Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments

Aims: The aims of this study were to identify the Fusarium isolates based on translation elongation factor (tef) 1α sequence, to determine the genetic diversity among isolates and species using selected microsatellite markers and to examine the pathogenicity of Fusarium isolates causing fruit rot di...

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Main Authors: Findi, Ahmed H. M., Marhaban, Mohammad Hamiruce, Raja Ahmad, Raja Mohd Kamil, Hassan, Mohd Khair
Format: Article
Language:English
Published: Asian Research Publication Network 2017
Online Access:http://psasir.upm.edu.my/id/eprint/61954/1/Genetic%20network%20programming-reinforcement%20learning%20based%20safe%20and%20smooth%20mobile%20robot%20navigation%20in%20unknown%20dynamic%20environments.pdf
http://psasir.upm.edu.my/id/eprint/61954/
http://www.jatit.org/volumes/ninetyfive11.php
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spelling my.upm.eprints.619542019-03-07T09:03:02Z http://psasir.upm.edu.my/id/eprint/61954/ Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments Findi, Ahmed H. M. Marhaban, Mohammad Hamiruce Raja Ahmad, Raja Mohd Kamil Hassan, Mohd Khair Aims: The aims of this study were to identify the Fusarium isolates based on translation elongation factor (tef) 1α sequence, to determine the genetic diversity among isolates and species using selected microsatellite markers and to examine the pathogenicity of Fusarium isolates causing fruit rot disease of banana. Methods and Results: One‐hundred and thirteen microfungi isolates were obtained from fruit rot infected banana in Peninsular Malaysia. However, this study was focused on the dominant number of the discovered microfungi that belongs to the genus Fusarium; 48 isolates of the microfungi have been identified belonging to 11 species of Fusarium, namely Fusarium incarnatum, Fusarium equiseti, Fusarium camptoceras, Fusarium solani, Fusarium concolor, Fusarium oxysporum, Fusarium proliferatum, Fusarium verticillioides, Fusarium sacchari, Fusarium concentricum and Fusarium fujikuroi. All Fusarium isolates were grouped into their respective clades indicating their similarities and differences in genetic diversity among isolates. Out of 48 Fusarium isolates tested, 42 isolates caused the fruit rot symptom at different levels of severity based on Disease Severity Index (DSI). The most virulent isolate was F. proliferatum B2433B with DSI of 100%. Conclusions: All the isolated Fusarium species were successfully identified and some of them were confirmed as the causal agents of pre‐ and postharvest fruit rot in banana across Peninsular Malaysia. Significance and Impact of the Study: Our results will provide additional information regarding new report of Fusarium species in causing banana fruit rot and in the search of potential biocontrol agent of the disease. Asian Research Publication Network 2017-06 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/61954/1/Genetic%20network%20programming-reinforcement%20learning%20based%20safe%20and%20smooth%20mobile%20robot%20navigation%20in%20unknown%20dynamic%20environments.pdf Findi, Ahmed H. M. and Marhaban, Mohammad Hamiruce and Raja Ahmad, Raja Mohd Kamil and Hassan, Mohd Khair (2017) Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments. Journal of Theoretical and Applied Information Technology, 95 (11). 2339 - 2351. ISSN 1992-8645; ESSN: 1817-3195 http://www.jatit.org/volumes/ninetyfive11.php
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Aims: The aims of this study were to identify the Fusarium isolates based on translation elongation factor (tef) 1α sequence, to determine the genetic diversity among isolates and species using selected microsatellite markers and to examine the pathogenicity of Fusarium isolates causing fruit rot disease of banana. Methods and Results: One‐hundred and thirteen microfungi isolates were obtained from fruit rot infected banana in Peninsular Malaysia. However, this study was focused on the dominant number of the discovered microfungi that belongs to the genus Fusarium; 48 isolates of the microfungi have been identified belonging to 11 species of Fusarium, namely Fusarium incarnatum, Fusarium equiseti, Fusarium camptoceras, Fusarium solani, Fusarium concolor, Fusarium oxysporum, Fusarium proliferatum, Fusarium verticillioides, Fusarium sacchari, Fusarium concentricum and Fusarium fujikuroi. All Fusarium isolates were grouped into their respective clades indicating their similarities and differences in genetic diversity among isolates. Out of 48 Fusarium isolates tested, 42 isolates caused the fruit rot symptom at different levels of severity based on Disease Severity Index (DSI). The most virulent isolate was F. proliferatum B2433B with DSI of 100%. Conclusions: All the isolated Fusarium species were successfully identified and some of them were confirmed as the causal agents of pre‐ and postharvest fruit rot in banana across Peninsular Malaysia. Significance and Impact of the Study: Our results will provide additional information regarding new report of Fusarium species in causing banana fruit rot and in the search of potential biocontrol agent of the disease.
format Article
author Findi, Ahmed H. M.
Marhaban, Mohammad Hamiruce
Raja Ahmad, Raja Mohd Kamil
Hassan, Mohd Khair
spellingShingle Findi, Ahmed H. M.
Marhaban, Mohammad Hamiruce
Raja Ahmad, Raja Mohd Kamil
Hassan, Mohd Khair
Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments
author_facet Findi, Ahmed H. M.
Marhaban, Mohammad Hamiruce
Raja Ahmad, Raja Mohd Kamil
Hassan, Mohd Khair
author_sort Findi, Ahmed H. M.
title Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments
title_short Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments
title_full Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments
title_fullStr Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments
title_full_unstemmed Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments
title_sort genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments
publisher Asian Research Publication Network
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/61954/1/Genetic%20network%20programming-reinforcement%20learning%20based%20safe%20and%20smooth%20mobile%20robot%20navigation%20in%20unknown%20dynamic%20environments.pdf
http://psasir.upm.edu.my/id/eprint/61954/
http://www.jatit.org/volumes/ninetyfive11.php
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