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: 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)
items: APPLICATION OF MACHINE LEARNING IN AUTOMATION AND CLASSIFICATION OF TAX AND FINANCIAL DOCUMENTS (Aplicação de Machine Learning na Automação e Classificação de Documentos Fiscais e Financeiros)
items: FACIAL RECOGNITION OF MISSING PERSONS THROUGH MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE (Reconhecimento facial de pessoas desaparecidas através de aprendizado de máquina e inteligência artificial)
items: https://journal.sptech.school/index.php/ojs/article/view/26
items: COVID-19 CASE FORECASTING USING NEURAL NETWORK ALGORITHMS (PREVISÃO DE CASOS DE COVID-19 UTILIZANDO ALGORITMOS DE REDES NEURAIS)
items: DEEP LEARNING TECHNIQUES APPLIED IN STOCK MARKET FORECASTS (TÉCNICAS DE DEEP LEARNING APLICADAS EM PREVISÕES NO MERCADO DE AÇÕES)
items: Detecção de tumores baseada em aprendizado de máquina: uma abordagem para detecção mais assertiva.
items: Acne Severity Classification Using Convolutional Neural Networks (Classificação de Severidade de Acne Utilizando Redes Neurais Convolucionais)
items: Outsmarting Malware Using AI to Combat WannaCry and Beyond
items: AFFIRMATIVE ACTION POSITIONS ON LINKEDIN - DIVERSITY ANDINCLUSION, TECHNOLOGICAL INNOVATION
items: Challenges in Healthcare Information Management: How OpenHealth Can Help
items: Digital Transformation in the Real Estate Market: Case Study of the I9Dommus Proposal
items: Digital Transformation in UBS Clinics in the City of São Paulo

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

APPLICATION OF MACHINE LEARNING IN AUTOMATION AND CLASSIFICATION OF TAX AND FINANCIAL DOCUMENTS (Aplicação de Machine Learning na Automação e Classificação de Documentos Fiscais e Financeiros)
type: item
description: https://doi.org/10.5281/zenodo.15694882 A presente pesquisa teve como objetivo desenvolver uma solução automatizada para o processo de classificação e extração de dados de documentos fiscais em uma das principais instituições financeiras brasileiras. Atualmente, esse processo é realizado de forma manual, por meio da contratação de empresas terceiras e da alocação de recursos internos, o que acarreta altos custos operacionais e perda de eficiência. A aplicação proposta neste estudo visa classificar o tipo de documento fornecido pelo usuário, extrair as informações relevantes e cadastrá-las de forma organizada em um banco de dados. Para a classificação dos documentos fiscais e financeiros, foi utilizado o algoritmo Naive Bayes com modelos multinomiais. A extração dos dados foi realizada por meio do desenvolvimento de uma aplicação em Python, customizada para atender às especificidades do problema em questão. Os resultados obtidos demonstram que a solução proposta apresenta um alto grau de precisão na classificação e extração das informações, com potencial para otimizar significativamente os processos internos da instituição.
subject: Artigos
  topic: section
subject: Machine Learning
  topic: keywords
subject: Automação
  topic: keywords
subject: Notas fiscais e impostos
  topic: keywords
subject: Naïve-Bayes
  topic: keywords
creator: Gabriel P. S. Fernandes
creator: Victor M. S. Aquino
creator: Bruno R. Oliveira
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: 2025-06-19
publication date: 2025-06-19
starting page: 31
ending page: 31

FACIAL RECOGNITION OF MISSING PERSONS THROUGH MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE (Reconhecimento facial de pessoas desaparecidas através de aprendizado de máquina e inteligência artificial)
type: item
description: https://doi.org/10.5281/zenodo.15679023 The disappearance of people is a social problem of great magnitude, causing immeasurable suffering to the families and friends of missing individuals. Faced with this problem, this Course Conclusion Paper (TCC) proposes the development of the "SeekingLost" platform, an innovative tool that uses facial recognition to assist in the search for missing people. The SeekingLost platform will allow users to register photos of missing people, whether they are family, friends or acquaintances. The images will then be compared with a database of faces. The platform, equipped with a facial recognition model, calculates a percentage chance that the missing person matches the person present in the provided image.
subject: Artigos
  topic: section
subject: Missing People
  topic: keywords
subject: Facial Recognition
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Family Reunification
  topic: keywords
creator: Bruno L. G. Silva
creator: Eduardo D. Oliveira
creator: Renan M. Lima
creator: Roberto S. Alves
creator: Eduardo Verri
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-16
publication date: 2025-06-16
starting page: 40
ending page: 40

https://journal.sptech.school/index.php/ojs/article/view/26
type: item
title: Musical Plagiarism Detection: An Approach with Artificial Intelligence (Al) and Machine Learning (ML) (Detecção de plágio musical: uma abordagem com inteligência artificial (IA) e aprendizado de máquina (AM))
description: https://doi.org/10.5281/zenodo.15678650 This study explores the development of an automated system for detecting musical plagiarism, a journey intertwined with advanced signal processing techniques, machine learning (ML), and textual analysis. Each step, each note, reflects the complexity and sensitivity of the topic. Data collection, a cornerstone of this process, requires a robust, diverse, and dynamic database that embraces different musical genres, historical periods, and harmonic and melodic variations. As highlighted by Smith (1997), diversity in datasets is crucial to avoid biases in analysis and to enhance the models' generalization capacity. The system's architecture, designed to be modular and scalable, employs AWS services with the precision of a conductor: EC2 orchestrates model hosting, S3 stores data like valuable scores, and Lambda executes asynchronous processing. This structure not only supports massive data volumes but also enables real-time analysis and the addition of new features seamlessly. Every line of code respects the guidelines of the Copyright Law (Law 9,610/98), which allows the use of protected works for academic and research purposes. The initial results offer promising chords. The combination of MFCC and PCP, key tools in tonal analysis, proved powerful in identifying harmonic similarities with high accuracy. However, the system also hits a few dissonant notes: applying techniques like STFT and KNN to long-duration music reveals computational limitations, emphasizing the need for a more robust infrastructure and fine-tuned parameterization. Logan (2000) had already pointed out the value of MFCC in adapting audio analysis to human perception, and here, its impact resonates. Additionally, the system boldly expands its repertoire with added features. One of these is the classification of musical genres based on song lyrics, a task led by the Naive Bayes Multinomial and TF-IDF vectorization. Early performances show impressive results in c…
subject: Artigos
  topic: section
subject: Musical plagiarism
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: FFT
  topic: keywords
subject: STFT
  topic: keywords
subject: MFCC
  topic: keywords
subject: PCP
  topic: keywords
subject: Copyright
  topic: keywords
subject: AWS
  topic: keywords
subject: Modular Architecture
  topic: keywords
creator: João V. N. Ribeiro
creator: Juan G. Silva
creator: Mateus F. Cunha
creator: Yan C. Santos
creator: Alexander Barreira
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.050
date: 2025-06-16
publication date: 2025-06-16
starting page: 31
ending page: 31

COVID-19 CASE FORECASTING USING NEURAL NETWORK ALGORITHMS (PREVISÃO DE CASOS DE COVID-19 UTILIZANDO ALGORITMOS DE REDES NEURAIS)
type: item
description: https://doi.org/10.5281/zenodo.15671164 This paper explores the prediction of new COVID-19 cases using recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Data from the São Paulo State Data Analysis System (SEADE) was utilized. Additionally, a scalable cloud architecture using AWS was employed. The data underwent pre-processing techniques like scaling and moving average. Following processing, the models were evaluated using MAE (Mean Absolute Error) and R² metrics. Results indicate that the GRU model exhibited superior performance, likely attributed to its ability to efficiently handle low-dimensional data.
subject: Artigos
  topic: section
subject: LSTM
  topic: keywords
subject: GRU
  topic: keywords
subject: COVID-19
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Recurrent Neural Networks
  topic: keywords
creator: Léo Igor Nunes de Oliveira
creator: Américo Talarico
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-15
publication date: 2025-06-15
starting page: 31
ending page: 31

DEEP LEARNING TECHNIQUES APPLIED IN STOCK MARKET FORECASTS (TÉCNICAS DE DEEP LEARNING APLICADAS EM PREVISÕES NO MERCADO DE AÇÕES)
type: item
description: https://doi.org/10.5281/zenodo.15670699 The Brazilian stock market has proven to be very profitable in recent years, even in adverse scenarios. For example, in 2021, when many industries faced significant challenges such as economic crises and political instability, affecting a wide range of sectors, the market continued to attract investors, with some companies achieving impressive results and surpassing expectations. This growth reflects the importance of the stock market as one of the pillars of the Brazilian economy, being essential for the development of businesses of all sizes and sectors, from the largest corporations to small businesses. In times of crisis, the stock market offers opportunities for both investors and companies seeking to capitalize on their operations and expand their activities. Given the relevance of the stock market to the Brazilian economy, we aimed to analyze various factors that influence stock performance, such as economic policies and fluctuations in interest rates. To forecast stock prices for different companies, we collected historical stock price data along with economic indicators. This data was then processed and analyzed using time series models, classification and regression techniques. The time series models facilitated the identification of patterns over time, while the classification and regression techniques helped detect the key factors that influence price variations. With this combination of methods, it was possible to conduct a thorough analysis; now we present the results obtained from the application of each model.
subject: Artigos
  topic: section
subject: Machine Learning
  topic: keywords
subject: Times Series
  topic: keywords
subject: Trends Data
  topic: keywords
subject: Patterns
  topic: keywords
subject: Forecast Model
  topic: keywords
creator: Lucas M. Teixeira
creator: Luiz C. D. Martins Filho
creator: Nikolas B. Virionis
creator: Pedro A. Bonacelli
creator: Fernando D. Brandão
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-06-15
publication date: 2025-06-15
starting page: 56
ending page: 56

Detecção de tumores baseada em aprendizado de máquina: uma abordagem para detecção mais assertiva.
type: item
description: https://zenodo.org/records/15553746 Considering that early detection of brain tumors is essential to increase the chances of successful treatment, we developed a Convolutional Neural Network (CNN) model to assist doctors in detecting potential brain tumors in MRI scans. We achieved a model with 98% accuracy, providing doctors with support in identifying MRIs with a higher probability of containing a tumor.
subject: Artigos
  topic: section
subject: Rede Neural
  topic: keywords
subject: CNN
  topic: keywords
subject: Ressonância Magnética
  topic: keywords
subject: Médico
  topic: keywords
subject: Tumor
  topic: keywords
creator: Felipe Cruz
creator: Breno Fraga dos Anjos
creator: Beatriz Cardoso
creator: Eduardo Verri
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-05-30
publication date: 2025-05-30
starting page: 30
ending page: 30

Acne Severity Classification Using Convolutional Neural Networks (Classificação de Severidade de Acne Utilizando Redes Neurais Convolucionais)
type: item
description: This article aims to perform a comparative analysis and identify the best artificial intelligence model for acne classification, based on the IGA (Investigator Global Assessment of Acne), used by the FDA (Food and Drug Administration), a federal agency that is related to health and human services in the United States. Two models were used: (1) a learning model using neural networks based on CNN (Convolutional Neural Network); and (2) a second nonlinear Support Vector Machine (SVM) model that is used for classification and regression. A dataset consisting of 84 facial images was used, containing techniques for rotating these images for better generalization of the model. As results obtained, it is highlighted that the acne recognition model using convolutional neural networks had an accuracy of 85% on the test dataset. https://doi.org/10.5281/zenodo.15312095
subject: Artigos
  topic: section
subject: Acne
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Classification
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Containers
  topic: keywords
subject: Amazon Web Services
  topic: keywords
subject: Ansible
  topic: keywords
subject: Terraform
  topic: keywords
creator: Italo G. S. Rodrigues
creator: Rodrigo G. Hermann
creator: Enan L. Oliveira
creator: Mauricélio Lauand
creator: Marise Miranda
rights: Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 32
ending page: 32

Outsmarting Malware Using AI to Combat WannaCry and Beyond
type: item
description: DOI 10.5281/zenodo.10633019 https://hal.science/hal-04460792v2 Cybersecurity threats like WannaCry are constantly evolving, leaving traditional defenses struggling to keep up. This research explores a powerful new weapon: Artificial Intelligence (AI). Using Bayesian Networks, a type of AI, we developed a method that detects WannaCry with 67.47% accuracy, outperforming traditional signature-based approaches. But this is just the beginning. Our AI model can learn and adapt, making it effective against even unknown malware, offering a crucial advantage in the ever-changing cybersecurity landscape.
subject: Artigos
  topic: section
subject: AI-powered detection
  topic: keywords
subject: Beyond WannaCry
  topic: keywords
subject: Adaptable defense
  topic: keywords
subject: WannaCry countermeasure
  topic: keywords
creator: Americo Talarico
creator: Bruno Sena
creator: Guilherme Paternezzi
creator: Rhian Bottura
creator: Vinicius Bastos
creator: Domingos Sanches
creator: Marise Miranda
rights: Copyright (c) 2024 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2024-01-22
publication date: 2024-01-22
starting page: 27
ending page: 27

AFFIRMATIVE ACTION POSITIONS ON LINKEDIN - DIVERSITY ANDINCLUSION, TECHNOLOGICAL INNOVATION
type: item
description: DOI: 10.13140/RG.2.2.27996.19841 We noticed that LinkedIn has a high demand for affirmative vacancies, both from recruiters and candidates, we can help improve the experience of your customers and users. Through our solution, users will be able to enjoy a safe environment and find vacancies that best suit their profile, while recruiters will be able to more easily use the tools to make vacancies available and also find potential candidates.
subject: Artigos
  topic: section
subject: LinkedIn. Affirmative vacancies. Users
  topic: keywords
creator: Beatriz Tenório
creator: Dailton Junior
creator: Camila Mamed
creator: Flávia Verissimo
creator: Gustavo Henrique
creator: Alessandro Goulart
creator: Fernando Brandão
creator: Gerson Santos
creator: Rafael Petry
creator: Evandra Camargo
creator: Mauricélio Lauand
creator: Alex Barreira
creator: Vera Goulart
creator: Kaline Barreira
creator: Thiago Bonacelli
creator: Leonardo Marques
rights: Copyright (c) 2024 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2024-03-10
publication date: 2024-03-10
starting page: 30
ending page: 30

Challenges in Healthcare Information Management: How OpenHealth Can Help
type: item
description: DOI: 10.13140/RG.2.2.19345.44646 This article addresses the challenges faced by patients and clinics in the management of medical information. By examining the difficulties patients encounter when arriving at a new clinic without medical history and considering the problems of accumulation and disorganization of information in medical clinics, the need for an innovative solution arises. Modernization becomes urgent, as evidenced by reports highlighting the damages caused by the loss of sensitive information. The current scenario reveals a cumbersome and bureaucratic process, from initial registration to obtaining relevant medical information. This article proposes OpenHealth, a platform aimed at transforming the sharing of information between clinics and pharmacies, aiming to facilitate access to patient information, optimize workflows, and improve operational efficiency.
subject: Artigos
  topic: section
subject: Medical Information Management. Service Modernization. OpenHealth Platform
  topic: keywords
creator: Christian Raphael
creator: Matheus Fernandes
creator: Milene Oliveira
creator: Tatiana Mohr
creator: Alessandro Goulart
creator: Fernando Brandão
creator: Gerson Santos
creator: Evandra Camargo
creator: Mauricélio Lauand
creator: Alex Barreira
creator: Vera Goulart
creator: Kaline Barreira
creator: Thiago Bonacelli
creator: Leonardo Marques
rights: Copyright (c) 2024 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2024-03-10
publication date: 2024-03-10
starting page: 29
ending page: 29

Digital Transformation in the Real Estate Market: Case Study of the I9Dommus Proposal
type: item
description: DOI: 10.13140/RG.2.2.19188.16002 This article explores I9Dommus's initiative for digital transformation in the real estate sector, focusing on enhancing the buying and selling experience of properties. The main focus is to integrate innovative technologies and optimized processes to increase operational efficiency, strengthen data security, and enhance customer satisfaction. This proposal addresses the urgent need for modernization in a traditionally slow-to-adapt industry, presenting a detailed strategy for implementing significant digital changes. The analysis includes the implementation of advanced Data Analytics solutions and Customer Relationship Management (CRM) systems, aimed at improving decision-making and personalizing user experience. Data security, a crucial aspect in this digital age, is addressed through the introduction of encryption technologies and robust security systems, aligning with current
subject: Artigos
  topic: section
subject: Digital Transformation, Real Estate Sector, Innovative Technologies, Operational Efficiency, Data Security, Customer Satisfaction, Data Analytics, Customer Relationship Management (CRM)
  topic: keywords
creator: Emanuel de Sousa
creator: Gabriel Vieira
creator: Leonardo Freire
creator: Paula Pinheiro
creator: Alessandro Goulart
creator: Fernando Brandão
creator: Gerson Santos
creator: Rafael Petry
creator: Evandra Camargo
creator: Mauricélio Lauand
creator: Alex Barreira
creator: Vera Goulart
creator: Kaline Barreira
creator: Thiago Bonacelli
creator: Leonardo Marques
rights: Copyright (c) 2024 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2024-03-10
publication date: 2024-03-10
starting page: 33
ending page: 33

Digital Transformation in UBS Clinics in the City of São Paulo
type: item
description: DOI: 10.13140/RG.2.2.35126.51526 This paper proposes a digital transformation model for public health services in São Paulo, focusing on the population's needs and access to healthcare. The model aims to improve the quality and efficiency of care through the use of digital technologies. The Brazilian Constitution guarantees the right to health for all citizens. However, the current system faces challenges, such as long waiting times, inadequate infrastructure, and a shortage of professionals. This study analyzes the current state of the São Paulo public health system, focusing on the Basic Health Units (UBS). The analysis is based on the Audit Report of the Court of Accounts of the Municipality of São Paulo. The report found that there is a mismatch between supply and demand for healthcare services, long waiting times for appointments, and a lack of integration between different services. The proposed digital transformation model aims to address these challenges by: Implementing electronic medical records, Using telemedicine to provide remote consultations, Developing mobile apps to provide patients with access to information and services, Using data analytics to improve the quality of care and target resources more effectively. The digital transformation model has the potential to improve the quality and efficiency of public health services in Brazil. By implementing innovative digital technologies, the model can help to ensure that all citizens have access to the healthcare they need.
subject: Artigos
  topic: section
subject: digital transformation, public health, healthcare, access to care, patient experience
  topic: keywords
creator: Julia M. B. Lima
creator: Leonardo I. T. Gimenes
creator: Patrícia M. Gonçalves
creator: Vinicius A. R. Siva
creator: Alessandro Goulart
creator: Fernando Brandão
creator: Gerson Santo
creator: Rafael Petry
creator: Evandra Camargo
creator: Mauricélio Lauand
creator: Alex Barreira
creator: Vera Goulart
creator: Kaline Barreira
creator: Thiago Bonacelli
creator: Leonardo Marques
rights: Copyright (c) 2024 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
date: 2024-03-10
publication date: 2024-03-10
starting page: 16
ending page: 16