<?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>S2215-34702025000100222</article-id>
<article-id pub-id-type="doi">10.15359/ru.39-1.13</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[An Innovative Framework for Intelligent Computer Vision Empowered by Deep Learning]]></article-title>
<article-title xml:lang="es"><![CDATA[Un marco innovador para la visión artificial inteligente, potenciada por el aprendizaje profundo]]></article-title>
<article-title xml:lang="pt"><![CDATA[Uma estrutura inovadora para visão mecânica inteligente com base na aprendizagem profunda]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bikku]]></surname>
<given-names><![CDATA[Thulasi]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Thota]]></surname>
<given-names><![CDATA[Srinivasarao]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ayoade]]></surname>
<given-names><![CDATA[Abayomi Ayotunde]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Amaravati, Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>India</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Amrita Vishwa Vidyapeetham, Amaravati Amrita School of Physical Sciences Department of Mathematics]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>India</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,University of Lagos Faculty of Science Department of Mathematics]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>India</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>222</fpage>
<lpage>238</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S2215-34702025000100222&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S2215-34702025000100222&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S2215-34702025000100222&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  (Objective)  The field of computer vision has seen remarkable progress, largely due to the advancements in deep learning. These developments have revolutionized image recognition, interpretation, and application across numerous domains. This paper introduces a new framework designed to expand the potential of computer vision systems by harnessing the power of deep learning techniques. Deep neural networks are at the core of this new system, providing exceptional accuracy and reliability in tasks such as object recognition, image segmentation, and scene understanding.  (Methodology)  Furthermore, this framework offers a versatile platform for realtime image processing, paving the way for numerous applications in areas like industrial automation, medical diagnostics, and autonomous vehicles. This study comprehensively explores the architectural elements and methodologies that drive this innovative framework. It emphasizes the framework&#8217;s technological capabilities, scalability, adaptability, and potential for broad adoption across industries seeking advanced computer vision solutions.  (Results)  The proposed model, Convolutional Neural Network-Feature Pyramid Network (CNN-FPN), demonstrates superior performance across all evaluated metrics for object detection compared to existing models. Specifically, it achieves the highest scores in Accuracy (57.2%), Recall (60.4%), Precision (94.1%), F1Score (73.5%), and AUC (0.983). These results indicate that the proposed model offers superior performance and reliability in object detection tasks, demonstrating its potential for high-precision computer vision applications.  (Conclusions)  In conclusion, this innovative architecture represents a significant advancement in computer vision, enabled by the capabilities of deep learning. Our test findings demonstrate that compared to conventional algorithms, the enhanced CNN-FPN produced more accurate results.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  (Objetivo)  El campo de la visión por computadora ha experimentado un progreso notable, en gran parte, debido a los avances en el aprendizaje profundo. Estos desarrollos han revolucionado el reconocimiento, la interpretación y la aplicación de imágenes en numerosos dominios. Este artículo presenta un nuevo marco, diseñado para ampliar el potencial de los sistemas de visión por computadora, aprovechando el poder de las técnicas de aprendizaje profundo. Las redes neuronales profundas son fundamentales para este nuevo sistema y ofrecen una precisión y confiabilidad exclusivas en tareas esenciales como el reconocimiento de objetos, la segmentación de imágenes y la comprensión de escenas.  (Metodología)  Además, este marco ofrece una plataforma versátil para el procesamiento de imágenes en tiempo real, allanando el camino para numerosas aplicaciones en áreas como la automatización industrial, el diagnóstico médico y los vehículos autónomos. Este estudio explora, exhaustivamente, los elementos arquitectónicos y las metodologías que impulsan este marco innovador. Enfatiza las capacidades tecnológicas, la escalabilidad, la adaptabilidad y el potencial de este para una amplia adopción en todas las industrias que buscan soluciones avanzadas de visión por computadora.  (Resultados)  El modelo propuesto, Red neuronal convolucional-Red piramidal de características (CNN-FPN), demuestra un rendimiento superior en todas las métricas evaluadas para la detección de objetos, en comparación con los prototipos existentes. En concreto, logra las puntuaciones más altas en Precisión (57,2 %), Recuperación (60,4 %), Precisión (94,1 %), F1-Score (73,5 %) y AUC (0,983). Estos resultados indican que el modelo propuesto proporciona un rendimiento y confiabilidad superiores para tareas de detección de objetos, lo que muestra su potencial para aplicaciones de visión por computadora de alta precisión.  (Conclusiones)  En conclusión, esta arquitectura innovadora representa un avance significativo en la visión por computadora, la cual es posible gracias a las capacidades del aprendizaje profundo. Los resultados de nuestras pruebas demuestran que, en comparación con los algoritmos convencionales, el CNN-FPN mejorado produjo resultados más precisos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  (Objetivo)  O campo da visão computacional teve um progresso notável, em grande parte devido aos avanços na aprendizagem profunda. Esses desenvolvimentos revolucionaram o reconhecimento, a interpretação e a aplicação de imagens em vários domínios. Este documento apreseta uma nova estrutura projetada para ampliar o potencial dos sistemas de visão computacional, aproveitando o poder das técnicas de aprendizagem profunda. As redes neurais profundas são fundamentais para esse novo sistema e oferecem precisão e confiabilidade excepcionais em tarefas essenciais, como reconhecimento de objetos, segmentação de imagens e compreensão de cenas.  (Metodologia)  Além disso, essa estrutura oferece uma plataforma versátil para o processamento de imagens em tempo real, abrindo caminho para inúmeras aplicações em áreas como automação industrial, diagnóstico médico e veículos autônomos. Este estudo explora de forma abrangente os elementos arquitetônicos e as metodologias que impulsionam essa estrutura inovadora. Ele enfatiza os recursos tecnológicos, a escalabilidade, a adaptabilidade e o potencial da estrutura para ampla adoção em todos os setores que buscam soluções avançadas de visão computacional.  (Resultados)  O modelo proposto, Rede Neural Convolucional - Rede Piramidal de Recursos (CNN-FPN), demonstra desempenho superior em todas as métricas avaliadas para detecção de objetos em comparação com os modelos existentes. Especificamente, ele atinge as pontuações mais altas em Exatidão (57,2%), Recuperação (60,4%), Precisão (94,1%), F1-Score (73,5%) e AUC (0,983). Esses resultados indicam que o modelo proposto oferece desempenho e confiabilidade superiores para tarefas de detecção de objetos, mostrando seu potencial para aplicações de visão computacional de alta precisão.  (Conclusões)  Em conclusão, esta arquitetura inovadora representa um avanço significativo na visão computacional, possibilitado pelos recursos de aprendizagem profunda. Nossos resultados de teste mostram que, em comparação com os algoritmos convencionais, o CNN-FPN aprimorado produziu resultados mais precisos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Computer vision]]></kwd>
<kwd lng="en"><![CDATA[Deep learning]]></kwd>
<kwd lng="en"><![CDATA[Image Classification]]></kwd>
<kwd lng="en"><![CDATA[Neural networks]]></kwd>
<kwd lng="en"><![CDATA[Object detection]]></kwd>
<kwd lng="en"><![CDATA[Object recognition]]></kwd>
<kwd lng="en"><![CDATA[Super pixel]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[clasificación de imágenes]]></kwd>
<kwd lng="es"><![CDATA[detección de objetos]]></kwd>
<kwd lng="es"><![CDATA[reconocimiento de objetos]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[súper píxel]]></kwd>
<kwd lng="es"><![CDATA[visión por computadora]]></kwd>
<kwd lng="pt"><![CDATA[visão computacional]]></kwd>
<kwd lng="pt"><![CDATA[super pixel]]></kwd>
<kwd lng="pt"><![CDATA[aprendizagem profunda]]></kwd>
<kwd lng="pt"><![CDATA[detecção de objetos]]></kwd>
<kwd lng="pt"><![CDATA[classificação de imagens]]></kwd>
<kwd lng="pt"><![CDATA[reconhecimento de objetos]]></kwd>
<kwd lng="pt"><![CDATA[redes neurais.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Abdusalomov]]></surname>
<given-names><![CDATA[A. B.]]></given-names>
</name>
<name>
<surname><![CDATA[Islam]]></surname>
<given-names><![CDATA[B. M. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Nasimov]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Mukhiddinov]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Whangbo]]></surname>
<given-names><![CDATA[T. K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An improved forest fire detection method based on the detectron2 model and a deep learning approach]]></article-title>
<source><![CDATA[Sensors]]></source>
<year>2023</year>
<volume>23</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>1512</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alsakka]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Assaf]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[El-Chami]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Al-Hussein]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Computer vision applications in offsite construction]]></article-title>
<source><![CDATA[Automation in Construction]]></source>
<year>2023</year>
<volume>154</volume>
<page-range>104980</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ariyanto]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Purnamasari]]></surname>
<given-names><![CDATA[P. D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Object detection system for self- checkout cashier system based on faster region-based convolution neural network and YOLO9000]]></source>
<year>2021</year>
<conf-name><![CDATA[ 2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering]]></conf-name>
<conf-loc> </conf-loc>
<page-range>153-7</page-range><publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ballard]]></surname>
<given-names><![CDATA[D. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Animat vision]]></article-title>
<source><![CDATA[Computer vision: A reference guide]]></source>
<year>2021</year>
<page-range>52-7</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Batchu]]></surname>
<given-names><![CDATA[R. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Bikku]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Thota]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Seetha]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Ayoade]]></surname>
<given-names><![CDATA[A. A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A novel optimization-driven deep learning framework for the detection of DDoS attacks]]></article-title>
<source><![CDATA[Scientific Reports]]></source>
<year>2024</year>
<volume>14</volume>
<page-range>28024</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bhatt]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Patel]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Talsania]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Patel]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Vaghela]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Pandya]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Ghayvat]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[CNN variants for computer vision: History, architecture, application, challenges and future scope]]></article-title>
<source><![CDATA[Electronics]]></source>
<year>2021</year>
<volume>10</volume>
<numero>20</numero>
<issue>20</issue>
<page-range>2470</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bikku]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Malligunta]]></surname>
<given-names><![CDATA[K. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Thota]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Surapaneni]]></surname>
<given-names><![CDATA[P. P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Improved Quantum Algorithm: A Crucial Stepping Stone in Quantum-Powered Drug Discovery]]></article-title>
<source><![CDATA[Journal of Electronic Materials]]></source>
<year>2024</year>
<volume>54</volume>
<page-range>3434-43</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bikku]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Thota]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Shanmugasundaram]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A Novel Quantum Neural Network Approach to Combating Fake Reviews]]></article-title>
<source><![CDATA[International Journal of Networked and Distributed Computing]]></source>
<year>2024</year>
<volume>2024</volume>
<page-range>1-11</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Efthymiou]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Ramos-Calderer]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Bravo-Prieto]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Pérez-Salinas]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[García-Martín]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Garcia-Saez]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Carrazza]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Qibo: a framework for quantum simulation with hardware acceleration]]></article-title>
<source><![CDATA[Quantum Science and Technology]]></source>
<year>2021</year>
<volume>7</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>015018</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Han]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Guo]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Tao]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A survey on vision transformer]]></article-title>
<source><![CDATA[IEEE Transactions on Pattern Analysis and Machine Intelligence]]></source>
<year>2022</year>
<volume>45</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>87-110</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hasan]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Bao]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Shawon]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[DenseNet convolutional neural networks application for predicting COVID-19 using CT image]]></article-title>
<source><![CDATA[SN Computer Science]]></source>
<year>2021</year>
<volume>2</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>389</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[S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Deepseanet: Improving underwater object detection using efficientdet]]></source>
<year>2024</year>
<conf-name><![CDATA[ 2024 4th International Conference on Applied Artificial Intelligence (ICAPAI)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-11</page-range><publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kim]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Davis]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Hong]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Augmented intelligence: enhancing human decision making]]></article-title>
<source><![CDATA[Bridging Human Intelligence and Artificial Intelligence]]></source>
<year>2022</year>
<page-range>151-70</page-range><publisher-name><![CDATA[Springer International Publishing]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Manakitsa]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Maraslidis]]></surname>
<given-names><![CDATA[G. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Moysis]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Fragulis]]></surname>
<given-names><![CDATA[G. F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision]]></article-title>
<source><![CDATA[Technologies]]></source>
<year>2024</year>
<volume>12</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>15</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nazar]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Subash]]></surname>
<given-names><![CDATA[T. D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Navigating Augmented Realities: A Review Of Advancements, Applications, And Future Prospects]]></article-title>
<source><![CDATA[Educational Administration: Theory and Practice]]></source>
<year>2024</year>
<volume>30</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>4182-6</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pujari]]></surname>
<given-names><![CDATA[J. J.]]></given-names>
</name>
<name>
<surname><![CDATA[et al.]]></surname>
</name>
</person-group>
<source><![CDATA[Deep fake Image Verification using DCNN with MobileNetV2]]></source>
<year>2024</year>
<conf-name><![CDATA[ 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)]]></conf-name>
<conf-loc> </conf-loc>
<publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ravikumar]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Sriraman]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Acceleration of Image Processing and Computer Vision Algorithms]]></article-title>
<source><![CDATA[Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era]]></source>
<year>2023</year>
<page-range>1-18</page-range><publisher-name><![CDATA[IGI Global]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Safaldin]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Zaghden]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Mejdoub]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An Improved YOLOv8 to Detect Moving Objects]]></article-title>
<source><![CDATA[IEEE Access]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Szeliski]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[Computer vision: algorithms and applications]]></source>
<year>2022</year>
<publisher-name><![CDATA[Springer Nature]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Thota]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Bikku]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Rakshitha]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Hybrid optimization technique for matrix chain multiplication using Strassen&#8217;s algorithm]]></article-title>
<source><![CDATA[F1000Research]]></source>
<year>2025</year>
<page-range>1-14</page-range></nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Thota]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Gopisairam]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Bikku]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<source><![CDATA[Modelling LCR-Circuit into Integro-Differential Equation Using Variational Iteration Method and GRU-Based Recurrent Neural Network]]></source>
<year>2024</year>
<conf-name><![CDATA[ 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)]]></conf-name>
<conf-loc> </conf-loc>
</nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Ji]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Bridging multi-scale context-aware representation for object detection]]></article-title>
<source><![CDATA[IEEE Transactions on Circuits and Systems for Video Technology]]></source>
<year>2022</year>
</nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xavier]]></surname>
<given-names><![CDATA[A. I.]]></given-names>
</name>
<name>
<surname><![CDATA[Villavicencio]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Macrohon]]></surname>
<given-names><![CDATA[J. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Jeng]]></surname>
<given-names><![CDATA[J. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Hsieh]]></surname>
<given-names><![CDATA[J. G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Object detection via gradient-based mask R-CNN using machine learning algorithms]]></article-title>
<source><![CDATA[Machines]]></source>
<year>2022</year>
<volume>10</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>340</page-range></nlm-citation>
</ref>
<ref id="B24">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yadav]]></surname>
<given-names><![CDATA[S. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Jindal]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Rani]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[de Albuquerque]]></surname>
<given-names><![CDATA[V. H. C.]]></given-names>
</name>
<name>
<surname><![CDATA[dos Santos Nascimento]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Kumar]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An improved deep learning-based optimal object detection system from images]]></article-title>
<source><![CDATA[Multimedia Tools and Applications]]></source>
<year>2024</year>
<volume>83</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>30045-72</page-range></nlm-citation>
</ref>
<ref id="B25">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[Yolov7-sea: Object detection of maritime uav images based on improved yolov7]]></source>
<year>2023</year>
<conf-name><![CDATA[ Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision]]></conf-name>
<conf-loc> </conf-loc>
<page-range>233-8</page-range></nlm-citation>
</ref>
<ref id="B26">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Object detection algorithm based on improved YOLOv3]]></article-title>
<source><![CDATA[Electronics]]></source>
<year>2020</year>
<volume>9</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>537</page-range></nlm-citation>
</ref>
<ref id="B27">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Han]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Deveci]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Parmar]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A review of convolutional neural networks in computer vision]]></article-title>
<source><![CDATA[Artificial Intelligence Review]]></source>
<year>2024</year>
<volume>57</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>99</page-range></nlm-citation>
</ref>
<ref id="B28">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[Y. X.]]></given-names>
</name>
<name>
<surname><![CDATA[Chee]]></surname>
<given-names><![CDATA[K. W. G. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Paul]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Kim]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Lv]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Electronics Engineering Perspectives on Computer Vision Applications: An Overview of Techniques, Sub- areas, Advancements and Future Challenges]]></article-title>
<source><![CDATA[Cutting Edge Applications of Computational Intelligence Tools and Techniques]]></source>
<year>2023</year>
<page-range>113-42</page-range></nlm-citation>
</ref>
<ref id="B29">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zou]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Shi]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Guo]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Ye]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Object detection in 20 years: A survey]]></article-title>
<source><![CDATA[Proceedings of the IEEE]]></source>
<year>2023</year>
<volume>111</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>257-76</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
