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Bird voice classification is a crucial issue in wild bird protection work. However, the existing strategies of static classification are always unable to achieve the desired outcomes in a dynamic data stream context, as the standard machine learning approaches mainly focus on static learning, which is not suitable for mining dynamic data and has th...
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... Librosa (https://librosa.org/ (accessed on 20 May 2023)) to extract features and mixing the Mel spectrum and Mel frequency cepstrum coefficient (MFCC) [20] with dynamic features to enhance the effectiveness of bird song features (as shown in Figure 1). Subsequently, the Librosa library is used for the feature extraction of the Mel spectrum and MFCC. ...
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... a total of 148,021 samples were obtained. The data preprocessing process is shown in Figure 1. ...
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... a total of 148,021 samples were obtained. The data preprocessing process is shown in Figure 1. ...
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... when new data streams are input, incremental learning is performed based on the existing concept space, and the concept space is optimized. During the learning process, the data in the data stream are divided into different independent subblocks, as shown in Figure 1. Considering the impact of data block size on the parallel computing efficiency of PyC3S, it is assumed that these data block sizes are fixed in this paper. ...
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... when new data streams are input, incremental learning is performed based on the existing concept space, and the concept space is optimized. During the learning process, the data in the data stream are divided into different independent subblocks, as shown in Figure 1. Considering the impact of data block size on the parallel computing efficiency of PyC3S, it is assumed that these data block sizes are fixed in this paper. ...
Citations
... In the field of pattern classification, various classifiers have been developed to predict labels for query samples [1][2][3][4]. Among these, RBCM has recently garnered substantial attention. ...
Representation-based classification methods (RBCM) have recently garnered notable attention in the field of pattern classification. Diverging from conventional methods reliant on ℓ1 or ℓ2-norms, the nonnegative representation-based classifier (NRC) enforces a nonnegative constraint on the representation vector, thus enhancing the representation capabilities of positively correlated samples. While NRC has achieved substantial success, it falls short in fully harnessing the discriminative information associated with the training samples and neglects the locality constraint inherent in the sample relationships, thereby limiting its classification power. In response to these limitations, we introduce the locality-constraint discriminative nonnegative representation (LDNR) method. LDNR extends the NRC framework through the incorporation of a competitive representation term. Recognizing the pivotal role played by the estimated samples in the classification process, we include estimated samples that involve discriminative information in this term, establishing a robust connection between representation and classification. Additionally, we assign distinct local weights to different estimated samples, augmenting the representation capacity of homogeneous samples and, ultimately, elevating the performance of the classification model. To validate the effectiveness of LDNR, extensive comparative experiments are conducted on various pattern classification datasets. The findings demonstrate the competitiveness of our proposed method.
In clinical nursing, neonatal pain assessment is a challenging task for preventing and controlling the impact of pain on neonatal development. To reduce the adverse effects of repetitive painful treatments during hospitalization on newborns, we propose a novel method (namely pain concept-cognitive computing model, PainC3M) for evaluating facial pain in newborns. In the fusion system, we first improve the attention mechanism of vision transformer by revising the node encoding way, considering the spatial structure, edge and centrality of nodes, and then use its corresponding encoder as a feature extractor to comprehensively extract image features. Second, we introduce a concept-cognitive computing model as a classifier to evaluate the level of pain. Finally, we evaluate our PainC3M on various open pain data sets and a real clinical pain data stream, and the experimental results demonstrate that our PainC3M is very effective for dynamic classification and superior to other comparative models. It also provides a good approach for pain assessment of individuals with aphasia (or dementia).
