scholarly journals CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose

Author(s):  
Li Liu ◽  
Xianghao Zhan ◽  
Xianghao Zhan ◽  
Xikai Yang ◽  
Xiaoqing Guan ◽  
...  
Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2936 ◽  
Author(s):  
Xianghao Zhan ◽  
Xiaoqing Guan ◽  
Rumeng Wu ◽  
Zhan Wang ◽  
You Wang ◽  
...  

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.


Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1869 ◽  
Author(s):  
Zhan Wang ◽  
Xiyang Sun ◽  
Jiacheng Miao ◽  
You Wang ◽  
Zhiyuan Luo ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 964 ◽  
Author(s):  
You Wang ◽  
Zhan Wang ◽  
Junwei Diao ◽  
Xiyang Sun ◽  
Zhiyuan Luo ◽  
...  

A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of conformal predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated conformal prediction to classify the species of dendrobium with reliability and validity is promising.


Measurement ◽  
2020 ◽  
Vol 158 ◽  
pp. 107588 ◽  
Author(s):  
Xianghao Zhan ◽  
Zhan Wang ◽  
Meng Yang ◽  
Zhiyuan Luo ◽  
You Wang ◽  
...  

2021 ◽  
Vol 12 (04) ◽  
pp. 33-49
Author(s):  
Ezeofor Chukwunazo ◽  
Akpado Kenneth ◽  
Ulasi Afamefuna

This paper presents Predictive Model for Stem Borers’ classification in Precision Farming. The recent announcement of the aggressive attack of stem borers (Spodoptera species) to maize crops in Africa is alarming. These species migrate in large numbers and feed on maize leaf, stem, and ear of corn. The male of these species are the target because after mating with their female counterpart, thousands of eggs are laid which produces larvae that create the havoc. Currently, Nigerian farmers find it difficult to distinguish between these targeted species (Fall Armyworm-FAW, African Armyworm-AAW and Egyptian cotton leaf worm-ECLW only) because they look alike in appearance. For these reasons, the network model that would predict the presence of these species in the maize farm to farmers is proposed. The maize species were captured using delta pheromone traps and laboratory breeding for each category. The captured images were pre-processed and stored in an online Google drive image dataset folder created. The convolutional neural network (CNN) model for classifying these targeted maize moths was designed from the scratch. The Google Colab platform with Python libraries was used to train the model called MothNet. The images of the FAW, AAW, and ECLW were inputted to the designed MothNet model during learning process. Dropout and data augmentation were added to the architecture of the model for an efficient prediction. After training the MothNet model, the validation accuracy achieved was 90.37% with validation loss of 24.72%, and training accuracy 90.8% with loss of 23.25%, and the training occurred within 5minutes 33seconds. Due to the small amount of images gathered (1674), the model prediction on each image was of low confident. Because of this, transfer learning was deployed and Resnet 50 pretrained model selected and modified. The modified ResNet-50 model was fine-tuned and tested. The model validation accuracy achieved was 99.21%, loss of 3.79%, and training accuracy of 99.75% with loss of 2.55% within 10mins 5 seconds. Hence, MothNet model can be improved on by gathering more images and retraining the system for optimum performance while modified ResNet 50 is recommended to be integrated in Internet of Things device for maize moths’ classification on-site.


2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.


2001 ◽  
Vol 120 (5) ◽  
pp. A248-A248
Author(s):  
N KAWASAKI ◽  
K NARIAI ◽  
M NAKAO ◽  
K NAKADA ◽  
N HANYUU ◽  
...  

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