Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip

Zeng, Nianyin; Han, Ling; Wang, Zidong; Liu, Weibo; Liu, Songming; Alsaadi, Fuad E.; Liu, Xiaohui

doi:10.1016/j.neucom.2020.04.001

Cited by 116 publications

(34 citation statements)

References 44 publications

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“…The use of deep learning (Zeng et al 2018(Zeng et al , 2019(Zeng et al , 2020, and specially that of Convolutional Neural Networks (CNNs), has revolutionised the state-of-the-art of challenging problems such as speech recognition and image classification (Ronao and Cho 2015). Likewise, CNNs are gaining increasing attention within the field of HAR due to the numerous advantages they provide as compared to traditional state-ofthe-art HAR feature extraction and classification methods.…”

Section: The Use Of Convolutional Neural Network For Activity Recognmentioning

confidence: 99%

A deep learning based wearable system for food and drink intake recognition

Anderez

Lotfi

Pourabdollah

2020

J Ambient Intell Human Comput

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Eating difficulties and the subsequent need for eating assistance are a prevalent issue within the elderly population. Besides, a poor diet is considered a confounding factor for developing chronic diseases and functional limitations. Driven by the above issues, this paper proposes a wrist-worn tri-axial accelerometer based food and drink intake recognition system. First, an adaptive segmentation technique is employed to identify potential eating and drinking gestures from the continuous accelerometer readings. A posteriori, a study upon the use of Convolutional Neural Networks for the recognition of eating and drinking gestures is carried out. This includes the employment of three time series to image encoding frameworks, namely the signal spectrogram, the Markov Transition Field and the Gramian Angular Field, as well as the development of various multi-input multi-domain networks. The recognition of the gestures is then tackled as a 3-class classification problem (‘Eat’, ‘Drink’ and ‘Null’), where the ‘Null’ class is composed of all the irrelevant gestures included in the post-segmentation gesture set. An average per-class classification accuracy of 97.10% was achieved by the proposed system. When compared to similar work, such accurate classification performance signifies a great contribution to the field of assisted living.

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Section: The Use Of Convolutional Neural Network For Activity Recognmentioning

confidence: 99%

A deep learning based wearable system for food and drink intake recognition

Anderez

Lotfi

Pourabdollah

2020

J Ambient Intell Human Comput

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“…At present, the main clinical diagnostic methods are ultrasound, CT, and liver biopsy (7). For their invasiveness and complexity, they are not suitable for large-scale epidemiological screening (8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records

Zhao

Changjun

Luo

et al. 2021

Front. Public Health

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Fatty liver disease (FLD) is a common liver disease, which poses a great threat to people's health, but there is still no optimal method that can be used on a large-scale screening. This research is based on machine learning algorithms, using electronic physical examination records in the health database as data support, to a predictive model for FLD. The model has shown good predictive ability on the test set, with its AUC reaching 0.89. Since there are a large number of electronic physical examination records in most of health database, this model might be used as a non-invasive diagnostic tool for FLD for large-scale screening.

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“…Based on the software performance of the guided vehicle (J. C. Chen et al, 2021;Karim et al, 2021), Farooq et al optimize the routing CONTACT Xuefeng Deng dxf@sxau.edu.cn and decision-making performance, as well as the cooperative scheduling algorithm and the energy efficiency improved according to the optimal tracking system synthesis theory (Farooq et al, 2021;Gao et al, 2021;Holovatenko & Pysarenko, 2021). With the progress of artificial intelligence technology, the introduction to intelligent algorithms has promoted a variety of fields, such as image analysis and processing (Zeng, Li, et al, 2021;Zeng et al, 2019), algorithm optimization (Zeng, Song, et al, 2020;Zeng, Wang, et al, 2020) and other aspects.…”

Section: Introductionmentioning

confidence: 99%

A method for reliability detection of automated guided vehicle based on timed automata

Deng

Zhou

Sun

et al. 2021

Systems Science & Control Engineering

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To ensure the normal operation of the automated guided vehicle, the system must pass the reliability test in the process of design and manufacturing. Unreliable events are small probability events, which is not easy to be detected by using experimental methods. Besides, a lot of testing work is needed. In this paper, a time automata based reliability detection method of automated guided vehicle is proposed and the model of the vehicle is built in the design stage.The counting function related to reliability calculation is introduced to record the occurrence number of reliability-related events. The timed automata is used to qualitatively test the design model of the automated guided vehicle. After the model is proved to be correct, the timed automata model of the automated guided vehicle is automatically iterated by using the simulation function of the model detection tool. The simulation data can be used to quantitatively test the reliability of the design model of the automated guided vehicle. The simulation results show that the proposed method can calculate the reliability of the automated guided vehicle for the design stage. The reliability is evaluated quantitatively to provide reliability guarantee for engineering design.

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Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip

Cited by 116 publications

References 44 publications

A deep learning based wearable system for food and drink intake recognition

A deep learning based wearable system for food and drink intake recognition

Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records

A method for reliability detection of automated guided vehicle based on timed automata

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