Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
Deep learning for medical anomaly detection–a survey
Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …
extensively studied. Numerous approaches have been proposed across various medical …
Artificial intelligence forecasting of covid-19 in china
BACKGROUND An alternative to epidemiological models for transmission dynamics of
Covid-19 in China, we propose the artificial intelligence (AI)-inspired methods for real-time …
Covid-19 in China, we propose the artificial intelligence (AI)-inspired methods for real-time …
Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence
Artificial intelligence applications have changed the landscape of computer design, driving a
search for hardware architecture that can efficiently process large amounts of data. Three …
search for hardware architecture that can efficiently process large amounts of data. Three …
Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions
Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …
video, etc., are showing better performance than individual modalities (ie, unimodal) …
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications
Artificial intelligence and all its supporting tools, eg machine and deep learning in
computational intelligence-based systems, are rebuilding our society (economy, education …
computational intelligence-based systems, are rebuilding our society (economy, education …
A new divergence measure for belief functions in D–S evidence theory for multisensor data fusion
F **ao - Information Sciences, 2020 - Elsevier
Abstract Dempster–Shafer (D–S) evidence theory is useful for handling uncertainty
problems in multisensor data fusion. However, the question of how to handle highly …
problems in multisensor data fusion. However, the question of how to handle highly …
Machine learning in beyond 5G/6G networks—State-of-the-art and future trends
Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important
role in realizing and optimizing 6G network applications. In this paper, we present a brief …
role in realizing and optimizing 6G network applications. In this paper, we present a brief …
Autoencoders and their applications in machine learning: a survey
Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …
ability to learn data features and act as a dimensionality reduction method. With rapid …
Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction
The right choice of features to be extracted from individual or aggregated observations is an
extremely critical factor for the success of modern network traffic classification approaches …
extremely critical factor for the success of modern network traffic classification approaches …