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.
Artigo Técnico : vulpix.AI - Seu Agente de Marketing
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.
Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
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)
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.
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)
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 trabalhos futuros, propõe-se a aplicação de métodos de explicabilidade, como LIME, em maior escala, e a investigação de novas arquiteturas híbridas que combinem transformadores e redes convolucionais para alcançar ainda melhores resultados.
PREVISÃO DE CASOS DE DOENÇAS RESPIRATÓRIAS COM REDES NEURAIS LSTM: UMA ABORDAGEM PARA ANÁLISE TEMPORAL E IDENTIFICAÇÃO DE EPIDEMIAS
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.
Comparison of Machine Learning Models in Animal Adoption (Comparação de Modelos de Machine Learning na Adoção de Animais)
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.
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)
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.
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)
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.
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)
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.
Information and Communication Technology
Aplicação de Técnicas de Machine Learning e Processamento de Imagens para a Identificação de Selos Indicativos de Excessos em Embalagens Alimentícias
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.
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)
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.
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)
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.
FACIAL RECOGNITION OF MISSING PERSONS THROUGH MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE (Reconhecimento facial de pessoas desaparecidas através de aprendizado de máquina e inteligência artificial)
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.
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))
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 categorizing genres such as Country, Rock, and Rap, proving that statistical models can still be skilled soloists in the textual domain. But not everything is in harmony. The high computational cost of Support Vector Machines (SVM)-based models and the challenges of parameterizing tools like PCP for larger datasets are dissonances yet to be resolved. Even so, the progress made suggests that integrating hybrid models and deep neural networks will bridge these limitations. Recent studies, such as those by Logan (2000) and Serra et al. (2008), already point in this direction, and the future seems promising. In addition to the technical challenges, it is necessary to consider the legal and ethical impacts of the system in the musical landscape. A notable case is the lawsuit between Robin Thicke and Marvin Gaye over the song Blurred Lines, which highlighted how harmonic similarity can become a central point in plagiarism disputes. In 2015, a jury determined that the song infringed the copyrights of "Got to Give It Up," initially resulting in a fine of $7.4 million, later reduced to $5.3 million, in addition to 50% of future royalties being allocated to Gaye's family (THE GUARDIAN, 2018). The solution proposed in this article, by providing a detailed and impartial technical analysis, could significantly contribute to clarifying the nature of similarities between songs, promoting greater fairness in judicial decisions. When applied responsibly and without harming the rights of artists, the system has the potential to protect both original creations and the integrity of the creative process, encouraging artistic development and innovation in the musical field.
Copyright (c) 2025 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.050
COVID-19 CASE FORECASTING USING NEURAL NETWORK ALGORITHMS (PREVISÃO DE CASOS DE COVID-19 UTILIZANDO ALGORITMOS DE REDES NEURAIS)
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.
DEEP LEARNING TECHNIQUES APPLIED IN STOCK MARKET FORECASTS (TÉCNICAS DE DEEP LEARNING APLICADAS EM PREVISÕES NO MERCADO DE AÇÕES)
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.
Detecção de tumores baseada em aprendizado de máquina: uma abordagem para detecção mais assertiva.
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.
Acne Severity Classification Using Convolutional Neural Networks (Classificação de Severidade de Acne Utilizando Redes Neurais Convolucionais)
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
Outsmarting Malware Using AI to Combat WannaCry and Beyond
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.
Copyright (c) 2024 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
AFFIRMATIVE ACTION POSITIONS ON LINKEDIN - DIVERSITY ANDINCLUSION, TECHNOLOGICAL INNOVATION
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.
Challenges in Healthcare Information Management: How OpenHealth Can Help
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.
Medical Information Management. Service Modernization. OpenHealth Platform
Digital Transformation in the Real Estate Market: Case Study of the I9Dommus Proposal
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
Digital Transformation, Real Estate Sector, Innovative Technologies, Operational Efficiency, Data Security, Customer Satisfaction, Data Analytics, Customer Relationship Management (CRM)
Digital Transformation in UBS Clinics in the City of São Paulo
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.
digital transformation, public health, healthcare, access to care, patient experience
COMPARISON OF AI TECHNIQUES FOR DETERMINING NEWS RELIABILITY
DOI: 10.5281/zenodo.10734624 The internet provides a wider range of access to information, however, this comes with the burden of greater dissemination of fake news, enhanced through social networks, thus increasing misinformation (ANTONIO, 2023). This work sought to build, implement and compare artificial intelligence techniques to define the reliability of news published on the internet. It used trained algorithms to test the reliability of a piece of news and indicate whether it is true or false. By delimiting the Brazilian news scene and creating a web platform for user interaction, this work was able to evaluate the accuracy of the Recurrent Neural Network (RNN), Decision Tree, Randon Forest, K-Nearest Neighbors (KNN) and Logistic Regression methodologies in assessing the veracity of news. All showed considerable accuracy, with the RNN technique showing the best accuracy.
IDENTIFICAÇÃO DE ASSINATURAS EM QUADROS POR MEIO DE CARACTERÍSTICAS SIFT E CLASSIFICAÇÃO COM KNN
DOI: 10.5281/zenodo.10732940 With the emergence, popularization and ease of consumption of generative Artificial Intelligences – AIs capable of generating content that does not yet exist, be it written, visual, among others – it has become necessary to create tools that can combat plagiarism and misuse of intellectual property. An example of this problem is a case that became extremely popular in which an image created by the generative AI Midjourney won a digital art competition (Oliveira, Pedro, 2022), raising several debates about it. This article aims to analyze the images of signatures, in order to reach a conclusion on the topic: is it possible to distinguish the handwriting of a human and an AI? To this end, the following techniques were used: SIFT (Scale-Invariant Feature Transform) to extract characteristics that distinguish the two types of signatures. The K-Means algorithm to group the data that will be transcribed into a histogram that will be consumed by the K-Neareast Neighbors (KNN) algorithm that will classify the data obtained. Using a base of original and artificially generated images of the painter's handwriting, Claude Monet - from the Impressionist art movement - this research aims to develop and conclude the possibility of differentiating a human handwriting from that generated by an AI algorithm based on the signature present in the work, through Machine Learning algorithm technologies.
Copyright (c) 2023 SPTech World Journal https://creativecommons.org/licenses/by-nc/4.0
APLICATIVO DE ANÁLISE DE DADOS PARA REALIZAR DIETAS
DOI: https://doi.org/10.5281/zenodo.10729493 Em um mundo onde a preocupação com hábitos alimentares saudáveis é cada vez mais evidente, nosso projeto se destaca como uma iniciativa crucial. Nosso objetivo primordial é conscientizar e proporcionar uma alimentação saudável aos nossos usuários por meio do desenvolvimento de um aplicativo inovador. A essência deste projeto se fundamenta na compreensão da singularidade de cada indivíduo e em suas necessidades dietéticas únicas. Com base nesse princípio, desenvolvemos um aplicativo que emprega tecnologias de vanguarda, tais como Input, Embedding, Flatten, Dense e Concatenate, para capturar e analisar os dados individuais de cada usuário. Esse processo nos habilita a personalizar uma dieta específica, alinhada com as metas de saúde pessoais de cada indivíduo. Nós acreditamos que a conscientização e a educação são os primeiros passos cruciais para a transformação de hábitos alimentares prejudiciais. Nosso aplicativo não é apenas uma ferramenta de prescrição de dietas; ele atua como um guia educacional, empoderando os usuários a fazerem escolhas informadas sobre sua alimentação.
Dieta. Alimentação. Saúde. Educação. Específica.
Application to assist in the recognition and identification of lost pets using the Artificial Neural Networks technique
DOI: https://doi.org/10.5281/zenodo.10724842 O número de animais de estimação abandonados no Brasil aumentou significativamente entre 2018 e 2020, com um aumento de 126% no início da pandemia (IPB, 2022). Este estudo explora o impacto emocional do abandono em cães e a felicidade do reencontro com seus donos. Os cães sofrem de abandono, aguardando o regresso dos seus donos (Bradshaw, 2016). Este período de espera enfraquece-os, pois quebra a sua rotina e afecta o seu autocuidado. A perda de um animal de estimação pode ser comparada à perda de um ente querido. No Brasil, o processo de reencontro de animais de estimação com seus donos ainda é precário. Porém, quando o reencontro acontece, a felicidade é imensa para ambas as partes, melhorando a saúde física e mental. A conexão nesse momento transcende os sentidos da vida. Este trabalho tem como objetivo implementar uma ferramenta de reconhecimento de padrões utilizando técnicas de Inteligência Artificial (IA) para ajudar a identificar animais de estimação perdidos. A ferramenta utilizará algoritmos de reconhecimento de imagem e de aprendizagem automática para fazer corresponder os animais de estimação perdidos aos seus donos. A ferramenta será desenvolvida utilizando tecnologias de código aberto e será disponibilizada ao público gratuitamente. Os benefícios esperados da ferramenta incluem: Aumento das hipóteses de reunir os animais de estimação perdidos com os seus donos; Redução do stress e da ansiedade tanto para os animais de estimação como para os donos; Aumento da sensibilização para o problema do abandono de animais de estimação e, Incentivo à adoção de animais de estimação. O desenvolvimento desta ferramenta é um passo significativo para resolver o problema do abandono de animais de estimação no Brasil. Ao aproveitar o poder da IA, podemos ajudar a criar uma sociedade mais compassiva e responsável para todos os animais.
Reconehcimento de padrões animais doméstico
Machine Learning-Based Phishing Detection: An Approach for Cyber Attack Prevention
DOI:https://doi.org/10.5281/zenodo.10719793 The article addresses the growing threat of virtual scams, especially phishing, driven by technological advancement. Explores various attack techniques, origin, current statistics and future challenges related to this scenario. Highlights the need for intervention through the development of a Machine Learning (ML)-based phishing detection system in Python. In this phishing analysis project, an article from IBM (International Business Machines Corporation) is used as an initial guide, providing a solid framework for the investigation. However, since planning, the proposal was to explore an alternative premise. While the article was valuable as a starting point, the project is deliberately guided by another approach. This strategic decision resulted in new perspectives and innovative discoveries that significantly expanded the scope and depth of study. The research uses an extensive dataset to train the system, focusing on exploratory analysis and the choice of ML models. Models such as Random Forest, data normalization and Principal Component Analysis (PCA) are experimented with to reduce dimensionalities and improve model efficiency. The practical application of the system is discussed, highlighting its role in defending against constantly evolving cyber threats. The article also describes data processing, algorithm implementation and evaluation of model effectiveness, using metrics such as Area under the ROC Curve. Furthermore, the text discusses the creation of a plug-in for browsers, especially Chrome, which serves as a URL validator, allowing users to check the legitimacy of links before clicking. This initiative is accompanied by a detailed explanation of the plugin development using HTML, CSS and JavaScript. Finally, the article details the architecture and technologies used, highlighting the choice of AWS for approval and production environments, including virtual machines, cloud storage and application observability.
Social engineering. Machine Learning. Threat Detection. Cybersecurity. Virtual Attacks. Exploratory Data Analysis. Decision tree. Principal Component Analysis (PCA). Browser Plugin. URL validator. AWS (Amazon Web Services). Cloud Computing. Observability. Ransomware. Cyber Threats. Technological Intervention. Real-Time Machine Learning. Secure Digital Environment.
ESTIMATING THE RISK OF VEHICLE CRIME OCCURRENCE IN SÃO PAULO STATE USING NEURAL NETWORKS
DOI:https://doi.org/10.5281/zenodo.10719541 The increasing number of vehicle-related crimes and rising costs of financial services are impacting the level of customization offered to vehicle insurance customers. To address this issue, this study proposes a platform that leverages machine learning and neural networks to analyze data from the São Paulo State Secretariat of Public Security (SSP) and the Brazilian Institute of Geography and Statistics (IBGE). The platform aims to provide a more accurate assessment of an insured individual's risk profile based on personal information, such as city and neighborhood of residence.
FRUITLENS: USING ARTIFICIAL INTELLIGENCE TO RECOGNIZE AND CLASSIFY FRUITS AND THEIR RIPENESS STAGES
https://doi.org/10.5281/zenodo.10717019 Given the advance of technology, numerous challenges faced by modern society have been able to be solved or mitigated. However, even with such progress, one of the major difficulties is food waste. A significant portion of this loss comprises fruits and vegetables, and many cases of this wastage can occur due to a lack of knowledge about their ripening stages. Therefore, the project aims to implement machine learning techniques for the classification of fruits into three categories: banana, apple, and orange. Additionally, it seeks to classify them into five ripening stages: green, nearly ripe, ripe, very ripe, and rotten.
The Importance of Neural Networks in Aiding the Identification of Monkeypo
DOI 10.5281/zenodo.10713093 With the arrival of covid-19 and a global quarantine scenario, the use of technology in the healthcare sector has grown dramatically, and telemedicine has become a great resource for healthcare professionals and patients. It is estimated that demand for the service increased by 226% in the first two months of 2022, compared to the previous year. However, after the preventive measures and the decrease in cases and deaths, telemedicine has remained a great ally for its many benefits, such as the speed of care, less exposure to contagious diseases, and no travel costs. COVID-19 was not the only contagious disease during this pandemic period, while the focus of governments and people was on controlling and reducing covid-19 cases, other diseases were boosting their numbers, such as monkeypox. Shortly after the covid crisis, there was a huge increase in monkeypox, a disease caused by by the orthopoxvirus genus. It is a disease characterized by skin rashes and can be transmitted by physical contact and airborne droplets, so the patient also needs to be isolated so that there is no contagion. The focus of our study is the comparison between two neural networks, Resnet18 and Xception, in the context of image recognition, to identify symptoms on patients' skin. Notably, Resnet18 has emerged as the option with the best cost-benefit ratio. This choice provides convenience, speed, and safety, ensuring effectiveness in symptom identification without unnecessarily exposing the health condition.