Robust least squares twin support vector machine for human activity recognition

Khemchandani, Reshma; Sharma, Sweta

doi:10.1016/j.asoc.2016.05.025

Cited by 68 publications

(23 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The support vector machine (SVM) is an algorithm that uses a non-linear function to transform the original data into a high dimension. In [11], multi-class activity classification problem is addressed and a hierarchical approach based on a robust least squares twin SVM algorithm is proposed. This algorithm handles the heteroscedastic noise and outliers present in activity recognition framework.…”

Section: Related Workmentioning

confidence: 99%

Contextual data classification for a ubiquitous intelligent environment

et al. 2020

View full text Add to dashboard Cite

Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The K-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample's membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.

show abstract

Section: Related Workmentioning

confidence: 99%

Contextual data classification for a ubiquitous intelligent environment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Many researchers are concentrating their efforts on solving different challenges in health care [11,12], specifically through human activity recognition [13,14]. This has opened the door to the detection of abnormal events in an automatic manner.…”

Section: Related Workmentioning

confidence: 99%

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

et al. 2019

View full text Add to dashboard Cite

Cardiovascular diseases are the leading cause of death worldwide. Therefore, getting help in time makes the difference between life and death. In many cases, help is not obtained in time when a person is alone and suffers a heart attack. This is mainly due to the fact that pain prevents him/her from asking for help. This article presents a novel proposal to identify people with an apparent heart attack in colour images by detecting characteristic postures of heart attack. The method of identifying infarcts makes use of convolutional neural networks. These have been trained with a specially prepared set of images that contain people simulating a heart attack. The promising results in the classification of infarcts show 91.75% accuracy and 92.85% sensitivity.

show abstract

“…Zhao, Zhang, and Zhou () use a powerful tool to the model of nonlinear systems. Reshma Khemchandani and Sweta Sharma () propose a robust least squares support vector machine to solve human activity recognition. Dalian, Yong, and Yingjie () propose a novel sparse least squares SVM, named the ramp loss least squares support vector machine, for binary classification.…”

Section: Introductionmentioning

confidence: 99%

Passenger travel behaviour on Chinese high‐speed railways using machine learning based on revealed‐preference data

et al. 2019

View full text Add to dashboard Cite

Existing studies on the selection of railway travel modes almost always compare high‐speed trains and regular‐speed trains. In this study, three high‐speed trains (Revival, Harmony, and Electric multiple unites (EMU)) on the Beijing–Shanghai line, and the first‐ and second‐class seats on these three trains are selected as research objects to analyse the travel selection behaviour of Chinese high‐speed train passengers. Three methods are selected to study this behaviour: the Support Vector Machine, Nested Logit model, and Multiple Logit model. The results of these three models are calibrated using LIBSVM software and STATA software and show that age, funding source, income level, and purpose of travel are major factors that affect Chinese railway passengers' choices on high‐speed railways. Finally, it is shown that the Support Vector Machine is the most accurate of these three methods, followed by the Nested Logit model. The results of this study can complement existing research on the travel selection behaviour of Chinese railway passengers and has important implications for enabling China's high‐speed rail operators to adjust their passenger transportation products.

show abstract

Robust least squares twin support vector machine for human activity recognition

Cited by 68 publications

References 21 publications

Contextual data classification for a ubiquitous intelligent environment

Contextual data classification for a ubiquitous intelligent environment

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

Passenger travel behaviour on Chinese high‐speed railways using machine learning based on revealed‐preference data

Contact Info

Product

Resources

About