pubs2024

@inproceedings{Alves2024623,

title = {Review for Augmented Reality Shopping Application for Mobile Systems},

author = {C. Alves and J. Machado and J. L. Reis},

editor = {Del Rio Araujo M. dos Santos J.P. Reis J.L.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171888848&doi=10.1007%2f978-981-99-0333-7_45&partnerID=40&md5=bfb14fae4f33db950967b78bf1a04b3a},

doi = {10.1007/978-981-99-0333-7_45},

issn = {21903018},

year  = {2024},

date = {2024-01-01},

journal = {Smart Innovation, Systems and Technologies},

volume = {344},

pages = {623-634},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The evolution of e-commerce has, in recent times, made a significant advance largely due to the forced digital transformation and to Sars-cov-2. Thus, augmented reality (AR) technology is increasingly being adopted by companies either to increase brand value or to improve the shopping experience for their consumers. This work aims to present a comparative study between some shopping applications using AR, augmented reality shopping applications (ARSAs), which are carried out in stores or online. The applications were studied in articles in Open Access format and the focus was to understand what their conclusions were as well as the problems and trends of future research toward this type of technologies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},

note = {cited By 0; Conference of International Conference on Marketing and Technologies, ICMarkTech 2022 ; Conference Date: 1 December 2022 Through 3 December 2022; Conference Code:294609},

keywords = {Augmented reality, Augmented reality technology; Brand values; Comparatives studies; Digital transformation; E- commerces; Mobile app; Mobile systems; OpenAccess; Shopping app; Type of technology, Mobile commerce},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cruz2024439,

title = {Decentralize Healthcare Marketplace},

author = {G. Cruz and T. Guimarães and M. F. Santos and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183845666&doi=10.1016%2fj.procs.2023.12.231&partnerID=40&md5=7930f35c92e1fa1e5b922e34be970ec1},

doi = {10.1016/j.procs.2023.12.231},

issn = {18770509},

year  = {2024},

date = {2024-01-01},

journal = {Procedia Computer Science},

volume = {231},

pages = {439-444},

publisher = {Elsevier B.V.},

abstract = {This paper presents the development of a decentralized healthcare marketplace, where patients can anonymously sell their data in the form of Non-Fungible Tokens (NFTs) to researchers. The platform aims to merge NFTs, decentralized finance, and patients' record management, empowering patients, expanding research opportunities, and enhancing healthcare outcomes through blockchain technology. The methodology employed for this research includes field research and discussions with professionals, ensuring credible and valuable insights. The article concludes that blockchain technology offers a transformative solution for healthcare record management, addressing issues of data security, privacy, interoperability, and transparency. Future work focuses on scalability and performance optimization, regulatory compliance, and governance, as well as interoperability and data standardization, among other areas, to unlock the platform's full potential. © 2024 The Authors. Published by Elsevier B.V.},

note = {cited By 0; Conference of 14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023 ; Conference Date: 7 November 2023 Through 9 November 2023; Conference Code:196395},

keywords = {Block-chain; Decentralised; Decentralized finance; Field research; Healthcare; Healthcare record; Patient record; Record management; Research opportunities; Security/privacy, Blockchain, Commerce; Health care; Information management; Interoperability; Regulatory compliance},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2024452,

title = {Towards a Standardized Real-Time Data Repository based on Laboratory Test Results},

author = {R. Sousa and H. Peixoto and T. Guimarães and A. Abelha and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183899245&doi=10.1016%2fj.procs.2023.12.233&partnerID=40&md5=92265b57dddfc7c279187e0735fa722b},

doi = {10.1016/j.procs.2023.12.233},

issn = {18770509},

year  = {2024},

date = {2024-01-01},

journal = {Procedia Computer Science},

volume = {231},

pages = {452-457},

publisher = {Elsevier B.V.},

abstract = {Healthcare facilities use huge quantities of real-time and analytical data to discover meaningful information from patient clinical lab results. Advanced analytics and machine learning algorithms help doctors identify and treat patients more accurately. Accurate models must be trained, tested, and validated with enough data. New real-time data allows healthcare practitioners to quickly and accurately analyse patient demands. Healthcare organizations can improve patient care and outcomes through knowledge discovery. The goal of this effort is to develop a real-time data repository based on patient clinical exams. This collection feeds real-time monitoring panels and machine or deep learning algorithms that forecast patient progression from clinical lab results. Integrate HL7 messages from diverse sources, preprocess them, and add them to an API-accessible data warehouse. In conclusion, the proposed method creates an international-standard data warehouse. This data warehouse can increase healthcare decision-making accuracy and efficacy when utilised with machine learning models, improving patient care and outcomes through more personalised treatment options. © 2024 The Authors. Published by Elsevier B.V.},

note = {cited By 0; Conference of 14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023 ; Conference Date: 7 November 2023 Through 9 November 2023; Conference Code:196395},

keywords = {API; Clinical test result; Clinical tests; Data repositories; Data standards; Health data; Health data standard; Patient care; Real-time data; Real-time information systems, Data warehouses, Decision making; Deep learning; Learning algorithms; Patient treatment; Real time systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chaves2024195,

title = {Collaborative Platform for Intelligent Monitoring of Diabetic Foot Patients - Colab4IMDF},

author = {A. Chaves and R. Sousa and J. Machado and A. Abelha and H. Peixoto},

editor = {Ferras C. Diez J.H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187779099&doi=10.1007%2f978-3-031-54256-5_18&partnerID=40&md5=7cc7dae3a967fb508b0024598e23f8e6},

doi = {10.1007/978-3-031-54256-5_18},

issn = {23673370},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {933 LNNS},

pages = {195-204},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This research work aims to set the basis for the development of a multidisciplinary platform designed to assist healthcare professionals, their institutions and more important, diabetic patients to manage diabetic foot syndrome complications. The system’s primary function is the automated classification of diabetic foot ulcers (DFU), with the ultimate goal of expediting treatment and improving patient well-being. Given the sporadic nature of diabetic patients’ medical consultations, the time gaps between appointments can exacerbate symptoms. To address this issue, the project advocates for sustained communication between physicians and patients, facilitated by the exchange of patient images, thereby reducing the need for frequent in-person visits. This manuscript shows project’s initial phase that involves the research and modelling of an architecture that ranges from mHealth to Deep Learning algorithm capable of rapidly and accurately classifying user-submitted images, offering clinical decision support and autonomous identification of potential DFU complications from the patient’s perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799},

keywords = {Clinical research; Decision support systems; Deep learning; mHealth; Patient treatment, Collaborative platform; Diabetic foot; Diabetic foot ulcer; Diabetic foot wound follow-up; Diabetics patients; Follow up; Health information systems; Healthcare standards; Microservice; Multi agent, Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Alves2024205,

title = {Immersive Shopping Experiences: The Role of Augmented Reality in E-Commerce},

author = {C. Alves and J. Machado},

editor = {Ferras C. Diez J.H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187788679&doi=10.1007%2f978-3-031-54256-5_19&partnerID=40&md5=a725ca93ea25b7c23887aaa3f71d2857},

doi = {10.1007/978-3-031-54256-5_19},

issn = {23673370},

year  = {2024},

date = {2024-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {933 LNNS},

pages = {205-213},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Augmented Reality (AR) is a technology that is emerging in the mobile world, more specifically on smartphones. In this sense, new mobile devices have been launched and several studies on this technology already exist. However, regarding the current era, it will still be necessary to determine the main difficulties and challenges of this technology in electronic commerce. This article aims to investigate and identify in studies with a high impact factor in the literature what the next steps could be as well as factors to mitigate difficulties in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799},

keywords = {'current; High impact; Immersive; Impact factor; M-commerce; Smart phones, Augmented reality, Electronic commerce; Mobile telecommunication systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2024467,

title = {The Interplay of Inflation, Healthcare Spending, and Suicide Rates: An Empirical Analysis},

author = {R. Sousa and C. Ribeiro and C. Cardoso and B. Freixo and H. Peixoto and A. Abelha and J. Machado},

editor = {Ferras C. Diez J.H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187789261&doi=10.1007%2f978-3-031-54235-0_42&partnerID=40&md5=a953118bdafa8c1a1c74d6512e1723c9},

doi = {10.1007/978-3-031-54235-0_42},

issn = {23673370},

year  = {2024},

date = {2024-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {932 LNNS},

pages = {467-476},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This paper investigates the complex interrelation between economic indicators, such as inflation and government spending, and their consequences for public health and suicide rates. Scholars and policymakers are increasingly focused on understanding how economic volatility affects individuals’ resource access and stress levels, thus influencing societal well-being. This study analyzes data from three public datasets using an ETL process developed in Python. After being stored in an Apache HBase database, this data can be visualized in interactive dashboards developed in PowerBI. The data sources include the HCPI, government healthcare expenditures, and suicide rates across different time periods and locations. Key findings highlight the impact of inflation on healthcare costs, emphasizing the need for strategic healthcare planning in the face of economic fluctuations. Moreover, the research reveals intricate relationships between suicide rates and economic variables, suggesting the importance of considering mental health and social well-being in economic policies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799},

keywords = {Big data; Economics; Health care, Consumer price index; Economic indicators; Empirical analysis; Government spending; Harmonized consumer price index; Health spending; Inflation rates; Policy makers; Resource access; Suicide rate, Python},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pimenta2023197,

title = {A Comprehensive Study on Personal and Medical Information to Predict Diabetes},

author = {N. Pimenta and R. Sousa and H. Peixoto and J. Machado},

editor = {Sitek P. Mehmood R. Omatu S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144976640&doi=10.1007%2f978-3-031-20859-1_20&partnerID=40&md5=a454129222acd9990e3dcc663066b957},

doi = {10.1007/978-3-031-20859-1_20},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {583 LNNS},

pages = {197-207},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Diabetes mellitus is without a doubt one of the most well-known and prevalent diseases in people’s daily lives. Creating a tool that can predict the disease would benefit professionals and healthcare systems alike, benefiting both families and countries’ economies in general. Data Mining can be a useful factor in the development of this predictive tool. Data was explored in this study in order to determine which attributes, techniques, and approaches can effectively improve this predictive objective. The main approaches to investigating the data using CRISP-DM were classification and association rules, a methodology that allows searching and finding hidden patterns and relations within data. Results obtained and represented show sensitivity and accuracy values higher than 70%, using J48 and SVM classification algorithms, and allowed to examine that social-economical attributes are not enough to illness prediction. The same applies when only those most indicative characteristics are used—i.e. physical activity, healthy eating and lifestyle, regular health exams—which indicates that a greater set of information is needed so as to be designed an effective model. The best results were obtained using J48 and SVM classification techniques. This can be considered a step towards discovering major indicators of diabetes mellitus and the development of models capable of predicting its presence, enable the creation decision support systems that can improve professionals’ response when dealing with the disease. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 19th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:287829},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2023195,

title = {Prediction Models Applied to Lung Cancer Using Data Mining},

author = {R. Sousa and R. Sousa and H. Peixoto and J. Machado},

editor = {Badica C. Jander K. Braubach L.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159416696&doi=10.1007%2f978-3-031-29104-3_22&partnerID=40&md5=7380c3a23a29e62b89ad3a27e67b79ae},

doi = {10.1007/978-3-031-29104-3_22},

issn = {1860949X},

year  = {2023},

date = {2023-01-01},

journal = {Studies in Computational Intelligence},

volume = {1089 SCI},

pages = {195-200},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Lung cancer is the most common cause of cancer death in men and the second leading cause of cancer death in women worldwide. Even though early detection of cancer can aid in the complete cure of the disease, the demand for techniques to detect the occurrence of cancer nodules at an early stage is increasing. Its cure rate and prediction are primarily dependent on early disease detection and diagnosis. Knowledge discovery and data mining have numerous applications in the business and scientific domains that provide useful information in healthcare systems. Therefore, the present work aimed to compare several prediction models as well as the features to be used, with the help of Weka and RapidMiner tools. Both classification and association rules techniques were implemented. The results obtained were quite satisfactory, with emphasis on the Naive Bayes model, which obtained an accuracy of 95.03% for cross-validation 10 folds and 94.59% for percentage split 66%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 15th International Symposium on Intelligent Distributed Computing, IDC 2022 ; Conference Date: 14 September 2022 Through 16 September 2022; Conference Code:293309},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lopes2023297,

title = {Big Data in Healthcare Institutions: An Architecture Proposal},

author = {J. Lopes and R. Sousa and A. Abelha and J. Machado},

editor = {Zeng D. Huang H. Hou R.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163403837&doi=10.1007%2f978-3-031-33614-0_20&partnerID=40&md5=32838fc94653d1c8bf46169b58d98913},

doi = {10.1007/978-3-031-33614-0_20},

issn = {18678211},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {480 LNICST},

pages = {297-311},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Healthcare institutions are complex organizations dedicated to providing care to the population. Continuous improvement has made the care provided a factor of excellence in the population, improving people’s daily lives and increasing average life expectancy. Even so, the resulting aging has caused patterns to increase day by day and the paradigm of medicine to shift from reaction to prevention. Often, the principle of evidence-based medicine is compromised by lack of evidence on pathogenic mechanisms, risk prediction, lack of resources, and effective therapeutic strategies. This is even more evident in pandemic situations. The current data management tools (centered in a single machine) do not have an ideal behavior for the processing of large amounts of information. This fact combined with the lack of sensitivity for the health area makes it imminent the need to create and implement an architecture that performs this management and processing effectively. In this sense, this paper aims to study the problem of knowledge construction from Big Data in health institutions. The main goal is to present an architecture that deals with the adversities of the big data universe when applied to health. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 0; Conference of 11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022 ; Conference Date: 10 December 2022 Through 11 December 2022; Conference Code:295539},

keywords = {Architecture; Computer architecture; Health care; Information management; Information use; Medical information systems; Real time systems, Average life expectancy; Continuous improvements; Daily lives; Evidence-based medicine; Healthcare information system; Healthcare institutions; Pathogenic mechanisms; Real-time information systems; Risk predictions; Systems architecture, Big data},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Silva2023844,

title = {A Data Acquisition and Consolidation System based on openEHR applied to Physical Medicine and Rehabilitation},

author = {I. Silva and D. Ferreira and H. Peixoto and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164453711&doi=10.1016%2fj.procs.2023.03.113&partnerID=40&md5=05a9e82d67ef672349732c517afce7de},

doi = {10.1016/j.procs.2023.03.113},

issn = {18770509},

year  = {2023},

date = {2023-01-01},

journal = {Procedia Computer Science},

volume = {220},

pages = {844-849},

publisher = {Elsevier B.V.},

abstract = {Shoulder pathologies are prevalent and reduce the quality of life. Due to the shoulder joint's complexity, healthcare professionals face challenges to evaluate, diagnose, and treat these pathologies. On the other hand, the acquisition, presentation, and analysis of patient data in healthcare is complex and hindered by the absence of standardization and interoperability in Database Management Systems. Hence, in this study, we propose a web platform based on openEHR structures that incorporates user interface forms for registering patient physical examinations. The benefits of implementing this approach include structured and standardized data collection, and better communication and information exchange between different systems. © 2023 Elsevier B.V.. All rights reserved.},

note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},

keywords = {Data acquisition; eHealth; Hospital data processing; Information management; Medical applications; Medical computing; Pathology; User interfaces, Electronic health; Electronic health record; Health care professionals; Health records; OpenEHR; Patient data; Physical medicine; Quality of life; Shoulder joints; Shoulder pathology, Interoperability},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Braga2023820,

title = {An Architecture Proposal for Noncommunicable Diseases Prevention},

author = {D. Braga and D. Oliveira and R. Rosario and P. Novais and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164473680&doi=10.1016%2fj.procs.2023.03.109&partnerID=40&md5=befd9725885c6b3009c00b6c112ad1ff},

doi = {10.1016/j.procs.2023.03.109},

issn = {18770509},

year  = {2023},

date = {2023-01-01},

journal = {Procedia Computer Science},

volume = {220},

pages = {820-825},

publisher = {Elsevier B.V.},

abstract = {Noncommunicable Diseases (NCDs) are a leading global health challenge, causing 41 million deaths per year. Risk factors include genetics, environmental factors, and lifestyle choices. Adopting healthy lifestyles can prevent or delay the onset of NCDs, but health misinformation can lead people to make poor decisions about their health. To combat this, it is proposed to develop an Intelligent System using Artificial Intelligence techniques to collect and analyze data from social media about health topics to combat misinformation in public health and forecast NCDs, providing guidelines to prevent their spread. Methods: A systems overall architecture is presented. An innovative and novel solution that addresses the spread of information concerning health and NCDs contributes to inform public policies and infodemic management strategies. © 2023 Elsevier B.V.. All rights reserved.},

note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},

keywords = {Disease prevention; Environmental factors; Global health; Healthy lifestyles; Lifestyle choices; Misinformation; Non-communicable disease; Online social listening; Prevention; Risk factors, Information management; Social networking (online), Intelligent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Miranda2023826,

title = {Data Platforms for Real-time Insights in Healthcare: Systematic Review},

author = {R. Miranda and C. Alves and A. Abelha and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164491019&doi=10.1016%2fj.procs.2023.03.110&partnerID=40&md5=a066521d09ece0ce9d718e34c451fe31},

doi = {10.1016/j.procs.2023.03.110},

issn = {18770509},

year  = {2023},

date = {2023-01-01},

journal = {Procedia Computer Science},

volume = {220},

pages = {826-831},

publisher = {Elsevier B.V.},

abstract = {The ever-growing usage and popularity of Internet of Things devices, coupled with Big Data technologies and machine learning algorithms, have allowed for data engineers to explore new opportunities in healthcare and continuous care. Furthermore, there is a need to reduce the gap on time from when information is created to when actions and insights can be offered. However, a challenge in implementing a large-scale data processing architecture is deciding which tools are appropriate, and how to apply them in the best way possible. For example, streaming systems are now mature enough that hospitals worldwide can use their extremely large datasets, along with data producers, to predict and influence future events. Thus, the main objective of this systematic review is to identify the state-of-the-art in data platforms on healthcare that allow the creation of metrics and actions in real-time. The PRISMA guideline for reporting systematic reviews was implemented to deliver a transparent and consistent report, validating the technological advances in a critical sector. Multiple pertinent articles and papers were retrieved from the SCOPUS abstract and citation database on May 13, 2022, using several relevant keywords to identify potentially relevant documents published from January 2020 onward. These documents must have already been published in English and been already published, and accessible through the B-ON consortium that allows Portuguese students to legally download from most publishers. Over seven studies have been selected for deeper discussion based on their relevance and impact for this review, showcasing their main objectives, data sources, and tools used, as well as their approaches for interoperability and support of machine learning algorithms for decision support. In closing, the collected articles have shown that while Big Data is currently in use at health institutions of all sizes, the ability of processing large amounts of data from sensors and events, and notifying stakeholders as quickly as possible is still in its infancy. © 2023 Elsevier B.V.. All rights reserved.},

note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},

keywords = {Business-intelligence; Data engineering; Data lake; Data mining event-driven microservice; Data platform; Event-driven; Machine-learning; Real- time; Streaming systems; Systematic Review, Computer architecture; Data Analytics; Data handling; Decision support systems; Health care; Large dataset; Learning algorithms; Learning systems; Machine learning; Real time systems, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Vaz2023850,

title = {Enhancing Clinical Management of Bariatric Surgery Using Business Intelligence},

author = {L. Vaz and H. Peixoto and J. Duarte and C. Alvarez and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164522803&doi=10.1016%2fj.procs.2023.03.114&partnerID=40&md5=33c5a7e1ee9afa79be7d332a07083b79},

doi = {10.1016/j.procs.2023.03.114},

issn = {18770509},

year  = {2023},

date = {2023-01-01},

journal = {Procedia Computer Science},

volume = {220},

pages = {850-855},

publisher = {Elsevier B.V.},

abstract = {There is a problem with collecting information in healthcare services as it is scattered among various sources. This leads to potential impact on patient care focus. To address this issue, a Business Intelligence platform was developed and implemented at the Centre for Surgical Treatment of Obesity at Centro Hospitalar do Tâmega e Sousa. The platform developed enables knowledge extraction and aids healthcare professionals to easily access helpful information and perform better decisions, specifically in regards to the growing global concern of obesity and the increasing prevalence of bariatric surgery. © 2023 Elsevier B.V.. All rights reserved.},

note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},

keywords = {Bariatric surgery; Business Intelligence platform; Business-intelligence; Clinical management; Healthcare services; Knowledge extraction; Obesity; Patient care; Potential impacts; Surgical treatment, Extraction, Information analysis; Nutrition; Surgery},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2023347,

title = {Implementing a Software-as-a-Service Strategy in Healthcare Workflows},

author = {R. Sousa and H. Peixoto and A. Abelha and J. Machado},

editor = {Analide C. Sitek P. Ossowski S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172215447&doi=10.1007%2f978-3-031-38333-5_35&partnerID=40&md5=65351fd6a4cb540be0182fe1e9567940},

doi = {10.1007/978-3-031-38333-5_35},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {740 LNNS},

pages = {347-356},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The spread of healthcare technology has resulted in a massive amount of data, particularly in the form of laboratory test results, which play an important role in medical diagnosis and treatment. However, managing and interpreting such large amounts of data has proven increasingly difficult, particularly for resource-constrained healthcare facilities. To address this issue, we present a multi-agent system for effective laboratory test result management based on Software-as-a-Service (SaaS) technology. This paper contains a case study that evaluates the system’s performance and efficacy. The study’s goal is to examine the viability of using a multi-agent system and SaaS technology to manage laboratory test data, highlighting the system’s advantages over conventional alternatives. In the age of big data, the deployment of this system could dramatically improve healthcare service efficiency, quality, and cost-effectiveness. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023 ; Conference Date: 12 July 2023 Through 14 July 2023; Conference Code:298499},

keywords = {Big data; Cost effectiveness; Diagnosis; Health care; Information management; Real time systems; Software agents; Software as a service (SaaS); Software testing; Web services, Case-studies; Cloud paradigm; Healthcare facility; Healthcare technology; Healthcare workflow; Laboratory test; Large amounts of data; Real-time information systems; Service strategy; Software-as-a- Service (SaaS), Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Miranda202397,

title = {Smart Cities Using Crowdsensing and Geoferenced Notifications},

author = {R. Miranda and E. Ribeiro and D. Durães and H. Peixoto and R. Machado and A. Abelha and J. Machado},

editor = {Cardona O. Isaza G. Castillo Ossa L.F.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172225087&doi=10.1007%2f978-3-031-36957-5_9&partnerID=40&md5=6e2f6d526f51ff74d61fc27085a1bf3a},

doi = {10.1007/978-3-031-36957-5_9},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {732 LNNS},

pages = {97-110},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {As the internet and the Internet of Things continue to expand, the idea of Smart Cities has begun to take hold. Smart Cities use connected devices and data to improve the environment and quality of life of their citizens. Technologies such as crowdsensing and geofencing allow citizens to contribute to initiatives and receive notifications when near areas of interest, respectively. This paper presents a systematic review of past works on the implementation of crowdsensing and geofencing technologies in Smart Cities, with the goal of identifying their purpose, strategies, and tools. The review examines seventeen relevant papers identified through the Scopus citation and abstract database. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 2nd International Conference on Sustainable Smart Cities and Territories, SSCT 2023 ; Conference Date: 21 June 2023 Through 23 June 2023; Conference Code:300219},

keywords = {Area of interest; Crowdsensing; Geofence; Location based; Location-based notification; Quality of life; Smart notification; Strategies and tools; Systematic Review, Crowdsourcing, Internet of things; Smart city},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Coelho2023673,

title = {Multi-agent System for Multimodal Machine Learning Object Detection},

author = {E. Coelho and N. Pimenta and H. Peixoto and D. Durães and P. Melo-Pinto and V. Alves and L. Bandeira and J. Machado and P. Novais},

editor = {Martinez de Pison F. J. Perez Garcia H. Garcia Bringas P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172230717&doi=10.1007%2f978-3-031-40725-3_57&partnerID=40&md5=79d45a455c79da96394a5b0e05a56908},

doi = {10.1007/978-3-031-40725-3_57},

issn = {03029743},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {14001 LNAI},

pages = {673-681},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Multi-agent systems have shown great promise in addressing complex problems that traditional single-agent approaches are not be able to handle. In this article, we propose a multi-agent system for the conception of a multimodal machine learning problem on edge devices. Our architecture leverages docker containers to encapsulate knowledge in the form of models and processes, enabling easy management of the system. Communication between agents is facilitated by Message Queuing Telemetry Transport, a lightweight messaging protocol ideal for Internet of Things and edge computing environments. Additionally, we highlight the significance of object detection in our proposed system, which is a crucial component of many multimodal machine learning tasks, by enabling the identification and localization of objects within diverse data modalities. In this manuscript an overall architecture description is performed, discussing the role of each agent and the communication protocol between them. The proposed system offers a general approach to multimodal machine learning problems on edge devices, demonstrating the advantages of multi-agent systems in handling complex and dynamic environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of Proceedings of the 18th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2023 ; Conference Date: 5 September 2023 Through 7 September 2023; Conference Code:299919},

keywords = {Agent approach; Complex problems; Learning objects; Machine learning problem; Machine-learning; Multi-modal; Multi-modality; Multimodal machine learning; Objects detection; Single-agent, Internet protocols; Machine learning; Network architecture; Object detection; Object recognition, Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ramos2023351,

title = {Information Technology Monitoring in Healthcare: A Case Study},

author = {V. Ramos and C. Marques and H. Peixoto and J. Machado},

editor = {Ibarra W. Ferras C. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172692659&doi=10.1007%2f978-3-031-33261-6_30&partnerID=40&md5=b17b14aec696139f74f78283fdc0660d},

doi = {10.1007/978-3-031-33261-6_30},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {692 LNNS},

pages = {351-361},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The healthcare environment is particularly relevant when discussing information technology infrastructure monitoring since availability and communication are vital for the proper functioning of healthcare units. It is important to be able to easily monitor and observe each unit from a single point of access so that actions can be swiftly taken when there is a problem. This paper proposes a multi-site and multi-organization web and microservices-based information technology infrastructure monitoring solution. In addition to exploring the developed system and its architecture, it presents a case study resulting from the system’s implementation in an organization and holds a discussion about the obtained results to determine whether a multi-platform monitoring system improves information technology availability in the healthcare industry. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2023 ; Conference Date: 24 April 2023 Through 26 April 2023; Conference Code:298099},

keywords = {Case-studies; Health information systems; Healthcare; Healthcare environments; Information technology infrastructure; Infrastructure monitoring; IT infrastructure monitoring; IT infrastructures; Microservice; Technology monitoring, Health care, Information systems; Information use},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa202316,

title = {COVID-19 Cases and Their Impact on Global Air Traffic},

author = {R. Sousa and J. Gomes and J. Gomes and M. Arcipreste and P. Guimarães and D. Oliveira and J. Machado},

editor = {Peixoto H. Machado J.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172721784&doi=10.1007%2f978-3-031-38204-8_2&partnerID=40&md5=64a4cb6986ae3fc0935b9c82e33c5145},

doi = {10.1007/978-3-031-38204-8_2},

issn = {18678211},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {485 LNICST},

pages = {16-27},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The air transport industry has marked unprecedented changes throughout the pandemic period of Covid-19 infection. Mostly in the number of flights canceled, liquidation of airlines and disconnection between points worldwide. The existing documentation relating to air traffic, in the specific period of this study, can be extracted, processed and visualized through tools widely used to support case study assumptions, especially in the context of Big Data. This document addresses to the use of a Big Data architecture to survey, analyze and explore different data sources and consequent loading, transformation and visual representation of the results obtained in order to verify the impact of the number of cases of infection by Covid-19 in air traffic. Based on the results obtained through the described methodology, it can be stated that the number of cases of infection by Covid-19 presents a significant impact on the number of flights that occurred ever since (around 50% less flights). © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},

keywords = {Air traffics; Air transport industry; Big data architecture; Case-studies; Covid-19 world impact; Data architectures; Data-source; GDP; Global air traffic; Survey analysis, Air transportation; Aviation; COVID-19; Metadata, Big data},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa202328,

title = {The Impact of Contingency Measures on the COVID-19 Reproduction Rate},

author = {R. Sousa and D. Oliveira and F. Hak and J. Machado},

editor = {Peixoto H. Machado J.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172722736&doi=10.1007%2f978-3-031-38204-8_3&partnerID=40&md5=f2bd2a51fa043e2d5a2f974d3f33ef48},

doi = {10.1007/978-3-031-38204-8_3},

issn = {18678211},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {485 LNICST},

pages = {28-37},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The SARS-CoV-2 virus had a major impact on the health of the world’s population, causing governments to take progressively more cautious measures. All of these measures took into account the pandemic situation in the region in real time, with the aim of slowing down the spread of the infection as much as possible and reducing the associated mortality. This article aims to study the impact of preventive measures on the spread of COVID-19 and the consequent impact on excess deaths. In order to obtain the results presented, Big Data techniques were used for data storage and processing. As a result it can be concluded that Gross Domestic Product (GDP) is directly proportional to the Human Development Index (HDI), Higher GDP per capita are associated with a higher number of new cases of COVID-19 and R-index is inversely proportional to the severity of the contingency measures. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},

keywords = {Big data analyse; Contigency; Contigency measure; Correlation; Covid-19; Gross domestic products; Powerbi; Preventive measures; Proliferation rate; Real- time, Big data; Cell proliferation; Data handling; Digital storage, COVID-19},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Neto202391,

title = {Recommendation of Medical Exams to Support Clinical Diagnosis Based on Patient’s Symptoms},

author = {C. Neto and D. Ferreira and H. Cunha and M. Pires and S. Marques and R. Sousa and J. Machado},

editor = {Peixoto H. Machado J.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172725999&doi=10.1007%2f978-3-031-38204-8_8&partnerID=40&md5=e29bde9aad6cf908bce88ef83007236a},

doi = {10.1007/978-3-031-38204-8_8},

issn = {18678211},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {485 LNICST},

pages = {91-100},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Nowadays, it is essential that the error in the decisions made by health professionals is as small as possible. This applies to any medical area, including the recommendation of medical exams based on certain symptoms for the diagnosis of diseases. This study aims to explore the use of different Machine Learning techniques to increase the confidence of the medical exams prescribed by healthcare professionals. A successful implementation of this proposal could reduce the probability of medical errors in what concerns the prescription of medical exams and, consequently, the diagnosis of medical conditions. Thus, in this paper, six Machine Learning models were applied and optimized, namely, RF, DT, k-NN, NB, SVM and RNN, in order to find the most suitable model for the problem at hand. The results obtained with this study were promising, achieving high accuracy values with RF, DT and k-NN. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},

keywords = {Clinical diagnosis; CRISP-DM; Diagnoses of disease; Health care professionals; Health professionals; Machine learning techniques; Medical areas; Medical conditions; Medical errors; Medical exam, Diagnosis; Learning systems; Nearest neighbor search, Support vector machines},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chaves2023230,

title = {Intelligent Systems in Healthcare: An Architecture Proposal},

author = {A. Chaves and L. Montenegro and H. Peixoto and A. Abelha and L. Gomes and J. Machado},

editor = {Hornos M. J. Julian Inglada V. Novais P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174435986&doi=10.1007%2f978-3-031-43461-7_23&partnerID=40&md5=f1fab121626cc690c7357a2d9bd721ba},

doi = {10.1007/978-3-031-43461-7_23},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {770 LNNS},

pages = {230-238},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Multi-Agent Systems has existed for decades and has focused on principles such as loose coupling, distribution, reactivity, and local state. Despite substantial tool and programming language research and development, industry adoption of these systems has been restricted, particularly in the healthcare arena. Artificial intelligence, on the other hand, entails developing computer systems that can execute tasks that normally require human intelligence, such as decision-making, problem-solving, and learning. The goal of this article is to develop and implement an architecture that includes multi-agent systems with microservices, leveraging the capabilities of both methodologies in order to harness the power of Artificial Intelligence in the healthcare industry. It assesses the proposed architecture’s merits and downsides, as well as its relevance to various healthcare use cases and the influence on system scalability, adaptability, and maintainability. Indeed, the proposed architecture is capable of meeting the objectives while maintaining scalability, flexibility, and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.},

note = {cited By 1; Conference of 14th International Symposium on Ambient Intelligence, ISAmI 2023 ; Conference Date: 12 July 2023 Through 14 July 2023; Conference Code:301589},

keywords = {Coupling distribution; Development industry; Intelligent healthcare; Local state; Loose couplings; Microservice; Programming language researches; Proposed architectures; Reactivity state; Research and development, Decision making; Health care; Intelligent agents; Intelligent systems; Scalability, Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{IGustiNgurahAgungAgniPrema2023,

title = {Discovery of Hospital Billing Process in a Regional Hospital Using Process Mining},

author = {N. I Gusti Ngurah Agung Agni Prema and P. Naufal Avilandi and Fathan and R. Andreswari and J. M. F. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175616569&doi=10.1109%2fICADEIS58666.2023.10271040&partnerID=40&md5=9da901ec00a5a466f5c8da5360d3e6d7},

doi = {10.1109/ICADEIS58666.2023.10271040},

isbn = {9798350303414},

year  = {2023},

date = {2023-01-01},

journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Delays and complexities in the billing process for healthcare services can inconvenience patients and hinder the efficient functioning of regional hospitals. This study aims to utilize process mining techniques to analyze event logs and identify bottleneck activities within the billing process. By evaluating the process, the research aims to understand the causality behind these bottlenecks and propose effective solutions for enhancing efficiency and reducing time costs. The research employs the PM4Py open-source toolkit, including PM4Py-GPU for computationally intensive tasks. Through fitness alignments between event logs and process models, it is found that approximately 47.128 percent of traces match the process model, exhibiting a good level of conformity with an average fitness of 0.8888. Notably, the study reveals that over 3 percent of billing processes in regional hospitals exhibit repetitive occurrences of specific activities consecutively. The identification of this repetitive activity pattern prompts a deeper investigation into its root causes and implications for resource utilization and performance. By addressing these causative factors, the research aims to propose optimized approaches to streamline the billing process, thus enhancing overall efficiency and customer satisfaction levels for the hospitals. Overall, the findings of this study contribute to a comprehensive understanding of the billing process in healthcare services and provide valuable insights for hospitals to implement targeted improvements, reduce delays, and offer high-quality services to their patients. © 2023 IEEE.},

note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},

keywords = {Celonis; Effective solution; Event logs; Healthcare services; Hospital biling; Mining techniques; PM4Py; Process mining; Process-models; Time cost, Customer satisfaction; Data mining; Efficiency; Health care, Hospitals},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Arfilinia2023,

title = {Multidimensional Sentiment Analysis of Tourism Object in DKI Jakarta, Banten, East Java, Central Java and West Java using Support Vector Machine Algorithm},

author = {A. Arfilinia and R. Andreswari and F. Hamami and J. M. F. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175628715&doi=10.1109%2fICADEIS58666.2023.10270985&partnerID=40&md5=9141e7a3e64b959a43789749f4526161},

doi = {10.1109/ICADEIS58666.2023.10270985},

isbn = {9798350303414},

year  = {2023},

date = {2023-01-01},

journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Information technology provides several conveniences, one of which is facilitating tourists in searching for information about tourist attractions. One of the services that can be used is Google My Business. The comments or reviews from tourists are numerous, thus requiring a significant amount of time to review them one by one. Therefore, a method is needed to address this issue, which is sentiment analysis. In this study, a multidimensional sentiment analysis was conducted on tourist attractions in the provinces of DKI Jakarta, Banten, East Java, Central Java, and West Java. Data was collected between January and March 2023 using the Data Miner tool. Two labeling techniques, namely Transformer and Textblob, were compared for labeling the data. The confusion matrix was employed as the evaluation tool, and the Support Vector Machine (SVM) technique was used to implement sentiment analysis. Labeling using transformers obtains an accuracy of 0.8169 or 81.6%, then the average precision, recall and f1-score are 68%, 54%, abd 58%. While labeling using textblob, obtained an accuracy of 0.9257 or 92.5%, the average precision is 88%, recall and f1-score results are 87%. The accuracy, precision, recall, and f1-score results indicate that labeling using Textblob outperforms the labeling using Transformers. © 2023 IEEE.},

note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},

keywords = {Confusion matrix; F1 scores; Labelings; Multi-dimensional sentiments; Multidimensional sentiment analyse; Sentiment analysis; Support vectors machine; Textblob; Transformer, Image resolution; Matrix algebra; Sentiment analysis, Support vector machines},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Fauzan2023,

title = {Breast Cancer Detection on Histopathology Images Using Pre-trained Computer Vision Models},

author = {D. F. Fauzan and R. Fauzi and O. N. Pratiwi and J. M. Ferreira Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175636654&doi=10.1109%2fICADEIS58666.2023.10270900&partnerID=40&md5=0526f55af1d7651d110203f5f4f0b012},

doi = {10.1109/ICADEIS58666.2023.10270900},

isbn = {9798350303414},

year  = {2023},

date = {2023-01-01},

journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Breast cancer is the most common type of cancer worldwide. According to the World Health Organization (WHO), there were 7.8 million women alive in 2020 who had been diagnosed with breast cancer, and it has claimed more women's lives than any other kind of cancer. With the recent rise of artificial intelligence, breast cancer detection using deep learning techniques is getting more popular. However, creating a deep learning model for a specific task from scratch costs a lot of time and money. Transfer learning is a well-known method that can make deep learning developments more efficient by leveraging pre-trained models. Using the BreakHis dataset, this paper will compare three cutting-edge pre-trained computer vision models: DenseNet, RegNet, and BiT, in predicting malignant or benign tumor tissue from breast histopathology images to determine which model is better for that specific task. Although the DenseNet model achieves the highest score with 93.7% Area Under the ROC Curve (AUC) and 97.4% Average Precision Score (APS), the BiT model is more suitable for deployment in a real-world setting since it can predict more malignant cases correctly than the other two models with a sensitivity score of 90.79%. © 2023 IEEE.},

note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},

keywords = {Breast Cancer; Breast cancer detection; Cancer detection; Deep learning; Learning models; Learning techniques; Specific tasks; Transfer learning; Vision model; World Health Organization, Computer vision, Deep learning; Diseases; Learning systems; Medical imaging; Transfer learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dias2023288,

title = {Enhancing Data Science Interoperability: An Innovative System for Managing OpenEHR Structures},

author = {M. Dias and R. Sousa and J. Duarte and H. Peixoto and A. Abelha and J. Machado},

editor = {Bonsangue M. M. Anutariya C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177871998&doi=10.1007%2f978-981-99-7969-1_21&partnerID=40&md5=19b18d908fc002f4b8eab31c03dfc5f0},

doi = {10.1007/978-981-99-7969-1_21},

issn = {18650929},

year  = {2023},

date = {2023-01-01},

journal = {Communications in Computer and Information Science},

volume = {1942 CCIS},

pages = {288-299},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The concept of e-Health is increasingly used in the healthcare industry, referring to the development and application of software and hardware solutions for efficient collection, storage, manipulation, and communication of data, to improve healthcare delivery. Data Science Interoperability is critical to create a data repository from which to implement artificial intelligence models that support the decision-making process. The lack of interoperability in Health Information Systems (HIS) has been a significant challenge. The need for systems that promote interoperability between HIS within the same institution or even between HIS from different institutions is a worldwide concern. A system that contributes to improving interoperability in healthcare through the use of the openEHR standard is proposed with this paper. The system provides a way to manipulate clinical data by creating an artifact built with React and NextJS that allows the conversion of openEHR standardized data into a JSON object. The artifact being a web application presents a new way for users to check openEHR data, while the API can be used by developers to work with openEHR data in a more accessible and supported way. The results of this research and engineering effort have been successful in presenting a new approach to implementing yet another tool to help healthcare professionals and biomedical software engineers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.},

note = {cited By 0; Conference of 1st International Conference on Data Science and Artificial Intelligence, DSAI 2023 ; Conference Date: 27 November 2023 Through 29 November 2023; Conference Code:304419},

keywords = {Application programming interfaces (API); Application programs; Data Science; Decision making; Digital storage; Health care; Information systems; Information use, Data science interoperability; Development and applications; E health; Ehealth; Health information systems; Healthcare industry; Innovative systems; Openehr; Software and hardwares; Software solution, Interoperability},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Hernández2023300,

title = {A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents},

author = {G. Hernández and A. González-Briones and J. Machado and P. Chamoso and P. Novais},

editor = {Bonsangue M. M. Anutariya C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177874390&doi=10.1007%2f978-981-99-7969-1_22&partnerID=40&md5=97e552640b738ab3e2668bc83b3e3e02},

doi = {10.1007/978-981-99-7969-1_22},

issn = {18650929},

year  = {2023},

date = {2023-01-01},

journal = {Communications in Computer and Information Science},

volume = {1942 CCIS},

pages = {300-313},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {There may be multiple reasons for tooth extraction, such as deep cavities, an infection that has destroyed an important portion of the tooth or the bone that surrounds it, or for orthodontic reasons, such as the lack of space for all the teeth in the mouth. In the case of orthodontics, however, there is a relationship between tooth extraction and the craniofacial morphological pattern. The purpose of this study is to establish whether such a relationship exists in adolescents and to evaluate it and to serve as a tool to support medical decision making. Machine Learning techniques can now be applied to datasets to discover relationships between different variables. Thus, this study involves the application of a series of Machine Learning techniques to a dataset containing information on orthodontic tooth extraction in adolescents. It has been discovered that by following simple rules it is possible to identify the need of treatment in 98.7% of the cases, while the remaining can be regarded as “limited cases”, in which an expert’s opinion is necessary. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.},

note = {cited By 0; Conference of 1st International Conference on Data Science and Artificial Intelligence, DSAI 2023 ; Conference Date: 27 November 2023 Through 29 November 2023; Conference Code:304419},

keywords = {Craniofacial; Craniofacial morphological growth; Deep cavity; Dental extraction; Machine learning approaches; Machine learning techniques; Machine-learning; Morphological patterns; Orthodontic treatments; Tooth extraction, Decision making; Dentistry; Learning algorithms; Machine learning, Extraction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zhinin-Vera202315,

title = {A Reinforcement Learning Algorithm for Improving the Generation of Telerehabilitation Activities of ABI Patients},

author = {L. Zhinin-Vera and A. Moya and E. Navarro and J. Jaen and J. Machado},

editor = {Urzaiz G. Bravo J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178594918&doi=10.1007%2f978-3-031-48306-6_2&partnerID=40&md5=3823104564976273c57dde7b795187c6},

doi = {10.1007/978-3-031-48306-6_2},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {835 LNNS},

pages = {15-26},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Acquired Brain Injury (ABI) is a condition caused by an injury or disease that disrupts the normal functioning of the brain. In recent years, there has been a significant increase in the incidence of ABI, highlighting the need for a comprehensive approach that improves the rehabilitation process and, thus, provides people with ABI with a better quality of life. Developing appropriate rehabilitation activities for these patients is a major challenge for experts in the field, as their poor design can hinder the recovery process. One way to address this problem is through the use of smart systems that generate such rehabilitation activities in an automatic way that can then be modified by therapists as they deem appropriate. This automatic generation of rehabilitation activities uses experts’ knowledge to determine their suitability according to the patient’s needs. The problem is that this knowledge may be ill-defined, hampering the rehabilitation process. This paper investigates the possibility of applying Deep Q-Networks, a Reinforcement Learning (RL) algorithm, to evolve and adapt that information according to the outcomes of the rehabilitation process of groups of patients. This will help minimize possible errors made by experts and improve the rehabilitation process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 15th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2023 ; Conference Date: 28 November 2023 Through 29 November 2023; Conference Code:304769},

keywords = {Acquired brain injuries; Condition; Deep q-network; Gesture interaction; Multi-modal; Multimodal gesture interaction; Rehabilitation activities; Reinforcement learning algorithms; Reinforcement learnings; Telerehabilitation, Brain; Deep learning; Learning algorithms; Patient rehabilitation, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Moya2023217,

title = {Clustering ABI Patients for a Customized Rehabilitation Process},

author = {A. Moya and L. Zhinin-Vera and E. Navarro and J. Jaen and J. Machado},

editor = {Urzaiz G. Bravo J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178644656&doi=10.1007%2f978-3-031-48642-5_21&partnerID=40&md5=5f6a0755f77fbc70a3d9148792855ca9},

doi = {10.1007/978-3-031-48642-5_21},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {842 LNNS},

pages = {217-228},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Acquired Brain Injury (ABI) is a medical condition resulting from injury or disease that affects the functioning of the brain. The incidence of ABI has increased in recent years, highlighting the need for a comprehensive approach to treatment and rehabilitation to improve patients’ quality of life. Developing appropriate therapies for these patients is a challenging task because of the wide diversity of effects and severity they may suffer. This problem exacerbates the complexity of designing the rehabilitation activities, which is a time-consuming and complicated task that may cause poor patient recovery, if such activities are poorly designed. In order to overcome this problem, it is common practice to create groups of patients with similar complaints and deficits and to design rehabilitation activities that may be reused internally by such groups, facilitating comparative analyses. Usually, such grouping is conducted by specialists who may neglect to detect commonalities due to the huge amount of information to be processed. In this work, a clustering of ABI patients is performed following a systematic methodology, from preprocessing the data to applying appropriate clustering algorithms, in order to guarantee an adequate clustering of ABI patients. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 15th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2023 ; Conference Date: 28 November 2023 Through 29 November 2023; Conference Code:304769},

keywords = {Acquired brain injuries; Amount of information; Clusterings; Comparative analyzes; Medical conditions; Quality of life; Rehabilitation activities; Systematic methodology, Clustering algorithms; Patient treatment, Patient rehabilitation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Denanti2023,

title = {The Correlation of Headline News Sentiment and Stock Return during Dividend Period},

author = {S. P. Denanti and I. Yunita and T. Widarmanti and J. M. Ferreira Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180008658&doi=10.1109%2fICONDBTM59210.2023.10327342&partnerID=40&md5=cf21fdc9bbb11f8c4a8c6e089f91285e},

doi = {10.1109/ICONDBTM59210.2023.10327342},

isbn = {9798350328028},

year  = {2023},

date = {2023-01-01},

journal = {2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Stock price fluctuations require investors to gather more information to make informed decisions for optimal returns. Dividend announcements are the basis for investors to make investment decisions, as they contain asymmetric information about company performance. However, during the dividend period stock prices often fluctuate, which can make it difficult for investors to make decisions. Therefore, market participants can use sentiment analysis to assess company performance and assist in making investment decisions. The purpose of this study is to analyse headline sentiment during the dividend period, and how it relates to the stock returns of companies included in the LQ45 Index from 2018 to 2022. In conducting the sentiment analysis, the FinBERT model was used to classify dividend news headlines into positive, negative, and neutral sentiment. Then, a Spearman rank correlation test is conducted with the closing price of the stock to see the relationship. The results show that the sentiment formed by news headlines is dominated by neutral sentiment (46%), followed by positive sentiment (28%) and negative sentiment (26%). The study, conducted over the 2018-2022 dividend period, shows a positive relationship between news headlines and stock returns. The analysis shows that the sentiment conveyed in news headlines has a statistically significant positive correlation with changes in company stock returns. These findings suggest that the sentiment expressed in news headlines can serve as a valuable indicator for predicting and understanding fluctuations in stock returns during dividend periods. © 2023 IEEE.},

note = {cited By 0; Conference of 2023 International Conference on Digital Business and Technology Management, ICONDBTM 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:195011},

keywords = {Asymmetric information; Company performance; Dividend period; FinBERT; Headline news; Informed decision; Investment decisions; Sentiment analysis; Stock price fluctuation; Stock returns, Costs; Financial markets; Investments, Sentiment analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Montenegro2023274,

title = {AI-Based Medical Scribe to Support Clinical Consultations: A Proposed System Architecture},

author = {L. Montenegro and L. M. Gomes and J. M. Machado},

editor = {Vale Z. Moniz N. Moniz N.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180626879&doi=10.1007%2f978-3-031-49011-8_22&partnerID=40&md5=a02dc441c31b5e5b04b0b741a3aaa2bd},

doi = {10.1007/978-3-031-49011-8_22},

issn = {03029743},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {14116 LNAI},

pages = {274-285},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {AI applications in hospital frameworks can improve patient-care quality and efficient workflows and assist in digital transformation. By designing Smart Hospital infrastructures, creating an efficient framework enables patient information exchange between hospitals, point of care, and remote patient monitoring. Deep learning (DL) solutions play important roles in these infrastructures’ digital transformation process and architectural design. Literature review shows that DL solutions based on Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) are rising concerning clinical data digitalisation, population health management, and improving patient care. Nevertheless, one of the literature’s shortcomings highlights the limited research using these solutions in real-world medical environments. As part of smart hospitals, smart medical scribes have been presented in several studies as a promising solution. However, just a few studies have tested it in real settings. Moreover, it was limited to non-existent studies on non-English systems, even yet to be found similar studies for European Portuguese. The proposed study evaluates NLP-based solutions in real-life Portuguese clinical settings focused on patient care for Smart Healthcare applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 22nd EPIA Conference on Artificial Intelligence, EPIA 2023 ; Conference Date: 5 September 2023 Through 8 September 2023; Conference Code:305499},

keywords = {AI applications; Automatic speech recognition; Digital transformation; Language processing; Natural language processing; Natural languages; Patient care; Smart healthcare; Smart hospital; Systems architecture, Deep learning; Health care; Natural language processing systems; Population statistics; Speech recognition, Hospitals},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Neto2022119,

title = {Prediction Models for Coronary Heart Disease},

author = {C. Neto and D. Ferreira and J. Ramos and S. Cruz and J. Oliveira and A. Abelha and J. Machado},

editor = {Yigitcanlar T. Omatu S. Matsui K.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115256287&doi=10.1007%2f978-3-030-86261-9_12&partnerID=40&md5=bc7086cdc28db5a781d5729387dd7f0c},

doi = {10.1007/978-3-030-86261-9_12},

issn = {23673370},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {327 LNNS},

pages = {119-128},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {In the current days, it is known that a great amount of effort is being applied to improving healthcare with the use of Artificial Intelligence technologies in order to assist healthcare professionals in the decision-making process. One of the most important field in healthcare diagnoses is the identification of Coronary Heart Disease since it has a high mortality rate worldwide. This disease occurs when the heart’s arteries are incapable of providing enough oxygen-rich blood to the heart. Thus, this study attempts to develop Data Mining models, using Machine Learning algorithms, capable of predicting, based on patients’ data, if a patient is at risk of developing any kind of Coronary Heart Disease within the next 10 years. To achieve this goal, the study was conducted by the CRISP-DM methodology and using the RapidMiner software. The best model was obtained using the Decision Tree algorithm and with Cross-Validation as the sampling method, obtaining an accuracy of 0.884, an AUC value of 0.942 and an F1-Score of 0.881. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 18th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2021 ; Conference Date: 6 October 2021 Through 8 October 2021; Conference Code:264809},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Neto2022283,

title = {Classification of Dementia in Adults},

author = {C. Neto and D. Ferreira and J. Nunes and L. Braga and L. Martins and L. Cunha and J. Machado},

editor = {Riola Rodriguez J. M. Fajardo-Toro C.H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119352719&doi=10.1007%2f978-981-16-4884-7_23&partnerID=40&md5=c3b7bd8b729c23c8a694e7e6807db3cd},

doi = {10.1007/978-981-16-4884-7_23},

issn = {21903018},

year  = {2022},

date = {2022-01-01},

journal = {Smart Innovation, Systems and Technologies},

volume = {255},

pages = {283-293},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Dementia is a broad term for a large number of conditions, and it is often associated with Alzheimer’s disease. A reliable diagnosis of this disease, especially in the early stages, may prevent further complications. As such, machine learning algorithms can be applied in order to validate and correctly classify cases of dementia or non dementia in adults, assisting physicians in the diagnosis and management of this clinical condition. In this study, a dataset containing magnetic resonance imaging comparisons of demented/non demented adults was used to conduct a Data Mining process, following the Cross Industry Standard Process for Data Mining methodology, with the main goal of classifying instances of dementia. Different machine learning algorithms were applied during this process, more specifically Support Vector Machines, Decision Trees, Logistic Regression, Neural Networks, Naïve Bayes and Random Forest. The maximum accuracy of 95.41% was achieved with the Naïve Bayes algorithm using Split Validation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},

note = {cited By 0; Conference of Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 ; Conference Date: 18 August 2021 Through 20 August 2021; Conference Code:267889},

keywords = {Alzheimer; Clinical conditions; Condition; Cross industry; Data mining process; Industry standards; Machine decisions; Machine learning algorithms; Standards process; Support vectors machine, Classification (of information), Data mining; Decision trees; Diagnosis; Learning algorithms; Magnetic resonance imaging; Neurodegenerative diseases; Support vector machines},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Guisasola2022271,

title = {Detecting Autism Spectrum Disorder Using Data Mining},

author = {A. C. Guisasola and D. Ferreira and C. Neto and A. Abelha and J. Machado},

editor = {Riola Rodriguez J. M. Fajardo-Toro C.H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119376845&doi=10.1007%2f978-981-16-4884-7_22&partnerID=40&md5=0b7fd71f51e6e6841168346ba356b94e},

doi = {10.1007/978-981-16-4884-7_22},

issn = {21903018},

year  = {2022},

date = {2022-01-01},

journal = {Smart Innovation, Systems and Technologies},

volume = {255},

pages = {271-281},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Autism spectrum disorder (ASD) is a set of neurodevelopmental disorders that affect cognitive development, social and communication skills, and behavior of affected individuals. The faster traces of ASD are identified, the faster the stimulation will begin and the more effective the gains in neuropsychomotor development will be. That being said, the earlier the diagnosis of ASD, the easier it is to control the disorder. Therefore, this study aims to classify the cases of ASD as “yes” if a patient has been diagnosed with ASD or “no” if a patient has not, using data mining (DM) models with classification techniques. The methodology of cross-industry standard process for data mining (CRISP-DM) was followed, and to induce the data mining models, the Rapidminer software was used. The results were quite promising reaching a level of accuracy of 97%, specificity of 95.45%, sensitivity of 100%, and precision of 95.65%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},

note = {cited By 0; Conference of Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 ; Conference Date: 18 August 2021 Through 20 August 2021; Conference Code:267889},

keywords = {Autism spectrum disorders; Cognitive communications; Cognitive development; Communication skills; Cross industry; Cross-industry standard process for data mining; Data mining models; Industry standards; Social skills; Standards process, Classification (of information); Diseases, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Duarte202253,

title = {A Comparative Study of Data Mining Techniques Applied to Renal-Cell Carcinomas},

author = {A. Duarte and H. Peixoto and J. Machado},

editor = {Goleva R. Silva B. Spinsante S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127847164&doi=10.1007%2f978-3-030-99197-5_5&partnerID=40&md5=df7b9e6c1d403d0e3049ede31cd77307},

doi = {10.1007/978-3-030-99197-5_5},

issn = {18678211},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {432 LNICST},

pages = {53-62},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Despite being one of the deadliest diseases and the enormous evolution in fighting it, the best methods to predict kidney cancer, namely Renal-Cell Carcinomas (RCC), are not well-known. One of the solutions to accelerate the current knowledge about RCC is through the use of Data Mining techniques based on patients' personal and clinical data. Therefore, it is crucial to understand which techniques are the most suitable to extract knowledge about this disease. In this paper, we followed the CRISP-DM methodology to simulate different techniques to determine the ones with the best predictive performance. For this purpose, we used a dataset of 821 records of RCC patients, obtained from The Cancer Genome Atlas. The present work tests different Data Mining techniques, that can be used to predict the 5-year life expectancy of patients with renal cancer and to predict the number of days to death for patients who have a life expectancy of less than 5 years. The results obtained demonstrated that the best algorithm for estimating the vital status at 5 years was Random Forest. This algorithm presented an accuracy of 87.65% and an AUROC of 0.931. For the prediction of days to death, the best performance was obtained with the k-Nearest Neighbors algorithm with a root mean square error of 354.6 days. The work suggested that Data Mining techniques can help to understand the influence of various risk factors on the life expectancy of patients with RCC. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 1; Conference of 8th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2021 ; Conference Date: 24 November 2021 Through 26 November 2021; Conference Code:275359},

keywords = {'current; Clinical data; Comparatives studies; CRISP-DM; Data-mining techniques; Kidney cancer; Life expectancies; Rapidminer; Renal cell carcinoma; Survival, Data mining, Decision trees; Diseases; Forecasting; Mean square error; Nearest neighbor search},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Marques202263,

title = {Predicting Diabetes Disease in the Female Adult Population, Using Data Mining},

author = {C. Marques and V. Ramos and H. Peixoto and J. Machado},

editor = {Goleva R. Silva B. Spinsante S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127917985&doi=10.1007%2f978-3-030-99197-5_6&partnerID=40&md5=d0b1061ffb8b1c4ae3d164d82c9a4a30},

doi = {10.1007/978-3-030-99197-5_6},

issn = {18678211},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {432 LNICST},

pages = {63-73},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The aim of this study is to predict, through data mining, the incidence of diabetes disease in the Pima Female Adult Population. Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces and is a major cause of blindness, kidney failure, heart attacks, stroke and lower limb amputation. The information collected from this population combined with the data mining techniques, may help to detect earlier the presence of this decease. To achieve the best possible ML model, this work uses the CRISP-DM methodology and compares the results of five ML models (Logistic Regression, Naive Bayes, Random Forest, Gradient Boosted Trees and k-NN) obtained from two different datasets (originated from two different data preparation strategies). The study shows that the most promising model as k-NN, which produced results of 90% of accuracy and also 90% of F1 Score, in the most realistic evaluation scenario. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 0; Conference of 8th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2021 ; Conference Date: 24 November 2021 Through 26 November 2021; Conference Code:275359},

keywords = {Adult populations; Chronic disease; CRISP-DM; Data-mining techniques; Female adults; Heart attack; Logistics regressions; Lower-limb amputations; ML model; Naive bayes, Barium compounds; Decision trees; Insulin; Logistic regression; Nearest neighbor search; Population statistics, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Marques2022720,

title = {Pervasive Monitoring System for Services and Servers in Healthcare Environment},

author = {C. Marques and V. Ramos and H. Peixoto and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132186057&doi=10.1016%2fj.procs.2022.03.097&partnerID=40&md5=e46c2b47a98b1e8446e72ab0b00c1ea2},

doi = {10.1016/j.procs.2022.03.097},

issn = {18770509},

year  = {2022},

date = {2022-01-01},

journal = {Procedia Computer Science},

volume = {201},

number = {C},

pages = {720-725},

publisher = {Elsevier B.V.},

abstract = {Information systems are continuously evolving in nature and complexity. In the healthcare environment, information and information exchange are critical for providing health care at all levels. Hence, the healthcare environment is particularly relevant when discussing IT infrastructure monitoring and disaster prevention since availability and communication are vital for the proper functioning of healthcare units, whether acting in isolation or on a network. This work focuses on understanding what comprises a good monitoring solution, analyzing the monitoring solutions currently available in the optics of a multi-location healthcare environment and, finally, proposing a pervasive and comprehensive conceptual architecture for a monitoring system that is capable of handling such environments. © 2022 Elsevier B.V.. All rights reserved.},

note = {cited By 3; Conference of 13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022 ; Conference Date: 22 March 2022 Through 25 March 2022; Conference Code:147558},

keywords = {Disaster prevention, Environment information; Health information systems; Healthcare environments; Information exchanges; Infrastructure monitoring; IT infrastructures; IT monitoring system; Microservice; Monitoring system; Pervasive monitoring, Health care; Information systems; Information use; Monitoring},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Silva2022726,

title = {Rule-based Clinical Decision Support System using the OpenEHR Standard},

author = {S. T. Silva and F. Hak and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132194511&doi=10.1016%2fj.procs.2022.03.098&partnerID=40&md5=c8c752f5ce49f27dbc82c7ed9abc4291},

doi = {10.1016/j.procs.2022.03.098},

issn = {18770509},

year  = {2022},

date = {2022-01-01},

journal = {Procedia Computer Science},

volume = {201},

number = {C},

pages = {726-731},

publisher = {Elsevier B.V.},

abstract = {Clinical Decision Support Systems present diverse ramifications that ultimately help healthcare professionals in their decision-making process. These systems can manifest themselves in the form of computerized guidelines that, depending on their goal and stipulated directives, help and optimize healthcare professional's daily tasks. However, nowadays there is a certain resistance from the healthcare community towards using these systems, with valid justifications such as: the absence of transparency in defined rules, the lack of interoperability within these systems and the difficulty in their usage as they prove themselves unintuitive and hard. With the intention of culminating these flaws, a clinical decision support system was developed based on the openEHR module in order to ensure standardization and semantic interoperability. This system will be oriented towards management and creation of clinical guidelines based on the specifications of the openEHR standard. © 2022 Elsevier B.V.. All rights reserved.},

note = {cited By 1; Conference of 13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022 ; Conference Date: 22 March 2022 Through 25 March 2022; Conference Code:147558},

keywords = {Artificial intelligence; Decision making; Decision support systems; Health care; Semantics, Clinical decision support systems; Daily tasks; Decision module language; Decision modules; Decision-making process; Guideline; Health care professionals; Openehr; Rule; Rule based, Interoperability},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burduk2022,

title = {Efficiency forecasting of electric bundle assembly with use of ANN model},

author = {A. Burduk and B. Dybała and J. MacHado},

editor = {Xavior M. A. Burduk A. Batako A.D.L.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134739780&doi=10.1088%2f1742-6596%2f2198%2f1%2f012001&partnerID=40&md5=76ecb3dac2c40e4ab964e68e0041091a},

doi = {10.1088/1742-6596/2198/1/012001},

issn = {17426588},

year  = {2022},

date = {2022-01-01},

journal = {Journal of Physics: Conference Series},

volume = {2198},

number = {1},

publisher = {Institute of Physics},

abstract = {Management of production systems requires making immediate decisions based on the data generated in bulk by IT systems. In this case, it can be helpful to use models of artificial neural networks (ANN) that, on the grounds of accessible data, will determine results of the made decision. One of the key problems in production companies is determination of execution time and cost of a production order. The problem is especially important in a company manufacturing a variable product line with a big part of manual operations. In the article, the way of building an ANN model for efficiency forecasting of the assembly process of electric bundles is presented. With regard to the very wide and variable product line, the products with different complexity degree are manufactured on three types of assembly lines. The assembly processes are performed on the assembly lines manually by groups of workers, so efficiency of the process is influenced mostly by skills and experience of these workers. Therefore, numbers of new assembly workers assigned to individual assembly lines and quantities of new products in the production schedule are selected as explanatory variables in the ANN model. The explained variable in the ANN model is production volume of the manufactured electric bundles. © Published under licence by IOP Publishing Ltd.},

note = {cited By 0; Conference of 15th Global Congress on Manufacturing and Management, GCMM 2021 ; Conference Date: 25 November 2020 Through 27 November 2020; Conference Code:180965},

keywords = {Artificial neural network modeling; Assembly line; Assembly process; Decision-based; IT system; Production companies; Production system; Productline; Use-model; Workers', Assembly, Assembly machines; Information management; Manufacture; Neural networks; Production control; Production efficiency},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Witeck202243,

title = {A Bibliometric Review and Analysis of Traffic Lights Optimization},

author = {G. R. Witeck and A. M. A. C. Rocha and G. O. Silva and A. Silva and D. Durães and J. Machado},

editor = {Misra S. Murgante B. Gervasi O.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135878912&doi=10.1007%2f978-3-031-10562-3_4&partnerID=40&md5=cb31d8b72c37fbf91545fbf66609cc1e},

doi = {10.1007/978-3-031-10562-3_4},

issn = {03029743},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {13378 LNCS},

pages = {43-54},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The significant increase in the number of vehicles in urban areas emerges the challenge of urban mobility. Researchers in this area suggest that most daily delays in urban travel times are caused by intersections, which could be reduced if the traffic lights at these intersections were more efficient. The use of simulation for real intersections can be effective in optimizing the cycle times and improving the traffic light timing to coordinate vehicles passing through intersections. From these themes emerge the research questions: How are the existing approaches (optimization techniques and simulation) to managing traffic lights smartly? What kind of data (offline and online) are used for traffic lights optimization? How beneficial is it to propose an optimization approach to the traffic system? This paper aims to answer these questions, carried out through a bibliometric literature review. In total, 93 articles were analyzed. The main findings revealed that the United States and China are the countries with the most studies published in the last ten years. Moreover, Particle Swarm Optimization is a frequently used approach, and there is a tendency for studies to perform optimization of real cases by real-time data, showing that the praxis of smart cities has resorted to smart traffic lights. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; Conference Date: 4 July 2022 Through 7 July 2022; Conference Code:281299},

keywords = {Bibliometric; Cycle time; Number of vehicles; Optimisations; Research questions; Traffic light; Travel-time; Urban areas; Urban mobility; Urban travels, Particle swarm optimization (PSO); Travel time, Smart city},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Silva202268,

title = {On Tuning the Particle Swarm Optimization for Solving the Traffic Light Problem},

author = {G. O. Silva and A. M. A. C. Rocha and G. R. Witeck and A. Silva and D. Durães and J. Machado},

editor = {Misra S. Murgante B. Gervasi O.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135961179&doi=10.1007%2f978-3-031-10562-3_6&partnerID=40&md5=b2d6908c90ece4348548f01963ab55a9},

doi = {10.1007/978-3-031-10562-3_6},

issn = {03029743},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {13378 LNCS},

pages = {68-80},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {In everyday routines, there are multiple situations of high traffic congestion, especially in large cities. Traffic light timed regulated intersections are one of the solutions used to improve traffic flow without the need for large-scale and costly infrastructure changes. A specific situation where traffic lights are used is on single-lane roads, often found on roads under maintenance, narrow roads or bridges where it is impossible to have two lanes. In this paper, a simulation-optimization strategy is tested for this scenario. A Particle Swarm Optimization algorithm is used to find the optimal solution to the traffic light timing problem in order to reduce the waiting times for crossing the lane in a simulated vehicle system. To assess vehicle waiting times, a network is implemented using the Simulation of Urban MObility software. The performance of the PSO is analyzed by testing different parameters of the algorithm in solving the optimization problem. The results of the traffic light time optimization show that the proposed methodology is able to obtain a decrease of almost 26% in the average waiting times. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 2; Conference of 22nd International Conference on Computational Science and Its Applications , ICCSA 2022 ; Conference Date: 4 July 2022 Through 7 July 2022; Conference Code:281299},

keywords = {Bridges; Computer software; Particle swarm optimization (PSO); Street traffic control; Traffic signals, Large cities; Particle swarm; Particle swarm optimization; Simulation of urban mobility; Swarm optimization; Traffic flow; Traffic light; Traffic light problem; Urban mobility; Waiting time, Traffic congestion},

pubstate = {published},

tppubtype = {inproceedings}

}