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(searched for: doi:10.1109/icis.2016.7550922)
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Pandiyan P, Rajasekaran T, Vishnu Kumar K, Sivaramakrishnan R, Thigarajan T
Published: 30 December 2020
Abstract:
This paper presents classification of fish species using support vector machine (SVM) algorithm with four kernel functions such as linear, polynomial, sigmoid and radial basis functions. The datasets for performing this research is obtained from Fish-Pak website which has required number of images for classifying the two different fish species namely Catla and Rohu with three fish features like head, body and scale data. The number of images for Rohu fish species is not equal to the Catla type fish species therefore image augmentation technique is used to balance the number of images. The simulation results reveal that SVM with radial basis function-based kernel provides the accuracy of 78 %.
Mixia Wang, Yilin Song, Song Zhang, Shengwei Xu, Yu Zhang, Guihua Xiao, Ziyue Li, Fei Gao, Feng Yue, Ping Zhuang, et al.
Journal of Ambient Intelligence and Humanized Computing pp 1-8; https://doi.org/10.1007/s12652-019-01576-9

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, Brooke N. Klatt, Susan L. Whitney, Kathleen H. Sienko,
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Volume 27, pp 179-186; https://doi.org/10.1109/tnsre.2019.2891000

Abstract:
Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). Here, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments outside of the clinic. We recruited sixteen participants to perform standing balance exercises. For each exercise, we recorded trunk sway data and had a PT rate balance performance on a scale of 1 to 5. The rating scale was adapted from the Functional Independence Measure. From the trunk sway data, we extracted a 61-dimensional feature vector representing performance of each exercise. Given these labeled data, we trained a multi-class support vector machine (SVM) to map trunk sway features to PT ratings. Evaluated in a leave-one-participant-out scheme, the model achieved a classification accuracy of 82%. Compared to participant self-assessment ratings, the SVM outputs were significantly closer to PT ratings. The results of this pilot study suggest that in the absence of PTs, ML techniques can provide accurate assessments during standing balance exercises. Such automated assessments could reduce PT consultation time and increase user compliance outside of the clinic.
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