scholarly journals The promise of next-generation taxonomy

Megataxa ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 35-38
Author(s):  
MIGUEL VENCES
Keyword(s):  
Machine Learning ◽  
Global Changes ◽  
Next Generation ◽  
Dna Taxonomy ◽  
Technological Developments ◽  
Baseline Information ◽  
Life On Earth ◽  
Diversity Of Life

Documenting, naming and classifying the diversity of life on Earth provides baseline information on the biosphere, which is crucially important to understand and mitigate the global changes of the Anthropocene. We should meet three main challenges, using new technological developments without throwing the well-tried and successful foundations of Linnaean nomenclature overboard. 1. Fully embrace cybertaxonomy, machine learning and DNA taxonomy to ease, not burden the workflow of taxonomists. 2. Emphasize diagnosis over description, images over words. 3. Understand promises and pitfalls of omics approaches to avoid taxonomic inflation.

CLASS-BASED MACHINE LEARNING FOR NEXT GENERATION WELLBORE DATA PROCESSING AND INTERPRETATION

2019 ◽  
Author(s):  
Vikas Jain ◽  
Po-Yen Wu ◽  
Ridvan Akkurt ◽  
Brook Hodenfield ◽  
Tianmin Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Next Generation

scholarly journals Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Metabolites ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 445
Author(s):  
Morena M. Tinte ◽  
Kekeletso H. Chele ◽  
Justin J. J. van der Hooft ◽  
Fidele Tugizimana
Keyword(s):  
Machine Learning ◽  
Abiotic Stress ◽  
Stress Responses ◽  
Abiotic Stresses ◽  
Industrial Revolution ◽  
Chemical Space ◽  
Big Data Analytics ◽  
Plant Responses ◽  
Next Generation ◽  
Computational Tools

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

scholarly journals Toward a next generation particle precipitation model: Mesoscale prediction through machine learning (a case study and framework for progress)

Space Weather ◽  
2021 ◽  
Author(s):  
Ryan M. McGranaghan ◽  
Jack Ziegler ◽  
Téo Bloch ◽  
Spencer Hatch ◽  
Enrico Camporeale ◽  
...  
Keyword(s):  
Machine Learning ◽  
Next Generation ◽  
Particle Precipitation ◽  
Precipitation Model

scholarly journals Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research

BioScience ◽  
2020 ◽  
Vol 70 (7) ◽  
pp. 610-620 ◽  
Author(s):  
Katelin D Pearson ◽  
Gil Nelson ◽  
Myla F J Aronson ◽  
Pierre Bonnet ◽  
Laura Brenskelle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Discovery ◽  
Herbarium Specimens ◽  
Plant Fitness ◽  
Science And Society ◽  
Ecological Processes ◽  
Critical Determinant ◽  
Phenological Data ◽  
Life On Earth ◽  
Growth And Reproduction

Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

Many: The Diversity of Life on Earth by Nicola Davies

2017 ◽  
Vol 71 (3) ◽  
pp. 113-113
Author(s):  
Deborah Stevenson
Keyword(s):  
Life On Earth ◽  
Diversity Of Life

scholarly journals Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 607 ◽  
Author(s):  
Ihab Ahmed Najm ◽  
Alaa Khalaf Hamoud ◽  
Jaime Lloret ◽  
Ignacio Bosch
Keyword(s):  
Machine Learning ◽  
Congestion Control ◽  
Learning Model ◽  
Training Dataset ◽  
Transmission Protocol ◽  
Control Mechanisms ◽  
Next Generation ◽  
Control Approach ◽  
Machine Learning Model ◽  
Prediction Approach

The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

Sign in / Sign up

Export Citation Format

Share Document