Zobrazeno 1 - 10
of 215
pro vyhledávání: '"Luca, Longo"'
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 3, Pp 2049-2073 (2024)
Explainable Artificial Intelligence (XAI) is a research area that clarifies AI decision-making processes to build user trust and promote responsible AI. Hence, a key scientific challenge in XAI is the development of methods that generate transparent
Externí odkaz:
https://doaj.org/article/649ade1d712b48b79b5424817e2249d5
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, the
Externí odkaz:
https://doaj.org/article/299da7684c81477b8e5b81a8ab710084
Autor:
Bujar Raufi, Luca Longo
Publikováno v:
BioMedInformatics, Vol 4, Iss 1, Pp 853-876 (2024)
Background: Creating models to differentiate self-reported mental workload perceptions is challenging and requires machine learning to identify features from EEG signals. EEG band ratios quantify human activity, but limited research on mental workloa
Externí odkaz:
https://doaj.org/article/353a7f3e522e468e93826b59999c064e
Publikováno v:
IEEE Access, Vol 12, Pp 70434-70463 (2024)
Due to market deregulation and globalisation, competitive environments in various sectors continuously evolve, leading to increased customer churn. Effectively anticipating and mitigating customer churn is vital for businesses to retain their custome
Externí odkaz:
https://doaj.org/article/61a83a8788d24a068ede83ea2edf9a39
Autor:
Luca Longo, Ruairi O’Reilly
This open access book constitutes selected papers presented during the 30th Irish Conference on Artificial Intelligence and Cognitive Science, held in Munster, Ireland, in December 2022. The 41 presented papers were thoroughly reviewed and selected
Autor:
Robert S. Sullivan, Luca Longo
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 4, Pp 1433-1455 (2023)
Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and health
Externí odkaz:
https://doaj.org/article/6ebb63fe5a004a148ca8765a3d077321
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 464-473 (2023)
Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human
Externí odkaz:
https://doaj.org/article/4f3a4c7243214b1eb514431baf7a2b74
Autor:
Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 1429-1439 (2023)
Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300
Externí odkaz:
https://doaj.org/article/305daea33f7643c9b8529e554f7e7b51
Autor:
Arjun Vinayak Chikkankod, Luca Longo
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 4, Iss 4, Pp 1042-1064 (2022)
Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or frequency domains. Noise and artifacts during the data acquisition phase contaminate these signals adding difficulties in their analysis. Techniques such as Independent
Externí odkaz:
https://doaj.org/article/07f3eba3256e49f2b22718c444616f19
Publikováno v:
Brain Sciences, Vol 14, Iss 4, p 335 (2024)
Early-stage Alzheimer’s disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identif
Externí odkaz:
https://doaj.org/article/7b49668201ff487484b3db2bfd649718