Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Dimitris K. Iakovidis"'
Publikováno v:
IEEE Access, Vol 12, Pp 25668-25683 (2024)
Medical image synthesis has emerged as a promising solution to address the limited availability of annotated medical data needed for training machine learning algorithms in the context of image-based Clinical Decision Support (CDS) systems. To this e
Externí odkaz:
https://doaj.org/article/ca28c7d5c29e44018a783e42f5ff719e
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-22 (2023)
Abstract The adoption of convolutional neural network (CNN) models in high-stake domains is hindered by their inability to meet society’s demand for transparency in decision-making. So far, a growing number of methodologies have emerged for develop
Externí odkaz:
https://doaj.org/article/40f05eb929a04c67bd9b70637e96a375
Publikováno v:
IEEE Access, Vol 9, Pp 51970-51982 (2021)
Machine Learning (ML) applications are growing in an unprecedented scale. The development of easy-to-use machine-learning application frameworks has enabled the development of advanced artificial intelligence (AI) applications with only a few lines o
Externí odkaz:
https://doaj.org/article/4a9da234743a4d608941a9084b404eb3
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 12, Iss 2 (2019)
A novel accurate and robust metric called II-Learn for measuring the increase of intelligence of a system after a learning process is proposed. We define evolving learning systems, as systems that are able to make at least one measurable evolutionary
Externí odkaz:
https://doaj.org/article/3b360f7613bd463d9f198e5e487886ec
Autor:
Dimitris K. Iakovidis
Publikováno v:
Sensors, Vol 20, Iss 18, p 5071 (2020)
Sensor technologies are crucial in biomedicine, as the biomedical systems and devices used for screening and diagnosis rely on their efficiency and effectiveness [...]
Externí odkaz:
https://doaj.org/article/5aacfbd50dc24d19a2c9c31d85d275e0
Publikováno v:
Sensors, Vol 20, Iss 8, p 2385 (2020)
Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a no
Externí odkaz:
https://doaj.org/article/0956fe11ded842a0b7f36f064b039e07
Autor:
Anastasios Koulaouzidis, Dimitris K. Iakovidis, Diana E. Yung, Evangelos Mazomenos, Federico Bianchi, Alexandros Karagyris, George Dimas, Danail Stoyanov, Henrik Thorlacius, Ervin Toth, Gastone Ciuti
Publikováno v:
Endoscopy International Open, Vol 06, Iss 02, Pp E205-E210 (2018)
Background and study aims Capsule endoscopy (CE) is invaluable for minimally invasive endoscopy of the gastrointestinal tract; however, several technological limitations remain including lack of reliable lesion localization. We present an approach to
Externí odkaz:
https://doaj.org/article/b83359ecb9624f47b17aa6e7efa1e86f
Autor:
Anastasios Koulaouzidis, Dimitris K. Iakovidis, Diana E. Yung, Emanuele Rondonotti, Uri Kopylov, John N. Plevris, Ervin Toth, Abraham Eliakim, Gabrielle Wurm Johansson, Wojciech Marlicz, Georgios Mavrogenis, Artur Nemeth, Henrik Thorlacius, Gian Eugenio Tontini
Publikováno v:
Endoscopy International Open, Vol 05, Iss 06, Pp E477-E483 (2017)
Background and aims Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amo
Externí odkaz:
https://doaj.org/article/220602af524b4d4bacf77d5271a304bb
Publikováno v:
Symmetry, Vol 10, Iss 12, p 663 (2018)
The Symmetric Traveling Salesman Problem (sTSP) is an intensively studied NP-hard problem. It has many important real-life applications such as logistics, planning, manufacturing of microchips and DNA sequencing. In this paper we propose a cluster le
Externí odkaz:
https://doaj.org/article/fa612cec68a64680a138a65de9758fb2
Publikováno v:
Information Sciences. 624:881-907
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bca23c2eae01a7d43c37f6c0688b7d12
https://doi.org/10.1016/j.ins.2023.01.009
https://doi.org/10.1016/j.ins.2023.01.009