<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2215-3470</journal-id>
<journal-title><![CDATA[Uniciencia]]></journal-title>
<abbrev-journal-title><![CDATA[Uniciencia]]></abbrev-journal-title>
<issn>2215-3470</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional, Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2215-34702025000100239</article-id>
<article-id pub-id-type="doi">10.15359/ru.39-1.14</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Quantum-AI Empowered Intelligent Surveillance: Advancing Public Safety Through Innovative Contraband Detection]]></article-title>
<article-title xml:lang="es"><![CDATA[Vigilancia inteligente potenciada por IA cuántica: desarrollando la seguridad pública mediante la detección de contrabando innovadora]]></article-title>
<article-title xml:lang="pt"><![CDATA[Vigilância inteligente alimentada por IA quântica: desenvolvimento da segurança pública por meio da detecção inovadora de contrabando]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Shah]]></surname>
<given-names><![CDATA[Syed Atif Ali]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Algeelani]]></surname>
<given-names><![CDATA[Nasir]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Al-Sammarraie]]></surname>
<given-names><![CDATA[Najeeb]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Al-Madinah International University Faculty of Computer and Information Technology School of Engineering and Applied Sciences, Bahria University, Islamabad, Pakistan]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Malasya</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Hanze University of Applied Science Institute of Engineering, Electrical and Electronic Engineering ]]></institution>
<addr-line><![CDATA[Groningen ]]></addr-line>
<country>Netherlands</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Hanze University of Applied Science Institute of Engineering, Electrical and Electronic Engineering Faculty of Computer and Information Technology, Al-Madinah International University, Malaysia]]></institution>
<addr-line><![CDATA[Groningen ]]></addr-line>
<country>Netherlands</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>39</volume>
<numero>1</numero>
<fpage>239</fpage>
<lpage>253</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S2215-34702025000100239&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S2215-34702025000100239&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S2215-34702025000100239&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  (Objective) This research aims to develop an intelligent surveillance model, Quantum-RetinaNet, by integrating a RetinaNet model with Quantum Convolutional Neural Networks (QCNN) to enhance accuracy and processing speed, thus addressing limitations of conventional CNN-based approaches. The study evaluates Quantum-RetinaNet&#8217;s performance in real-time scenarios to determine its potential as a practical and scalable solution for intelligent monitoring in densely populated areas.  (Methodology) This research integrates a RetinaNet model with Quantum Convolutional Neural Networks (Quantum CNN or QCNN), designating the resulting framework as Quantum-RetinaNet. By harnessing the quantum capabilities of QCNN, Quantum-RetinaNet achieves a balance between accuracy and processing speed. This innovative integration positions it as a game-changer, addressing the challenges of active monitoring in densely populated scenarios. As demand for efficient surveillance solutions grows, Quantum-RetinaNet offers a compelling alternative to existing CNN models, upholding accuracy standards without sacrificing real-time performance.  (Results) The unique attributes of Quantum-RetinaNet have far-reaching implications for the future of intelligent surveillance. Its enhanced processing speed is poised to revolutionize the field, addressing the critical demand for systems that provide both rapid and precise monitoring  (Conclusions) As Quantum-RetinaNet becomes the new standard, it ensures public safety and security while pushing the boundaries of AI in surveillance.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  (Objetivo) Los sistemas de vigilancia han surgido como elementos cruciales para mantener la paz y la seguridad en el mundo moderno: su ubicuidad ayuda a monitorear actividades sospechosas de manera eficaz. Sin embargo, en entornos densamente poblados, el monitoreo activo continuo se vuelve poco práctico, lo que obliga al desarrollo de sistemas de vigilancia inteligentes. La integración de IA en la vigilancia fue una gran revolución; sin embargo, los problemas de velocidad han impedido su implementación generalizada. No obstante, la inteligencia artificial cuántica ha llevado a un avance significativo. Se ha demostrado que los sistemas de vigilancia basados en inteligencia artificial cuántica son más precisos y capaces de funcionar bien en escenarios en tiempo real nunca vistos.  (Metodología) Esta investigación integra un modelo RetinaNet con Quantum CNN, denominándolo Quantum-RetinaNet. Al aprovechar las capacidades cuánticas del QCNN, Quantum-RetinaNet logra un equilibrio entre precisión y velocidad. Esta innovadora integración lo posiciona como un elemento innovador, que aborda los desafíos del monitoreo activo en escenarios densamente poblados. A medida que crece la demanda de soluciones de vigilancia eficientes, Quantum-RetinaNet ofrece una alternativa convincente a los modelos CNN existentes, manteniendo los estándares de precisión sin sacrificar el rendimiento en tiempo real.  (Resultados) Los atributos únicos de Quantum-RetinaNet tienen implicaciones de largo alcance para el futuro de la vigilancia inteligente. Su velocidad de procesamiento mejorada está lista para revolucionar el campo, con lo cual se satisface la necesidad apremiante de un monitoreo rápido pero preciso.  (Conclusiones) A medida que Quantum-RetinaNet se convierte en el nuevo estándar, garantiza la seguridad pública al tiempo que amplía los límites de la IA en la vigilancia.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  (Objetivo) Os sistemas de vigilância surgiram como elementos essenciais para manter a paz e a segurança no mundo moderno: sua onipresença ajuda a monitorar atividades suspeitas de forma eficaz. No entanto, em ambientes densamente povoados, o monitoramento ativo contínuo torna-se pouco prático, exigindo o desenvolvimento de sistemas de vigilância inteligentes. A integração da IA na vigilância foi uma grande revolução; no entanto, problemas de velocidade impedem sua implementação generalizada. No entanto, a inteligência artificial quântica tem levado a um avanço significativo. Os sistemas de vigilância baseados em inteligência artificial quântica têm se mostrado mais precisos e capazes de apresentar um bom desempenho em cenários em tempo real jamais visto.  (Metodologia) Esta pesquisa integra um modelo RetinaNet com o Quantum CNN, chamando-o de Quantum-RetinaNet. Ao aproveitar os recursos quânticos do QCNN, o Quantum-RetinaNet alcança um equilíbrio entre precisão e velocidade. Essa integração inovadora o posiciona como um elemento revolucionário, abordando os desafios do monitoramento ativo em cenários densamente povoados. À medida que cresce a demanda por soluções de vigilância eficientes, a Quantum- RetinaNet oferece uma alternativa atraente aos modelos CNN existentes, mantendo os padrões de precisão sem sacrificar o desempenho em tempo real.  (Resultados) Os atributos exclusivos da Quantum-RetinaNet têm implicações de longo alcance para o futuro da vigilância inteligente. Sua velocidade de processamento aprimorada está pronta para revolucionar o campo, atendendo à necessidade urgente de monitoramento rápido, mas preciso.  (Conclusões) À medida que a Quantum-RetinaNet se torna o novo padrão, ela garante a segurança pública e, ao mesmo tempo, amplia os limites da IA na vigilância.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Quantum AI]]></kwd>
<kwd lng="en"><![CDATA[Deep Learning]]></kwd>
<kwd lng="en"><![CDATA[Quantum Deep Learning]]></kwd>
<kwd lng="en"><![CDATA[CNN]]></kwd>
<kwd lng="en"><![CDATA[QCNN]]></kwd>
<kwd lng="en"><![CDATA[intelligent surveillance]]></kwd>
<kwd lng="en"><![CDATA[weapon detection]]></kwd>
<kwd lng="es"><![CDATA[IA cuántica]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo cuántico]]></kwd>
<kwd lng="es"><![CDATA[CNN]]></kwd>
<kwd lng="es"><![CDATA[QCNN]]></kwd>
<kwd lng="es"><![CDATA[vigilancia inteligente]]></kwd>
<kwd lng="es"><![CDATA[detección de armas]]></kwd>
<kwd lng="pt"><![CDATA[IA quântica]]></kwd>
<kwd lng="pt"><![CDATA[aprendizagem profunda]]></kwd>
<kwd lng="pt"><![CDATA[aprendizagem profunda quântica]]></kwd>
<kwd lng="pt"><![CDATA[CNN]]></kwd>
<kwd lng="pt"><![CDATA[QCNN]]></kwd>
<kwd lng="pt"><![CDATA[vigilância inteligente]]></kwd>
<kwd lng="pt"><![CDATA[detecção de armas]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Agurto]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Tian]]></surname>
<given-names><![CDATA[G.Y]]></given-names>
</name>
<name>
<surname><![CDATA[Bowring]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Lockwood]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<source><![CDATA[A review of concealed weapon detection and research in perspective]]></source>
<year>2007</year>
<conf-name><![CDATA[ 2007 IEEE International Conference on Networking, Sensing and Control]]></conf-name>
<conf-loc> </conf-loc>
<page-range>443-8</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ali Shah]]></surname>
<given-names><![CDATA[S.A]]></given-names>
</name>
<name>
<surname><![CDATA[Ahmad Al-Khasawneh]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Uddin]]></surname>
<given-names><![CDATA[M.I]]></given-names>
</name>
</person-group>
<source><![CDATA[Review of Weapon Detection Techniques within the Scope of Street-Crimes]]></source>
<year>2021</year>
<conf-name><![CDATA[ 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>26-37</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ali Shah]]></surname>
<given-names><![CDATA[S.A]]></given-names>
</name>
<name>
<surname><![CDATA[Uddin]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Aziz]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Ahmad]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Al-Khasawneh]]></surname>
<given-names><![CDATA[M.A]]></given-names>
</name>
<name>
<surname><![CDATA[Sharaf]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An enhanced deep neural network for predicting workplace absenteeism]]></article-title>
<source><![CDATA[Complexity]]></source>
<year>2020</year>
<page-range>1-12</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ashraf]]></surname>
<given-names><![CDATA[A.H]]></given-names>
</name>
<name>
<surname><![CDATA[Imran]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Qahtani]]></surname>
<given-names><![CDATA[A.M]]></given-names>
</name>
<name>
<surname><![CDATA[Alsufyani]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Almutiry]]></surname>
<given-names><![CDATA[O]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Weapons detection for security and video surveillance using CNN and yolo-v5s]]></article-title>
<source><![CDATA[Computers, Materials &amp; Continua]]></source>
<year>2022</year>
<volume>70</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>2761-75</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Atif]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Shah]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Abdel-Wahab]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Ageelani]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Najeeb]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Street-crimes modelled arms recognition technique employing deep learning and quantum deep learning]]></article-title>
<source><![CDATA[Indonesian Journal of Electrical Engineering and Computer Science]]></source>
<year>2023</year>
<volume>30</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>528-44</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Danilov]]></surname>
<given-names><![CDATA[V.V]]></given-names>
</name>
<name>
<surname><![CDATA[Klyshnikov]]></surname>
<given-names><![CDATA[K.Y]]></given-names>
</name>
<name>
<surname><![CDATA[Gerget]]></surname>
<given-names><![CDATA[O.M]]></given-names>
</name>
<name>
<surname><![CDATA[Kutikhin]]></surname>
<given-names><![CDATA[A.G]]></given-names>
</name>
<name>
<surname><![CDATA[Ganyukov]]></surname>
<given-names><![CDATA[V.I]]></given-names>
</name>
<name>
<surname><![CDATA[Frangi]]></surname>
<given-names><![CDATA[A.F]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Real-time coronary artery stenosis detection based on modern neural networks]]></article-title>
<source><![CDATA[Scientific Reports]]></source>
<year>2021</year>
<volume>11</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>1-14</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dong]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Zhong]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Shi]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Practical synchronization of neural networks with delayed impulses and external disturbance via hybrid control]]></article-title>
<source><![CDATA[Neural Networks]]></source>
<year>2023</year>
<volume>157</volume>
<page-range>54-64</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gelana]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Yadav]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<source><![CDATA[Firearm detection from surveillance cameras using image processing and machine learning techniques]]></source>
<year>2019</year>
<volume>851</volume>
<conf-name><![CDATA[ Advances in Intelligent Systems and Computing]]></conf-name>
<conf-loc> </conf-loc>
<page-range>89-98</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gopinath]]></surname>
<given-names><![CDATA[B.Y]]></given-names>
</name>
<name>
<surname><![CDATA[Krishna]]></surname>
<given-names><![CDATA[V.S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Concealed weapon detection using image processing]]></article-title>
<source><![CDATA[International Journal of Electronics and Communication Technology]]></source>
<year>2014</year>
<volume>5</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>13-7</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Grega]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Matiola&#324;ski]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Guzik]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Leszczuk]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automated detection of firearms and knives in a CCTV image]]></article-title>
<source><![CDATA[Sensors]]></source>
<year>2016</year>
<volume>16</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>1-20</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ho]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[C.-Y]]></given-names>
</name>
<name>
<surname><![CDATA[Jordan]]></surname>
<given-names><![CDATA[M.I]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Convergence rates for Gaussian mixtures of experts]]></article-title>
<source><![CDATA[Journal of Machine Learning Research]]></source>
<year>2019</year>
<volume>23</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>1-81</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jain]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Vikram]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Mohana]]></surname>
<given-names><![CDATA[Kashyap]]></given-names>
</name>
<name>
<surname><![CDATA[Jain]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<source><![CDATA[Weapon detection using artificial intelligence and deep learning for security applications]]></source>
<year>2020</year>
<conf-name><![CDATA[ International Conference on Electronics and Sustainable Communication Systems]]></conf-name>
<conf-loc> </conf-loc>
<page-range>193-8</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kambhatla]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Ahmed]]></surname>
<given-names><![CDATA[K.R]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Real-time deep learning weapon detection techniques for mitigating lone wolf attacks]]></article-title>
<source><![CDATA[arXiv Preprint]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Khalid]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Waqar]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Ain Tahir]]></surname>
<given-names><![CDATA[H.U]]></given-names>
</name>
<name>
<surname><![CDATA[Edo]]></surname>
<given-names><![CDATA[O.C]]></given-names>
</name>
<name>
<surname><![CDATA[Tenebe]]></surname>
<given-names><![CDATA[I.T]]></given-names>
</name>
</person-group>
<source><![CDATA[Weapon detection system for surveillance and security]]></source>
<year>2023</year>
<conf-name><![CDATA[ 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-7</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Khan]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Gul]]></surname>
<given-names><![CDATA[M.A]]></given-names>
</name>
<name>
<surname><![CDATA[Uddin]]></surname>
<given-names><![CDATA[M.I]]></given-names>
</name>
<name>
<surname><![CDATA[Ali Shah]]></surname>
<given-names><![CDATA[S.A]]></given-names>
</name>
<name>
<surname><![CDATA[Ahmad]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Al Firdausi]]></surname>
<given-names><![CDATA[M.D]]></given-names>
</name>
<name>
<surname><![CDATA[Zaindin]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Summarizing online movie reviews: A machine learning approach to big data analytics]]></article-title>
<source><![CDATA[Scientific Programming]]></source>
<year>2020</year>
<volume>2021</volume>
<page-range>1</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lai]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Maples]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<source><![CDATA[Developing a real-time gun detection classifier]]></source>
<year>2017</year>
</nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Olmos]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Tabik]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Lamas]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Pérez-Hernández]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Herrera]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A binocular image fusion approach for minimizing false positives in handgun detection with deep learning]]></article-title>
<source><![CDATA[Information Fusion]]></source>
<year>2019</year>
<volume>49</volume>
<page-range>271-80</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Piyadasa]]></surname>
<given-names><![CDATA[T.D]]></given-names>
</name>
</person-group>
<source><![CDATA[Concealed weapon detection using convolutional neural networks]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tiwari]]></surname>
<given-names><![CDATA[R.K]]></given-names>
</name>
<name>
<surname><![CDATA[Verma]]></surname>
<given-names><![CDATA[G.K]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A computer vision-based framework for visual gun detection using Harris interest point detector]]></article-title>
<source><![CDATA[Procedia Computer Science]]></source>
<year>2015</year>
<volume>54</volume>
<page-range>703-12</page-range></nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Uddin]]></surname>
<given-names><![CDATA[M.I]]></given-names>
</name>
<name>
<surname><![CDATA[Ali Shah]]></surname>
<given-names><![CDATA[S.A]]></given-names>
</name>
<name>
<surname><![CDATA[Al-Khasawneh]]></surname>
<given-names><![CDATA[M.A]]></given-names>
</name>
<name>
<surname><![CDATA[Alarood]]></surname>
<given-names><![CDATA[A.A]]></given-names>
</name>
<name>
<surname><![CDATA[Alsolami]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Optimal policy learning for COVID-19 prevention using reinforcement learning]]></article-title>
<source><![CDATA[Journal of Information Science]]></source>
<year>2022</year>
<volume>48</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>336-48</page-range></nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Uddin]]></surname>
<given-names><![CDATA[M.I]]></given-names>
</name>
<name>
<surname><![CDATA[Shah]]></surname>
<given-names><![CDATA[S.A.A]]></given-names>
</name>
<name>
<surname><![CDATA[Al-Khasawneh]]></surname>
<given-names><![CDATA[M.A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A novel deep convolutional neural network model to monitor people following guidelines to avoid COVID-19]]></article-title>
<source><![CDATA[Journal of Sensors]]></source>
<year>2020</year>
<page-range>1-10</page-range></nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Uddin]]></surname>
<given-names><![CDATA[M.I]]></given-names>
</name>
<name>
<surname><![CDATA[Zada]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Aziz]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Saeed]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Zeb]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Ali Shah]]></surname>
<given-names><![CDATA[S.A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prediction of future terrorist activities using deep neural networks]]></article-title>
<source><![CDATA[Complexity]]></source>
<year>2020</year>
<page-range>1-16</page-range></nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="">
<collab>United Nations (UN)</collab>
<source><![CDATA[Small arms review conference 2006: United Nations conference to review progress made in the implementation of the programme of action to prevent, combat and eradicate the illicit trade in small arms and light weapons in all its aspects]]></source>
<year>2006</year>
<publisher-loc><![CDATA[United Nations ]]></publisher-loc>
</nlm-citation>
</ref>
<ref id="B24">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Dong]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Wei]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery]]></article-title>
<source><![CDATA[Remote Sensing]]></source>
<year>2019</year>
<volume>11</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>1-20</page-range></nlm-citation>
</ref>
<ref id="B25">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xie]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Novel deep learning pipeline for automatic weapon detection]]></article-title>
<source><![CDATA[arXiv Preprint]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B26">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Han]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Rao]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<source><![CDATA[A new feature pyramid network for object detection]]></source>
<year>2019</year>
<conf-name><![CDATA[ Proceedings of the 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS 2019)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>428-31</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
