scholarly journals Botanical Digitization: Application of MorphoLeaf in 2D Shape Visualization, Digital Morphometrics, and Species Delimitation, using Homologous Landmarks of Cucurbitaceae Leaves as a Model

2020 ◽  
Author(s):  
Oluwatobi A. Oso ◽  
Adeniyi A. Jayeola
Keyword(s):  
Deep Learning ◽  
Species Delimitation ◽  
Data Extraction ◽  
High Variability ◽  
Plant Leaves ◽  
Pca Analysis ◽  
Digital Identification ◽  
Plant Groups ◽  
Blade Area ◽  
Outline Analysis

ABSTRACTMorphometrics has been applied in several fields of science including botany. Plant leaves are been one of the most important organs in the identification of plants due to its high variability across different plant groups. The differences between and within plant species reflect variations in genotypes, development, evolution, and environment. While traditional morphometrics has contributed tremendously to reducing the problems that come with the identification of plants and delimitation of species based on morphology, technological advancements have led to the creation of deep learning digital solutions that made it easy to study leaves and detect more characters to complement already existing leaf datasets. In this study, we demonstrate the use of MorphoLeaf in generating morphometric dataset from 140 leaf specimens from seven Cucurbitaceae species via scanning of leaves, extracting landmarks, data extraction, landmarks data quantification, and reparametrization and normalization of leaf contours. PCA analysis revealed that blade area, blade perimeter, tooth area, tooth perimeter, height of (each position of the) tooth from tip, and the height of each (position of the) tooth from base are important and informative landmarks that contribute to the variation within the species studied. Our results demonstrate that MorphoLeaf can quantitatively track diversity in leaf specimens, and it can be applied to functionally integrate morphometrics and shape visualization in the digital identification of plants. The success of digital morphometrics in leaf outline analysis presents researchers with opportunities to apply and carry out more accurate image-based researches in diverse areas including, but not limited to, plant development, evolution, and phenotyping.

scholarly journals Technical Note: Quantifying music-dance synchrony with the application of a deep learning-based 2D pose estimator

2020 ◽  
Author(s):  
Filip Potempski ◽  
Andrea Sabo ◽  
Kara K Patterson
Keyword(s):  
Deep Learning ◽  
Motor Performance ◽  
Data Extraction ◽  
Effective Means ◽  
Technical Note ◽  
Novel Method ◽  
Synchronous Condition ◽  
The Mean ◽  
Repeated Training

AbstractDance interventions are more effective at improving gait and balance outcomes than other rehabilitation interventions. Repeated training may culminate in superior motor performance compared to other interventions without synchronization. This technical note will describe a novel method using a deep learning-based 2D pose estimator: OpenPose, alongside beat analysis of music to quantify movement-music synchrony during salsa dancing. This method has four components: i) camera setup and recording, ii) tempo/downbeat analysis and waveform cleanup, iii) OpenPose estimation and data extraction, and iv) synchronization analysis. Two trials were recorded: one in which the dancer danced synchronously to the music and one where they did not. The salsa dancer performed a solo basic salsa step continuously for 90 seconds to a salsa track while their movements and the music were recorded with a webcam. This data was then extracted from OpenPose and analyzed. The mean synchronization value for both feet was significantly lower in the synchronous condition than the asynchronous condition, indicating that this is an effective means to track and quantify a dancer’s movement and synchrony while performing a basic salsa step.

scholarly journals Deep learning the high variability and randomness inside multimode fibers

2019 ◽  
Vol 27 (15) ◽  
pp. 20241 ◽  
Author(s):  
Pengfei Fan ◽  
Tianrui Zhao ◽  
Lei Su
Keyword(s):  
Deep Learning ◽  
High Variability ◽  
Multimode Fibers

scholarly journals Automated data extraction software for medical summary using text mining (T-Library)

2019 ◽  
Author(s):  
Tomohide Yamada ◽  
Yoshinobu Kondo ◽  
Ryo Momosaki
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Clinical Research ◽  
Data Extraction ◽  
Healthcare Providers ◽  
Clinical Information ◽  
Patient Records ◽  
Unstructured Text ◽  
Risk Engine ◽  
Easy Operation

The electronic medical record (EMR) is a source of clinical information and is used for clinical research. Clinical researchers leverage this information by employing staffs to manually extracting data from the unstructured text. This process can be both error-prone and labor-intensive. This software (T-Library) is a software which automatically extracts key clinical data from patient records and can potentially help healthcare providers and researchers save money, make treatment decisions and manage clinical trials. This software saves labor for data transcription in clinical research. This is a vital step toward getting researchers rapid access to the information they need. This is also the attempt to cluster patients’ morbid states and establish accurate and constantly updated risk engine of complications’ crises, using deep learning. Strengths: 1) Quick and Easy operation URL: http://www.picoron.com/tlibrary/

Chemical Named Entity Recognition Using Deep Learning Techniques

2021 ◽  
pp. 59-73
Author(s):  
Hema R. ◽  
Ajantha Devi
Keyword(s):  
Deep Learning ◽  
Chemical Elements ◽  
Data Extraction ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Related Data ◽  
Named Entity ◽  
Chemical Structures ◽  
Learning Techniques ◽  
Chemical Named Entity Recognition

Chemical entities can be represented in different forms like chemical names, chemical formulae, and chemical structures. Because of the different classification frameworks for chemical names, the task of distinguishing proof or extraction of chemical elements with less ambiguous is considered a major test. Compound named entity recognition (NER) is the initial phase in any chemical-related data extraction strategy. The majority of the chemical NER is done utilizing dictionary-based, rule-based, and machine learning procedures. Recently, deep learning methods have evolved, and, in this chapter, the authors sketch out the various deep learning techniques applied for chemical NER. First, the authors introduced the fundamental concepts of chemical named entity recognition, the textual contents of chemical documents, and how these chemicals are represented in chemical literature. The chapter concludes with the strengths and weaknesses of the above methods and also the types of the chemical entities extracted.

Deep Learning Models for the Detection of Plant Leaf Diseases: A Systematic Review

2019 ◽  
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Systems ◽  
Learning Models ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Learning Framework ◽  
Software Frameworks ◽  
Software Packages ◽  
Learning Frameworks

Machine learning systems use different algorithms to detect the diseases affecting the plant leaves. Nevertheless, selecting a suitable machine learning framework differs from study to study, depending on the features and complexity of the software packages. This paper introduces a taxonomic inspection of the literature in deep learning frameworks for the detection of plant leaf diseases. The objective of this study is to identify the dominating software frameworks in the literature for modelling machine learning plant leaf disease detecting systems.

scholarly journals Implementation of CNN for Plant Leaf Classification

2021 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Mohammad Diqi ◽  
Sri Hasta Mulyani
Keyword(s):  
Deep Learning ◽  
Medicinal Plants ◽  
Programming Language ◽  
Classification Model ◽  
Plant Leaves ◽  
Plant Leaf ◽  
The Public ◽  
Java Programming ◽  
Python Programming ◽  
Deep Learning Model

Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.

scholarly journals Automated data extraction software for medical summary using text mining (T-Library)

2019 ◽  
Author(s):  
Tomohide Yamada ◽  
Yoshinobu Kondo ◽  
Ryo Momosaki
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Clinical Research ◽  
Data Extraction ◽  
Healthcare Providers ◽  
Clinical Information ◽  
Patient Records ◽  
Unstructured Text ◽  
Risk Engine ◽  
Easy Operation

The electronic medical record (EMR) is a source of clinical information and is used for clinical research. Clinical researchers leverage this information by employing staffs to manually extracting data from the unstructured text. This process can be both error-prone and labor-intensive. This software (T-Library) is a software which automatically extracts key clinical data from patient records and can potentially help healthcare providers and researchers save money, make treatment decisions and manage clinical trials. This software saves labor for data transcription in clinical research. This is a vital step toward getting researchers rapid access to the information they need. This is also the attempt to cluster patients’ morbid states and establish accurate and constantly updated risk engine of complications’ crises, using deep learning. Strengths: 1) Quick and Easy operation URL: http://www.picoron.com/tlibrary/

scholarly journals Deep learning in non coding variant (a brief overview)

2020 ◽  
Vol 18 (3) ◽  
pp. 1432
Author(s):  
Lee Kuan Xin ◽  
Afnizanfaizal Abdullah
Keyword(s):  
Deep Learning ◽  
Data Extraction ◽  
Current Trend ◽  
Genomic Data ◽  
Biological Data ◽  
Data Driven ◽  
Business World ◽  
Major Interest ◽  
Variant Analysis ◽  
Generation Sequencing

<span>The 21st centuries were deemed to be the era of big data. Data driven research had become a necessity. This hold true not only in the business world, yet also in the field of biomedical world. From a few years of biological data extraction and derivation. With the advancement of Next Generation Sequencing, genomics data had grown to become an ambiguous giant which could not keep up with the pace of its advancement in it analysis counter parts. This results in a large amount of unanalysed genomic data. These genomic data consist not only plain information, researcher had discovered the potential of most gene called the non-coding variant and still failing in identifying their function. With the growth in volume of data, there is also a growth of hardware or technologies. With current technologies, we were able to implement a more complex and sophisticated algorithm in analysis these genomics data. The domain of deep learning had become a major interest of researcher as it was proven to have achieve a significant success in deriving insight from various field. This paper aims to review the current trend of non-coding variant analysis using deep learning approach.</span>

scholarly journals Improving generalization of deep learning models for diagnostic pathology by increasing variability in training data: experiments on osteosarcoma subtypes

2020 ◽  
Author(s):  
Haiming Tang ◽  
Nanfei Sun ◽  
Steven Shen
Keyword(s):  
Deep Learning ◽  
Model Performance ◽  
High Variability ◽  
Training Data ◽  
Classification Model ◽  
Training Dataset ◽  
Learning Models ◽  
Diagnostic Pathology ◽  
Model Generalization ◽  
Histopathological Images

Artificial intelligence (AI) has an emerging progress in diagnostic pathology. A large number of studies of applying deep learning models to histopathological images have been published in recent years. While many studies claim high accuracies, they may fall into the pitfalls of overfitting and lack of generalization due to the high variability of the histopathological images. We use the example of Osteosarcoma to illustrate the pitfalls and how the addition of model input variability can help improve model performance. We use the publicly available osteosarcoma dataset to retrain a previously published classification model for osteosarcoma. We partition the same set of images into the training and testing datasets differently than the original study: the test dataset consists of images from one patient while the training dataset consists images of all other patients. The performance of the model on the test set using the new partition schema declines dramatically, indicating a lack of model generalization and overfitting.We also show the influence of training data variability on model performance by collecting a minimal dataset of 10 osteosarcoma subtypes as well as benign tissues and benign bone tumors of differentiation. We show the additions of more and more subtypes into the training data step by step under the same model schema yield a series of coherent models with increasing performances. In conclusion, we bring forward data preprocessing and collection tactics for histopathological images of high variability to avoid the pitfalls of overfitting and build deep learning models of higher generalization abilities.

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