Results 111 to 120 of about 2,085,409 (278)

eXplainable Artificial Intelligence (XAI) applications in solar photovoltaic systems [PDF]

EPJ Web of Conferences
This article aims to provide a brief overview of explainable Artificial Intelligence (XAI) applications in the solar photovoltaic (PV) systems.
Mellit Adel, Hajji Bekkay, Rabhi Hamid
doaj   +1 more source

Hybrid framework for on‐the‐fly diagnosis of energy inefficiency in multi‐unit processes based on data‐driven and knowledge‐based integration

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Pinpointing the true roots of energy waste in large, multi‐unit industrial systems is notoriously difficult: the data are high‐dimensional, and process units are tightly interlinked. This paper presents a powerful hybrid diagnostic framework that integrates explainable AI (XAI), Granger causality (GC), and fault tree analysis (FTA).
Mohamed El Koujok, Hakim Ghezzaz, Mouloud Amazouz   +2 more
wiley   +1 more source

Automated B‐cell and plasma cell identification using unsupervised clustering by FlowSOM and an excel‐based classification model

Cytometry Part B: Clinical Cytometry, EarlyView.
Abstract Manual gating for plasma cell (PC) identification in multiparametric flow cytometry (MFC) is time‐consuming and operator‐dependent, especially when PCs are scarce. Artificial intelligence approaches such as unsupervised clustering (e.g., FlowSOM) map high‐dimensional data that still require expert interpretation.
Ethan James Gantana, Ernest Musekwa, Erica‐Mari Nell, Zivanai Cuthbert Chapanduka   +3 more
wiley   +1 more source

Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract)

Explainable Artificial Intelligence (XAI), a promising future technology in the field of healthcare, has attracted significant interest. Despite ongoing efforts in the development of XAI approaches, there has been inadequate evaluation of explanation ...
Kim, Hyeoneui, Jung, Jinsun
core   +1 more source

National Security Commission on Artificial Intelligence: Interim Report, November 2019 [PDF]

, 2019
Report presenting the National Security Commission on Artificial Intelligence's initial (NSCAI)'s preliminary assessment of artificial intelligence (AI).
National Security Commission on Artificial Intelligence (U.S.)
core  

Artificial Intelligence in Ophthalmology: From Methodological Advances to Clinical Translation and Future Directions

Eye &ENT Research, EarlyView.
ABSTRACT Artificial intelligence (AI) is reshaping ophthalmology from task‐specific image analysis toward multimodal, longitudinal, and clinically integrated decision support. This narrative review summarizes the methodological evolution of ophthalmic AI, including traditional machine learning, task‐specific deep learning, self‐supervised learning ...
Yuxin Liu, Hanruo Liu
wiley   +1 more source

eXplainable AI (XAI) -Lecture 2-

, 2021
Segona sessió del seminari impartit pel professor convidat Sebastian Lapuschkin, de l'Institut Fraunhofer de Berlin, sobre Explainable AI6584.mp4 6584 ...
Lapuschkin, Sebastian
core   +2 more sources

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

BMJ Health & Care Informatics
Background Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer.
Worku Jimma, Daraje kaba Gurmessa
doaj   +1 more source

Explainable artificial Intelligence ( XAI ) and ethical decision-making in business [PDF]

The question of whether machines can think, proposed by Alan M. Turing (Turing, 1950), has become increasingly relevant in today's world. Artificial Intelligence (AI) has made remarkable progress,surpassing human capabilitiesin varioustaskstraditionally ...
Galdón Salvador, José Luis, Marín Díaz, Gabriel, Galán Hernández, José Javier   +2 more
core  

Explainable artificial intelligence in medical research: A synopsis for clinical practitioners—Comprehensive XAI methodologies

Enhancing the interpretability and transparency of AI models for scientific and medical research is the goal of Explainable Artificial Intelligence (XAI).
Yagin, Fatma Hilal, Pinar, Abdulvahap
core   +1 more source