Abstract
Phishing attacks aims to steal sensitive information and, unfortunately, are becoming a common practice on the web. Email phishing is one of the most common types of attacks on the web and can have a big impact on individuals and enterprises. There is still a gap in prevention when it comes to detecting phishing emails, as new attacks are usually not detected. The goal of this work was to develop a model capable of identifying phishing emails based on machine learning approaches. The work was performed in collaboration with E-goi, a multi-channel marketing automation company. The data consisted of emails collected from the E-goi servers in the electronic mail format. The problem consisted of a classification problem with unbalanced classes, with the minority class corresponding to the phishing emails and having less than 1% of the total emails. Several models were evaluated after careful data selection and feature extraction based on the email content and the literature regarding these types of problems. Due to the imbalance present in the data, several sampling methods based on under-sampling techniques were tested to see their impact on the model's ability to detect phishing emails. The final model consisted of a neural network able to detect more than 80% of phishing emails without compromising the remaining emails sent by E-goi clients.

Abstract
The domain of Human Activity Recognition (HAR) has undergone a remarkable evolution, driven by advancements in sensor technology, artificial intelligence (AI), and machine learning algorithms. The aim of this article consists of taking as a basis the previously obtained results to implement other techniques to analyze the same dataset and improve the results previously obtained in the different studies, such as neural networks with different configurations, random forest, support vector machine, CN2 rule inducer, Naive Bayes, and AdaBoost. The methodology consists of data collection from smartphone sensors, data cleaning and normalization, feature extraction techniques, and the implementation of various machine learning models. The study analyzed machine learning models for recognizing human activities using data from smartphone sensors. The results showed that the neural network and random forest models were highly effective across multiple metrics. The models achieved an area under the curve (AUC) of 98.42%, a classification accuracy of 90.14%, an F1-score of 90.13%, a precision of 90.18%, and a recall of 90.14%. With significantly reduced computational cost, our approach outperforms earlier models using the same dataset and achieves results comparable to those of contemporary deep learning-based approaches. Unlike prior studies, our work utilizes non-normalized data and integrates magnetometer signals to enhance performance, all while employing lightweight models within a reproducible visual workflow. This approach is novel, efficient, and deployable on mobile devices in real-time. This approach makes it an ideal fit for real-time mobile applications.

Abstract

Abstract
DSentiment analysis has proven its importance in business and research. With the metaverse market expansion and abundant high-quality data, understanding how businesses can leverage technologies such as sentiment analysis to improve their marketing strategies becomes significant. This paper synthesizes and organizes information relevant to sentiment analysis using Virtual Reality technology. To minimize bias and ensure accuracy, a systematic review was conducted. Papers from Springer, ScienceDirect, and IEEE Xplore, published since 2022, were analyzed. This yielded a total of 12 studies included in this review after screening of 304 papers. This research shows that sentiment analysis, together with Artificial Intelligence, is crucial for businesses aiming to expand their influence in the metaverse. These tools enable high customization and optimization of interactions, making them more engaging, while providing real-time insights into the consumers' likes, dislikes and emotions. This allows companies to identify what works and what needs improvement in their metaverse platform.

Abstract
This paper proposes a novel and comprehensive framework for the integration of manufacturing management processes, spanning strategic and operational levels, within and across organizational boundaries. The framework combines a robust set of technologies-such as cyber-physical systems, digital twins, AI, and blockchain-designed to support real-time decision-making, interoperability, and collaboration in Industry 4.0 and 5.0 contexts. Implemented and validated in a Portuguese manufacturing group comprising three interoperating factories, the framework demonstrated its ability to improve agility, coordination, and stakeholder integration through a multi-layered architecture and modular software platform. Quantitative and qualitative feedback from 32 participants confirmed enhanced decision support, operational responsiveness, and external collaboration. While tailored to a specific industrial setting, the results highlight the framework's scalability and adaptability, positioning it as a meaningful contribution toward sustainable, human-centric digital transformation in manufacturing environments.

Abstract
The cryptocurrency market presents immense opportunities and significant risks due to its high volatility. Accurate price forecasting is crucial for informed investment decisions, enabling investors to optimize portfolio allocation, mitigate risk, and potentially maximize returns. Existing forecasting methods often struggle with the inherent non-stationarity and complexity of cryptocurrency price dynamics. This study addresses this challenge by developing a system for high-frequency forecasting of the closing prices of ten leading cryptocurrencies. We compare various machine learning models, including recurrent neural networks (RNNs), time series analysis (ARIMA), and conventional regression algorithms, using minute-step Bitcoin price data over a 30-day period to predict prices 60 min ahead. Our findings demonstrate that the GRU neural network exhibits superior predictive accuracy (MAPE = 0.09%, MSE = 5954.89, RMSE = 77.17, MAE = 60.20), outperforming other models considered. This improved forecasting accuracy contributes to the existing literature by providing empirical evidence for GRU's effectiveness in the volatile cryptocurrency market and offers practical insights for investment strategies. A web application integrating the best-performing model further facilitates real-time price prediction for multiple cryptocurrencies.