Evidence map›Paper›PMID 42568132›Full record

ArticleJournal of medical primatology2026

A Smart-Alarm Detection and Deterrent System for Long-Tailed Macaque Intrusion in Human Residential Zones.

Kanyanut Homsapaya, Janya Jadejaroen, Orawan Watchanupaporn

Abstract read
In one paragraph

Article in Journal of medical primatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kanyanut HomsapayaDepartment of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Chonburi, Thailand.ORCID https://orcid.org/0009-0009-1437-3688
Janya JadejaroenDepartment of Resources and Environment, Faculty of Science at Sriracha, Kasetsart University, Chonburi, Thailand.ORCID https://orcid.org/0009-0004-2108-0203
Orawan WatchanupapornDepartment of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Chonburi, Thailand.ORCID https://orcid.org/0009-0004-5193-1005

Funding

Faculty of Science at Sriracha, Kasetsart University
6 · The paper itself

Abstract

backgroundHuman-macaque conflict has become an increasing concern in urban and residential areas. Existing deterrent systems, particularly ultrasonic-based approaches relying on fixed frequencies or simple motion detection, have limited effectiveness and high false alarm rates. Moreover, most systems lack integration between detection, risk assessment, and adaptive response mechanisms.

objectiveTo develop a smart-alarm system for detecting and mitigating long-tailed macaque intrusion in human residential environments.

methodsComputer vision-based object detection was integrated with real-time risk assessment using distance and group size as key parameters. The system dynamically activates deterrent sound signals and sends real-time alerts via communication platforms. The system was deployed and evaluated in a real-world setting (Sriracha Campus, Kasetsart University).

resultsThe system could effectively detect macaques and provide appropriate, timely deterrence responses, thereby reducing human-macaque conflict.

conclusionsThis novel AI-based integrated framework combines detection, behavior analysis, and risk assessment for nonlethal wildlife management in shared human-animal environments.

Indexed as

Human-Animal InteractionMacacaAnimalsDetection AlgorithmsHumansCNN‐based object detectioncomputer visiondeep learninghuman–macaque conflictlong‐tailed macaquereal‐time monitoringrisk‐based alertingsmart‐alarm system

Identifiers

PMID42568132
PMCPMC13451578

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.