pubs2024

@inproceedings{Pinto2020562,

title = {Data mining to predict early stage chronic kidney disease},

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

editor = {Yasar A. Shakshuki E.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099880679&doi=10.1016%2fj.procs.2020.10.079&partnerID=40&md5=3e496eba589d1580b145c7043e27343e},

doi = {10.1016/j.procs.2020.10.079},

issn = {18770509},

year  = {2020},

date = {2020-01-01},

journal = {Procedia Computer Science},

volume = {177},

pages = {562-567},

publisher = {Elsevier B.V.},

abstract = {Chronic Kidney Disease (CKD) is a condition characterized by a gradual loss of kidney function over time. In national and international guidelines, CKD is organized into different degrees of risk stratification using commonly available markers. It is usually asymptomatic in its early stages, and early detection is important to reduce future risks. This study used the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and the WEKA software to build a system that can classify the chronic condition of kidney disease based on accuracy, sensitivity, specificity and precision. The results obtained were considered satisfactory, achieving the most suitable result of 97.66% of accuracy, 96.13% of sensitivity, 98.78% of specificity and 98.31% of precision with the J48 algorithm. © 2020 The Authors. Published by Elsevier B.V.},

note = {cited By 5; Conference of 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 ; Conference Date: 2 November 2020 Through 5 November 2020; Conference Code:166555},

keywords = {Chronic conditions; Chronic kidney disease; CRISP-DM; Cross industry; Kidney disease; Kidney function; Risk stratification, Computer science; Computers, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jesus2020549,

title = {Review of Trends in Automatic Human Activity Recognition Using Synthetic Audio-Visual Data},

author = {T. Jesus and J. Duarte and D. Ferreira and D. Durães and F. Marcondes and F. Santos and M. Gomes and P. Novais and F. Gonçalves and J. Fonseca and N. Lori and A. Abelha and J. Machado},

editor = {Camacho D. Novais P. Analide C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097138094&doi=10.1007%2f978-3-030-62365-4_53&partnerID=40&md5=4f6f299f14229712acda0326b1718e1d},

doi = {10.1007/978-3-030-62365-4_53},

issn = {03029743},

year  = {2020},

date = {2020-01-01},

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

volume = {12490 LNCS},

pages = {549-560},

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

abstract = {An in-depth study of knowledge and technologies was made related to the various scientific, technical, and industrial domains necessary for the acquisition of skills and capabilities for the design and development of a multisensory fusion system for vehicle cockpits. After an extensive literature review, it was possible to determine the baselines of the solution to be developed and obtain a pipeline prototype. © 2020, Springer Nature Switzerland AG.},

note = {cited By 4; Conference of 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference Date: 4 November 2020 Through 6 November 2020; Conference Code:251049},

keywords = {Artificial intelligence, Audio-visual data; Design and Development; Human activity recognition; In-depth study; Literature reviews; Multi-sensory fusion, Computer science; Computers},

pubstate = {published},

tppubtype = {inproceedings}

}