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  • 1
    Online Resource
    Online Resource
    Machine Learning in Aquaculture : Hunger Classification of Lates calcarifer (2020)
    Singapore :Springer Nature Singapore :
    Details
    Keywords: Animal culture. ; Computational intelligence. ; Computer simulation. ; Signal processing. ; Animal Science. ; Computational Intelligence. ; Computer Modelling. ; Signal, Speech and Image Processing .
    Description / Table of Contents: 1 Introduction -- 2 Monitoring and feeding integration of demand feeder systems -- 3 Image processing features extraction on fish behaviour -- 4 Time-series identification of fish feeding behaviour.
    Abstract: This book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour.
    Type of Medium: Online Resource
    Pages: VI, 60 p. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9789811522376
    Series Statement: SpringerBriefs in Applied Sciences and Technology,
    URL: https://doi.org/10.1007/978-981-15-2237-6
    DOI: 10.1007/978-981-15-2237-6
    DDC: 636
    Language: English
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  • 2
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    Technical and tactical performance indicators discriminating winning and losing team in elite Asian beach soccer tournament (2019)
    Public Library of Science
    Details
    Publication Date: 2019-06-27
    Electronic ISSN: 1932-6203
    Topics: Medicine , Natural Sciences in General
    Published by Public Library of Science
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  • 3
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    The classification of EEG-based winking signals: a transfer learning and random forest pipeline (2021)
    PeerJ
    Details
    Publication Date: 2021-03-31
    Description: Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.
    Electronic ISSN: 2167-8359
    Topics: Biology , Medicine
    Published by PeerJ
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  • 4
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    The classification of movement intention through machine learning models: the identification of significant time-domain EMG features (2021)
    PeerJ
    Details
    Publication Date: 2021-02-25
    Description: Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject’s intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects’ biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
    Electronic ISSN: 2376-5992
    Topics: Computer Science
    Published by PeerJ
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