Deep Neural Network
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2022 ◽  
Vol 42 (1) ◽  
pp. 351-360
Author(s):  
Kexin Zhang ◽  
Bin Lin ◽  
Jixin Chen ◽  
Xinlong Wu ◽  
Chao Lu ◽  
...  

2022 ◽  
Vol 192 ◽  
pp. 106560
Author(s):  
Thani Jintasuttisak ◽  
Eran Edirisinghe ◽  
Ali Elbattay

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Minami Masumoto ◽  
Ittetsu Fukuda ◽  
Suguru Furihata ◽  
Takahiro Arai ◽  
Tatsuto Kageyama ◽  
...  

AbstractBhas 42 cell transformation assay (CTA) has been used to estimate the carcinogenic potential of chemicals by exposing Bhas 42 cells to carcinogenic stimuli to form colonies, referred to as transformed foci, on the confluent monolayer. Transformed foci are classified and quantified by trained experts using morphological criteria. Although the assay has been certified by international validation studies and issued as a guidance document by OECD, this classification process is laborious, time consuming, and subjective. We propose using deep neural network to classify foci more rapidly and objectively. To obtain datasets, Bhas 42 CTA was conducted with a potent tumor promotor, 12-O-tetradecanoylphorbol-13-acetate, and focus images were classified by experts (1405 images in total). The labeled focus images were augmented with random image processing and used to train a convolutional neural network (CNN). The trained CNN exhibited an area under the curve score of 0.95 on a test dataset significantly outperforming conventional classifiers by beginners of focus judgment. The generalization performance of unknown chemicals was assessed by applying CNN to other tumor promotors exhibiting an area under the curve score of 0.87. The CNN-based approach could support the assay for carcinogenicity as a fundamental tool in focus scoring.


2021 ◽  
Author(s):  
Ya Dong ◽  
Xingzhong Xiong ◽  
Tianyu Li ◽  
Lin Zhang ◽  
Jienan Chen

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhiping Wan ◽  
Zhiming Xu ◽  
Jiajun Zou ◽  
Shaojiang Liu ◽  
Weichuan Ni ◽  
...  

Passive sensing networks can maintain the operation of the network by capturing energy from the environment, thereby solving the energy limitation problem of network nodes. Therefore, passive sensing networks are widely used in data collection in complex environments. However, the complexity of the network deployment environment makes passive sensing nodes unable to obtain stable energy from the surroundings. Therefore, better routing strategies are needed to save network energy consumption. In response to this problem, this paper proposes an IPv6 passive-aware network routing algorithm for the Internet of Things. This method is based on the characteristics of passive sensing networks. By analyzing the successful transmission rate of the network node transmission link, transmission energy consumption, end-to-end transmission delay, and waiting delay of IPv6 packets, the utility evaluation function of the route is obtained. After the utility evaluation function is obtained, the network routing is selected through the utility evaluation function. Then, the utility value and the deep neural network method are combined to train the classification model. The classification model assigns the best routing strategy according to the characteristics of the current network, thereby improving the energy consumption and delay performance of the network.


2021 ◽  
Vol 153 ◽  
pp. 111530
Author(s):  
Zeinab Hajimohammadi ◽  
Fatemeh Baharifard ◽  
Ali Ghodsi ◽  
Kourosh Parand

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