Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

He, Ping; Jia, Pengfei; Qiao, Siqi; Duan, Shukai

doi:10.3390/s17102279

Cited by 21 publications

(14 citation statements)

References 28 publications

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“…Compared to dimension r duction with PCA, autoencoder can preserve more non-linear relationships in the resu ing feature space. A recent study [61] also showed that an autoencoder network built fro unlabeled data can generate highly discriminative features for another labeled datase Zhao et al [62] proposed a stacked sparse autoencoder model (SSAE), which was com bined with a backpropagation neural network (BPNN) to perform feature extraction f Chinese liquor classification (Figure 5). After the model was trained, an extra predictio layer was appended to the encoder of autoencoder for prediction.…”

Section: Feature Extraction Through Learningmentioning

confidence: 99%

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Liu

2021

Sensors

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Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

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Section: Feature Extraction Through Learningmentioning

confidence: 99%

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Liu

2021

Sensors

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“…Such an assumption has proven to learn good representations of the input data from the unlabeled data and help aid the classification task in a supervised setting [14]. Self taught learning has shown to be effective in various fields such as audio classification [15], [16], E-Nose in Wound Infection Detection [17], facial beauty prediction [18], image classification [14]. In a study, the authors created codebooks of features called basis vectors corresponding to each activity using sparse encoding [19] from the unlabeled data.…”

Section: Related Workmentioning

confidence: 99%

STAR: A Scalable Self-taught Learning Framework for Older Adults’ Activity Recognition

Ramamurthy

Ghosh

Gangopadhyay

et al. 2021

2021 IEEE International Conference on Smart Computing (SMARTCOMP)

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Activity Recognition (AR) in older adults living with Neurocognitive disorders caused by diseases such as Alzheimer's is still a challenging research problem. The inherent natural variation in performing an activity increases while repeating the same activity for an older adult, let alone the variation introduced when another older adult performs the same activity. Moreover, the challenges in acquiring the labeled data while preserving the privacy, availability of annotators with domain knowledge, aversion towards cameras even for a minimal amount of time for ground truth data collection, and psychological and mental health status make AR for older adults challenging. In this paper, we postulate a self-taught learning-based approach that helps recognize activities with variations that are not being directly seen during the training phase. We hypothesize that the features extracted using deep architectures from unlabeled data instances can learn general underlying representations of activities efficiently and help improve activity classification in a supervised setting, although the data instances in labeled data do not follow the generative distribution of that of unlabeled data. We posit real data from a retirement community center using our in-house SenseBox infrastructure and survey-based assessments concurrently done by a clinical evaluator to study the relationship between activities and functional/behavioral health of older adults. We evaluate our proposed self-taught learningbased approach, STAR, using the presented in-house Alzheimer's Activity Recognition (AAR) dataset acquired in a real-world deployment in 25 homes which outperforms the state-of-the-art algorithm by about 20%.

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“…However, the majority of these studies use supervised learning, which often requires large amounts of labeled defective samples for model training [ 23 ]. The autoencoder (AE) network is a typical unsupervised method that has been widely used in shape retrieval [ 24 ], scene description [ 25 ], target recognition [ 26 , 27 ] and object detection [ 28 ]. It can be trained without any labeled ground truth or human intervention.…”

Section: Related Work and Foundationsmentioning

confidence: 99%

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Mei

Wang

Wen

2018

Sensors

236

121

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Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

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Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

Cited by 21 publications

References 28 publications

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

STAR: A Scalable Self-taught Learning Framework for Older Adults’ Activity Recognition

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

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