GAVIN

Annotation is an effective reading strategy people often undertake while interacting with digital text. It involves highlighting pieces of text and making notes about them. Annotating while reading in a desktop environment is considered trivial but, in a mobile setting where people read while hand-holding devices, the task of highlighting and typing notes on a mobile display is challenging. In this article, we introduce GAVIN, a gaze-assisted voice note-taking application, which enables readers to seamlessly take voice notes on digital documents by implicitly anchoring them to text passages. We first conducted a contextual enquiry focusing on participants’ note-taking practices on digital documents. Using these findings, we propose a method which leverages eye-tracking and machine learning techniques to annotate voice notes with reference text passages. To evaluate our approach, we recruited 32 participants performing voice note-taking. Following, we trained a classifier on the data collected to predict text passage where participants made voice notes. Lastly, we employed the classifier to built GAVIN and conducted a user study to demonstrate the feasibility of the system. This research demonstrates the feasibility of using gaze as a resource for implicit anchoring of voice notes, enabling the design of systems that allow users to record voice notes with minimal effort and high accuracy.

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Sentiment Analysis for Health Care

Big Data ◽

10.4018/978-1-4666-9840-6.ch031 ◽

2016 ◽

pp. 676-689 ◽

Cited By ~ 4

Author(s):

Muhammad Taimoor Khan ◽

Shehzad Khalid

Keyword(s):

Machine Learning ◽

Health Care ◽

Sentiment Analysis ◽

High Accuracy ◽

Machine Learning Techniques ◽

Commercial Products ◽

Learning Techniques

Sentiment analysis for health care deals with the diagnosis of health care related problems identified by the patients themselves. It takes the patients opinions into perspective to make policies and modifications that could directly address their problems. Sentiment analysis is used with commercial products to great effect and has outgrown to other application areas. Aspect based analysis of health care, not only recommend the services and treatments but also present their strong features for which they are preferred. Machine learning techniques are used to analyze millions of review documents and conclude them towards an efficient and accurate decision. The supervised techniques have high accuracy but are not extendable to unknown domains while unsupervised techniques have low accuracy. More work is targeted to improve the accuracy of the unsupervised techniques as they are more practical in this time of information flooding.

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Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

Advances in Civil Engineering ◽

10.1155/2018/5481705 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Palika Chopra ◽

Rajendra Kumar Sharma ◽

Maneek Kumar ◽

Tanuj Chopra

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

High Accuracy ◽

Machine Learning Techniques ◽

Coefficient Of Determination ◽

Software Environment ◽

R Software ◽

Learning Techniques ◽

Compressive Strength Of Concrete ◽

Indispensable Tool

A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R2) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

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A Study of Machine Learning Algorithms on Email Spam Classification

10.29007/qshd ◽

2020 ◽

Author(s):

N Sutta ◽

Z Liu ◽

X Zhang

Keyword(s):

Machine Learning ◽

High Accuracy ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Spam Detection ◽

Classification Models ◽

Detection Techniques ◽

Learning Techniques ◽

Comprehensive Comparison ◽

Email Spam

Despite the fact that different techniques have been developed to filter spam, due to the spammer’s rapid adoption of new spam detection techniques, we are still overwhelmed with spam emails. Currently, machine learning techniques are the most effective ways to classify and filter spam emails. In this paper, a comprehensive comparison and analysis of the performance of various classification models on the 2007 TREC Public Spam Corpus are exhibited in various cases of without or with N- Grams as well as using separate or combined datasets. It is shown that the inclusion of the N-Grams in the pre-processing phase provides high accuracy results for classification models in most of the cases, and the models using the split approach with combined datasets give better results than models using the separate dataset.

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Sentiment Analysis for Health Care

International Journal of Privacy and Health Information Management ◽

10.4018/ijphim.2015070105 ◽

2015 ◽

Vol 3 (2) ◽

pp. 78-91 ◽

Cited By ~ 7

Author(s):

Muhammad Taimoor Khan ◽

Shehzad Khalid

Keyword(s):

Machine Learning ◽

Health Care ◽

Sentiment Analysis ◽

High Accuracy ◽

Machine Learning Techniques ◽

Commercial Products ◽

Learning Techniques

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Crop Yield Prediction Using Machine Learning Techniques

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset20736 ◽

2020 ◽

pp. 312-315

Author(s):

Ashwini I. Patil ◽

Ramesh A. Medar ◽

Vinod Desai

Keyword(s):

Machine Learning ◽

Crop Yield ◽

Model Performance ◽

High Accuracy ◽

Machine Learning Techniques ◽

Yield Prediction ◽

The People ◽

Learning Techniques ◽

High Production ◽

Day By Day

Today Indian economy depends upon agriculture. More than 70% of the people in India have taken it as a main occupation, day by day for a particular crop; the formers are not getting proper yield as well as profit due to environmental conditions like soil quality, weather, heavy rainfall, drought, seed damages, fertilizers, pesticides. The farmers not able to produce high production, so taking the historical agricultural data records we can predict the crop yield using machine learning techniques like Linear regression, comparative analysis are done with decision tree, KNN algorithms, using these to achieve the high accuracy and model performance is computed.

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FAKE NEWS DETECTION USING VARIOUS MACHINE LEARNING TECHNIQUES

EPRA International Journal of Research & Development (IJRD) ◽

10.36713/epra6715 ◽

2021 ◽

pp. 134-137

Author(s):

Akanksha Akulwar ◽

Anish Khobragade

Keyword(s):

Machine Learning ◽

High Accuracy ◽

Machine Learning Techniques ◽

Support Vector ◽

Fake News ◽

Data Set ◽

Online Platforms ◽

Learning Techniques ◽

Original Classification ◽

Better Than

Online platforms are being used for outspreading malicious talk, which creates an impact on the minds of millions. Many distinct approaches have been brought forward to detect this fake news, but very few have been carried out in the actual world. We address this problem of estimating the rumor authentication in a real-world in less time with significantly high accuracy. We design and implement an approach addressing the above issue. We accessed whether the news is fake or not using various Machine learning techniques. We evaluate this algorithm on a set of data set scrapped from random online sites. The result shows that the performance of this improved algorithm is better than the original classification method. And finally, we consider various sizes of data to view and compare the accuracy. KEYWORDS—Support Vector Machine, Decision Tree, Fake Content, Naive Bayes, Machine Learning Models

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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