stdClass Object
(
    [id] => 15198
    [paper_index] => 202502-06-020403
    [title] => SYSTEMATIC REVIEW: AI FOR WILDLIFE CONSERVATION - PREVENTING ELEPHANT DEATHS FROM TRAIN COLLISIONS USING ARTIFICIAL INTELLIGENCE
    [description] => 
    [author] => Dinesh Deckker, Subhashini Sumanasekara
    [googlescholar] => 
    [doi] => https://doi.org/10.36713/epra20403
    [year] => 2025
    [month] => February
    [volume] => 12
    [issue] => 2
    [file] => fm/jpanel/upload/2025/March/202502-06-020403.pdf
    [abstract] => Railway lines crossing elephant habitats pose significant danger to these animals throughout regions where train lines and animals co-exist. The effectiveness of traditional mitigation approaches such as fencing, speed restrictions, and manual monitoring reduces because elephants show unpredictable movement patterns. Artificial intelligence supplies a fundamental solution to train some of its limitations through real-time monitoring features, alongside predictive analytics, plus automated alerts and training intervention systems. The review investigates existing AI-based solutions for train-elephant collision prevention while analyzing their performance and AI architectural structures that benefit conservation activities. This paper evaluates AI applications in wildlife conservation through structured research of Indian and African case studies, developing policy recommendations and identifying existing challenges
    [keywords] => 
    [doj] => 2025-03-01
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 12
    [journal_id] => 6
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Environmental Economics, Commerce and Educational Management 
    [short_code] => IJCM
    [eissn] => 2348-814X
    [pissn] => 
    [home_page_wrapper] => images/products_image/6.ECEM.png
)
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