scholarly journals An image dataset of cleared, x-rayed, and fossil leaves vetted to plant family for human and machine learning

PhytoKeys ◽  
2021 ◽  
Vol 187 ◽  
pp. 93-128
Author(s):  
Peter Wilf ◽  
Scott L. Wing ◽  
Herbert W. Meyer ◽  
Jacob A. Rose ◽  
Rohit Saha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Late Cretaceous ◽  
Late Eocene ◽  
Western Hemisphere ◽  
Modern World ◽  
Leaf Architecture ◽  
Plant Family ◽  
Fossil Leaves ◽  
Image Dataset ◽  
New Research

Leaves are the most abundant and visible plant organ, both in the modern world and the fossil record. Identifying foliage to the correct plant family based on leaf architecture is a fundamental botanical skill that is also critical for isolated fossil leaves, which often, especially in the Cenozoic, represent extinct genera and species from extant families. Resources focused on leaf identification are remarkably scarce; however, the situation has improved due to the recent proliferation of digitized herbarium material, live-plant identification applications, and online collections of cleared and fossil leaf images. Nevertheless, the need remains for a specialized image dataset for comparative leaf architecture. We address this gap by assembling an open-access database of 30,252 images of vouchered leaf specimens vetted to family level, primarily of angiosperms, including 26,176 images of cleared and x-rayed leaves representing 354 families and 4,076 of fossil leaves from 48 families. The images maintain original resolution, have user-friendly filenames, and are vetted using APG and modern paleobotanical standards. The cleared and x-rayed leaves include the Jack A. Wolfe and Leo J. Hickey contributions to the National Cleared Leaf Collection and a collection of high-resolution scanned x-ray negatives, housed in the Division of Paleobotany, Department of Paleobiology, Smithsonian National Museum of Natural History, Washington D.C.; and the Daniel I. Axelrod Cleared Leaf Collection, housed at the University of California Museum of Paleontology, Berkeley. The fossil images include a sampling of Late Cretaceous to Eocene paleobotanical sites from the Western Hemisphere held at numerous institutions, especially from Florissant Fossil Beds National Monument (late Eocene, Colorado), as well as several other localities from the Late Cretaceous to Eocene of the Western USA and the early Paleogene of Colombia and southern Argentina. The dataset facilitates new research and education opportunities in paleobotany, comparative leaf architecture, systematics, and machine learning.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 374 ◽  
Author(s):  
Sudhanshu Kumar ◽  
Monika Gahalawat ◽  
Partha Pratim Roy ◽  
Debi Prosad Dogra ◽  
Byung-Gyu Kim
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Age Groups ◽  
Modern World ◽  
Support Vector ◽  
Digital Information ◽  
Age And Gender ◽  
And Gender ◽  
The Impact

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

scholarly journals Coal petrology analysis and implications in depositional environments from upper Cretaceous to Miocene: a study case in the Eastern Cordillera of Colombia

2021 ◽  
Author(s):  
Clara Guatame ◽  
Marco Rincón
Keyword(s):  
Late Cretaceous ◽  
Foreland Basin ◽  
Late Eocene ◽  
Depositional Environments ◽  
Estuarine System ◽  
Eastern Cordillera ◽  
Stratigraphic Position ◽  
San Fernando ◽  
Coal Facies ◽  
Oil Sector

AbstractThe Piedemonte Llanero Basin is located on the eastern side of the Eastern Cordillera of the Colombian Andes. It has been the subject of numerous geological studies carried out for the oil sector, mainly. This study presents the coal-petrographical features of 15 coal seams of four geological formations from Late Cretaceous to Middle Miocene (Chipaque formation, Palmichal group, Arcillas del Limbo formation, and San Fernando formation). Analysis of 33 samples indicates enrichment in vitrinite, while liptinite and inertinite concentrations vary according to the stratigraphic position. Reflectance indicates that the coal range gradually decreases from highly volatile bituminous C (Chipaque formation) to subbituminous C (San Fernando formation). The microlithotypes with the highest concentrations are clarite and vitrinertoliptite. Maceral composition and coal facies indicate changes in the depositional conditions of the sequence. The precursor peat from Late Cretaceous to Late Paleocene accumulated under limnic conditions followed by telmatic in Late Eocene–Early Miocene. The coal facies indices show wet conditions in forest swamps with variations in the flooding surface, influxes of brackish water and good tissue preservation. The tectonic conditions along the Piedemonte Llanero basin is evident, from post-rift to foreland basin, evidenced by oxic and anoxic periods reflected in the maceral composition and its morphology. The coal environment corresponds to an estuarine system started in the Chipaque formation evolving to the lacustrine conditions in the San Fernando formation.

scholarly journals Aporte Teórico-Metodologico para o Estudo da Pluviosidade em Ambientes Urbanos Vulneráveis: Estudo de Caso do Clima Urbano de Duque de Caxias/RJ (Theoretical and Methodological Rain for the Study of Vulnerable in Urban Environments: a Case Study of ...)

2011 ◽  
Vol 4 (3) ◽  
pp. 476
Author(s):  
Antonio Carlos Da Silva Oscar Júnior ◽  
Ana Maria De Paiva Macedo Brandão
Keyword(s):  
Research Agenda ◽  
Rio De Janeiro ◽  
Urban Climate ◽  
Urban Environments ◽  
Modern World ◽  
Weather And Climate ◽  
New Space ◽  
New Research

Hodiernamente as ciências do tempo e do clima assumem protagonismo no meio cientifico devido às questões e polêmicas atuais acerca das mudanças climáticas. Tendo em vista esse novo espaço, esse trabalho tem como objetivo trazer uma contribuição teórico-metodologica para aqueles que desejam se debruçar sobre essas novas questões que afligem o mundo moderno. Para aprofundar as discussões deste artigo, abordaremos o caso de Duque de Caxias, localizado na Baixada Fluminense do Rio de Janeiro, usando a também como caso exemplo para explicar como as dinâmicas socioeconômicas, deixando suas marcas no território intensificam os riscos naturais e aprofundam as vulnerabilidades sociais. No aflorar dessa nova agenda de pesquisas é papel dos Geógrafos aprofundarem suas análises em prol de um ordenamento territorial, e gestão do espaço condizente com as novas necessidades da sociedade. Palavras-Chave: Clima Urbano, Mudanças Climáticas, Planejamento Urbano.  Theoretical and Methodological Rain for the Study of Vulnerable in Urban Environments: a Case Study of Urban Climate Duque de Caxias-RJ  ABSTRACT Today the sciences of weather and climate took center stage in the middle due to scientific issues and controversies about the current climate. In light of this new space, this work aims to bring a theoretical and methodological contributions for those Who wish to dwell on these new issues that plague the modern world. For further discussion of this article, we discuss the case of Duque de Caxias, located in the Baixada Fluminense in Rio de Janeiro, also using as a case example to explain how socio-economic dynamics, leaving it’s mark in the territory of natural hazards intensify and deepen the vulnerabilities social. Flourishin this new research agenda is the role of geographers deepen their analysis in favor of a use and land management consistent with the changing needs of society.  Keywords: Urban Climate, Climate Change, Runoff, Urban Management

scholarly journals The Open Data Challenge: An Analysis of 124,000 Data Availability Statements and an Ironic Lesson about Data Management Plans

2020 ◽  
Vol 2 (4) ◽  
pp. 554-568
Author(s):  
Chris Graf ◽  
Dave Flanagan ◽  
Lisa Wylie ◽  
Deirdre Silver
Keyword(s):  
Machine Learning ◽  
Data Management ◽  
Open Data ◽  
Research Data ◽  
Data Availability ◽  
Policy Changes ◽  
Management Plans ◽  
Trends Over Time ◽  
New Research ◽  
Over Time

Data availability statements can provide useful information about how researchers actually share research data. We used unsupervised machine learning to analyze 124,000 data availability statements submitted by research authors to 176 Wiley journals between 2013 and 2019. We categorized the data availability statements, and looked at trends over time. We found expected increases in the number of data availability statements submitted over time, and marked increases that correlate with policy changes made by journals. Our open data challenge becomes to use what we have learned to present researchers with relevant and easy options that help them to share and make an impact with new research data.

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

scholarly journals A second peirosaurid crocodyliform from the Mid-Cretaceous Kem Kem Group of Morocco and the diversity of Gondwanan notosuchians outside South America

2021 ◽  
Vol 8 (10) ◽  
Author(s):  
Cecily S. C. Nicholl ◽  
Eloise S. E. Hunt ◽  
Driss Ouarhache ◽  
Philip D. Mannion
Keyword(s):  
South America ◽  
Late Cretaceous ◽  
North Africa ◽  
Lateral Surface ◽  
Middle Jurassic ◽  
Tooth Enamel ◽  
Phylogenetic Analyses ◽  
Late Eocene ◽  
South American ◽  
Early Late

Notosuchians are an extinct clade of terrestrial crocodyliforms with a particularly rich record in the late Early to Late Cretaceous (approx. 130–66 Ma) of Gondwana. Although much of this diversity comes from South America, Africa and Indo-Madagascar have also yielded numerous notosuchian remains. Three notosuchian species are currently recognized from the early Late Cretaceous (approx. 100 Ma) Kem Kem Group of Morocco, including the peirosaurid Hamadasuchus rebouli . Here, we describe two new specimens that demonstrate the presence of at least a fourth notosuchian species in this fauna. Antaeusuchus taouzensis n. gen. n. sp. is incorporated into one of the largest notosuchian-focused character-taxon matrices yet to be compiled, comprising 443 characters scored for 63 notosuchian species, with an increased sampling of African and peirosaurid species. Parsimony analyses run under equal and extended implied weighting consistently recover Antaeusuchus as a peirosaurid notosuchian, supported by the presence of two distinct waves on the dorsal dentary surface, a surangular which laterally overlaps the dentary above the mandibular fenestra, and a relatively broad mandibular symphysis. Within Peirosauridae, Antaeusuchus is recovered as the sister taxon of Hamadasuchus . However, it differs from Hamadasuchus with respect to several features, including the ornamentation of the lateral surface of the mandible, the angle of divergence of the mandibular rami, the texture of tooth enamel and the shape of the teeth, supporting their generic distinction. We present a critical reappraisal of the non-South American Gondwanan notosuchian record, which spans the Middle Jurassic–late Eocene. This review, as well as our phylogenetic analyses, indicate the existence of at least three approximately contemporaneous peirosaurid lineages within the Kem Kem Group, alongside other notosuchians, and support the peirosaurid affinities of the ‘trematochampsid’ Miadanasuchus oblita from the Maastrichtian of Madagascar. Furthermore, the Cretaceous record demonstrates the presence of multiple lineages of approximately contemporaneous notosuchians in several African and Madagascan faunas, and supports previous suggestions regarding an undocumented pre-Aptian radiation of Notosuchia. By contrast, the post-Cretaceous record is depauperate, comprising rare occurrences of sebecosuchians in north Africa prior to their extirpation.

Machine Learning Techniques Application

2021 ◽  
pp. 1396-1417
Author(s):  
Karthikeyan P. ◽  
Karunakaran Velswamy ◽  
Pon Harshavardhanan ◽  
Rajagopal R. ◽  
JeyaKrishnan V. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Modern World ◽  
Interdisciplinary Field ◽  
Sound Image ◽  
Learning Techniques

Machine learning is the part of artificial intelligence that makes machines learn without being expressly programmed. Machine learning application built the modern world. Machine learning techniques are mainly classified into three techniques: supervised, unsupervised, and semi-supervised. Machine learning is an interdisciplinary field, which can be joined in different areas including science, business, and research. Supervised techniques are applied in agriculture, email spam, malware filtering, online fraud detection, optical character recognition, natural language processing, and face detection. Unsupervised techniques are applied in market segmentation and sentiment analysis and anomaly detection. Deep learning is being utilized in sound, image, video, time series, and text. This chapter covers applications of various machine learning techniques, social media, agriculture, and task scheduling in a distributed system.

scholarly journals Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2509 ◽  
Author(s):  
Kamran Shaukat ◽  
Suhuai Luo ◽  
Vijay Varadharajan ◽  
Ibrahim A. Hameed ◽  
Shan Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Modern World ◽  
The Internet ◽  
Cyber Threats ◽  
Risk Detection ◽  
Learning Techniques ◽  
Made In

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

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