Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Artificial intelligence (AI) facilitates scientists to devise intelligent machines that work and behave like humans to resolve difficulties and problems by utilizing minimal resources. The Healthcare sector has benefited due to this. Mosquito-transmitted diseases pose a significant health risk. Despite all advances, present strategies for curbing these diseases still depend largely on controlling the mosquito vectors. This strategy demands an army of entomology experts for thorough monitoring, determining, and finally eradicating the targeted mosquito population. Deep learning (DL) algorithms may substitute such unmanageable processes. The current review focuses on how AI, with particular emphasis on deep learning, demonstrates effectiveness in quick detection, identification, monitoring, and finally controlling the target mosquito populations with minimal resources. It accelerates the pace of operation and data exploration on ongoing evolutionary status, tendency to feed blood, and age grading of mosquitoes. The successful combination of computer and biological sciences will provide practical insight and generate a new research niche in this study area.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1007/s00436-022-07752-9 | DOI Listing |
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