scholarly journals Fish Classification Using DNA Barcode Sequences through Deep Learning Method

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1599
Author(s):  
Lina Jin ◽  
Jiong Yu ◽  
Xiaoqian Yuan ◽  
Xusheng Du
Keyword(s):  
Deep Learning ◽  
Dna Sequences ◽  
Dna Barcode ◽  
Kernel Density ◽  
Original Data ◽  
High Dimensionality ◽  
Learning Method ◽  
Resources Management ◽  
Fish Taxonomy ◽  
The World

Fish is one of the most extensive distributed organisms in the world. Fish taxonomy is an important component of biodiversity and the basis of fishery resources management. The DNA barcode based on a short sequence fragment is a valuable molecular tool for fish classification. However, the high dimensionality of DNA barcode sequences and the limitation of the number of fish species make it difficult to reasonably analyze the DNA sequences and correctly classify fish from different families. In this paper, we propose a novel deep learning method that fuses Elastic Net-Stacked Autoencoder (EN-SAE) with Kernel Density Estimation (KDE), named ESK model. In stage one, the ESK preprocesses original data from DNA barcode sequences. In stage two, EN-SAE is used to learn the deep features and obtain the outgroup score of each fish. In stage three, KDE is used to select a threshold based on the outgroup scores and classify fish from different families. The effectiveness and superiority of ESK have been validated by experiments on three datasets, with the accuracy, recall, F1-Score reaching 97.57%, 97.43%, and 98.96% on average. Those findings confirm that ESK can accurately classify fish from different families based on DNA barcode sequences.

scholarly journals A deep learning model for fish classification base on DNA barcode

2021 ◽  
Author(s):  
Lina Jin ◽  
Jiong Yu ◽  
Xiaoqian Yuan ◽  
Xusheng Du
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Dna Barcode ◽  
Original Data ◽  
Morphological Characters ◽  
Coi Gene ◽  
Resources Management ◽  
Fish Taxonomy ◽  
Dominant Fish ◽  
Deep Learning Model

AbstractFish is one of the most extensive distributed organisms in the world, fish taxonomy is an important part of biodiversity and is also the basis of fishery resources management. However, the morphological characters are so subtle to identify and intact specimens are not available sometimes, making the research and application of morphological method laborious and time-consuming. DNA barcoding based on a fragment of the cytochrome c oxidase subunit I (COI) gene is a valuable molecular tool for species identification and biodiversity studies. In this paper, a novel deep learning classification approach that fuses Elastic Net-Stacked Autoencoder (EN-SAE) with Kernel Density Estimation (KDE), named ESK-model, is proposed bases on DNA barcode. In stage one, ESK-model preprocesses the original data from COI fragments. In stage two, EN-SAE is used to learn the deep features and obtain the outgroup score of each fish. In stage three, KDE is used to select the threshold base on the outgroup scores and classify fish from different families. The effectiveness and superiority of ESK-model have been validated by experiment on three dominant fish families and comparisons with state-of-the-art methods. Those findings confirm that the ESK-model can accurately classify fish from different family base on DNA barcode.

scholarly journals The dna barcoding data and genetic distance of leaf beetles (Coleoptera, Chrysomelidae) in Vietnam

2020 ◽  
Vol 42 (4) ◽  
Author(s):  
Nguyen Thi Dinh
Keyword(s):  
Genetic Distance ◽  
Dna Barcoding ◽  
Dna Sequences ◽  
Phylogenetic Relationships ◽  
Dna Barcode ◽  
Coi Gene ◽  
Biodiversity Assessment ◽  
Leaf Beetles ◽  
The World ◽  
Living Organisms

DNA barcoding is a useful tool in identifying species, biodiversity assessment, and revealing phylogenetic relationships of living organisms in the world. However, the DNA barcode data for leaf beetles in Vietnam is lacking. In this study, sixteen DNA sequences of 658 bp of COI gene from nine species (five genera; three subfamilies) of Chrysomelidae in Vietnam were (obtained). Intra- and inter-specific diversities, and phylogenetic relationships of these species were analyzed. 

New or little-known taxa of Anurophorinae (Collembola) with anal spines from East Asia with notes on DNA barcode

Zootaxa ◽  
2017 ◽  
Vol 4318 (2) ◽  
pp. 312 ◽  
Author(s):  
MIKHAIL POTAPOV ◽  
TAIZO NAKAMORI ◽  
SEIKOH SAITOH ◽  
NATALIA KUZNETSOVA ◽  
ANATOLY BABENKO
Keyword(s):  
Cytochrome C ◽  
Cytochrome C Oxidase ◽  
Ribosomal Rna ◽  
Dna Sequences ◽  
Abdominal Segment ◽  
Dna Barcode ◽  
Mitochondrial Cytochrome ◽  
Dna Barcodes ◽  
The World ◽  
Taxonomical Position

The paper concerns two species of Collembola having a strong spine armature at the end of abdomen. Sahacanthella saoriae sp.nov. (Hokkaido, Japan), the second species of the genus, is described. Octodonthophora ornata (Magadan district, Russia) is redescribed with remarks on its taxonomical position. A key to the world genera of Anurophorinae with anal spines is given. The genera having four anal spines on the fifth abdominal segment are illustrated. DNA sequences of genes for mitochondrial cytochrome c oxidase subunit I, 16S ribosomal RNA, and nucler 28S ribosomal RNA were sequenced as DNA barcodes for S. saoriae sp.nov. and several taxa of Anurophorinae. 

scholarly journals Hyper-parameter optimization of convolutional neural networks for classifying COVID-19 X-ray images

2021 ◽  
pp. 56-56
Author(s):  
Grega Vrbacic ◽  
Spela Pecnik ◽  
Vili Podgorelec
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Optimization Algorithms ◽  
Learning Method ◽  
High Demand ◽  
X Ray ◽  
Depth Analysis ◽  
The World ◽  
Depth View

For more than a year the COVID-19 epidemic is threatening people all over the world. Numerous researchers are looking for all possible insights into the new corona virus SARS-CoV-2. One of the possibilities is an in-depth analysis of X-ray images from COVID-19 patients, commonly conducted by a radiologist, which are due to high demand facing with overload. With the latest achievements in the field of deep learning, the approaches using transfer learning proved to be successful when tackling such problem. However, when utilizing deep learning methods, we are commonly facing the problem of hyper-parameter settings. In this research, we adapted and generalized transfer learning based classification method for detecting COVID-19 from X-ray images and employed different optimization algorithms for solving the task of hyper-parameter settings. Utilizing different optimization algorithms our method was evaluated on a dataset of 1446 X-ray images, with the overall accuracy of 84.44%, outperforming both conventional CNN method as well as the compared baseline transfer learning method. Besides quantitative analysis, we also conducted a qualitative in-depth analysis using the local interpretable modelagnostic explanations method and gain some in-depth view of COVID-19 characteristics and the predictive model perception.

scholarly journals Pembelajaran Mendalam untuk Membentuk Karakter Siswa sebagai Pembelajar

2017 ◽  
Vol 2 (2) ◽  
pp. 97
Author(s):  
Moh Khoerul Anwar
Keyword(s):  
Deep Learning ◽  
Data Collection ◽  
Literature Review ◽  
Research Method ◽  
Learning Method ◽  
Trust Value ◽  
Learning Groups ◽  
Student Profile ◽  
New Knowledge ◽  
The World

This article aims to develop learning methods to establish the character of students as learners. Learning method developed is deep learning to establish the character of students as learners. This research method used document study based on research results and literature review. Deep learning is a learning that leverages the power of new partnerships to engage students in practicing the learning process through discovering and mastering existing knowledge and then creating and using new knowledge in the world so that the outcomes of deep learning seek to increase students' understanding of their strengths and weaknesses, data collection regarding student profile information and trust value building among members of student learning groups. Tujuan dari penulisan ini adalah untuk mengembangkan metode pembelajaran mendalam untuk membentuk karakter siswa sebagai pembelajar. Metode penelitian yang digunakan dalam penulisan ini adalah studi dokumen berbasis hasil penelitian dan kajian literatur. Pembelajaran mendalam merupakan pembelajaran yang memanfaatkan kekuatan kemitraan baru untuk melibatkan para siswa dalam mempraktekkan proses pembelajaran melalui menemukan dan menguasai pengetahuan yang ada dan kemudian menciptakan serta menggunakan pengetahuan baru di dunia sehingga hasil dari pembelajaran mendalam berupaya pada peningkatan pemahaman siswa tentang kelebihan dan kelemahannya, pengumpulan data mengenai informasi profil siswa dan pembangunan nilai kepercayaan diantara anggota kelompok belajar siswa.

scholarly journals Various Methods for Object Detection Based on Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 1642-1646
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Technology Use ◽  
Detection Task ◽  
Correct Result ◽  
Learning Technology ◽  
Learning Method ◽  
Learning Techniques ◽  
The World

The growing technology in the world made-up the deep learning method, which classifies different vehicles from a video. In deep learning technology use different strategies such as RCNN, Fast RCNN, RPN, faster RCNN, YOLO, SSD. All methods offer various accuracy of the identification of the vehicle. The convolutional natural network determining an object detection task exploitation in deep learning. Object detection is very important in AI as well as in videos using pc vision. Through this paper demystifies the important role of deep learning supported by CNN for object detection. And the methodology offers additional correct result. Deep learning techniques shows the development of object detection in various area and the different technics are assessed during this paper.

scholarly journals DEEP LEARNING BASED MASK DETECTION IN SMART HOME ENTRIES DURING THE EPIDEMIC PROCESS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 159-163
Author(s):  
B. Cerit ◽  
R. Bayir
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Smart Home ◽  
Smart Homes ◽  
Illumination Level ◽  
Learning Method ◽  
Level Control ◽  
Control Card ◽  
The People ◽  
The World

Abstract. In this study, "smart home" systems were designed against Covid-19 virus, which negatively affects life all over the world, and viruses that may become epidemics later. Our homes need to be more hygienic and safe than yesterday. One of these hygiene rules is the masks that cover our nose and mouth. It is very important to use a mask to prevent further spread of the virus. Whether or not the people in smart homes are wearing masks at home will be diagnosed with the deep learning method. Hosts will be warned if they do not have masks. Brightness level control card and illuminator have been added to smart home entrances to better identify people's faces. With PID, the illumination level is fixed at the desired value, and with IOT technology, people can follow the illumination level at the smart home entrance from the mobile application.

Rotational Characteristics of the Green Solar Corona: 1947-1991

1994 ◽  
Vol 144 ◽  
pp. 139-141 ◽  
Author(s):  
J. Rybák ◽  
V. Rušin ◽  
M. Rybanský
Keyword(s):  
Solar Corona ◽  
World Wide ◽  
Rotation Period ◽  
Original Data ◽  
Coronal Emission ◽  
Time Intervals ◽  
Data Set ◽  
The World ◽  
Coronal Emission Line ◽  
Homogeneous Data

AbstractFe XIV 530.3 nm coronal emission line observations have been used for the estimation of the green solar corona rotation. A homogeneous data set, created from measurements of the world-wide coronagraphic network, has been examined with a help of correlation analysis to reveal the averaged synodic rotation period as a function of latitude and time over the epoch from 1947 to 1991.The values of the synodic rotation period obtained for this epoch for the whole range of latitudes and a latitude band ±30° are 27.52±0.12 days and 26.95±0.21 days, resp. A differential rotation of green solar corona, with local period maxima around ±60° and minimum of the rotation period at the equator, was confirmed. No clear cyclic variation of the rotation has been found for examinated epoch but some monotonic trends for some time intervals are presented.A detailed investigation of the original data and their correlation functions has shown that an existence of sufficiently reliable tracers is not evident for the whole set of examinated data. This should be taken into account in future more precise estimations of the green corona rotation period.

Sign in / Sign up

Export Citation Format

Share Document