Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data (Englisch)
- Neue Suche nach: Riaboff, L.
- Neue Suche nach: Shalloo, L.
- Neue Suche nach: Smeaton, A.F.
- Neue Suche nach: Couvreur, S.
- Neue Suche nach: Madouasse, A.
- Neue Suche nach: Keane, M.T.
- Neue Suche nach: Riaboff, L.
- Neue Suche nach: Shalloo, L.
- Neue Suche nach: Smeaton, A.F.
- Neue Suche nach: Couvreur, S.
- Neue Suche nach: Madouasse, A.
- Neue Suche nach: Keane, M.T.
In:
Computers and Electronics in Agriculture
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192
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2021
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ISSN:
- Aufsatz (Zeitschrift) / Elektronische Ressource
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Titel:Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data
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Beteiligte:Riaboff, L. ( Autor:in ) / Shalloo, L. ( Autor:in ) / Smeaton, A.F. ( Autor:in ) / Couvreur, S. ( Autor:in ) / Madouasse, A. ( Autor:in ) / Keane, M.T. ( Autor:in )
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Erschienen in:
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Verlag:
- Neue Suche nach: The Authors
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Erscheinungsdatum:30.11.2021
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ISSN:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:Behaviour , Ruminant , Prediction , Accelerometer sensor , Raw data , Methodology , Processing techniques , Accx , X-axis acceleration , Accy , Y-axis acceleration , Accz , Z-axis acceleration , Ada , Adaptative Boosting , ANFIS , Adaptive Neuro Fuzzy Inference System , CNN , Convolutional Neural Network , DL , Deep Learning , DT , Decision tree , FD , Frequency Domain , FDR , False Discovery Rate , GPS , Global Positioning System , HMM , Hidden Markov Model , kNN , k-Nearest Neighbours , LDA , Linear Discriminant Analysis , LOOV , Leave-One-Out Validation , LR , Logistic Regression , LSTM , Long Short-Time Memory , MI , Motion intensity , MLP , Multilayer Perceptron , MT , Manual Thresholding , NB , Naive Bayes , OBDA , Overall Body Dynamic Acceleration , PCA , Principal Component Analysis , PNN , Probabilistic Neural Network , QDA , Quadratic Discriminant Analysis , RF , Random Forest , RS , Random Subspace , RFID , Radio-Frequency Identification , RNN , Recurrent Neural Network , RSS , Received Signal Strength , SEML , Supervised Ensemble Machine Learning , SM , Statistical Models , SML , Supervised Machine Learning , SVM , Support Vector Machine , TD , Time Domain , TFD , Time Frequency Domain , UML , Unsupervised Machine Learning , USB , Universal Serial Bus , VeDBA , Vector Dynamic Body Acceleration , WS , Window Size , XGB , eXtreme Gradient Boosting
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