Weapon Detector System by Using X-ray Image Processing Technique

Thaiparnit, Sattarpoom; Chumuang, Narumol; Ketcham, Mahasak

doi:10.1109/iscit.2018.8587853

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…However, no genuine remedy to these problems has been created or implemented ( Schuldt, 2021 ). These issues have prompted study and development efforts in the realm of surveillance technologies ( Thaiparnit, Chumuang & Ketcham, 2018 ; Inyaem et al, 2010 ; Inyaem, Meesad & Haruechaiyasak, 2009 ). One such initiative involves a security program concept that verifies identities of individuals by analyzing their facial features before granting access to boarding areas, entrances and passenger cabins ( Pumpong et al, 2021 ; Bite, 2010 ; Milne et al, 2019 ).…”

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confidence: 99%

Enhancing monitoring of suspicious activities with AI-based and big data fusion

Vorapatratorn

2024

PeerJ Computer Science

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This study provides an AI-based detection tool for the surveillance of suspicious activities using data fusion. The system leverages time, location, and specific data pertaining to individuals, objects, and vehicles associated with the agency. The study’s training data was obtained from Thailand’s military institution. The study focuses on comparing the efficiency between MySQL and Apache Hive for big data processing. According to the findings, MySQL is better suited for quick data retrieval and low storage capacity, while Hive demonstrates higher scalabilities for larger datasets. Furthermore, the study explores the practical utilization of web applications interfaces, enabling real-time display, analysis, and identification suspicious activity results. The web application, built with NuxtJS and MySQL, includes statistics charts and maps that show the status of suspicious items, cars, and people, as well as data filtering options. The system utilizes machine-learning algorithms to train the suspicious identification model, with the best-performing algorithms being the decision tree, reaching 98.867% classification accuracy.

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