The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform

Ifeoma Nwogu - One of the best experts on this subject based on the ideXlab platform.

  • BTAS - Novel Biometrics: Analysis of an Unattended Health Monitoring System
    2018 IEEE 9th International Conference on Biometrics Theory Applications and Systems (BTAS), 2018
    Co-Authors: Nicholas J Conn, David A. Borkholder, Ifeoma Nwogu
    Abstract:

    In-home monitoring technologies have the potential to transform the healthcare system. With the increasing number and variety of connected devices that can be accessed by multiple users, the need for seamless authentication and identification mechanisms is greater now than ever. Seamless, non-interruptive mechanisms are required to personalize these devices and non-intrusive biometric technologies are gaining popularity as an answer to this automated personalization challenge. In this work, we evaluate the biometric efficacies of a fully integrated toilet (FIT) seat, designed for monitoring a subjects cardiac health parameters such as electrocardiogram (ECG), ballistocardiogram (BCG) and weight measures, all in the home without any change in daily habits. We assess the system on data obtained from 22 healthy subjects with measurements taken over an 8-week period, in order to obtain the optimal combination of features, classifiers and Enrollment Record size. We found the multi-class SVM classifier along with all features extracted from the raw measurements to be the best performing combination. This combination produced an area under curve (AUC) value of 0.86. When tested for its person identification performance, we obtained an average accuracy of 75%. We have therefore demonstrated that the biometric capacities of the FIT seat shows a strong potential for deployment in real-life, multi-user, in-home settings.

Nicholas J Conn - One of the best experts on this subject based on the ideXlab platform.

  • BTAS - Novel Biometrics: Analysis of an Unattended Health Monitoring System
    2018 IEEE 9th International Conference on Biometrics Theory Applications and Systems (BTAS), 2018
    Co-Authors: Nicholas J Conn, David A. Borkholder, Ifeoma Nwogu
    Abstract:

    In-home monitoring technologies have the potential to transform the healthcare system. With the increasing number and variety of connected devices that can be accessed by multiple users, the need for seamless authentication and identification mechanisms is greater now than ever. Seamless, non-interruptive mechanisms are required to personalize these devices and non-intrusive biometric technologies are gaining popularity as an answer to this automated personalization challenge. In this work, we evaluate the biometric efficacies of a fully integrated toilet (FIT) seat, designed for monitoring a subjects cardiac health parameters such as electrocardiogram (ECG), ballistocardiogram (BCG) and weight measures, all in the home without any change in daily habits. We assess the system on data obtained from 22 healthy subjects with measurements taken over an 8-week period, in order to obtain the optimal combination of features, classifiers and Enrollment Record size. We found the multi-class SVM classifier along with all features extracted from the raw measurements to be the best performing combination. This combination produced an area under curve (AUC) value of 0.86. When tested for its person identification performance, we obtained an average accuracy of 75%. We have therefore demonstrated that the biometric capacities of the FIT seat shows a strong potential for deployment in real-life, multi-user, in-home settings.

Jason Hollowell - One of the best experts on this subject based on the ideXlab platform.

David A. Borkholder - One of the best experts on this subject based on the ideXlab platform.

  • BTAS - Novel Biometrics: Analysis of an Unattended Health Monitoring System
    2018 IEEE 9th International Conference on Biometrics Theory Applications and Systems (BTAS), 2018
    Co-Authors: Nicholas J Conn, David A. Borkholder, Ifeoma Nwogu
    Abstract:

    In-home monitoring technologies have the potential to transform the healthcare system. With the increasing number and variety of connected devices that can be accessed by multiple users, the need for seamless authentication and identification mechanisms is greater now than ever. Seamless, non-interruptive mechanisms are required to personalize these devices and non-intrusive biometric technologies are gaining popularity as an answer to this automated personalization challenge. In this work, we evaluate the biometric efficacies of a fully integrated toilet (FIT) seat, designed for monitoring a subjects cardiac health parameters such as electrocardiogram (ECG), ballistocardiogram (BCG) and weight measures, all in the home without any change in daily habits. We assess the system on data obtained from 22 healthy subjects with measurements taken over an 8-week period, in order to obtain the optimal combination of features, classifiers and Enrollment Record size. We found the multi-class SVM classifier along with all features extracted from the raw measurements to be the best performing combination. This combination produced an area under curve (AUC) value of 0.86. When tested for its person identification performance, we obtained an average accuracy of 75%. We have therefore demonstrated that the biometric capacities of the FIT seat shows a strong potential for deployment in real-life, multi-user, in-home settings.

Tossapon Boongoen - One of the best experts on this subject based on the ideXlab platform.

  • Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings
    International Journal of Machine Learning and Cybernetics, 2017
    Co-Authors: Natthakan Iam-on, Tossapon Boongoen
    Abstract:

    Increasing student retention has been a common goal of many academic institutions, especially in the university level. The negative effects of student attrition are evident to students, parents, university and the society as a whole. The first-year students are at the greatest risk of dropping out or not completing their degree on time. With this insight, a number of data mining methods have been developed for early detection of students at risk of dropout, hence the immediate application of assistive measure. As compared to western countries, this subject has attracted only a few studies in Thai university, with educational data mining being limited to the use of conventional classification models. This paper presents the most recent investigation of student dropout at Mae Fah Luang University, Thailand, and the novel reuse of link-based cluster ensemble as a data transformation framework for more accurate prediction. The empirical study on mixed-type data collection related to students’ demographic detail, academic performance and Enrollment Record, suggests that the proposed approach is usually more effective than several benchmark transformation techniques, across different classifiers.