Biblioteca Digital do IPB

Publications Repository of the Polytechnic Institute of Bragança

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The Digital Library of IPB (Biblioteca Digital do IPB), promotes and provides open access to scientific literature produced by the IPB academic community, promoting integration, visibility and sharing of scientific information granting the preservation of intellectual memory of the Instituto Politécnico de Bragança.

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Recent Submissions

Performance benchmarking of or-tools methods for capacitated vehicle routing problems with time windows

Publication . Sena, Inês; Ribeiro, Tiago B.; Silva, Adriano S.; Fernandes, Florbela P.; Costa, Lino A.; Pereira, Ana I.

The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a significant challenge in combinatorial optimization, with extensive practical applications in logistics and transportation. This study aims to conduct a comparative analysis of the various methods available in OR-Tools for solving the CVRPTW across datasets of different sizes and types using the Solomon and the Gehring and Homberger benchmarks. The analysis provided insights into the relative strengths of each method, with a primary focus on Guided Local Search (GLS) and Tabu Search (TS), showing consistent performance and adaptability to different dataset characteristics. The results indicate that GLS is the most robust method overall, and TS can outperform it in specific scenarios. In conclusion, this study offers insights for selecting the most effective method to solve vehicle routing problems based on the characteristics and scale of the problem.

2026Conference paper

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Interconnection between lifestyle, health, and academic outcomes: an analysis on study habits and well-being

Publication . Azevedo, Beatriz Flamia; Bezerra, Ana J.B.; Sirmakessis, Spiros; Pereira, Ana I.

Balancing academic demands with personal and professional responsibilities has become an increasingly challenging task, making it difficult to maintain well-being and potentially leading to serious health problems. The stress resulting from these multiple daily tasks, combined with the pressure to perform at high academic levels, directly impacts students’ mental and emotional health, significantly compromising their quality of life. In this work, statistical and clustering techniques are employed to analyze the dataset “Daily Lifestyle and Academic Performance of Students”. The objective of this work is to explore the relationship between students’ daily habits, level of stress, and the impact on academic performance. The results point out that many students have difficulty managing time and maintaining well-being (low-stress levels) with high academic performance since, according to the results, the higher the academic outcome, the higher the student’s stress level.

2026Conference paper

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Influence of habits and comorbidities on liver disease

Publication . Leite, Gabriel A.; Azevedo, Beatriz Flamia; Pacheco, Maria F.; Fernandes, Florbela P.; Pereira, Ana I.

The prevalence of hepatocellular carcinoma is expected to continue increasing worldwide, and its difficulty in early detection highlights the need for advanced monitoring technologies. As the disease progresses, it has a serious impact on patients’ health, and in severe cases, liver transplantation becomes the only viable solution, reinforcing its importance as a global health problem. This study proposes the use of different artificial intelligence methods to compare and understand them related to liver disease. Well-known algorithms such as Random Forest and Multi-Layer Perceptron were tested, as well as ensemble methods that exploit different modeling structures. The results showed that AdaBoost, Random Forest, and Gradient Boosting performed best with Area Under the Curve of 0.89, 0.86, and 0.84 respectively. To analyze their influence on clinical results, the best-performing model was reapplied only to the non-biochemical features that compose the dataset. The results indicate that portal vein thrombosis, diabetes, and hypertension are the most influential variables, with contributions of 29.48%, 20.50%, and 16.60%, respectively.

2026Conference paper

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Forecasting COVID-19 in european countries using long short-term memory

Publication . Carvalho, Kathleen; Teixeira, Rita de Almeida; Reis, Luis Paulo; Teixeira, João Paulo

Effective time series forecasts are increasingly important in supporting judgment in various decisions. Various prediction models are available to support these projections based on how each area provides a diverse set of data with variable behavior. Artificial neural networks (ANNs) significantly contribute to medical research since using predictive ideas allows for the study of disease progression in the future, as well as the behavior of other variables. This study implemented the proposed model based on Long Short-Term Memory (LSTM) to forecast COVID-19 daily new cases, deaths, and ICU patients. The methodology uses quantitative and qualitative data from six European countries: Austria, France, Germany, Italy, Portugal, and Spain to predict the last 242 days of the COVID-19 pandemic. The dataset uses the healthcare parameters of the number of daily new cases, deaths, ICU patients, and mitigation procedures, such as the percentage of the population fully vaccinated, the mandatory use of masks, and the lockdown. Two approaches were used to evaluate the model’s performance: the mean absolute error (MAE) and the mean square error (MSE). The results demonstrate that the LSTM model efficiently captures general trends in COVID-19 metrics but shows limitations when predicting data with low values or high variability, such as daily deaths. The model reported the lowest errors for Spain and Portugal, while France and Germany exhibited higher error rates due to differences in data reporting and pandemic dynamics. These findings highlight the importance of contextualizing predictive models based on specific regional characteristics.

2026Conference paper

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HaaS - a platform for password cracking in distributed heterogeneous systems

Publication . Lima, Carlos; Alves, Rui; Rufino, José

Traditional passwords and respective cryptographic hashes are still widely used for user authentication. Breaking these hashes, to recover the original passwords, may be necessary for a variety of legitimate reasons. Hashcat, a widely used password auditing tool, is able to exploit the parallel processing power of many GPUs to accelerate the breaking of cryptographic hashes. Moreover, there are already ways of performing this task in a distributed environment, though they face several challenges, including the difficulty of assembling and managing distributed deployments, and using them in a user-friendly and resource-efficient way. This work presents Hashcat-as-a-Service (HaaS), a novel platform that targets these challenges. It combines Hashcat with web technologies, containerization and remote OpenCL middleware, to allow the user-friendly management of Hashcat instances that leverage the processing power of distributed GPUs, including support of instances migration in order to maximize GPU utilization. The evaluation of HaaS in different configurations demonstrated promising results, confirming its ability to handle intensive workloads and its flexibility to adapt to different usage scenarios and resources availability, making HaaS a relevant contribution in the password recovery field.

2025Conference paper

Open access