Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often stru...
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Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often struggle to capture the complex spatial and temporal dynamics of human movements, leading to suboptimal classification performance. To address this limitation, this study introduces a novel encoding approach using locality-constrainedlinearcoding (LLC) to enhance the discriminative power of hand-crafted features extracted from low-cost wearable sensors—an accelerometer and a gyroscope. The proposed LLC-based encoding scheme enables robust feature representation, improving the accuracy of HAR models. The encoded features are classified using a diverse set of Machine Learning (ML) and Deep Learning (DL) algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), AdaBoost, Gradient Boosting Machine (GBM), and Deep Belief Network (DBN). Extensive quantitative evaluations demonstrate that LLC significantly outperforms conventional feature encoding techniques, leading to improved classification accuracy. Among the tested models, DBN achieves a state-of-the-art accuracy of 99%, highlighting its superiority for HAR tasks. The contributions of this research are threefold: (1) it establishes the necessity of an advanced encoding scheme (LLC) for feature enhancement in HAR, (2) it provides a rigorous comparative analysis of multiple ML and DL classifiers, and (3) it introduces a scalable and cost-effective HAR framework suitable for real-world applications. Performance is comprehensively assessed using robust evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The findings of this study offer new insights into feature encoding for HAR, setting a foundation for future advancements in sensor-based act
Visual target tracking has long been a challenging problem because of the variable appearance of the target with changing spatiotemporal factors. Therefore, it is important to design an effective and efficient appeara...
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Visual target tracking has long been a challenging problem because of the variable appearance of the target with changing spatiotemporal factors. Therefore, it is important to design an effective and efficient appearance model for tracking tasks. This study proposes a tracking algorithm based on locality-constrainedlinearcoding (LLC) under a particle filtering framework. A local feature descriptor is presented that can evenly represent the local information of each patch in the tracking region. LLC uses the locality constraints to project each local feature descriptor into its local-coordinate system. Compared with sparse coding, LLC can be performed very quickly for appearance modelling because it has an analytical solution derived by a three-step matrix calculation, and the computational complexity of the proposed tracking algorithm is o(eta x m x n). Both quantitative and qualitative experimental results demonstrate that the authors' proposed algorithm performs favourably against the 10 state-of-the-art trackers on 12 challenging test sequences. However, related experimental results show that the performance of their tracker is not effective enough for small tracking targets owing to a lack of sufficient local region information.
In recent years, the classification of breast cancer has been the topic of interest in the field of Healthcare informatics, because it is the second main cause of cancer-related deaths in women. Breast cancer can be i...
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In recent years, the classification of breast cancer has been the topic of interest in the field of Healthcare informatics, because it is the second main cause of cancer-related deaths in women. Breast cancer can be identified using a biopsy where tissue is removed and studied under microscope. The diagnosis is based on the qualification of the histopathologist, who will look for abnormal cells. However, if the histopathologist is not well-trained, this may lead to wrong diagnosis. With the recent advances in image processing and machine learning, there is an interest in attempting to develop a reliable pattern recognition based systems to improve the quality of diagnosis. In this paper, we compare two machine learning approaches for the automatic classification of breast cancer histology images into benign and malignant and into benign and malignant sub-classes. The first approach is based on the extraction of a set of handcrafted features encoded by two coding models (bag of words and locality constrained linear coding) and trained by support vector machines, while the second approach is based on the design of convolutional neural networks. We have also experimentally tested dataset augmentation techniques to enhance the accuracy of the convolutional neural network as well as "handcrafted features + convolutional neural network" and "convolutional neural network features + classifier" configurations. The results show convolutional neural networks outperformed the handcrafted feature based classifier, where we achieved accuracy between 96.15% and 98.33% for the binary classification and 83.31% and 88.23% for the multi-class classification.
A unified fault modelling approach for machines with varying operating speeds is of interest in automating production facilities. Building a unified fault model is challenging due to the presence of speed-specific att...
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A unified fault modelling approach for machines with varying operating speeds is of interest in automating production facilities. Building a unified fault model is challenging due to the presence of speed-specific attributes in the features derived. In this work, it is shown how feature selection, mapping, normalisation and fusion of heterogeneous systems can help enhance the performance of speed-independent (SI) machine-fault diagnosis systems. Statistical features, obtained after applying feature selection, are used as input to a support vector machine (SVM) back-end classifier as the baseline system. Entropy-based feature selection algorithm is proposed to improve the performance of the fault diagnosis system. Furthermore, to make the fault diagnosis system independent of speed, locality constrained linear coding (LLC), Fisher vector encoding (FVE) and mean and variance normalisation (MVN) are used. The LLC-MVN system and FVE-MVN system map the input features in terms of SI basis vectors to make the features robust to speed-specific variations. Finally, the decision scores of the time-domain LLC-MVN-SVM, frequency-domain LLC-MVN-SVM systems and variational mode decomposition-based FVE-MVN-SVM system were fused with appropriate weighting factors. The detection error trade-off curve is also used as a performance measure for intelligent fault diagnosis systems.
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