SPTech World Journal
type: channel
description: A Revista SPTech World Journal é uma fonte líder de pesquisa em tecnologia da informação. A revista publica artigos de pesquisa de alta qualidade em diversas áreas, incluindo ciência da computação, engenharia de software e sistemas de informação. A revista é revisada por pares por um painel de especialistas, garantindo que a pesquisa publicada seja da mais alta qualidade.
language: pt-BR
publication name: SPTech World Journal
items: INTELLIGENT PHARMACEUTICAL INVENTORY MANAGEMENT SYSTEM WITH MACHINE LEARNING (SISTEMA INTELIGENTE DE GESTÃO DE ESTOQUE FARMACÊUTICO COM MACHINE LEARNING)
items: https://journal.sptech.school/index.php/ojs/article/view/58
items: https://journal.sptech.school/index.php/ojs/article/view/57
items: https://journal.sptech.school/index.php/ojs/article/view/56
items: https://journal.sptech.school/index.php/ojs/article/view/55
items: Digital Transformation in Retail: Connecting Supermarkets for Buying and Selling Stagnant Inventory(Transformação Digital no Varejo: Conexão entre Supermercados para Compra e Venda de Estoque parado)
items: https://journal.sptech.school/index.php/ojs/article/view/53
items: https://journal.sptech.school/index.php/ojs/article/view/52
items: AUTOMATION OF THE PROCESS OF SENDING ADMISSION DOCUMENTS VIA OCR (Automatização do Processo de Envio de Documentos Admissionais Via OCR)
items: Marine Debris Recognition with Artificial Intelligence (Detecção de resíduos marinhos utilizando inteligência artificial)
items: https://journal.sptech.school/index.php/ojs/article/view/49
items: Application for detecting breast cancer in mammography images using neural network technology(Aplicação para detectar câncer de mama em imagens de mamografias com a técnica de redes neurais)
items: https://journal.sptech.school/index.php/ojs/article/view/47
items: https://journal.sptech.school/index.php/ojs/article/view/46
items: https://journal.sptech.school/index.php/ojs/article/view/45
items: APPLICATION OF PROHIBITED OBJECT DETECTION IN AIRPORT BAGGAGE:A hybrid approach with YOLO for gun and knife Identification
items: APPLICATION OF MACHINE LEARNING IN THE IDENTIFICATION OF FACIAL DERMATOLOGICAL DISEASES
items: Machine Learning Platform for Retinography: Architecture, Engineering and Multiclass Classifier (Plataforma de Machine Learning para Retinografia: Arquitetura, Engenharia e Classificador Multiclasse)
items: https://journal.sptech.school/index.php/ojs/article/view/41
items: MACHINE LEARNING APPROACH FOR SOILING DETECTION AND ANALYSIS ON SOLAR PANELS (Análise de modelos para detecção de sujidade e danos em placas fotovoltaicas)
items: Artigo Técnico : vulpix.AI - Seu Agente de Marketing
items: Application of the Simplex Method in Optimizing a Gradual Implementation in Credit Modalities (Aplicação do método Simplex na otimização de uma implementação gradual em modalidades de crédito)
items: https://journal.sptech.school/index.php/ojs/article/view/36
items: PREVISÃO DE CASOS DE DOENÇAS RESPIRATÓRIAS COM REDES NEURAIS LSTM: UMA ABORDAGEM PARA ANÁLISE TEMPORAL E IDENTIFICAÇÃO DE EPIDEMIAS
items: Comparison of Machine Learning Models in Animal Adoption (Comparação de Modelos de Machine Learning na Adoção de Animais)
items: Deep Learning: Techniques for Ovarian Cancer Subtype Classification and Outlier Detection (Deep Learning: Técnicas para Classificação de Subtipos de Câncer Ovariano e Detecção de Outliers)
items: https://journal.sptech.school/index.php/ojs/article/view/32
items: https://journal.sptech.school/index.php/ojs/article/view/31
items: Aplicação de Técnicas de Machine Learning e Processamento de Imagens para a Identificação de Selos Indicativos de Excessos em Embalagens Alimentícias
items: Use Of Predictive Recurring Neural Network Models In The Prediction Of Dengue Cases (Previsão de casos de dengue utilizando modelos preditivos derede neural recorrente - RNN)

INTELLIGENT PHARMACEUTICAL INVENTORY MANAGEMENT SYSTEM WITH MACHINE LEARNING (SISTEMA INTELIGENTE DE GESTÃO DE ESTOQUE FARMACÊUTICO COM MACHINE LEARNING)
type: item
description: https://doi.org/10.5281/zenodo.20738661 O presente artigo apresenta o desenvolvimento de uma prova de conceito (PoC) para um sistema inteligente de gestão de estoque farmacêutico com uso de Inteligência Artificial (IA). O objetivo é utilizar o procedimento de previsão Prophet para oferecer um modelo que suporte a tomada de decisão sobre demanda com base em dados. O desenvolvimento inclui pesquisa de campo com representante farmacêutico, levantamento de dados do setor e estruturação de um modelo preditivo aplicado sobre históricos de vendas. A proposta é a redução de perdas por vencimento e o aumento da disponibilidade de produtos essenciais. O trabalho contribui para a modernização do setor farmacêutico.
subject: Artigos
  topic: section
subject: Gestão de estoque
  topic: keywords
subject: Prophet
  topic: keywords
subject: Previsão
  topic: keywords
subject: Farmácias
  topic: keywords
creator: Eduardo Alonzo Begattini
creator: Felipe Amaral Benedicto
creator: Mariana Cabral de Souza Martins
creator: Matheus Tonini Matsumoto Pantaleão
creator: Ruy Barbosa Castro
creator: Alexander Barreira
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-17
publication date: 2026-06-17
starting page: 22
ending page: 22

https://journal.sptech.school/index.php/ojs/article/view/58
type: item
title: DIGITAL TRANSFORMATION IN CUSTOMER SERVICE: IMPLEMENTING AI TO IMPROVE EFFICIENCY AND CUSTOMER EXPERIENCE IN SMALL BUSINESSES (Transformação Digital no Atendimento: Implementação de IA na Eficiência e Experiência de Clientes em pequenos Comércios.)
description: https://doi.org/10.5281/zenodo.20737408 Este artigo apresenta o desenvolvimento de uma solução voltada para a centralização de pedidos em pequenos comércios, promovendo a transformação digital no atendimento ao cliente. O objetivo principal é integrar canais comunicação em uma única plataforma, utilizando inteligência artificial para automatizar o atendimento, sugerir produtos com base em histórico de compras e gerar relatórios de desempenho. A metodologia utilizada incluiu a pesquisa aplicada com abordagem qualitativa, aliada ao desenvolvimento de um protótipo funcional. Foram realizadas entrevistas com pequenos comerciantes para identificar as principais dores e validar os requisitos da solução. O projeto propõe uma arquitetura escalável com foco em governança de dados, fluxo de pedidos e observabilidade. Os resultados demonstram ganhos significativos na organização dos atendimentos, na redução de erros operacionais e na melhoria da experiência do cliente. Além disso, destaca-se o potencial da ferramenta para ampliar a capacidade de análise e tomada de decisão dos gestores dos pequenos negócios. Concluiu-se que a implementação de IA, mesmo em soluções simples, pode democratizar o acesso à tecnologia e fortalecer a competitividade de pequenos empreendedores no cenário digital atual.
subject: Artigos
  topic: section
subject: Transformação digital
  topic: keywords
subject: Pequenos comércios
  topic: keywords
subject: Atendimento automatizado
  topic: keywords
subject: Inteligência artificial
  topic: keywords
subject: Omnichannel
  topic: keywords
creator: Erick Breno Pontes Pio
creator: Henrique Medeiros Alves
creator: Henrique Oliveira da Silva
creator: Joaquim Gimenes Pires
creator: Nicolas Gonzaga
creator: Gislayno Ficuciello Monteiro
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-17
publication date: 2026-06-17
starting page: 60
ending page: 60

https://journal.sptech.school/index.php/ojs/article/view/57
type: item
title: APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE FOR INTELLIGENT PROCESSING OF INVOICES IN SMALL CONDOMINIUM MANAGEMENT COMPANIES (APLICAÇÃO DE INTELIGÊNCIA ARTIFICIAL GENERATIVA PARA PROCESSAMENTO INTELIGENTE DE NOTAS FISCAIS EM ADMINISTRADORAS DE CONDOMÍNIO DE PEQUENO PORTE)
description: https://doi.org/10.5281/zenodo.20736713 Este trabalho tem como objetivo aplicar uma automação no processo de contas a pagar de empresas de administração de condomínios de pequeno porte, explorando o impacto da aplicação de recursos de Inteligência Artificial Generativa do Google. Busca-se demonstrar como a integração de um sistema de IA generativa com o ERP Next pode reduzir custos e minimizar falhas decorrentes de atividades manuais, que ainda são comuns nesse setor e resultam em comunicação ineficaz e baixa visibilidade de custos. Dessa forma, o artigo apresenta uma abordagem prática para a extração e tratamento de documentos financeiros, utilizando ferramentas da Google como meio de modernizar e aprimorar a gestão financeira condominial.
subject: Artigos
  topic: section
subject: Inteligência Artificial Generativa
  topic: keywords
subject: Automação de Processos
  topic: keywords
subject: Contas a Pagar
  topic: keywords
subject: Administração de Condomínios
  topic: keywords
subject: ERP Next
  topic: keywords
subject: IDP
  topic: keywords
creator: GABRIELA SOUZA DIAS
creator: JULIA BARBOZA PEREIRA
creator: LEANDRO VIEIRA DA COSTA
creator: MURILO MIGUEL PINHONE DOS SANTOS
creator: VITOR CAMPOS MACAUBA
creator: Gerson Pinto dos Santos
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-17
publication date: 2026-06-17
starting page: 25
ending page: 25

https://journal.sptech.school/index.php/ojs/article/view/56
type: item
title: Automation of the Enrollment Process in Higher Education Institutions: Efficiency in the Verification of Academic Documents(Automação do processo de matrícula em Instituições de Ensino Superior: Eficiência na conferência de documentos acadêmicos
description: https://doi.org/10.5281/zenodo.20735301 This article addresses the automation of the enrollment process in Higher Education Institutions (HEIs), focusing on efficiency and reliability in the verification of enrollment documents. The study stems from the issue of dependency on manual validations, which directly impacts the productivity of academic offices. The application proposed in this article aims to validate the hypothesis that the use of Artificial Intelligence Generative (GenAI) and automation can optimize the enrollment workflow by reducing errors, processing time, and rework. The methodology adopted involved mapping the current enrollment process, developing a Proof of Concept (POC) using Large Language Models (LLM – GPT), and creating an integrated interface for uploading, reading, validating, and storing documents. The results indicated that the AI-based approach reduced document verification time by up to 80% and virtually eliminated inconsistencies associated with manual work, demonstrating feasibility and operational gains in the task. It is concluded that the use of intelligent solutions represents a significant advancement in the digital transformation of HEIs, promoting agility, security, and sustainability in the management of students’ academic data.
subject: Artigos
  topic: section
subject: Enrollment
  topic: keywords
subject: Higher Education Institutions
  topic: keywords
subject: Automation
  topic: keywords
subject: Artificial Intelligence Generative
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Document Verification
  topic: keywords
creator: Débora Rezende Flores
creator: Fábio Moledo de Seabra
creator: Júlia Araripe Lopes
creator: Marco Antonio Campos Junior
creator: Victor Roque Lage
creator: Mauricélio Lauand
creator: Alexander Barreira
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-17
publication date: 2026-06-17
starting page: 57
ending page: 57

https://journal.sptech.school/index.php/ojs/article/view/55
type: item
title: DIGITAL TRANSFORMATION AND SUSTAINABILITY: Using AI for energy efficiency in commercial buildings (TRANSFORMAÇÃO DIGITAL E SUSTENTABILIDADE: Uso de IA paraeficiência energética em edifícios comerciais)
description: https://doi.org/10.5281/zenodo.20734864 A sustentabilidade deixou de ser uma tendência para se tornar um imperativo global, impulsionando empresas a reduzir seu impacto ambiental. No setor imobiliário comercial, a eficiência energética é essencial diante da alta demanda energética e redução de emissões de carbono. No Brasil o crescimento e a valorização de edifícios sustentáveis impulsionam o uso de tecnologias como IoT e IA para otimizar o consumo energético. O presente artigo tem como objetivo analisar como a aplicação de IA pode impactar positivamente a eficiência energética em edifícios comerciais, contribuindo para a redução de custos, aumento da performance operacional e promoção da sustentabilidade no setor. Assim o objetivo desse trabalho é evidenciar como essas tecnologias podem otimizar o consumo de energia em edifícios e como elas podem ajudar a alcançar resultados mais consistentes em relação à eficiência energética e à sustentabilidade. Os resultados do estudo evidenciam um potencial de redução significativa do consumo energético, identificação antecipada de falhas e otimização operacional, demonstrando ganhos concretos em eficiência e sustentabilidade para edifícios comerciais.
subject: Artigos
  topic: section
subject: Sustentabilidade
  topic: keywords
subject: Inteligência Artificial
  topic: keywords
subject: Eficiência Energética
  topic: keywords
subject: Edifícios Comerciais
  topic: keywords
subject: Transformação Digital
  topic: keywords
subject: TI Verde
  topic: keywords
subject: IoT
  topic: keywords
subject: Energia Limpa
  topic: keywords
subject: ESG
  topic: keywords
creator: Mateus do Carmo Souza
creator: Mauricio Uesso Martins
creator: Patrik Rangel Souza
creator: Renato de Lima Tierno
creator: Verônica Antônia Zibordi Teixeira
creator: Fernanda Ferreira Caramico
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-17
publication date: 2026-06-17

Digital Transformation in Retail: Connecting Supermarkets for Buying and Selling Stagnant Inventory(Transformação Digital no Varejo: Conexão entre Supermercados para Compra e Venda de Estoque parado)
type: item
description: https://doi.org/10.5281/zenodo.20734110 O varejo brasileiro, responsável por cerca de 10% do PIB nacional, enfrenta um desafio recorrente: o acúmulo de estoques parados e produtos de baixa rotatividade, que comprometem a rentabilidade e a eficiência operacional. Esse problema decorre da instabilidade da demanda e da dificuldade em equilibrar níveis de estoque com as oscilações do consumo. Diante desse cenário, o presente artigo tem como objetivo apresentar o desenvolvimento da Regiro, uma plataforma B2B destinada a integrar empresas do setor supermercadista do estado de São Paulo, promovendo a otimização do processo de compra e venda de produtos excedentes. A metodologia adotada baseia-se na criação de uma solução tecnológica voltada à venda de estoques parados, ao conectar fornecedores e compradores em um ambiente digital seguro. A plataforma incorpora um sistema de precificação dinâmica com base em inteligência artificial (IA), capaz de analisar o comportamento do mercado e sugerir valores ideais para cada produto, além de um recurso de negociação automatizada, que permite ao comprador propor valores personalizados de acordo com a aquisição do pedido. O objetivo da Regiro é demonstrar como o uso da plataforma pode reduzir perdas financeiras decorrentes de estoques ociosos, liberar capital para reinvestimentos e aumentar a eficiência na cadeia de suprimentos supermercadista. Dessa forma, a plataforma almeja se consolidar como uma ferramenta estratégica para aprimorar a gestão de estoques, fomentar a cooperação entre empresas e fortalecer a inovação no varejo paulista.
subject: Artigos
  topic: section
subject: Varejo brasileiro
  topic: keywords
subject: Estoques parados
  topic: keywords
subject: Plataforma B2B
  topic: keywords
subject: Setor supermercadista
  topic: keywords
subject: Precificação dinâmica.
  topic: keywords
subject: Inteligência artificial
  topic: keywords
subject: Cadeia de suprimentos
  topic: keywords
subject: Inovação
  topic: keywords
creator: Felipe de Oliveira Pires
creator: Gabriel Felipe Macedo
creator: Gustavo Oliveira Goncalves
creator: Isabela Hantke Furlin Tenorio
creator: Pedro Henrique Moretti Pinsan de Oliveira Silva
creator: Teresa Sugimura
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-17
publication date: 2026-06-17
starting page: 40
ending page: 40

https://journal.sptech.school/index.php/ojs/article/view/53
type: item
title: A study on the applicability of emerging technologies and statistical concepts in optimizing inventory in the fashion retail sector (Um estudo sobre aplicabilidade de tecnologias emergentes e conceitos estatísticos na otimização de estoque do varejo de moda)
description: https://doi.org/10.5281/zenodo.20733641 O presente artigo analisa a aplicabilidade de tecnologias emergentes na gestão de estoques do varejo de moda, tendo como referencial o modelo de negócios Fast Fashion e seus desafios relacionados à volatilidade da demanda, sazonalidade e comportamento do consumidor. A partir de uma revisão teórica e de uma prova de conceito (POC) desenvolvida para simular a otimização de estoques, o estudo busca evidenciar como estas tecnologias podem aprimorar a precisão das previsões de demanda, reduzindo desperdícios e aumentando a eficiência operacional. Utilizaremos de algoritmos preditivos para correlacionar dados de vendas com variáveis sazonais, permitindo ajustes dinâmicos de estoque e melhor alinhamento entre oferta e demanda.
subject: Artigos
  topic: section
subject: Tecnologias emergentes
  topic: keywords
subject: Varejo de moda
  topic: keywords
subject: Gestão e otimização de estoque
  topic: keywords
creator: Bruna Nascimento Carvalho
creator: Gabriela Romanini Silva
creator: Hilary Leticia Araujo do Nascimento Lima
creator: Kaique Siqueira Santana
creator: Nathalia Aparecida Burlina Marques
creator: Fernando Duarte Brandão
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-17
publication date: 2026-06-17
starting page: 48
ending page: 48

https://journal.sptech.school/index.php/ojs/article/view/52
type: item
title: The Use of Artificial Intelligence in Bonus Distribution: Contributions to Transparent and Strategic Human Resources Management(A Utilização de Inteligência Artificial na Distribuição de Bonificações: Contribuições para a Gestão Transparente e Estratégica de Recursos Humanos)
description: https://doi.org/10.5281/zenodo.20633598 O objetivo deste artigo é analisar como a Inteligência Artificial pode contribuir para a distribuição eficiente e estratégica de bonificações no setor de Recursos Humanos. Para isso, desenvolveu-se uma prova de conceito (POC) que aplica técnicas de automação, normalização de dados e critérios objetivos de avaliação. O método incluiu o desenvolvimento de uma aplicação web, a definição de modelos matemáticos para cálculo de bonificações e a validação dos resultados por meio de simulações. Os resultados indicam ganho de precisão, maior transparência e redução de viés nas decisões relacionadas ao desempenho dos colaboradores. Conclui-se que o uso de IA fortalece o papel estratégico do RH ao permitir processos mais justos, rápidos e embasados em dados.
subject: Artigos
  topic: section
subject: inteligência artificial
  topic: keywords
subject: bonificações
  topic: keywords
subject: recursos humanos
  topic: keywords
subject: automação
  topic: keywords
subject: gestão estratégica
  topic: keywords
creator: Arthur Felix Latorre Simão
creator: Gabriel Cavalcanti Silva
creator: Gabriel Henrique de Camargo Bordin
creator: Luigi Perissinotto Ceolin
creator: Wilker Fructuoso
creator: Alexander Barreira
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-10
publication date: 2026-06-10
starting page: 17
ending page: 17

AUTOMATION OF THE PROCESS OF SENDING ADMISSION DOCUMENTS VIA OCR (Automatização do Processo de Envio de Documentos Admissionais Via OCR)
type: item
description: https://doi.org/10.5281/zenodo.20633042 Segundo a pesquisa “AI Recruitment Statistics: What Is the Future of Hiring?” (Estatísticas de recrutamento de Inteligência Artificial - IA: qual é o futuro das contratações?) realizada pela Tidio, empresa que utiliza inteligência artificial como base para apresentar soluções a seus clientes, 67% dos profissionais de Recursos Humanos acreditam que a IA possui muitos benefícios e exerce um impacto positivo no processo de recrutamento. Entre os entrevistados 44% acreditam que a IA liberará o tempo do recrutador para efetuar outras atividades. Aproximadamente 87% consideram a IA uma tecnologia útil que substituirá algumas etapas do processo de contratação (TIDIO, 2023). Diante dessa necessidade, uma das tecnologias mais utilizadas para automatizar o processo de admissão é a OCR (Optical Character Recognition), ou, em português, Reconhecimento Óptico de Caracteres, que realiza a leitura e extração automática de textos a partir de imagens ou documentos digitalizados. A DocFlow utiliza desta tecnologia para reduzir em até 95% o tempo necessário para a digitalização e validação de documentos RG, CPF, Título de Eleitor e Carteira de Trabalho. Esses documentos, por sua vez pode apresentar inconsistências, seja nos dados presentes, na preservação física ou na validade do documento em si. Com a integração do OCR à nossa plataforma, nossos clientes ganham eficiência e eficácia em todo o processo, sendo beneficiados com recursos de validação, acompanhamento do status e visão global sobre o impacto de todos os documentos e etapas que compõem o processo admissional da empresa.
subject: Artigos
  topic: section
subject: OCR
  topic: keywords
subject: Admissão
  topic: keywords
subject: IA
  topic: keywords
subject: RH
  topic: keywords
subject: Tecnologia
  topic: keywords
subject: Textract
  topic: keywords
creator: Gabriel Antônio Araujo de Medeiros
creator: Larissa da Silva Gouveia
creator: Lucas de Oliveira Navasconi
creator: Pedro Elias Cordeiro Neto
creator: Rafaela Souza Dias
creator: Rafael Petry
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-10
publication date: 2026-06-10
starting page: 24
ending page: 24

Marine Debris Recognition with Artificial Intelligence (Detecção de resíduos marinhos utilizando inteligência artificial)
type: item
description: https://doi.org/10.5281/zenodo.20601206 Abstract Marine pollution, particularly solid waste and plastics, poses an escalating threat to marine ecosystems, biodiversity, and human health. Contrary to the outdated perception of oceans as limitless waste repositories, it is now understood that plastic materials persist for centuries, fragmenting into microplastics that infiltrate the food chain. Research indicates that approximately 11 million metric tons of plastic enter the oceans annually, contributing to an accumulated total of nearly 200 million tons. In Brazil, this issue is exacerbated by intense port activities and insufficient waste management infrastructure. This reality underscores the urgent need for effective monitoring and prevention strategies. This study proposes a machine learning-based solution for the automatic detection and classification of marine debris. We evaluated multiple architectures, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), to determine the most effective approach for identifying pollution in underwater imagery. The results demonstrate that the CNN model achieved the highest performance, with 82.5% accuracy. Furthermore, we developed a web-based platform to democratize access to these environmental insights, aiming to assist NGOs and government agencies in mitigating critical pollution hotspots.
subject: Artigos
  topic: section
subject: marine pollution
  topic: keywords
subject: ocean plastics
  topic: keywords
subject: microplastics
  topic: keywords
subject: biodiversity
  topic: keywords
subject: machine learning monitoring
  topic: keywords
subject: marine waste
  topic: keywords
creator: João A. B. Conceição
creator: Victor H. M. Nascimento
creator: Leonardo T. Aguiar
creator: Guilherme A. A. Barros
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-08
publication date: 2026-06-08
starting page: 50
ending page: 50

https://journal.sptech.school/index.php/ojs/article/view/49
type: item
title: Detection of Viral and Bacterial Pneumonia Through Pattern Recognition and Machine Learning on Chest X-rays (Detecção de pneumonia viral e bacteriana por meio de reconhecimento de padrões e aprendizado de máquina em radiografias de tórax)
description: https://doi.org/10.5281/zenodo.20600900 Abstract During 2024, there were more than 245 thousand cases of pneumonia in Brazil which led to admissions to hospitals, an increase of 30% compared to the previous year. Therefore, our objective was the development of techniques that provide a quick and reliable identification of this disease. In that regard, we developed an image recognition application, capable of distinguishing unhealthy individuals from a pool of X-ray images, and identifying which type of pneumonia (viral or bacterial) present. This application would be accessible to healthcare professionals such as nurses and physicians via a user-friendly front end application such as a website. In order to identify the images, we trained a machine learning model using a dataset[1] containing labelled X-ray images of both healthy and unhealthy lungs, and later on we switched to a deep learning model. When an external test was run, the results were not impressive so we decided to return to the previous model and improve it using the KFold technique. The result obtained was a model with both accuracy and precision close to 75%, capable of reliably detecting unhealthy lungs and which type of pneumonia was present if any. This model did not use CNN and was composed of XGBoost with KFold and image treatment. After that the model was made available through a proof of concept website, which let the user upload an X-ray image of their chest and receive the model’s prediction, so even lay people could use the application.
subject: Artigos
  topic: section
subject: Pneumonia
  topic: keywords
subject: Image Recognition
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Felipe Corrêa de Lima
creator: Gabriel Duarte
creator: João Victor Hengler
creator: Pamela Labonia Morais
creator: Vinicius Alves Vasques
creator: Domingos Sanches
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-08
publication date: 2026-06-08
starting page: 54
ending page: 54

Application for detecting breast cancer in mammography images using neural network technology(Aplicação para detectar câncer de mama em imagens de mamografias com a técnica de redes neurais)
type: item
description: https://doi.org/10.5281/zenodo.20600286 ABSTRACT Given the alarming and growing rate of breast cancer cases, a computational model was developed to assist healthcare professionals and optimize the tumor diagnosis process. When detected early, breast cancer has a higher likelihood of effective treatment and better prognoses. However, the manual analysis of medical exams is time-consuming and prone to human error. According to the Brazilian National Cancer Institute (INCA), breast cancer is the most prevalent type among women in Brazil, with an estimated 73,610 new cases per year for the 2023–2025 period (INCA, 2024). International studies indicate that the use of artificial intelligence techniques in mammography interpretation can increase early tumor detection by up to 47%, while also reducing diagnostic errors and medical analysis time (Nature Medicine, 2023). This study aims to evaluate machine learning and computer vision models applied to breast cancer detection in mammograms. Four distinct models were implemented and compared: YOLO (You Only Look Once), designed for automatic detection of suspicious regions in images; Random Forest, used for classifying patterns extracted from the images; CNN (Convolutional Neural Network), applied for analyzing complex visual features; and ResNet (Residual Neural Network), employed for hierarchical feature extraction and accuracy enhancement. Among the evaluated models, ResNet achieved the best performance in terms of accuracy and generalization capability, demonstrating to be the most promising approach for automated support in early breast cancer diagnosis. Thus, this study seeks not only to accelerate the detection of breast abnormalities but also to provide intelligent support for clinical decision-making, contributing to more precise diagnoses, more effective treatments, and the preservation of lives through the integration of technology and medicine.
subject: Artigos
  topic: section
subject: Breast cancer
  topic: keywords
subject: Early diagnosis
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Image processing
  topic: keywords
subject: Machine learning
  topic: keywords
creator: Guilherme Coimbra Carneiro De Brito
creator: Gustavo Da Silva Nogueira
creator: João Pedro Miziara
creator: Matheus Gonçalves De Souza
creator: Ryan Yuji Miyazato
creator: Eduardo Verri
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-08
publication date: 2026-06-08
starting page: 44
ending page: 44

https://journal.sptech.school/index.php/ojs/article/view/47
type: item
title: Back to the Basics: A Resource-Efficient PCA and Voting Ensemble (KNN, SVM, XGBoost) for Brain Tumor Classification, Challenging Deep Learning Performance (De volta ao básico: uma votação de conjunto (KNN, SVM, XGBoost) com PCA e uso eficiente de recursos para classificação de tumores cerebrais, desafiando o desempenho do aprendizado profundo)
description: https://doi.org/10.5281/zenodo.20215092 The present study addresses the use of artificial intelligence (AI) in the field of neuro-oncology, particularly in the classification of brain tumors using Magnetic Resonance Imaging (MRI). This applied research, based on an exploratory method, adopts an inductive experimental approach to perform a comparative analysis between Deep Learning architectures (DeiT and ResNet) and classical supervised models optimized through Ensemble Learning (Voting Classifier: XGBoost, SVM, and k-NN). The study faced the challenge of domain shift, implementing a preprocessing pipeline with histogram equalization techniques (CLAHE) and a focus on PCA. The results highlighted the nuances of each approach: DeiT achieved high performance, reaching an average of 96% accuracy in generalization, though with high computational cost and low interpretability. In contrast, the Ensemble model achieved an average of 82% accuracy, standing out for its CPU efficiency and greater transparency. The study concludes that, although deep networks define the upper limit of accuracy, optimized classical approaches represent a viable and cost-effective alternative for medical decision support, enabling the selection of the most suitable tool according to infrastructure constraints and ethical requirements.
subject: Artigos
  topic: section
subject: PCA
  topic: keywords
subject: Tumor
  topic: keywords
subject: DeiT
  topic: keywords
subject: Ensemble learning
  topic: keywords
subject: Voting
  topic: keywords
subject: XGBoost
  topic: keywords
subject: SVM
  topic: keywords
subject: k-NN
  topic: keywords
subject: ResNet
  topic: keywords
subject: CLAHE
  topic: keywords
creator: Davi G. S. de Paula
creator: Gustavo Antonio
creator: Matheus Gomes Da Silva
creator: Pedro H. J. Varela
creator: Rafaella Piovezan Filipe
creator: Vinícius Da Silva Sousa
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-15
publication date: 2026-05-15
starting page: 63
ending page: 63

https://journal.sptech.school/index.php/ojs/article/view/46
type: item
title: CHALLENGE FOR THE CLASSIFICATION OF LABELED RECYCLABLE MATERIALS FOCUSED ON GLASS AND PLASTIC USING ARTIFICIAL INTELLIGENCE AND COMPUTER VISION(Desafio para a classificação de materiais recicláveis rotulados com foco em vidro e plástico utilizando inteligência artificial e visão computacional)
description: https://doi.org/10.5281/zenodo.20214267 This work presents the development of an automated system for identifying and classifying labeled recyclable materials, with emphasis on plastic and glass, using computer vision and artificial intelligence techniques. Considering the growing volume of urban solid waste and the low efficiency of manual sorting methods, the proposed solution aims to improve the accuracy, speed, and safety of the separation process. For this purpose, a dataset in YOLO format was prepared and processed through the Roboflow platform, and the YOLOv11 model was trained in two distinct versions, enabling comparative performance analysis. The results show significant improvements, especially in the “glass” class, whose recall increased from 66.67% to 89.66%. The comparison between versions highlights advancements in stability and overall model performance. The developed system demonstrates potential to optimize recycling processes, reduce operational costs, enhance the quality of recovered materials, and foster sustainable practices aligned with the circular economy.
subject: Artigos
  topic: section
subject: Machine Learning
  topic: keywords
subject: Object Detection
  topic: keywords
subject: AWS (Amazon Web Services)
  topic: keywords
subject: Roboflow
  topic: keywords
subject: Image Processing
  topic: keywords
subject: Recyclable Material Classification
  topic: keywords
subject: YOLOv11
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Classification Model
  topic: keywords
creator: Giovana S. Mainente
creator: Helder D. R. Alvarenga
creator: Pedro H. F. Dias
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-15
publication date: 2026-05-15
starting page: 39
ending page: 39

https://journal.sptech.school/index.php/ojs/article/view/45
type: item
title: D.E.E.P. – Dynamic Emotion Extraction Process: A Machine Learning-Based Approach for Facial Expression Analysis and Emotional Trends – Case studies with real images (Processo de extração dinâmica de emoções: Uma abordagem baseada em machine learning para análise de microexpressões faciais e tendências emocionais – Estudo de caso com imagens reais)
description: https://doi.org/10.5281/zenodo.20209786 This paper presents the development of D.E.E.P. (Dynamic Emotion Extraction Process), an innovative machine learning-based computational tool. The primary goal of D.E.E.P. is to identify emotional and behavioral trends through detailed analysis of facial expressions and facial expressions extracted from videos. The comprehensive methodology encompasses visual data collection, sophisticated image processing, robust machine learning model training, and the subsequent generation of an emotional timeline. This second version of the work elaborates on the theoretical foundations supporting the solution, as well as the main challenges encountered during development, such as overfitting issues and initial difficulties with K-Means clustering. It also highlights the integration of a novel dataset obtained in collaboration with Dr. Wilma Bainbridge, Ph.D., from the University of Chicago, and the documentation of code progression. Preliminary results suggest the practical applicability and robustness of the proposed system.
subject: Artigos
  topic: section
subject: Emotion recognition
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Facial expressions
  topic: keywords
subject: Overfitting
  topic: keywords
creator: Abner B. M. Nunes
creator: Diana A. S. Lima
creator: Guilherme H. A. Dias
creator: Matheus B. Caus
creator: Victor Z. Rubinec
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-15
publication date: 2026-05-15
starting page: 71
ending page: 71

APPLICATION OF PROHIBITED OBJECT DETECTION IN AIRPORT BAGGAGE:A hybrid approach with YOLO for gun and knife Identification
type: item
description: https://doi.org/10.5281/zenodo.20187690 The accelerated growth of air transport in recent decades has brought increasing challenges to airport security, especially regarding the detection of unauthorized objects in baggage. Traditional methods—based on X-ray devices and the visual analysis performed by operators—present important limitations, such as human error and fatigue. Seeking to mitigate these flaws, this work proposed and comparatively evaluated different machine learning (ML) models to automatically identify unauthorized objects in X-ray images. The methodology included the collection and pre-processing of the images, the training of the YOLO model, and the evaluation of its performance using metrics such as accuracy, precision, and recall. The results demonstrated that this approach proved to be an agile and reliable solution for identifying threats. This study highlights the potential of artificial intelligence (AI) as a complementary tool in risk prevention in critical environments. The proposal aims to reduce dependence on human examination, increase screening efficiency, and can be integrated into automated inspection systems, contributing to the modernization of processes in the face of the continuous growth in passenger flow.
subject: Artigos
  topic: section
subject: Airport Security
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Threat Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Davi Bordon Silva
creator: Luis Henrique Feitosa Nunes
creator: Matheus Leal de Oliveira
creator: Miguel Carvalho Schrempp
creator: Vitor Gonçalves da Silva
creator: Giuliana Miniguitti França
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal
date: 2026-05-15
publication date: 2026-05-15
starting page: 60
ending page: 60

APPLICATION OF MACHINE LEARNING IN THE IDENTIFICATION OF FACIAL DERMATOLOGICAL DISEASES
type: item
description: https://doi.org/10.5281/zenodo.20187222 The shortage of dermatologists in Brazil creates barriers to equitable access to dermatological care. This study proposes machine learning strategies for automated diagnosis of facial dermatological lesions using public datasets. It addresses issues such as ethnic representativeness, algorithmic bias, and technological limitations, highlighting the potential of automated tools to enhance healthcare access.
subject: Artigos
  topic: section
subject: Dermatology
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Healthcare access
  topic: keywords
creator: Agda Sayuri Sato Taniguchi
creator: Ivan Miranda
creator: Maria Eduarda Neves
creator: Ricardo Teixeira dos Santos
creator: Vitor Fernando da Silva Costa
creator: Marise Miranda
creator: Marcelo Rosim
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-15
publication date: 2026-05-15
starting page: 57
ending page: 57

Machine Learning Platform for Retinography: Architecture, Engineering and Multiclass Classifier (Plataforma de Machine Learning para Retinografia: Arquitetura, Engenharia e Classificador Multiclasse)
type: item
description: https://doi.org/10.5281/zenodo.20186594 This study presents the investigative path in building and evaluating machine learning models for the automatic identification of visual disorders from retinal images. The focus is strictly computational: selecting and organizing public datasets, designing reproducible preprocessing pipelines, feature extraction, choosing and comparing algorithms, and experimental validation. This is not a clinical study nor does it provide medical recommendations; the authors are not ophthalmologists. The goal is to design a platform and develop a classifier, applying principles of software engineering, data engineering/MLOps, and pattern-recognition algorithms. The experiments demonstrate the feasibility of the approach and, through comparative analysis, investigate the performance of the algorithms and their configurations, highlighting differences and similarities; the results must be interpreted within the scope of computing, not as clinical evidence or guidance.
subject: Artigos
  topic: section
subject: Machine Learning
  topic: keywords
subject: Pattern Recognition
  topic: keywords
subject: Cataract
  topic: keywords
subject: Glaucoma
  topic: keywords
subject: Eyes
  topic: keywords
subject: Visual Disorders.
  topic: keywords
creator: Danylo D. Gomes
creator: Igor T. Regali
creator: Kauan B. Cavazani
creator: Leonardo V. Paulino
creator: Vinicius S. Cardoso
creator: Domingos Sanches
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-15
publication date: 2026-05-15
starting page: 71
ending page: 71

https://journal.sptech.school/index.php/ojs/article/view/41
type: item
title: Comparative Evaluation of Conventional Classification Techniques and Deep Learning Models in the Detection of Fungal Phytopathogens in Agricultural Crops (Avaliação Comparativa de Técnicas Convencionais de Classificação e Modelos de Deep Learning na Detecção de Fitopatógenos Fúngicos em Cultivos Agrícolas)
description: https://doi.org/10.5281/zenodo.20183961 This project performed a comparative analysis between classical Machine Learning models and a Convolutional Neural Network (CNN) for the binary classification of rust in plant leaves, evaluating performance in both controlled and unseen (PlantDoc) data scenarios. The study went beyond traditional accuracy analysis by introducing a decision framework based on Predictive Efficiency (IEP) and Real-Time Viability (IVTR) indices. Results demonstrated that CNN superiority is not absolute without calibration: only after applying Fine-Tuning techniques with weighted loss did the model achieve maximum diagnostic reliability (F1-Score of 0.68 for the target class). Simultaneously, a hybrid approach, utilizing the CNN for feature extraction and Logistic Regression for classification, emerged as the most balanced solution, presenting the study's highest IVTR. Conversely, aggressive dimensionality reduction pre-processing, such as binarization, severely degraded predictive performance. The central conclusion is that optimal model selection depends on deployment constraints: the fine-tuned CNN is mandatory for clinical precision, while hybrid architectures offer the best trade-off for edge devices.
subject: Artigos
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Categorization
  topic: keywords
subject: Plants
  topic: keywords
subject: Cultivation
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Rafael A. Raposo
creator: Daniel F. Siqueira
creator: Gabriel V. Correa
creator: Hugo H. Galdino
creator: Marco A. S. Magalhães
creator: Pedro G. F. Jesus
creator: Eduardo Verri
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-15
publication date: 2026-05-15
starting page: 59
ending page: 59

MACHINE LEARNING APPROACH FOR SOILING DETECTION AND ANALYSIS ON SOLAR PANELS (Análise de modelos para detecção de sujidade e danos em placas fotovoltaicas)
type: item
description: https://doi.org/10.5281/zenodo.20166284 This work presents an innovative solution for monitoring solar panels by using images captured through surveillance cameras and drones, aiming to detect the level of dirtiness on photovoltaic modules. The proposed methodology employs machine learning techniques to analyze and categorize the degree of dirt accumulation on the panels, facilitating maintenance for system owners. By providing an automated and accurate assessment of the panels’ condition, the solution seeks to enhance the efficiency of solar energy generation, contributing to the optimal performance of photovoltaic systems and promoting energy sustainability for users.
subject: Artigos
  topic: section
subject: Solar energy
  topic: keywords
subject: Photovoltaic panels
  topic: keywords
subject: Dirt detection
  topic: keywords
subject: Computer vision
  topic: keywords
subject: Computer vision, Machine learning
  topic: keywords
subject: Automated monitoring
  topic: keywords
creator: Brayan C. Aquino
creator: Cauã S. Ciconelli
creator: Felipe G. T. Leite
creator: Filipe F. Guiraldini
creator: João V. S. S. Santana
creator: Claudio Frizzarini
creator: Marise Miranda
rights: Copyright (c) 2026 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-15
publication date: 2026-05-15
starting page: 46
ending page: 46

Artigo Técnico : vulpix.AI - Seu Agente de Marketing
type: item
description: https://doi.org/10.5281/zenodo.17820175 Este artigo apresenta a vulpix.AI, uma plataforma inovadora projetada para auxiliar pequenos empresários a otimizar suas estratégias de marketing digital. Abordamos o contexto do projeto, seus objetivos e a justificativa para sua criação, destacando a necessidade de ferramentas acessíveis e eficazes no cenário atual do e-commerce. Detalhamos a metodologia de desenvolvimento, incluindo as escolhas tecnológicas para backend (Spring Boot), serviço de inteligência artificial (FastAPI), infraestrutura como código (Terraform) e serviços AWS (RDS, S3, EC2), além das estratégias de segurança implementadas. Os resultados obtidos com a implementação da vulpix.AI demonstram o potencial da plataforma em expandir a base de clientes e aumentar lucros para pequenos negócios, redefinindo a abordagem do marketing digital. Por fim, discutimos as implicações das escolhas técnicas e o impacto da solução no mercado.
subject: Artigos
  topic: section
subject: Inteligência Artificial
  topic: keywords
subject: Marketing Digital
  topic: keywords
subject: E-commerce
  topic: keywords
subject: Terraform
  topic: keywords
subject: Serviços AWS
  topic: keywords
subject: Segurança
  topic: keywords
subject: FastAPI
  topic: keywords
creator: Enzo Pereira Martins
creator: Leonardo Aparecido de Paula Santos
creator: Lucas Oristânio Faes
creator: Renan Zammonaro Casalle Gonçalves
creator: Alexander Alves Barreira
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-04
publication date: 2025-12-04
starting page: 16
ending page: 16

Application of the Simplex Method in Optimizing a Gradual Implementation in Credit Modalities (Aplicação do método Simplex na otimização de uma implementação gradual em modalidades de crédito)
type: item
description: https://doi.org/10.5281/zenodo.17858458 The article addresses the fictional case of the company AutoProvision. The main goal is to determine the best combination of clients across different credit modalities (Working Capital, Overdraft, Personal Loan, etc.) to maximize expected profit during an initial phase of gradual project implementation. For this, we use Linear Programming, modeling the problem with decision variables (number of clients per modality), an objective function (maximize total profit), and constraints (capital limits, total client capacity, demand per modality, and a minimum for diversification). The model was implemented in Python using the PuLP library and solved using the Simplex Method. The results define the optimal allocation of clients for each credit line in this first cycle, resulting in a data-driven action plan for the start of AutoProvision's operations, as well as serving as a basis for future analyses and adjustments as the business evolves. The work demonstrates the practical application of optimization tools in a business context, including visualization and analysis of the results.
subject: Artigos
  topic: section
subject: Simplex Method
  topic: keywords
subject: Optimization
  topic: keywords
subject: Linear Programming
  topic: keywords
subject: Operations Research
  topic: keywords
subject: Credit Modalities
  topic: keywords
subject: Credit Portfolio
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Gradual Implementation
  topic: keywords
subject: PuLP (Python Library)
  topic: keywords
subject: Python (Programming Language)
  topic: keywords
subject: Data-Driven Decision Making
  topic: keywords
subject: Mathematical Model
  topic: keywords
subject: Sensitivity Analysis
  topic: keywords
subject: Finance
  topic: keywords
subject: Jupyter Notebook
  topic: keywords
subject: Working Capital
  topic: keywords
subject: Overdraft
  topic: keywords
subject: Personal Loan
  topic: keywords
subject: Payroll Loan
  topic: keywords
subject: Mortgage Loan
  topic: keywords
creator: Joaquim G. Pires
creator: Marise Miranda
creator: Alexander Alves Barreira
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-29
publication date: 2025-07-29
starting page: 23
ending page: 23

https://journal.sptech.school/index.php/ojs/article/view/36
type: item
title: IMAGE CLASSIFICATION FOR IDENTIFICATION OF DEEPFAKES GENERATED BY ARTIFICIAL INTELLIGENCE USING SELF-ATTENTION AND MACHINE LEARNING MODEL (CLASSIFICAÇÃO DE IMAGENS PARA IDENTIFICAÇÃO DE DEEPFAKES GERADAS POR INTELIGÊNCIA ARTIFICIAL UTILIZANDO MODELO DE AUTOATENÇÃO E APRENDIZADO DE MÁQUINA)
description: https://doi.org/10.5281/zenodo.15708576 Com o crescente uso de tecnologias de inteligência artificial (IA) para gerar conteúdos visuais, a necessidade de ferramentas eficazes para distinguir o real do sintético tornou-se indispensável. A disseminação de deepfakes e a manipulação de imagens representam ameaças significativas à integridade da informação e à segurança digital. Este trabalho desenvolveu e avaliou modelos de classificação de imagens para identificar conteúdos gerados por IA com alta precisão, empregando técnicas modernas de aprendizado de máquina e aprendizado profundo. Utilizamos uma base de dados composta por mais de 12 mil imagens, classificadas em conjuntos de treinamento, validação e teste. Diversos modelos foram explorados, incluindo KNN, PCA + SVM, Random Forest e arquiteturas avançadas baseadas em transformadores, como ViT e DeiT. No entanto, os melhores resultados foram alcançados com o modelo ResNet50, após a implementação de técnicas de regularização como dropout, L2 regularization e data augmentation, que mitigaram problemas de overfitting e garantiram uma maior capacidade de generalização do modelo. Os resultados mostraram que a ResNet50, com essas melhorias, alcançou uma acurácia de 97,90%, superando os modelos baseados em transformadores testados, como o DeiT e o ViT. Isso demonstra a eficácia das redes convolucionais, quando ajustadas corretamente, para capturar padrões sutis e características essenciais que distinguem imagens reais de deepfakes. Embora o modelo tenha apresentado um desempenho robusto, o campo da detecção de deepfakes permanece em constante evolução, demandando aprimoramentos contínuos. O aumento da diversidade dos conjuntos de dados e o desenvolvimento de modelos mais resistentes a manipulações adversárias são desafios importantes que precisam ser abordados. Este estudo reforça o potencial das redes convolucionais, combinadas com técnicas de regularização e pré-processamento, na detecção de conteúdos gerados por IA. Como…
subject: Artigos
  topic: section
subject: Inteligência Artificial
  topic: keywords
subject: Classificação de Imagens
  topic: keywords
subject: Redes convolucionais
  topic: keywords
subject: Transformadores
  topic: keywords
subject: Deepfakes
  topic: keywords
subject: Detecção de Falsificação
  topic: keywords
subject: Visão Computacional
  topic: keywords
subject: Segurança Digital
  topic: keywords
creator: Gabriel R. S. Medeiros
creator: Jeremy G. F. Freitas
creator: Pedro H. L. Santos
creator: Victor F. Garcia
creator: Marise Miranda
creator: Cesar Caetano
creator: Domingos Sanches
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-20
publication date: 2025-06-20
starting page: 96
ending page: 96

PREVISÃO DE CASOS DE DOENÇAS RESPIRATÓRIAS COM REDES NEURAIS LSTM: UMA ABORDAGEM PARA ANÁLISE TEMPORAL E IDENTIFICAÇÃO DE EPIDEMIAS
type: item
description: https://doi.org/10.5281/zenodo.15708452 O objetivo deste artigo é apresentar o desenvolvimento e implementação do Anti- Plague, um sistema para análises preditivas de epidemias referente a doenças respiratórias. O projeto utiliza aprendizado de máquina e inteligência artificial para prever a ocorrência de surtos em diferentes regiões, com base em dados históricos de imunização, indicadores socioeconômicos e informações em tempo real de surtos anteriores. A metodologia inclui a coleta e tratamento de dados de diversas fontes, a construção de modelos preditivos com redes neurais e a validação dos resultados. A arquitetura do sistema foi implementada na nuvem, utilizando a infraestrutura da AWS, garantindo escalabilidade e disponibilidade. Os resultados demonstram a capacidade do Anti-Plague em fornecer previsões precisas, com potencial de apoiar decisões rápidas e eficazes na contenção de surtos futuros.
subject: Artigos
  topic: section
subject: Previsão de Epidemias
  topic: keywords
subject: Aprendizado de Máquina
  topic: keywords
subject: Epidemias
  topic: keywords
subject: Inteligência Artificial
  topic: keywords
creator: Felipe D. Salles
creator: Fernando M. Marques
creator: Petterson S. Viturino
creator: Roberta A. Pires
creator: Alexander Barreira
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-20
publication date: 2025-06-20
starting page: 39
ending page: 39

Comparison of Machine Learning Models in Animal Adoption (Comparação de Modelos de Machine Learning na Adoção de Animais)
type: item
description: https://doi.org/10.5281/zenodo.15707701 Neste trabalho, explora-se o uso de inteligência artificial e aprendizado de máquina para otimizar a adoção de animais resgatados. A metodologia envolveu o pré-processamento da base de dados fornecida, que incluiu a limpeza e transformação das variáveis para garantir que os modelos recebessem dados relevantes e estruturados. Após essa etapa, foram executados diversos modelos de aprendizado de máquina, como KNN, Decision Tree, Random Forest e Gradient Boosting, para prever o tempo de adoção dos animais. O desempenho de cada modelo foi avaliado e suas métricas de precisão, recall e outras foram comparadas, a fim de identificar a abordagem mais eficiente. Espera-se que os resultados ajudem as instituições de resgate a alocarem recursos de forma mais inteligente e reduzir a superlotação, priorizando animais com maior chance de adoção rápida.
subject: Artigos
  topic: section
subject: Animal Adoption
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Resource Management
  topic: keywords
subject: Predictability
  topic: keywords
subject: Overcrowding
  topic: keywords
subject: Rescue Institutions
  topic: keywords
subject: Abandoned Animals
  topic: keywords
subject: Overcrowding in Rescue Institutions
  topic: keywords
creator: Enzo L. G. Godoy
creator: Erik S. Pacheco
creator: Lucas M. Souza
creator: Américo Talarico
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-20
publication date: 2025-06-20
starting page: 29
ending page: 29

Deep Learning: Techniques for Ovarian Cancer Subtype Classification and Outlier Detection (Deep Learning: Técnicas para Classificação de Subtipos de Câncer Ovariano e Detecção de Outliers)
type: item
description: https://doi.org/10.5281/zenodo.15706387 This study investigates the application of deep neural networks for ovarian cancer subtype classification and outlier detection, aiming to improve diagnostic accuracy and address interobserver variability. Using the OCEAN Dataset and advanced deep learning techniques, EfficientNetV2 and ResNet152 architectures were compared. The findings demonstrate the superior performance of ResNet152, especially in rare subtypes, underscoring its potential for precise and scalable clinical diagnostics. This work enhances clinical practice by offering a scalable and accurate diagnostic support model.
subject: Artigos
  topic: section
subject: Ovarian Cancer Subtype Classfication
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: AI-assisted Diagnosis
  topic: keywords
subject: Histopathological Analysis
  topic: keywords
creator: Giovana R. Nascimento
creator: Gerson Santos
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-20
publication date: 2025-06-20
starting page: 29
ending page: 29

https://journal.sptech.school/index.php/ojs/article/view/32
type: item
title: Prediction Models Based on Players' Historical Data for Evaluating Competitive Balance in League of Legends Matches (Modelos de Previsão Baseados em Dados Históricos dos Jogadores para Avaliar do Equilíbrio Competitivo em Partidas de League of Legends)
description: https://doi.org/10.5281/zenodo.15702048 This study aims to investigate possible trends in the ranked queues of the game League of Legends, developing predictive models to evaluate the balance of matches based on players' historical data. The data was collected through the game's official API, including statistics such as KDA, gold per minute, vision score, damage per minute, and objectives achieved (towers, dragons, and barons). Different machine learning techniques, including Random Forest, Logistic Regression, and SVM, were implemented and compared, using Python for backend development and data processing. The models were evaluated using metrics such as accuracy, F1 Score, recall, and support. The Random Forest model showed the best performance during training, achieving 96% accuracy. However, when tested with live match data, its performance dropped significantly, with the best result reaching 48%. The confusion matrix revealed the models' limitations in capturing more complex nuances of the matches. Additionally, an analysis of the win-rate proportion between teams was conducted, contributing to a broader understanding of the factors influencing match balance. The results suggest that, although the selected attributes are relevant, they are insufficient to identify patterns indicating imbalance in matches. This evidence highlights the effectiveness of the matchmaking system and the impact of Matchmaking Rating (MMR) on maintaining balance in matches.
subject: Artigos
  topic: section
subject: Machine Learning
  topic: keywords
subject: Ranked Queues
  topic: keywords
subject: Match Analysis
  topic: keywords
creator: Flávia O. Silva
creator: Gabriel L. L. Pedrosa
creator: Lucas H. A. Paula
creator: Américo Talarico
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-19
publication date: 2025-06-19
starting page: 56
ending page: 56

https://journal.sptech.school/index.php/ojs/article/view/31
type: item
title: Investigation of Academic Performance in the Analysis and Systems Development Course in Brazil: Framework for Monitoring and Evaluating Grades (Framework for Analyzing the Patterns that Influence the Progress of Students in the Early Semesters of Systems Analysis and Development Courses in Brazil)
description: https://doi.org/10.5281/zenodo.15701706 The course in Analysis and Systems Development (ADS) is essential to meet the growing demand in the Information and Communication Technology (ICT) market in Brazil, which seeks qualified professionals to support digital transformation. Although salaries in the ICT sector are higher than the national average, Brazil faces challenges in meeting this demand due to the limited number of graduates annually from programs such as ADS. This study analyzes the academic performance of students in the ADS course, focusing on the factors that influence their academic progress, using grades obtained in specific subjects during the first two semesters of the course as the basis. The research was conducted at a private higher education institution, involving data from 160 enrolled students. Subjects such as "Algorithms" and "Programming Language," as well as "Information Technology" and "Systems Analysis," were analyzed, selected for their similar syllabi, allowing for performance analysis over two consecutive semesters. The results indicated that difficulties in the first semester, especially in fundamental subjects such as "Algorithms," affect performance in subsequent subjects, suggesting the existence of patterns in the flow of academic performance. Furthermore, it was observed that performance between subjects shows important relationships, indicating that poor performance in one subject can impact other areas. The categorization of performance profiles allowed for the identification of specific student groups that may benefit from support strategies, such as enhanced teaching, remedial activities, and personalized monitoring.
subject: Artigos
  topic: section
subject: Academic Performance
  topic: keywords
subject: Analysis and Development of Systems
  topic: keywords
subject: ADS
  topic: keywords
subject: Information and Communication Technology
  topic: keywords
subject: ICT
  topic: keywords
subject: Information Technology
  topic: keywords
subject: IT
  topic: keywords
subject: Factors of Academic Performance
  topic: keywords
subject: Grades and Performance
  topic: keywords
subject: Private Higher Education
  topic: keywords
subject: Academic Performance in Subjects
  topic: keywords
subject: Educational Challenges
  topic: keywords
subject: Academic Support Strategies
  topic: keywords
subject: Education in Technology
  topic: keywords
subject: Categories of Academic Performance
  topic: keywords
subject: Student Performance Profiles
  topic: keywords
subject: Performance Analysis
  topic: keywords
subject: Student Classification
  topic: keywords
subject: Monitoring of Academic Progress
  topic: keywords
subject: Performance Flow
  topic: keywords
subject: Performance Stability
  topic: keywords
subject: Academic Instability
  topic: keywords
subject: Performance Standard
  topic: keywords
subject: Comparison Betweeen Subjects
  topic: keywords
subject: Relationship Between Subjects
  topic: keywords
subject: Curricular Connection
  topic: keywords
subject: Interdisciplinary Performance
  topic: keywords
subject: Differences in Academic Performance
  topic: keywords
subject: Comparative Performance Analysis
  topic: keywords
subject: Areas of Academic Focus
  topic: keywords
subject: Continuous Academic Monitoring
  topic: keywords
subject: Grade Categorization
  topic: keywords
subject: Grade Evolution Analysis
  topic: keywords
subject: Subjects Comparison
  topic: keywords
creator: Henrique P. S. Silva
creator: João Victor R. Araujo
creator: Cláudio Frizzarini
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-19
publication date: 2025-06-19
starting page: 48
ending page: 48

Aplicação de Técnicas de Machine Learning e Processamento de Imagens para a Identificação de Selos Indicativos de Excessos em Embalagens Alimentícias
type: item
description: https://doi.org/10.5281/zenodo.15700909 Este estudo investigou a viabilidade da aplicação de técnicas de Machine Learning e processamento de imagens em dois cenários distintos relacionados à análise de embalagens alimentícias. O estudo principal focou na detecção de selos de alerta (indicando excesso de gordura, açúcar ou sódio) utilizando uma base de dados própria com 92 imagens distintas coletadas pelas autoras em supermercados. Para o reconhecimento de padrões visuais, foram empregadas redes neurais convolucionais (CNNs), com o uso de data augmentation para aumentar a diversidade do conjunto de dados e aprimorar a robustez do modelo. Os resultados obtidos demonstraram uma acurácia de 0.83, com métricas de precisão (0.89), recall (0.89) e F1-score (0.89) para a detecção de selos em embalagens planas, como as de barras de chocolate. Antes do estudo principal, foi realizado um estudo experimental que visou a extração automática de informações nutricionais de embalagens, com o objetivo de transcrever essas informações para facilitar o acesso de pessoas com deficiência visual, analfabetos e crianças. A base de dados utilizada foi a OpenFoodFacts API. Para este estudo, foi empregado o método de Reconhecimento Óptico de Caracteres (OCR), com destaque para o uso da ferramenta Tesseract. Apesar de serem aplicadas técnicas de tratamento de imagem, como a redução de ruídos, sombras e curvaturas, foi identificado que, com o código utilizado, não era possível obter bons resultados em imagens que apresentassem sombras ou curvaturas. Essas condições adversas comprometeram o desempenho do OCR, dificultando a extração precisa do texto das embalagens. O estudo conclui que, embora o reconhecimento de texto em embalagens alimentícias exija melhorias adicionais no pré-processamento de imagens, a abordagem proposta para a detecção de selos apresenta-se promissora, com desempenho satisfatório e aplicabilidade prática no cenário analisado.
subject: Artigos
  topic: section
subject: Processamento de Imagens
  topic: keywords
subject: Reconhecimento Óptico de Caracteres
  topic: keywords
subject: Redes Neurais Convolucionais
  topic: keywords
subject: Informações Nutricionais
  topic: keywords
creator: Ana L. Freitas
creator: Isabella S. Correa
creator: Rafaela A. Soares
creator: Gerson Santos
creator: Marise Miranda
creator: Domingos Sanches
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-19
publication date: 2025-06-19
starting page: 53
ending page: 53

Use Of Predictive Recurring Neural Network Models In The Prediction Of Dengue Cases (Previsão de casos de dengue utilizando modelos preditivos derede neural recorrente - RNN)
type: item
description: https://doi.org/10.5281/zenodo.15699989 This research addresses the prediction of dengue outbreaks using statistical data, aiming to develop a predictive model that assists in the early identification of dengue outbreaks in the State of São Paulo. A quantitative approach was employed, utilizing statistical analysis techniques and mathematical models to process and interpret data collected from epidemiological and meteorological sources. Factors such as temperature, humidity, age, population, gender, waste collection, and access to treated water were analyzed, as well as the incidence of dengue cases in different regions of the State between the years 2010 and 2024. The methodology included the application of time series in predictive models developed using Recurrent Neural Networks (RNN) with three types of layers: Simple RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results indicated that the combination of climatic variables and historical dengue data allows for the accurate prediction of new outbreaks. It was concluded that the application of statistical models in predicting dengue outbreaks can be a valuable tool for public health authorities, enabling the implementation of preventive and control measures more effectively and promptly, thus contributing to reducing the impact of dengue in affected communities.
subject: Artigos
  topic: section
subject: Dengue
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Neural Network
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Amanda F. Caramico
creator: Felipe X. O. Braga
creator: Igor S. B. Souza
creator: Kevin G. Dias
creator: Américo Talarico
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal
date: 2025-06-19
publication date: 2025-06-19
starting page: 52
ending page: 52