Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data

Park, Se Jin; Soh, Jihee; Lee, Hyunju

doi:10.1186/s12859-021-04146-z

Cited by 17 publications

(15 citation statements)

References 57 publications

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“…e study found that the adults of pests are very similar in color, mostly brown [10], but there are great differences in morphological characteristics. At present, we have extracted more than 10 morphological features such as area, perimeter, complexity, rectangularity, duty cycle, circularity, and invariant moment as the original features of stored grain pests based on the binary images of four main pests: flat grain thief, rice weevil, pseudograin thief, and bark beetle [11,12]. However, the fundamental task of feature extraction is how to find the most effective features from many features.…”

Section: Feature Extraction and Normalizationmentioning

confidence: 99%

Intelligent Storage Data Classification System Based on the BP Neural Network

Li¹

2022

Journal of Control Science and Engineering

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In order to solve the problem of multifeature recognition and classification of many kinds of pests, this study puts forward a method of pest feature classification using the BP neural network. Through the preprocessing of stored grain pest images, five characteristic parameters are obtained and optimized and input into the BP network for training. The experimental results show that sample 3 of flat grain thief and sample 4 of bark beetle are not well recognized. Because these two kinds of pests have small bodies and thin legs, some detailed features are eliminated after image processing, resulting in a low recognition rate. But the overall recognition rate can reach 95%. Conclusion. The experiment has obtained good recognition results. This method is accurate and effective for the classification and recognition of stored grain pests and provides a scientific basis for the scientific decision-making of controlling stored grain pests.

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Section: Feature Extraction and Normalizationmentioning

confidence: 99%

Intelligent Storage Data Classification System Based on the BP Neural Network

Li¹

2022

Journal of Control Science and Engineering

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“…The second is monitoring through portable devices [9]. The advantage of using the wearable portable body motion gesture recognition device is that there are no restrictions on the monitoring places of the elderly, the elderly can go anywhere at will and can monitor their movement status, which is superior to video surveillance [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Accurate Detection of Intelligent Running Posture Based on Artificial Intelligence Sensor

Zhang¹,

Cheng²

2022

Journal of Sensors

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In order to solve the problem of low accuracy of human posture recognition during motion, the author proposes a human posture recognition and detection method based on multiple sensors. The method uses acceleration sensor, angular velocity sensor, and single-chip microcomputer to collect data; uses time domain and frequency domain analysis methods to analyze the collected data; and then uses Bayesian classifier to classify and identify the current motion posture of the human body. The obtained results are as follows: in the traditional method, with the increase of subjects, the accuracy of the classification method continues to decline; when the number of subjects reaches 50, the accuracy rate is only 60%; in the experiment, the zero crossing times of the acceleration signal axis are selected, and the axis area of the angular velocity signal is used as the characteristic item to distinguish the two different attitudes of going upstairs and going downstairs; the discrimination can reach about 90%. The authors combine the data collected by the acceleration sensor and the angular velocity sensor to perform feature extraction and classification, and the classification accuracy can reach more than 95%. It is proved that the method proposed by the authors can perform human gesture recognition very quickly and has a high accuracy rate.

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“…The second-order moment information and histogram information are fused to obtain the final texture measurement Z ∈ W 2CK×1 , which is as follows [18]: In order to test the effectiveness of the system in this paper to compress and extract urban building land, the eastern area of Dazu District in a city was selected as the experimental object, and the method of this paper was programmed in the computer with the operating system of Windows XP using Java language.…”

Section: Remote Sensing Data Dimension Compressionmentioning

confidence: 99%

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

Zhang¹,

Wan

2022

Journal of Sensors

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In order to solve the problem of complex extraction caused by large feature dimension of remote sensing data, this paper proposes a dimension compression extraction method of urban building land remote sensing data under BIM Technology. Firstly, the remote sensing data is imported into the BIM model for lightweight processing to obtain the element information required for urban construction land and then analyze the urban construction land data, extract the key elements of BIM Technology through semantic filtering, and use the triangulation method to transform the remote sensing data into the triangulation model that can be processed by GIS model. Finally, the random projection method is used to reduce the dimension and compress the remote sensing data, and the remote sensing data extraction of urban construction land is realized through dictionary learning, vocabulary coding, and feature extraction. The experimental results show that the accuracy of extracting different land use types by this method is more than 99%, while the accuracy of extracting different land use types by depth learning method and PLS method is less than 98.5%. In addition, the signal-to-noise ratio of the image extracted by this method is significantly higher than that by depth learning method and PLS method. Conclusion. This method can effectively compress and extract the urban construction land in the remote sensing data, and the extraction accuracy of remote sensing data is high. It provides a technical basis for the approval of urban construction planning. It has the advantages of simple feature extraction and effective differentiation of ground objects.

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Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data

Cited by 17 publications

References 57 publications

Intelligent Storage Data Classification System Based on the BP Neural Network

Intelligent Storage Data Classification System Based on the BP Neural Network

Accurate Detection of Intelligent Running Posture Based on Artificial Intelligence Sensor

Software for Mapping and Extraction of Building Land Remote Sensing Data Based on BIM and Sensor Technology

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