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CIIC - Artigos em Revistas com Peer Review

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http://hdl.handle.net/10400.8/5924

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Browsing CIIC - Artigos em Revistas com Peer Review by Sustainable Development Goals (SDG) "09:Indústria, Inovação e Infraestruturas"

Now showing 1 - 10 of 28
  • Accessible software development: a conceptual model proposal
    Publication . Silva, João Sousa e; Gonçalves, Ramiro; Branco, Frederico; Au‑Yong‑Oliveira, Manuel; Martins, José; Pereira, António
    Equal access to all software and digital content should be a reality in the Digital Era. This argument is something defended both by existing regulations, norms and standards, and also business organizations and governments. Despite this acknowledgement, the reality is still far from the desired equality. For certain groups of disabled or impaired citizens, such as the visually impaired, the existence of e-accessibility compliance represents an opportunity to integrate, in a more simple and straightforward manner, their societies. Despite the existing poor results on e-accessibility compliance, the mentioned citizens insist on using digital devices in their daily lives. Even though, in the last decade, multiple standards and regulations have been published towards indicating how to develop accessible digital user interfaces, there are still two major issues surrounding its implementation: the complexity and disparity of the documents containing the abovementioned norms, and also the lack of e-accessibility know-how by software experts. With this in mind, a proposal for an accessible software development model that encompasses e-accessibility incorporation as one of the development process activities has been presented. This model might represent a very interesting support tool for software development organizations and a novel resource for learning and training institutions to be able to improve their computer science and informatics students’ skills on e-accessibility.
    2019-07-28Journal article Open access Show more
  • Artificial intelligence applied to the stone manufacturing industry: A systematic literature review
    Publication . Santos Silva, Alexandre; Antunes, Carolina; Miragaia, Rolando; Costa, Rogério Luís C.; Silva, Fernando; Ribeiro, José
    Natural stone has long been used in construction, as its properties provide functional and visual value, and the natural stone market currently holds significant importance in the global economy. It is important to consider integrating new technologies in the production chain to aid the industry in moving forward, increasing profit margins and reducing wasted material. This article reviews recent trends in using Artificial Intelligence and Machine Learning techniques in the industry between 2017 and 2024, following a methodology for Systematic Literature Reviews in computer science. It was found that extensive research has been conducted on the subject of tile classification, with solid solutions proposed, achieving results that can be considered robust enough for industrial application. Other subjects comprise tasks regarding stone cutting and defect detection, as well as variable prediction, and quarry activity monitoring. Some authors propose solutions to integrate new technologies into the complete production chain. While more research needs to be done on specific subjects, this review provides a solid first step to future research.
    2025-12Journal article Open access Show more
  • Artificial Intelligence-Driven User Interaction with Smart Homes: Architecture Proposal and Case Study
    Publication . Lemos, João; Ramos, João; Gomes, Mário; Coelho, Paulo
    The evolution of Smart Grids enabled the deployment of intelligent and decentralized energy management solutions at the residential level. This work presents a comprehensive Smart Home architecture that integrates real-time energy monitoring, appliance-level consumption analysis, and environmental data acquisition using smart metering technologies and distributed IoT sensors. All collected data are structured into a scalable infrastructure that supports advanced Artificial Intelligence (AI) methods, including Large Language Models (LLMs) and machine learning, enabling predictive analysis, personalized energy recommendations, and natural language interaction. Proposed architecture is experimentally validated through a case study on a domestic refrigerator. Two series of tests were conducted. In the first phase, extreme usage scenarios were evaluated: one with intensive usage and another with highly restricted usage. In the second phase, normal usage scenarios were tested without AI feedback and with AI recommendations following them whenever possible. Under the extreme scenarios, AI-assisted interaction resulted in a reduction in daily energy consumption of about 81.4%. In the normal usage scenarios, AI assistance resulted in a reduction of around 13.6%. These results confirm that integrating AI-driven behavioral optimization within Smart Home environments significantly improves energy efficiency, reduces electrical stress, and promotes more sustainable energy usage.
    2025-12-06Journal article Open access Show more
  • Body Area Networks in Healthcare: A Brief State of the Art
    Publication . Roda-Sanchez, Luis; Olivares, Teresa; Fernández-Caballero, Antonio; Vera, Daniel; Costa, Nuno; Pereira, António Manuel de Jesus
    A body area network (BAN) comprises a set of devices that sense their surroundings, activate and communicate with each other when an event is detected in its environment. Although BAN technology was developed more than 20 years ago, in recent years, its popularity has greatly increased. The reason is the availability of smaller and more powerful devices, more efficient communication protocols and improved duration of portable batteries. BANs are applied in many fields, healthcare being one of the most important through gathering information about patients and their surroundings. A continuous stream of information may help physicians with making well-informed decisions about a patient's treatment. Based on recent literature, the authors review BAN architectures, network topologies, energy sources, sensor types, applications, as well as their main challenges. In addition, the paper focuses on the principal requirements of safety, security, and sustainability. In addition, future research and improvements are discussed. © 2019 by the authors
    2019-08-08Journal article Open access Show more
  • Contact center: information systems design
    Publication . Rijo, Rui; Varajão, João; Gonçalves, Ramiro
    The economic sector of contact centers is growing by more than 8% a year. It is a multidisciplinary area in which information systems are decisive to organizations' success. Contact Centers' Information Systems deal with real time requisites and critical business information. A theorybuilding research shows a framework with 12 key design factors to consider, which managers might use to develop projects and researchers may adopt for further investigation in the area of Contact Center design. This work intends to provide a valuable link between the research community and practitioners in industry.
    2010-03-10Journal article Restricted access Show more
  • Decrypting messages: Extracting digital evidence from signal desktop for windows
    Publication . Paulino, Gonçalo; Negrão, Miguel; Frade, Miguel; Domingues, Patrício
    With growing concerns over the security and privacy of personal conversations, end-to-end encrypted instant messaging applications have become a key focus of forensic research. This study presents a detailed methodology along with an automated Python script for decrypting and analyzing forensic artifacts from Signal Desktop for Windows. The methodology is divided into two phases: i) decryption of locally stored data and ii) analysis and documentation of forensic artifacts. To ensure data integrity, the proposed approach enables retrieval without launching Signal Desktop, preventing potential alterations. Additionally, a reporting module organizes extracted data for forensic investigators, enhancing usability. Our approach is effective in extracting and analyzing encrypted Signal artifacts, providing a reliable method for forensic investigations.
    2025-09Journal article Open access Show more
  • Driving Behavior Classification Using a ConvLSTM
    Publication . Pingo, Alberto; Castro, João; Loureiro, Paulo; Mendes, Silvio; Bernardino, Anabela; Miragaia, Rolando; Husyeva, Iryna
    This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). Sensor data are collected from a smartphone application and undergo a preprocessing pipeline, including data normalization, labeling, and feature extraction, to enhance the model’s performance. By capturing temporal and spatial dependencies within driving patterns, the proposed ConvLSTM model effectively differentiates between normal and aggressive driving behaviors. The model is trained and evaluated against traditional stacked LSTM and Bidirectional LSTM (BiLSTM) architectures, demonstrating superior accuracy and robustness. Experimental results confirm that the preprocessing techniques improve classification performance, ensuring high reliability in driving behavior recognition. The novelty of this work lies in a simple data preprocessing methodology combined with the specific application scenario. By enhancing data quality before feeding it into the AI model, we improve classification accuracy and robustness. The proposed framework not only optimizes model performance but also demonstrates practical feasibility, making it a strong candidate for real-world deployment.
    2025-05-01Journal article Open access Show more
  • Elder care architecture - A physical and social approach
    Publication . Marcelino, Isabel; Barroso, João; Cruz, José Bulas; Pereira, António
    As we observe society in our days, we can see that people live longer; this means that we have an older population, more likely to have health issues. The special needs presented by the elderly are becoming a major concern for all of us, along with the lack of time demonstrated by society as a whole and, as a consequence, the lack of time is seen when families are not able to take care of their own elders. Many solutions are being presented in order to solve this problem. Some of them are taking advantage of the new technological developments in the body sensor networks area. In this paper we propose the architecture of a system called Elder Care. The Elder Care solution has two primary goals: monitoring vital signs, sending alerts to family and to specialized help and providing a social network in order to help end the elderly's social isolation.
    2010Journal article Open access Show more
  • Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks
    Publication . Moreira Bernardino, Anabela; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel Ángel; Bernardino, Eugénia Moreira
    In the last decades, nature-inspired algorithms have been widely used to solve complex combinatorial optimisation problems. Among them, Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) algorithms have been extensively employed as search and optimisation tools in various problem domains. Evolutionary and Swarm Intelligent algorithms are Artificial Intelligence (AI) techniques, inspired by natural evolution and adaptation. This paper presents two new nature-inspired algorithms, which use concepts of EAs and SI. The combination of EAs and SI algorithms can unify the fast speed of EAs to find global solutions and the good precision of SI algorithms to find good solutions using the feedback information. The proposed algorithms are applied to a complex NP-hard optimisation problem - the Terminal Assignment Problem (TAP). The objective is to minimise the link cost to form a network. The proposed algorithms are compared with several EAs and SI algorithms from literature. We show that the proposed algorithms are suitable for solving very large scaled problems in short computational times.
    2012Journal article Open access Show more
  • Explainable prototype-based image classification using adaptive feature extractors in medical images
    Publication . Vasconcellos, Nicolas; Tavora, Luis M. N.; Miragaia, Rolando; Grilo, Carlos; Thomaz, Lucas
    Prototype-based classifiers are a category of Explainable Artificial Intelligence methods that use representative samples from the data, called prototypes, to classify new inputs based on a similarity criterion. However, these methods often rely on pre-trained Convolutional Neural Networks as feature extractors, which may not be adapted for the specific type of data being used, thus not suited for identifying the most representative prototypes. In this paper, we propose a method named Explainable Prototype-based Image Classification, a cluster-oriented training strategy that enhances the performance and explainability of prototype-based classifiers. Our method uses a novel loss function, called Cluster Density Error, to fine-tune the feature extractor and preserve the most representative feature vectors in the latent space. We also use Principal Component Analysis-based approach to reduce the dimensionality and complexity of the feature vectors. We conduct experiments on four medical image datasets and compare the results with those from different prototype-based classifiers and state-of-the-art non-explainable learning methods. The proposed method demonstrated superior explainable capabilities and comparable classification performance to the compared methods. Specifically, the proposed method achieved up to 95.01% accuracy and 0.992 AUC using only 43 prototypes. This translated to an improvement in accuracy and AUC score of 21.54% and 9.06%, respectively, and a substantial reduction in the number of prototypes by 98,38%
    2025-12Journal article Open access Show more