scholarly journals Classes for the masses: Systematic classification of unknowns using fragmentation spectra

2020 ◽  
Author(s):  
Kai Dührkop ◽  
Louis Felix Nothias ◽  
Markus Fleischauer ◽  
Marcus Ludwig ◽  
Martin A. Hoffmann ◽  
...  
Keyword(s):  
Digestive System ◽  
Training Data ◽  
Microbial Colonization ◽  
Structural Annotation ◽  
Systematic Classification ◽  
Class Level ◽  
Structure Databases ◽  
The Masses ◽  
Spectral Libraries

ABSTRACTMetabolomics experiments can employ non-targeted tandem mass spectrometry to detect hundreds to thousands of molecules in a biological sample. Structural annotation of molecules is typically carried out by searching their fragmentation spectra in spectral libraries or, recently, in structure databases. Annotations are limited to structures present in the library or database employed, prohibiting a thorough utilization of the experimental data. We present a computational tool for systematic compound class annotation: CANOPUS uses a deep neural network to predict 1,270 compound classes from fragmentation spectra, and explicitly targets compounds where neither spectral nor structural reference data are available. CANOPUS even predicts classes for which no MS/MS training data are available. We demonstrate the broad utility of CANOPUS by investigating the effect of the microbial colonization in the digestive system in mice, and through analysis of the chemodiversity of different Euphorbia plants; both uniquely revealing biological insights at the compound class level.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

Deep Learning based Tomato’s Ripe and Unripe Classification System

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Ccd Camera ◽  
Agricultural Products ◽  
Training Data ◽  
Maturity Level ◽  
Agriculture Sector ◽  
The Past ◽  
State Of Art

Effective productivity estimates of fresh produced crops are very essential for efficient farming, commercial planning, and logistical support. In the past ten years, machine learning (ML) algorithms have been widely used for grading and classification of agricultural products in agriculture sector. However, the precise and accurate assessment of the maturity level of tomatoes using ML algorithms is still a quite challenging to achieve due to these algorithms being reliant on hand crafted features. Hence, in this paper we propose a deep learning based tomato maturity grading system that helps to increase the accuracy and adaptability of maturity grading tasks with less amount of training data. The performance of proposed system is assessed on the real tomato datasets collected from the open fields using Nikon D3500 CCD camera. The proposed approach achieved an average maturity classification accuracy of 99.8 % which seems to be quite promising in comparison to the other state of art methods.

Classification of diseases of the gastrointestinal tract in the practice of a family doctor

2021 ◽  
pp. 8-13
Author(s):  
Aleksey Borisovich Petrukhin
Keyword(s):  
Gastrointestinal Tract ◽  
Digestive System ◽  
Family Doctor ◽  
The Body ◽  
Internal Organs ◽  
Important Place ◽  
High Prevalence ◽  
Classification Of Diseases ◽  
Diagnosis Of Diseases

Gastroenterology belongs to one of the leading branches of therapy. In the structure of diseases of the internal organs, diseases of the digestive system occupy a particularly important place due to their high prevalence, which increases with age. As a rule, these diseases have a chronic, progressive, recurrent course, which ultimately leads to severe disorders of the activity of many organs and systems of the body. The article presents the basic requirements for the formation of a clinical diagnosis of diseases of the gastrointestinal tract, which are most common in the practice of a family doctor.

CNN Based Technique for Systematic Classification of Field Photographs

2018 ◽  
Author(s):  
Kavitha D. ◽  
Ramachandra Hebbar ◽  
Vinod P.V. ◽  
Harsheetha M.P. ◽  
Jyothi L. ◽  
...  
Keyword(s):  
Systematic Classification

scholarly journals Physiological Implications of Hydrogen Sulfide in Plants: Pleasant Exploration behind Its Unpleasant Odour

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Zhuping Jin ◽  
Yanxi Pei
Keyword(s):  
Hydrogen Sulfide ◽  
Growth And Development ◽  
Defense Responses ◽  
Plant Responses ◽  
Plant Growth And Development ◽  
Systematic Classification ◽  
Physiological Processes ◽  
Overwhelming Evidence

Recently, overwhelming evidence has proven that hydrogen sulfide (H2S), which was identified as a gasotransmitter in animals, plays important roles in diverse physiological processes in plants as well. With the discovery and systematic classification of the enzymes producing H2Sin vivo, a better understanding of the mechanisms by which H2S influences plant responses to various stimuli was reached. There are many functions of H2S, including the modulation of defense responses and plant growth and development, as well as the regulation of senescence and maturation. Additionally, mounting evidence indicates that H2S signaling interacts with plant hormones, hydrogen peroxide, nitric oxide, carbon monoxide, and other molecules in signaling pathways.

Sign in / Sign up

Export Citation Format

Share Document