<b>AN INTRODUCTION TO SUPPORT VECTOR MACHINES AND OTHER KERNEL-BASED LEARNING METHODS</b> by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii+189 pp., ISBN 0-521-78019-5 (Hbk, £27.50).

Andrew, Alex M.

doi:10.1017/s0263574700232827

Cited by 54 publications

(21 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We also used Spearman's rank‐order correlation ( ρ ) to measure the monotonic relationship between each of the features and

L_{p}

. For each ray, those features with high ( ρ > 0.6) and statistically significant ( P < 0.001) correlation coefficients were selected for training a support vector machine (SVM) classifier to classify between the prostate and nonprostate patches. Rays with no selected feature were excluded for SVM training;

M \leq N_{1}

is the number of selected rays.…”

Section: Methodsmentioning

confidence: 99%

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling

et al. 2018

View full text Add to dashboard Cite

The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation.

show abstract

“…We also used Spearman's rank‐order correlation ( ρ ) to measure the monotonic relationship between each of the features and

L_{p}

M \leq N_{1}

is the number of selected rays.…”

Section: Methodsmentioning

confidence: 99%

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The human voice detection algorithm was based on Support Vector Machine (SVM) from MatLab and consisted of a training phase and a classification phase. The Hard-margin SVM [ 28 ] classifies data identifying the best hyperplane that divides all data points into two groups [ 29 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a Sensor System for Detecting Humans Trapped under Rubble: A Pilot Study

Zhang

Sessa

Kasai

et al. 2018

Sensors

View full text Add to dashboard Cite

Rapid localization of injured survivors by rescue teams to prevent death is a major issue. In this paper, a sensor system for human rescue including three different types of sensors, a CO2 sensor, a thermal camera, and a microphone, is proposed. The performance of this system in detecting living victims under the rubble has been tested in a high-fidelity simulated disaster area. Results show that the CO2 sensor is useful to effectively reduce the possible concerned area, while the thermal camera can confirm the correct position of the victim. Moreover, it is believed that the use of microphones in connection with other sensors would be of great benefit for the detection of casualties. In this work, an algorithm to recognize voices or suspected human noise under rubble has also been developed and tested.

show abstract

“…2. Support vector machines (SVMs) is a supervised machine learning algorithm used for both classification and regression problems [28,29]. Various applications have commonly used SVM because of its high performance in classification problems [30,31].…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Predicting Diabetes from Health-based Streaming Data using Social Media, Machine Learning and Stream Processing Technologies

Hassan¹,

Shaheen²

2020

International Journal of Engineering Research and Technology

View full text Add to dashboard Cite

Diabetes disease is one of the main healthcare challenges in all the world. Undiagnosed diabetes can increase the danger of cardiac stroke, diabetic nephropathy, and other disorders. Early detection of diabetes is necessary to take care of a healthy life. Nowadays, social media is a new dimension to deal with health care by exploiting the real-time shared patients' data to early detect diabetes disease. Furthermore, technologies typically associated with digitalization add value in healthcare, including artificial intelligence, data analytic technologies, and stream processing technologies. Therefore, in this research, we propose a real-time system for predicting diabetes disease from health-based social streaming data to indicate the current status for patient health. The proposed system aims to find the most accurate machine learning model which has the highest accuracy of diabetes prediction. We have used three types of feature selection techniques to select the most relevant features from the used dataset i.e., Recursive Feature Elimination, Univariate feature selection, and Feature Importance. Also, we have evaluated and compared four machine learning models with selected and full features i.e, , Random Forest, Support Vector Machine, Decision Tree, and Logistic Regression Classifier. The experimental results have determined that the random forest model has achieved the greatest accuracy among other models at 84.11%. For online prediction through social media, we have performed our proposed system to handle streaming Twitter data about patients' health. In doing so, Kafka and Spark streaming are integrated into the backend of the proposed system. Then, the random forest classifier is used to predict the patient's current health status in real-time.

show abstract

AN INTRODUCTION TO SUPPORT VECTOR MACHINES AND OTHER KERNEL-BASED LEARNING METHODS by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii+189 pp., ISBN 0-521-78019-5 (Hbk, £27.50).

Cited by 54 publications

References 1 publication

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling

Evaluation of a Sensor System for Detecting Humans Trapped under Rubble: A Pilot Study

Predicting Diabetes from Health-based Streaming Data using Social Media, Machine Learning and Stream Processing Technologies

Contact Info

Product

Resources

About