Article

@inproceedings{Miranda2022103,

title = {Crowdsensing on Smart Cities: A Systematic Review},

author = {R. Miranda and V. Ramos and E. Ribeiro and C. Rodrigues and A. Silva and D. Durães and C. Analide and A. Abelha and J. Machado},

editor = {Rodriguez Ribon J. C. Ferro M. Bicharra Garcia A.C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148698891&doi=10.1007%2f978-3-031-22419-5_9&partnerID=40&md5=400c3090fe6a5ffac109f06f5f487953},

doi = {10.1007/978-3-031-22419-5_9},

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 = {13788 LNAI},

pages = {103-106},

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

abstract = {With the rise of the internet and the Internet of Things (IoT), the concept of a Smart City began to materialise. Crowdsensing is the process of using portable sensing devices to gather information about people’s surroundings. Furthermore, the research on these domains has pivoted from solely technology concepts to now including how they improve the quality of life of citizens and their utility. This paper presents a systematic review aiming to identify the role and purpose of crowdsensing and the improvement of citizens’ lives in a smart city through this technique. Using the SCOPUS citation and abstract database, six papers were picked out as relevant for discussion in the review. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 2; Conference of 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022 ; Conference Date: 23 November 2022 Through 25 November 2022; Conference Code:289599},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Montenegro2022231,

title = {Evaluation of Transfer Learning to Improve Arrhythmia Classification for a Small ECG Database},

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

editor = {Rodriguez Ribon J. C. Ferro M. Bicharra Garcia A.C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148688129&doi=10.1007%2f978-3-031-22419-5_20&partnerID=40&md5=93bdc43f3a02b8d7307904e856cbc2f5},

doi = {10.1007/978-3-031-22419-5_20},

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 = {13788 LNAI},

pages = {231-242},

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

abstract = {Deep learning algorithms automatically extract features from ECG signals, eliminating the manual feature extraction step. Deep learning approaches require extensive data to be trained, and access to an ECG database with a large variety of cardiac rhythms is limited. Transfer learning is a possible solution to improve the results of cardiac rhythms classification in a small database. This work proposes a open-access robust 1D-CNN model to be trained with a public database containing cardiac rhythms with their annotations. This study explores transfer learning in a small database to improve arrhythmia classification tasks. Overall, the 1D-CNN model trained without TL achieved an average accuracy of 91.73 % and F1-score 67.18 %; meanwhile, the 1D-CNN model with TL achieved an average accuracy of 94.40 % and F1-score of 79.72 %. The F1-score has an overall improvement of 12.54 % over the baseline model for rhythm classification. Moreover, this method significantly improved the F1-score precision and recall, making the model trained with transfer learning more relevant and reliable. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022 ; Conference Date: 23 November 2022 Through 25 November 2022; Conference Code:289599},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Silva2022202,

title = {Traffic Light Optimization of an Intersection: A Portuguese Case Study},

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

editor = {Fernandes F. P. Kosir A. Pereira A.I.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148012915&doi=10.1007%2f978-3-031-23236-7_15&partnerID=40&md5=79c08f2493a1ee2d54f8046f4bcbb979},

doi = {10.1007/978-3-031-23236-7_15},

issn = {18650929},

year  = {2022},

date = {2022-01-01},

journal = {Communications in Computer and Information Science},

volume = {1754 CCIS},

pages = {202-214},

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

abstract = {Smart cities aim to rise strategies that reduce issues caused by the urban population growth and fast urbanization. Thus, traffic light optimization emerges as an important option for urban traffic management. The main goal of this study is to improve traffic light management at a specific intersection, in the City of Guimarães (Portugal), where high-intensity traffic and an active pedestrian area were observed, generating traffic queues. To achieve the goals, a simulation-based optimization strategy using the Particle Swarm Optimization combined with the Simulation of Urban Mobility software was used to minimize the average waiting time of the vehicles by determining the optimal value of the traffic light cycle. The computational results showed it is possible to decrease by 78.2% the average value of the waiting time. In conclusion, by better managing the traffic light cycle time, traffic flow without congestion or queues can be achieved. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022 ; Conference Date: 24 October 2022 Through 25 October 2022; Conference Code:289139},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2022199,

title = {The Covid-19 Influence on the Desire to Stay at Home: A Big Data Architecture},

author = {R. Sousa and D. Oliveira and A. Carneiro and L. Pinto and A. Pereira and A. Peixoto and H. Peixoto and J. Machado},

editor = {Tino P. Camacho D. Yin H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144825704&doi=10.1007%2f978-3-031-21753-1_20&partnerID=40&md5=07933409c2e62457b481165cbb7438e0},

doi = {10.1007/978-3-031-21753-1_20},

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 = {13756 LNCS},

pages = {199-210},

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

abstract = {The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic’s insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big Data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people’s desire to stay at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022 ; Conference Date: 24 November 2022 Through 26 November 2022; Conference Code:287419},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Oliveira2022265,

title = {OpenEHR modelling applied to Complementary Diagnostics Requests},

author = {D. Oliveira and A. Santos and D. Braga and I. Silva and R. Sousa and A. Abelha and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144824043&doi=10.1016%2fj.procs.2022.10.148&partnerID=40&md5=860a35222661de3243855a8f9f6a295a},

doi = {10.1016/j.procs.2022.10.148},

issn = {18770509},

year  = {2022},

date = {2022-01-01},

journal = {Procedia Computer Science},

volume = {210},

number = {C},

pages = {265-270},

publisher = {Elsevier B.V.},

abstract = {Complementary Diagnostic Requests (CDRs) are required for disease identification, monitoring, and prognosis. Diagnostic tests misuse, on the other hand, can lead to negative health outcomes as well as additional costs. Inappropriate diagnostic test requests are primarily the result of a lack of interoperability between Healthcare Information Systems (HIS). On one hand, clinicians can be mislead into which test is the best option for each clinical case, on the other hand missing previous results, leads to duplication or unnecessary tests. HIS is increasingly relying on standards based on dual architecture to promote interoperability as well as the structuring and consistency of clinical and demographic data. The OpenEHR standard's duo-based architecture allows for concise modelling of archetypes and templates for a given clinical case, which was used in this study. As a result, the purpose of this research was to build an openEHR template for the CDR registration as well as the architecture of a Data Warehouse (DW) system capable of storing all of the information needed for the diagnostic test request process. Afterwards, Business Intelligence (BI) indicators was developed in order to answers the needs for test registration and execution. © 2022 Elsevier B.V.. All rights reserved.},

note = {cited By 2; Conference of 13th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN / The 12th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2022 / Affiliated Workshops ; Conference Date: 26 October 2022 Through 28 October 2022; Conference Code:148658},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2022315,

title = {Medical Recommendation System Based on Daily Clinical Reports: A Proposed NLP Approach for Emergency Departments},

author = {R. Sousa and D. Oliveira and D. Durães and C. Neto and J. Machado},

editor = {Stahl F. Bramer M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144818897&doi=10.1007%2f978-3-031-21441-7_24&partnerID=40&md5=c46b6c78b09ded01e7ecd348b5a7dea6},

doi = {10.1007/978-3-031-21441-7_24},

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 = {13652 LNAI},

pages = {315-320},

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

abstract = {The operational management of an emergency department (ED) requires more attention from hospital administration since it can have a global impact on the institution’s management, increasing the probability of adverse events and worsening hospital expenses. Effective management of an ED potentially results in fewer hospitalisations after an ED admission. The purpose of the present study is to perform a multi-class prediction based on: a) structured data and unstructured data in an ED episode; and b) unstructured data generated during the inpatient event, just after the ED episode. The designed prediction model will lay the foundation for an ED Decision Support System based on symptoms and principal diagnoses. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 ; Conference Date: 13 December 2022 Through 15 December 2022; Conference Code:287589},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Afonso2022271,

title = {OpenEHR based bariatric surgery follow-up},

author = {A. Afonso and C. Alvarez and D. Ferreira and D. Oliveira and H. Peixoto and A. Abelha and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144818896&doi=10.1016%2fj.procs.2022.10.149&partnerID=40&md5=948521a51fd931ae8163d47a7fd3e7a0},

doi = {10.1016/j.procs.2022.10.149},

issn = {18770509},

year  = {2022},

date = {2022-01-01},

journal = {Procedia Computer Science},

volume = {210},

number = {C},

pages = {271-276},

publisher = {Elsevier B.V.},

abstract = {According to the World Health Organization (WHO), more than one billion people in the world are obese, and this number is still increasing. When this disease becomes a high risk to the individual's health and the non-invasive approach does not result in weight loss, it is usual to resort to an invasive intervention, bariatric surgery. Due to all the specifications, as well as the multidisciplinary treatment inherent to this procedure, the need for a specialized environment emerges. In this context, the main objective of this topic focuses on the development of a platform for registration and monitoring of bariatric surgery. For this purpose, a web platform was created, which integrates openEHR forms for the registration of appointments regarding this intervention, and uses openEHR modeling to build the interoperable template. The development of this project aims to provide greater ease and speed in patient care, assisting health professionals in their daily lives. © 2022 Elsevier B.V.. All rights reserved.},

note = {cited By 2; Conference of 13th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN / The 12th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2022 / Affiliated Workshops ; Conference Date: 26 October 2022 Through 28 October 2022; Conference Code:148658},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa202272,

title = {Sustainable and Social Energy on Smart Cities: Systematic Review},

author = {R. Sousa and D. Lopes and A. Silva and D. Durães and H. Peixoto and J. Machado and P. Novais},

editor = {Augusto M. F. Portela F. Guarda T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144627809&doi=10.1007%2f978-3-031-20316-9_6&partnerID=40&md5=e346e8afe9e3ac6ba198bcf05c775a97},

doi = {10.1007/978-3-031-20316-9_6},

issn = {18650929},

year  = {2022},

date = {2022-01-01},

journal = {Communications in Computer and Information Science},

volume = {1676 CCIS},

pages = {72-84},

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

abstract = {Sustainability and social energy are two concepts associated with smart cities. They aim to combat and contain the alarming environmental and socio-economic repercussions that urbanization has been causing on our planet. Smart sustainable cities drive to improve the life quality of citizens while ensuring that they meet the needs of the current and future generations. Sustainability is essential for urban transformation to achieve more resource-efficient, resilient and smart cities. The main objective of sustainable cities is to guide decisions for interventions in the city. Monitoring systems are examples of measures that aspire to ensure greater sustainability and energy efficiency, such as the application of air quality meters or smart water and light meters. Throughout the analysis of the collected data, it’s possible to develop alert systems and optimization models considering various metrics based on artificial intelligence. Therefore, allowing users to make better decisions to positively affect the course of actions in their cities and make it possible to apply sustainability and social energy measures. Thus, it is possible to reduce and improve the consumption of natural resources. Industry 5.0 is crucial in the evolution of smart cities. The complementarity role that this industry has been demonstrating is related to the technologies being developed, in which artificial intelligence plays an important role. This industry places its technology at the service of human beings, society and the environment. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 2nd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2022 ; Conference Date: 12 September 2022 Through 15 September 2022; Conference Code:286829},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Oliveira2022222,

title = {Improving the Effectiveness of Heart Disease Diagnosis with Machine Learning},

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

editor = {Fernandez A. Almeida A. Gonzalez-Briones A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141686201&doi=10.1007%2f978-3-031-18697-4_18&partnerID=40&md5=5b5ddaa362353509a172400b23b54b71},

doi = {10.1007/978-3-031-18697-4_18},

issn = {18650929},

year  = {2022},

date = {2022-01-01},

journal = {Communications in Computer and Information Science},

volume = {1678 CCIS},

pages = {222-231},

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

abstract = {Despite technological and clinical improvements, heart disease remains one of the leading causes of death worldwide. A significant shift in the paradigm would be for medical teams to be able to accurately identify, at an early stage, whether a patient is at risk of developing or having heart disease, using data from their health records paired with Data Mining tools. As a result, the goal of this research is to determine whether a patient has a cardiac condition by using Data Mining methods and patient information to aid in the construction of a Clinical Decision Support System. With this purpose, we use the CRISP-DM technique to try to forecast the occurrence of cardiac disorders. The greatest results were obtained utilizing the Random Forest technique and the Percentage Split sampling method with a 66% training rate. Other approaches, such as Naïve Bayes, J48, and Sequential Minimal Optimization, also produced excellent results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:285119},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Alves2022393,

title = {Survey for Big Data Platforms and Resources Management for Smart Cities},

author = {C. Alves and A. Chaves and C. Rodrigues and E. Ribeiro and A. Silva and D. Durães 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-s2.0-85139072493&doi=10.1007%2f978-3-031-15471-3_34&partnerID=40&md5=e9ed3982c3388f81adf3383436500587},

doi = {10.1007/978-3-031-15471-3_34},

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 = {13469 LNAI},

pages = {393-404},

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

abstract = {Currently, smart cities are a hot topic and their tendency will be to optimize resources and promote efficient strategies for the preservation of the planet as well as to increase the quality of life of its inhabitants. In this sense, this research presents an initial component of investigation about Big Data Platforms for Smart Cities in order to be implemented in integrated and innovative solutions for development in urban centers. For this, a survey was carried out on “Big Data Platforms”, “Data Science Platforms”, “Security & Privacy” and “Resources Management”. The extraction of the results of this research was done through the SCOPUS repository in articles from the last 5 years to conclude what has been done so far and what will be the trends in the coming years, define proposals for possible solutions for smart cities and identify the right technologies for the design of a smart city architecture. © 2022, Springer Nature Switzerland AG.},

note = {cited By 1; Conference of 17th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2022 ; Conference Date: 5 September 2022 Through 7 September 2022; Conference Code:283099},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pereira2022285,

title = {Comparison of Different Deployment Approaches of FPGA-Based Hardware Accelerator for 3D Object Detection Models},

author = {P. Pereira and A. Linhares Silva and R. Machado and J. Silva and D. Durães and J. Machado and P. Novais and J. Monteiro and P. Melo-Pinto and D. Fernandes},

editor = {Paiva A. Martins B. Marreiros G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138741507&doi=10.1007%2f978-3-031-16474-3_24&partnerID=40&md5=cee8ca98871acfc2bf58e53a13e9eefa},

doi = {10.1007/978-3-031-16474-3_24},

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 = {13566 LNAI},

pages = {285-296},

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

abstract = {GPU servers have been responsible for the recent improvements in the accuracy and inference speed of the object detection models targeted to autonomous driving. However, its features, namely, power consumption and dimension, make its integration in autonomous vehicles impractical. Hybrid FPGA-CPU boards emerged as an alternative to server GPUs in the role of edge devices in autonomous vehicles. Despite their energy efficiency, such devices do not offer the same computational power as GPU servers and have fewer resources available. This paper investigates how to deploy deep learning models tailored to object detection in point clouds in edge devices for onboard real-time inference. Different approaches, requiring different levels of expertise in logic programming applied to FPGAs, are explored, resulting in three main solutions: utilization of software tools for model adaptation and compilation for a proprietary hardware IP; design and implementation of a hardware IP optimized for computing traditional convolutions operations; design and implementation of a hardware IP optimized for sparse convolutions operations. The performance of these solutions is compared in the KITTI dataset with computer performances. All the solutions resort to parallelism, quantization and optimized access control to memory to reduce the usage of logical FPGA resources, and improve processing time without significantly sacrificing accuracy. Solutions probed to be effective for real-time inference, power limited and space-constrained purposes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 2; Conference of 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference Date: 31 August 2022 Through 2 September 2022; Conference Code:283109},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pereira2022551,

title = {A Comparison of Automated Time Series Forecasting Tools for Smart Cities},

author = {P. J. Pereira and N. Costa and M. Barros and P. Cortez and D. Durães and A. Silva and J. Machado},

editor = {Paiva A. Martins B. Marreiros G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138674457&doi=10.1007%2f978-3-031-16474-3_45&partnerID=40&md5=cc3bc2f19869e8c4a50d3a836654d054},

doi = {10.1007/978-3-031-16474-3_45},

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 = {13566 LNAI},

pages = {551-562},

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

abstract = {Most smart city sensors generate time series records and forecasting such data can provide valuable insights for citizens and city managers. Within this context, the adoption of Automated Time Series Forecasting (AutoTSF) tools is a key issue, since it facilitates the design and deployment of multiple TSF models. In this work, we adapt and compare eight recent AutoTSF tools (Pmdarima, Prophet, Ludwig, DeepAR, TFT, FEDOT, AutoTs and Sktime) using nine freely available time series that can be related with the smart city concept (e.g., temperature, energy consumption, city traffic). An extensive experimentation was carried out by using a realistic rolling window with several training and testing iterations. Also, the AutoTSF tools were evaluated by considering both the predictive performances and required computational effort. Overall, the FEDOT tool presented the best overall performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 2; Conference of 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference Date: 31 August 2022 Through 2 September 2022; Conference Code:283109},

keywords = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Saragih2021,

title = {Sentiment Analysis of Social Media Twitter with Case of Large Scale Social Restriction in Jakarta using Support Vector Machine Algorithm},

author = {P. S. Saragih and D. Witarsyah and F. Hamami and J. M. MacHado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126541289&doi=10.1109%2fICADEIS52521.2021.9701961&partnerID=40&md5=4f324699bafcb89732b5245f423ef393},

doi = {10.1109/ICADEIS52521.2021.9701961},

isbn = {9781665437097},

year  = {2021},

date = {2021-01-01},

journal = {2021 International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2021},

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

abstract = {When the Large-Scale Social Restrictions (LSSR or PSBB in Indonesian) policy was implemented it the policy was not entirely obeyed by the community which then reaped various opinions and responses on various social media, especially on Twitter. This study aims to conduct a sentiment analysis to find out the cause or phenomena that occur based on the opinions or views of Twitter. The Tweet data about the implementation of LSSR both part 1 and part 2 in Jakarta were obtained as many as 1080 opinions using the crawling method then the data is manually labelled with two labels, which are positive and negative after labelled the data is cleaned after and the data is processed by being weighted using the Bag of Words and TF-IDF extraction feature. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 then classified using the Support Vector Machines algorithm. The final result of this study shows that the classification accuracy results using the Support Vector Machine algorithm with 90:10 data splitting ratio using the TFIDF extraction feature is superior with an accuracy value of 85.185% and F1-Score 72.413%, which is better when compared to the Bag of words extraction feature which produces an accuracy value of 83.333% and F1-Score 66.666%. As for this study, Twitter users tend to give opinions with negative sentiments, which contain complaints and discomfort regarding the implementation of the LSSR policies, both the first LSSR and the second LSSR. Finally, the results of this research are also expected to be input for the government when making better policies in the future. © 2021 IEEE.},

note = {cited By 0; Conference of 2021 International Conference Advancement in Data Science, E-learning and Information Systems, ICADEIS 2021 ; Conference Date: 13 October 2021 Through 14 October 2021; Conference Code:177133},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Santos2021437,

title = {In-Car Violence Detection Based on the Audio Signal},

author = {F. Santos and D. Durães and F. S. Marcondes and N. Hammerschmidt and S. Lange and J. Machado and P. Novais},

editor = {Allmendinger R. Tino P. Camacho D.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126037019&doi=10.1007%2f978-3-030-91608-4_43&partnerID=40&md5=23842fca53bfddec1db51dc9346d55a4},

doi = {10.1007/978-3-030-91608-4_43},

issn = {03029743},

year  = {2021},

date = {2021-01-01},

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

volume = {13113 LNCS},

pages = {437-445},

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

abstract = {When it is intended to detect violence in the car, audio, speech processing, music, and ambient sound are some of the main points of this problem since it is necessary to find the similarities and differences between these domains. The recent increase in interest in deep learning has allowed practical applications in many areas of signal processing, often surpassing traditional signal processing on a large scale. This paper presents a comparative study of state-of-the-art deep learning architectures applied for inside car violence detection based only on the audio signal. The methodology proposed for audio signal representation was Mel-spectrogram, after an in-depth review of the literature. We build an In-Car video dataset in the experiments and apply four different deep learning architectures to solve the classification problem. The results have shown that the ResNet-18 model presents the best accuracy results on the test set. © 2021, Springer Nature Switzerland AG.},

note = {cited By 15; Conference of 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 ; Conference Date: 25 November 2021 Through 27 November 2021; Conference Code:269299},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2021209,

title = {Contactless Human-Computer Interaction Using a Deep Neural Network Pipeline for Real-Time Video Interpretation and Classification},

author = {R. Sousa and T. Jesus and V. Alves and J. Machado},

editor = {Santos M. F. Portela F. Guarda T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120534384&doi=10.1007%2f978-3-030-90241-4_17&partnerID=40&md5=c86fc35c9f2a3e50cd1b4e58b537b567},

doi = {10.1007/978-3-030-90241-4_17},

issn = {18650929},

year  = {2021},

date = {2021-01-01},

journal = {Communications in Computer and Information Science},

volume = {1485 CCIS},

pages = {209-220},

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

abstract = {Nowadays, all applications are developed with the user’s comfort in mind. Regardless of the application’s objective, it should be as simple as possible so that it is easily accepted by its users. With the evolution of technology, simplicity has evolved and has become intrinsically related to the automation of tasks. Therefore, many researchers have focused their investigations on the interaction between humans and computing devices. However, this interaction is usually still carried out via a keyboard and/or a mouse. We present an essemble of deep neural networks for the detection and interpretation of gestural movement, in various environments. Its purpose is to introduce a new form of interaction between the human and computing devices in order to evolve this paradigm. The use case focused on detecting the movement of the user’s hands in real time and automatically interpreting the movement. © 2021, Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 1st International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2021 ; Conference Date: 25 November 2021 Through 27 November 2021; Conference Code:268849},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Santos202165,

title = {Efficient Violence Detection Using Transfer Learning},

author = {F. Santos and D. Durães and F. S. Marcondes and S. Lange and J. Machado and P. Novais},

editor = {Duraes D. El Bolock A. De La Prieta F.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116505876&doi=10.1007%2f978-3-030-85710-3_6&partnerID=40&md5=c229aac41b0bc0f030eb19d748792d78},

doi = {10.1007/978-3-030-85710-3_6},

issn = {18650929},

year  = {2021},

date = {2021-01-01},

journal = {Communications in Computer and Information Science},

volume = {1472 CCIS},

pages = {65-75},

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

abstract = {In recent years several applications, namely in surveillance, human-computer interaction and video recovery based on its content has studied the detection and recognition of violence [22]. The purpose of violence detection is to automatically and effectively determine whether or not violence occurs in a short time. So, it is a crucial area since it will automatically enable the necessary means to stop the violence. To quickly solve this problem, we used models trained to solve general activity recognition problems such as Kinetics-400 to learn to extract general patterns that are very important to detect violent behaviour in videos. Our approach consists of using a state of the art pre-trained model in general activity recognition tasks (e.g. Kinetics-400) and then fine-tuning it to violence detection. We applied this approach in two violence datasets and achieved state-of-the-art results using only four input frames. © 2021, Springer Nature Switzerland AG.},

note = {cited By 4; Conference of International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2021 ; Conference Date: 6 October 2021 Through 9 October 2021; Conference Code:266119},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Durães2021290,

title = {Comparison of Transfer Learning Behaviour in Violence Detection with Different Public Datasets},

author = {D. Durães and F. Santos and F. S. Marcondes and S. Lange and J. Machado},

editor = {Lau N. Melo F.S. Marreiros G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115443718&doi=10.1007%2f978-3-030-86230-5_23&partnerID=40&md5=258a2451e74014f8e5df8c461bf232a0},

doi = {10.1007/978-3-030-86230-5_23},

issn = {03029743},

year  = {2021},

date = {2021-01-01},

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

volume = {12981 LNAI},

pages = {290-298},

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

abstract = {The detection and recognition of violence have been area of interest to research, mainly in surveillance, Human-Computer Interaction and information retrieval for video based on content. The primary purpose of detecting and recognizing violence is to automatically and in real-time recognize violence. Hence, it is a crucial area and object of several studies, as it will enable systems to have the necessary means to contain violence automatically. In this sense, pre-trained models are used to solve general problems of recognition of violent activity. These models were pre-trained with datasets from: hockey fight; movies; violence in real surveillance; and fighting in real situations. From this pre-training models, general patterns are extracted that are very important to detect violent behaviour in videos. Our approach uses a state-of-the-art pre-trained violence detection model in general activity recognition tasks and then tweaks it for violence detection inside a car. For this, we created our dataset with videos inside the car to apply in this study. © 2021, Springer Nature Switzerland AG.},

note = {cited By 3; Conference of 20th EPIA Conference on Artificial Intelligence, EPIA 2021 ; Conference Date: 7 September 2021 Through 9 September 2021; Conference Code:265179},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cunha2021156,

title = {A CRISP-DM Approach for Predicting Liver Failure Cases: An Indian Case Study},

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

editor = {Kalra J. Karwowski W. Ahram T.Z.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112285842&doi=10.1007%2f978-3-030-80624-8_20&partnerID=40&md5=6941c1bea4e3a230ddde5e6c03b7102b},

doi = {10.1007/978-3-030-80624-8_20},

issn = {23673370},

year  = {2021},

date = {2021-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {271},

pages = {156-164},

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

abstract = {We are currently living a period in which data processing and analysis are increasingly relevant and the health sector is no exception. In this way, through data mining processes, it is possible to make a number of predictions in the medical field, such as predicting medical conditions and disease progression. Acute Liver Failure (ALF) is a rare but critical disorder associated with high mortality. The aim of this work is to predict cases of acute hepatic insufficiency based on clinical data through data mining techniques. To this end, the CRISP-DM methodology was followed, in which five classifiers were applied, namely, Decision Tree, k-Nearest Neighbor, Random Forest, Rule Induction, and Naïve Bayes. Throughout this work, the RapidMiner software was used and the different models were analyzed based on Accuracy, Precision, Recall, Kappa Statistic, and Specificity. The best data mining model achieved an Accuracy of 0.925, a Precision of 0.869, a Recall of 1.000, a Kappa of 0.849, and a Specificity of 0.849, using split validation and the k-Nearest Neighbor algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 1; Conference of AHFE Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, 2021 ; Conference Date: 25 July 2021 Through 29 July 2021; Conference Code:262519},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Neto2021287,

title = {Step Towards Predicting Patient Length of Stay in Intensive Care Units},

author = {C. Neto and G. Pontes and A. Domente and F. Reinolds and J. Costa and D. Ferreira and J. Machado},

editor = {Dzemyda G. Adeli H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107362075&doi=10.1007%2f978-3-030-72654-6_28&partnerID=40&md5=aae7622245688001b9d7dde10eb6d271},

doi = {10.1007/978-3-030-72654-6_28},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1368 AISC},

pages = {287-297},

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

abstract = {Increasingly, hospitals are collecting huge amounts of data through new storage methods. These data can be use to extract hidden knowledge, which can be crucial to estimate the length of stay of admitted patients in order to improve the management of hospital resources. Hence, this article portrays the performance analysis of different data mining techniques through the application of learning algorithms in order to predict patients’ length of stay when admitted to an Intensive Care Unit. The data used in this study contains about 60,000 records and 28 features with personal and medical information. A full analysis of the results obtained with different Machine Learning algorithms showed that the model trained with the Gradient Boosted Trees algorithm and using only the features that were strongly correlated to the patient’s length of stay, achieved the best performance with 99,19% of accuracy. In this sense, an accurate understanding of the factors associated with the length of stay in intensive care units was achieved. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 30 March 2021 Through 2 April 2021; Conference Code:256979},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Oliveira2021893,

title = {Steps towards an healthcare information model based on openEHR},

author = {D. Oliveira and R. Miranda and F. Hak and N. Abreu and P. Leuschner and A. Abelha and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106726178&doi=10.1016%2fj.procs.2021.04.015&partnerID=40&md5=b476415f1452184e4a7bafbde7e6700d},

doi = {10.1016/j.procs.2021.04.015},

issn = {18770509},

year  = {2021},

date = {2021-01-01},

journal = {Procedia Computer Science},

volume = {184},

pages = {893-898},

publisher = {Elsevier B.V.},

abstract = {During COVID-19 pandemic crisis, healthcare institutions globally were experiencing a VUCA - Volatile, Uncertain, Complex, and Ambiguous - environment. Efficient clinical and administrative management had never been so emergent. To achieve this goal, different components of the Healthcare Information System (HIS) must cooperate and interoperate flawlessly. Data standardization is a necessary step towards normalization and interoperability between existing Legacy Systems (LSs), and provides for longitudinal, highly reliable and persistent Electronic Health Records (EHRs). The openEHR standard was chosen for its overall dual domain architecture, where the more dynamic clinical information model may evolve independently from the relatively stable Reference Model (RM). Its Information Model (IM) comprises demographic, administrative and clinical systems. Critical clinical terms have been aligned to the FHIR HL7 standard, as to further support interoperability. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)},

note = {cited By 4; Conference of 12th International Conference on Ambient Systems, Networks and Technologies, ANT 2021 / 4th International Conference on Emerging Data and Industry 4.0, EDI40 2021 / Affiliated Workshops ; Conference Date: 23 March 2021 Through 26 March 2021; Conference Code:145658},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chaves2021917,

title = {Development of FHIR based web applications for appointment management in healthcare},

author = {A. Chaves and T. Guimarães and J. Duarte and H. Peixoto and A. Abelha and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106686824&doi=10.1016%2fj.procs.2021.03.114&partnerID=40&md5=a0681e0e706d76a995e932f18cff4ba2},

doi = {10.1016/j.procs.2021.03.114},

issn = {18770509},

year  = {2021},

date = {2021-01-01},

journal = {Procedia Computer Science},

volume = {184},

pages = {917-922},

publisher = {Elsevier B.V.},

abstract = {The integration of Information Technology systems in healthcare is no new concept, however, the ever growing solutions offered by the IT field are pushing a revamp of older implementations of Hospital Information Systems. Contemporary web-based solutions are now readily available and promise independence from operating systems and desktop bound systems, while incorporating faster and more secure methods. The focus on interoperable systems has been setting new goals towards fully computerized hospital management and the progress of healthcare standards over the years has made interoperability an obligation. The work presented hereby reflects a FHIR web based application to overcome the problem presented by scheduling and appointment management. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)},

note = {cited By 3; Conference of 12th International Conference on Ambient Systems, Networks and Technologies, ANT 2021 / 4th International Conference on Emerging Data and Industry 4.0, EDI40 2021 / Affiliated Workshops ; Conference Date: 23 March 2021 Through 26 March 2021; Conference Code:145658},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Santos2021104,

title = {Modelling a Deep Learning Framework for Recognition of Human Actions on Video},

author = {F. Santos and D. Durães and F. Marcondes and M. Gomes and F. Gonçalves and J. Fonseca and J. Wingbermuehle and J. Machado and P. Novais},

editor = {Dzemyda G. Adeli H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105949815&doi=10.1007%2f978-3-030-72657-7_10&partnerID=40&md5=63526ae46868c827835600c4dba3711b},

doi = {10.1007/978-3-030-72657-7_10},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1365 AIST},

pages = {104-112},

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

abstract = {In Human action recognition, the identification of actions is a system that can detect human activities. The types of human activity are classified into four different categories, depending on the complexity of the steps and the number of body parts involved in the action, namely gestures, actions, interactions, and activities [1]. It is challenging for video Human action recognition to capture useful and discriminative features because of the human body's variations. To obtain Intelligent Solutions for action recognition, it is necessary to training models to recognize which action is performed by a person. This paper conducted an experience on Human action recognition compare several deep learning models with a small dataset. The main goal is to obtain the same or better results than the literature, which apply a bigger dataset with the necessity of high-performance hardware. Our analysis provides a roadmap to reach the training, classification, and validation of each model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Vieira2021511,

title = {Data Mining Approach to Classify Cases of Lung Cancer},

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

editor = {Dzemyda G. Adeli H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105949008&doi=10.1007%2f978-3-030-72657-7_49&partnerID=40&md5=071cf3523dd9f477acf20273d8405f0e},

doi = {10.1007/978-3-030-72657-7_49},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1365 AIST},

pages = {511-521},

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

abstract = {According to the World Cancer Research Fund, a leading authority on cancer prevention research, lung cancer is the most commonly occurring cancer in men and the third most commonly occurring cancer in women, with the 5-year relative survival percentage being significantly low. Smoking is the major risk factor for lung cancer and the symptoms associated with it include cough, fatigue, shortness of breath, chest pain, weight loss, and loss of appetite. In an attempt to build a model capable of identifying individuals with lung cancer, this study aims to build a data mining classification model to predict whether or not a patient has lung cancer based on crucial features such as the above mentioned symptoms. Through the CRISP-DM methodology and the RapidMiner software, different models were built, using different scenarios, algorithms, sampling methods, and data approaches. The best data mining model achieved an accuracy of 93%, a sensitivity of 96%, a specificity of 90% and a precision of 91%, using the Artificial Neural Network algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Neto2021210,

title = {Prediction Models for Polycystic Ovary Syndrome Using Data Mining},

author = {C. Neto and M. Silva and M. Fernandes and D. Ferreira and J. Machado},

editor = {Antipova T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103521052&doi=10.1007%2f978-3-030-71782-7_19&partnerID=40&md5=cd8917667f9e56f1982c1aba9ff64f02},

doi = {10.1007/978-3-030-71782-7_19},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1352},

pages = {210-221},

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

abstract = {Polycystic Ovary Syndrome is an endocrine abnormality that occurs in the female reproductive system and is considered a heterogeneous disorder because of the different criteria used for its diagnosis. Early detection and treatment are critical factors to reduce the risk of long-term complications, such as type 2 diabetes and heart disease. With the vast amount of data being collected daily in healthcare environments, it is possible to build Decision Support Systems using Data Mining and Machine Learning. Currently, healthcare systems have advanced skills like Artificial Intelligence, Machine Learning and Data Mining to offer intelligent and expert healthcare services. The use of efficient Data Mining techniques is able to reveal and extract hidden information from clinical and laboratory patient data, which can be helpful to assist doctors in maximizing the accuracy of the diagnosis. In this sense, this paper aims to predict, using the classification techniques and the CRISP-DM methodology, the presence of Polycystic Ovary Syndrome. This paper compares the performance of multiple algorithms, namely, Support Vector Machines, Multilayer Perceptron Neural Network, Random Forest, Logistic Regression and Gaussian Naïve Bayes. In the end, it was found that Random Forest provides the best classification, and the use of data sampling techniques also improves the results, allowing to achieve a sensitivity of 0.94, an accuracy of 0.95, a precision of 0.96 and a specificity of 0.96. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 8; Conference of International Conference on Advances in Digital Science, ICADS 2021 ; Conference Date: 19 February 2021 Through 21 February 2021; Conference Code:256499},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Abreu2021198,

title = {Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques},

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

editor = {Antipova T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103501921&doi=10.1007%2f978-3-030-71782-7_18&partnerID=40&md5=00cbc654502f69e638aa251a4aad2b0f},

doi = {10.1007/978-3-030-71782-7_18},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1352},

pages = {198-209},

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

abstract = {Diabetic retinopathy is one of the complications of diabetes that affects the small vessels of the retina, being the main cause of blindness in adults. An early detection of this disease is essential, as it can prevent blindness as well as other irreversible harmful outcomes. This article attempts to develop a data mining model capable of identifying diabetic retinopathy in patients based on features extracted from eye fundus images. The data mining process was carried out in the RapidMiner software and followed the CRISP-DM methodology. In particular, classification models were built by combining different scenarios, algorithms, and sampling methods. The data mining model which performed best achieved an accuracy of 76.90%, a precision of 85.92%, and a sensitivity of 67.40%, using the Logistic Regression algorithm and Split Validation as the sampling method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 2; Conference of International Conference on Advances in Digital Science, ICADS 2021 ; Conference Date: 19 February 2021 Through 21 February 2021; Conference Code:256499},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Castanheira202144,

title = {Overcoming challenges in healthcare interoperability regulatory compliance},

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

editor = {Larriba-Pey J. L. Vercelli G. Novais P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091511192&doi=10.1007%2f978-3-030-58356-9_5&partnerID=40&md5=74a0dcb5fadb890331a3a47d465a3e22},

doi = {10.1007/978-3-030-58356-9_5},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1239 AISC},

pages = {44-53},

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

abstract = {There has been a significant increase in the quantity of information stored digitally by health institutions. Such information contains personal data from the actors in their universe. Thus, is crucial that it is governed by a set of rules, in order to allow it to be understood without losing important data. With the increased use of digital tools for storing and exchanging information, ethical issues began to arise in the context of the privacy of personal data. Questions about access, processing, treatment and storage of personal data became increasingly important in society, leading to the creation of the General Data Protection Regulation (GDPR) in force at European level. GDPR is one of the main challenges in healthcare interoperability regulatory compliance, therefore the proposed architecture shows an approach to enforce GDPR compliance into Agency for Integration, Diffusion and Archive Platform (AIDA), which is held by several healthcare unities in Portugal, using technologies like ElasticSearch and Kibana. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.},

note = {cited By 2; Conference of 11th International Symposium on Ambient Intelligence, ISAmI 2020 ; Conference Date: 7 October 2020 Through 9 October 2020; Conference Code:245169},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Durães2021106,

title = {Detection violent behaviors: A survey},

author = {D. Durães and F. S. Marcondes and F. Gonçalves and J. Fonseca and J. Machado and P. Novais},

editor = {Larriba-Pey J. L. Vercelli G. Novais P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091503158&doi=10.1007%2f978-3-030-58356-9_11&partnerID=40&md5=ed2f6cb833a6cc4d92e3dca5aabec4c0},

doi = {10.1007/978-3-030-58356-9_11},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1239 AISC},

pages = {106-116},

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

abstract = {Violence detection behavior is a particular problem regarding the great problem action recognition. In recent years, the detection and recognition of violence has been studied for several applications, namely in surveillance. In this paper, we conducted a recent systematic review of the literature on this subject, covering a selection of various researched papers. The selected works were classified into three main approaches for violence detection: video, audio, and multimodal audio and video. Our analysis provides a roadmap to guide future research to design automatic violence detection systems. Techniques related to the extraction and description of resources to represent behavior are also reviewed. Classification methods and structures for behavior modelling are also provided. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.},

note = {cited By 13; Conference of 11th International Symposium on Ambient Intelligence, ISAmI 2020 ; Conference Date: 7 October 2020 Through 9 October 2020; Conference Code:245169},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Marcondes2021211,

title = {In-vehicle violence detection in carpooling: A brief survey towards a general surveillance system},

author = {F. S. Marcondes and D. Durães and F. Gonçalves and J. Fonseca and J. Machado and P. Novais},

editor = {Matsui K. Herrera-Viedma E. Dong Y.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089722186&doi=10.1007%2f978-3-030-53036-5_23&partnerID=40&md5=d4ebacd935c0a98d12558df672ef3b1f},

doi = {10.1007/978-3-030-53036-5_23},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1237 AISC},

pages = {211-220},

publisher = {Springer},

abstract = {Violence is a word that encompasses several meanings ranging from an actual fight to theft and several types of harassment. Therefore, violence detection through surveillance systems can be a quite difficult yet important task. The increasing use of carpooling services and vehicle sharing brought the need to implement a sufficient general surveillance system for monitoring these vehicles for assuring the passengers’ safety during the ride. This paper raised the literature for this matter, finding fewer research papers than it was expected for the in-vehicle perspective, noticeably to sexual harassment. Most of the research papers focused on out-vehicle issues such as runs over and vehicle theft. In-vehicle electronic components security and cockpit user experience were perceived as major concern areas. This paper discusses these findings and presents some insights about in-vehicle surveillance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.},

note = {cited By 14; Conference of 17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020 ; Conference Date: 17 June 2020 Through 19 June 2020; Conference Code:243089},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}