This study introduces class-incremental learning (CIL). This framework improves multi-class data support vector machines (SVMs). CIL is incremental feature selection and training. Both steps update SVM classifiers as new classes are added to a system. CIL learns one binary sub-classifier and reuses classifier models for subsequent classes. Another feature selection stage follows. Testing applies the classifier to subspacerelevant vector projections. The CIL system is compatible with any binary classification approaches that are used for text analysis. According to the findings of our research, the CIL-based SVM beat the most popular batch SVM learning methods, such as divide-by-2, 1-against-1, and 1-against-rest, and trained faster. These learning methods are used to train SVMs.
As a key research subject in the fields of health and human-machine interaction, human activity recognition (HAR) has emerged as a major research focus over the past few decades. Many artificial intelligence-based models are being created for activity recognition. However, these algorithms are failing to extract spatial and temporal properties, resulting in poor performance on real-world long-term HAR. A drawback in the literature is that there are only a small number of publicly available datasets for physical activity recognition that contain a small number of activities, owing to the scarcity of publicly available datasets. In this paper, a hybrid model for activity recognition that incorporates both convolutional neural networks (CNN) are developed. The CNN network is used for extracting spatial characteristics, while the LSTM network is used for learning time-related information. Using a variety of traditional and deep machine learning models, an extensive ablation investigation is carried out in order to find the best possible HAR solution. The CNN approach can achieve a precision of 90.89%, indicating that the model is suitable for HAR applications.
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