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INTELLIGENT ANTI – ROBBERY POLICE SYSTEM IN CHACHOENGSAO

INTELLIGENT  ANTI – ROBBERY  POLICE  SYSTEM  IN  CHACHOENGSAO

Abstract

The project uses artificial intelligence (AI) and deep learning to develop a smart police system (Smart Police) to analyze the identity of individuals and vehicles suspected of involvement in crimes. The system uses CCTV cameras to detect people with concealed weapons and track vehicles involved in crimes. The system also sends alerts to the police when a crime is detected. The Smart Police system is a collaboration between the Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, the Provincial Police Region 2, the Chachoengsao Foundation for Development, and the Smart City Office of Chachoengsao Province. The system is designed to prevent and deter crime, increase public safety and order, and build a network of cooperation between the government, the private sector, and the community. The system is currently under development, but it has the potential to be a valuable tool for law enforcement. The system could help to reduce crime and improve public safety in Chachoengsao Province and other parts of Thailand.

Objective

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Other Innovations

Processed pineapple

คณะบริหารธุรกิจ

Processed pineapple

The goal of the processed pineapple study is to reduce the number of pineapples wasted as a result of climate change and to determine the best way for processing different pineapple species. The drying method, also known as dehydration, is chosen for processing because it is appropriate for the variety's features and will provide the maximum benefit while reducing waste, resulting in the most value for the product. This study was carried out to develop the processing of new kinds, use various processing principles to adapt to this pineapple variety, raise the value of agricultural goods, significantly reduce waste, and provide marketing potential in the future.

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Genomic analysis, Bacteriocin dynamic production of putatively novel bacteriocin DCR3-2 inhibiting Listeria monocytogenes synthesized by Lactococcus lactis subsp. hordinae DCR3-2 isolated from Thai pickled crab (Pu-dong)

คณะวิทยาศาสตร์

Genomic analysis, Bacteriocin dynamic production of putatively novel bacteriocin DCR3-2 inhibiting Listeria monocytogenes synthesized by Lactococcus lactis subsp. hordinae DCR3-2 isolated from Thai pickled crab (Pu-dong)

Listeriosis is a severe foodborne illness characterized by a fatality rate exceeding 30%, attributed to the pathogen Listeria monocytogenes. This study evaluated 160 lactic acid bacteria (LAB) isolated from Thai pickled crabs for their potential as agents against L. monocytogenes and for their probiotic properties and probiogenomic characteristics. Among these strains, strain DRC3-2 exhibited activity through the synthesis of bacteriocin DRC3-2, which significantly inhibited L. monocytogenes ATCC 19115 in spot-on-lawn assays. Phenotypic and whole-genome analyses revealed that strain DRC3-2 thrived in environments with 2-6% NaCl, pH values ranging from 3 to 9, and temperatures between 25 and 45°C. Based on average nucleotide identity (ANI) and digital DNA‒DNA hybridization (dDDH) values, strain DRC3-2 was taxonomically classified as Lactococcus lactis subsp. hordinae. The production of bacteriocin DRC3-2 peaked during the late stationary phase, following its synthesis in the early exponential growth phase. BAGEL4 analysis identified the putative novel bacteriocin DRC3-2 as lactococcin A and B, with respective bit-scores of 40.05 and 36.58. In silico safety assessments confirmed the nonpathogenic nature of strain DRC3-2 in humans, highlighting its absence of antibiotic resistance genes. Finally, this investigation underscores the novel bacteriocin DRC3-2 for application in the prevention and treatment of L. monocytogenes infections.

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Web Application System Prototype for Hand Dental Instruments Identifying and Counting using Deep Learning

คณะเทคโนโลยีสารสนเทศ

Web Application System Prototype for Hand Dental Instruments Identifying and Counting using Deep Learning

This research presents the development of an AI-powered system designed to automate the identification and quantification of dental surgical instruments. By leveraging deep learning-based object detection, the system ensures the completeness of instrument sets post-procedure. The system's ability to process multiple images simultaneously streamlines the inventory process, reducing manual effort and potential errors. The extracted data on instrument quantity and type can be seamlessly integrated into a database for various downstream applications.

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