Lasagna

Liu, Cihang; Zhang, Lan; Liu, Zongqian; Liu, Kebin; Li, Xiangyang; Liu, Yunhao

doi:10.1145/2973750.2973752

Cited by 50 publications

(2 citation statements)

References 49 publications

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“…The rationale behind selecting this hybrid model is compelling: CNN excels at capturing spatial relationships within data, while LSTM is adept at modeling temporal dependencies. This combination allows us to leverage the strengths of both architectures [20]. One notable advantage of the hybrid model is that CNN accelerates the feature extraction process, enhancing training efficiency.…”

Section: ) Hybrid Deep Learning Model (Cnn-lstm)mentioning

confidence: 99%

A Comparison of Sampling Methods for Dealing with Imbalanced Wearable Sensor Data in Human Activity Recognition using Deep Learning

Ghazi,

Aknin

2023

IJACSA

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Human Activity Recognition (HAR) holds significant implications across diverse domains, including healthcare, sports analytics, and human-computer interaction. Deep learning models demonstrate great potential in HAR, but performance is often hindered by imbalanced datasets. This study investigates the impact of class imbalance on deep learning models in HAR and conducts a comprehensive comparative analysis of various sampling techniques to mitigate this issue. The experimentation involves the PAMAP2 dataset, encompassing data collected from wearable sensors. The research includes four primary experiments. Initially, a performance baseline is established by training four deep-learning models on the imbalanced dataset. Subsequently, Synthetic Minority Oversampling Technique (SMOTE), random under-sampling, and a hybrid sampling approach are employed to rebalance the dataset. In each experiment, Bayesian optimization is employed for hyperparameter tuning, optimizing model performance. The findings underscore the paramount importance of dataset balance, resulting in substantial improvements across critical performance metrics such as accuracy, F1 score, precision, and recall. Notably, the hybrid sampling technique, combining SMOTE and Random Undersampling, emerges as the most effective method, surpassing other approaches. This research contributes significantly to advancing the field of HAR, highlighting the necessity of addressing class imbalance in deep learning models. Furthermore, the results offer practical insights for the development of HAR systems, enhancing accuracy and reliability in real-world applications. Future works will explore alternative public datasets, more complex deep learning models, and diverse sampling techniques to further elevate the capabilities of HAR systems.

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Section: ) Hybrid Deep Learning Model (Cnn-lstm)mentioning

confidence: 99%

A Comparison of Sampling Methods for Dealing with Imbalanced Wearable Sensor Data in Human Activity Recognition using Deep Learning

Ghazi,

Aknin

2023

IJACSA

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“…Existing handwriting monitoring solutions can be divided into two categories: wearable-based devices [1][2][3][4][5] and wireless-based devices [6][7][8][9].…”

Section: Related Workmentioning

confidence: 99%

RF Sign: Signature Anticounterfeiting Real-Time Monitoring System Based on Single Tag

Zhu¹,

Wei²,

Lu³

et al. 2023

Wireless Communications and Mobile Computing

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Signatures are one of the most important means to ensure the authenticity of documents and are commonly used in life and work. In identifying imitation handwriting, it is easy to make mistakes that cannot correctly identify and evaluate different writing characteristics. In this paper, from the perspective of dynamic handwriting detection, we propose RF sign, a signature anticounterfeiting real-time monitoring model, which achieves passive recognition of signature behavior using only a single antenna with a single tag. The RF sign identifies different users by extracting fine-grained reflection features from the original RF signal. We introduced a dynamic time regularization and neural network technique for similarity calculation and signature recognition matching to achieve template matching and classification. We compiled a real-time signature handwriting detection system. The system effectively identifies the person’s signature by checking real-time spatial and temporal information. Comprehensive experiments show that the recognition accuracy of my signature can reach over 93% and is robust to input location, environmental changes, and user diversity.

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