TY - GEN
T1 - DeepKAF
T2 - IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)
AU - Amin, Kareem
AU - Kapetanakis, Stelios
AU - Polatidis, Nikolaos
AU - Althoff, Klaus-Dieter
AU - Dengel, Andreas
PY - 2020/9/11
Y1 - 2020/9/11
N2 - With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.
AB - With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.
KW - Case-based Reasoning
KW - Deep Learning
KW - Natural Language Processing
UR - http://inista.org/accepted-papers.php
UR - http://www.scopus.com/inward/record.url?scp=85091997286&partnerID=8YFLogxK
U2 - 10.1109/INISTA49547.2020.9194679
DO - 10.1109/INISTA49547.2020.9194679
M3 - Conference contribution with ISSN or ISBN
SN - 9781728168005
T3 - INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings
SP - 1
EP - 7
BT - INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings
A2 - Ivanovic, Mirjana
A2 - Yildirim, Tulay
A2 - Trajcevski, Goce
A2 - Badica, Costin
A2 - Bellatreche, Ladjel
A2 - Kotenko, Igor
A2 - Badica, Amelia
A2 - Erkmen, Burcu
A2 - Savic, Milos
PB - IEEE
Y2 - 24 August 2020 through 26 August 2020
ER -
