José Miguel Sales Dias

Prof. José Miguel Sales Dias

ISCTE - Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-Information Sciences and Technologies and Architecture Research Center
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Academic Career Length

30Years (1997-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2008 in Advances in Engineering Software
Verified
Journal Article
Closed access
JCJoão B. Cardoso
João B. Cardoso
JAJoão R. de Almeida
João R. de Almeida
José Miguel Sales DiasJosé Miguel Sales Dias
José Miguel Sales Dias
P. G. CoelhoP. G. Coelho
P. G. Coelho
2 February 2021 in ISPRS International Journal of Geo-Information
Verified
Journal Article
Open access
PDF file
Vitória AlbuquerqueVitória Albuquerque
Vitória Albuquerque
José Miguel Sales DiasJosé Miguel Sales Dias
José Miguel Sales Dias
Fernando BaçãoFernando Bação
Fernando Bação
Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.
21 September 2023 in Journal of Personalized Medicine
Verified
Journal Article
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LELuis B. Elvas
Luis B. Elvas
MNMiguel Nunes
Miguel Nunes
joao ferreirajoao ferreira
joao ferreira
José Miguel Sales DiasJosé Miguel Sales Dias
José Miguel Sales Dias
LRLuís Brás Rosario
Luís Brás Rosario
Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.
1 January 1997 in IEEE Computer Graphics and Applications
Verified
Journal Article
Closed access
José Miguel Sales DiasJosé Miguel Sales Dias
José Miguel Sales Dias
RGR. Galli
R. Galli
AAA.C. Almeida
A.C. Almeida
CBC.A.C. Belo
C.A.C. Belo
JRJosé Manuel Rebordão
José Manuel Rebordão
This paper describes the mWorld system that integrates data communication techniques and 3D virtual scene generation tools enabling 3D cooperative design work. A 3D virtual environment with multiuser support is introduced in the framework of an architecture that supports synchronous, asynchronous, and time-dependent communication. The application of such a system can be very wide, such as distributed virtual studios and cooperative CAD/CAM applications. The system allows several users to immerse in the same world space, where they can design a 3D virtual environment or an industrial product, or discuss an architect or urban planning project, cooperatively and from a distance. They may add, modify, or delete objects, change environment attributes, and navigate in the common environment, remotely and interactively. Any change made to the environment by any user is shown to other distant users in the group, since the system provides consistency control over the share data space. The editing access rights of any user to the environment is arbitrated by the system using several token control policies. In addition, they may obtain a 3D virtual scene, an architecture design, or product design elements from remote locations just as if they were local. To aid the collaborative sessions, a parallel audio conferencing tool is available, using the same communication platform.
31 December 2021 in Buildings
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Journal Article
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Maria AnastasiadouMaria Anastasiadou
Maria Anastasiadou
VSVitor M. Santos
Vitor M. Santos
José Miguel Sales DiasJosé Miguel Sales Dias
José Miguel Sales Dias
The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.
22 November 2021 in Energies
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Journal Article
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AAAHMED ABDELAZIZ
AHMED ABDELAZIZ
VSVitor M. Santos
Vitor M. Santos
José Miguel Sales DiasJosé Miguel Sales Dias
José Miguel Sales Dias
The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.