as a test time greater than the personalized ULN. OFI was categorized into types 1 to 3 based on a clustering algorithm. A web app was developed to deploy the model clinically. RESULTS Overall, 288 patients and 129 spine-healthy individuals were included. The model predicted “expected” test times with a mean absolute error of 1.18 (95% CI 1.13–1.21) seconds and R2 of 0.37 (95% CI 0.34–0.41). Based on the implemented personalized testing strategy, 191 patients (66.3%) exhibited OFI. Type 1, 2, and 3 impairments were seen in 64 (33.5%), 91 (47.6%), and 36 (18.8%) patients, respectively. Increasing detected levels of OFI were associated with statistically significant increases in subjective functional impairment, extreme anxiety and depression symptoms, being bedridden, extreme pain or discomfort, inability to carry out activities of daily living, and a limited ability to work. CONCLUSIONS In the era of “precision medicine,” simple population-based thresholds may eventually not be adequate to monitor quality and safety in neurosurgery. Individualized assessment integrating machine learning techniques provides more detailed and objective clinical assessment. The personalized testing strategy demonstrated concurrent validity with quality-of-life measures, and the freely accessible web app (https://neurosurgery.shinyapps.io/5RSTS/) enabled clinical application.

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