Results 31 to 40 of about 86,374 (257)

Patient therapy outcome modeling in cancer organoids is improved by cancer‐associated fibroblasts and organoid assembly convolution

open access: yesMolecular Oncology, EarlyView.
Patient‐derived organoids (PDOs) from pancreatic, colorectal, and gastric cancers were used to evaluate standard and experimental therapies. Incorporating cancer‐associated fibroblasts (CAFs) into organoid cultures improved patient therapy outcome prediction.
Marcin Grochowski   +12 more
wiley   +1 more source

Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression

open access: yesScientific Reports
Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to develop learning models in processing and analyzing brain data.
Zainab H. Ali   +3 more
doaj   +1 more source

Artificial Intelligence: Basic Concepts

open access: yesПедагогически форум, 2023
This study presents basic concepts embedded in the scientific field of artificial intelligence with an emphasis on key aspects and methods of application.
Mihail Kozhuharov
doaj   +1 more source

Multiple Embeddings for Quantum Machine Learning

open access: yesCoRR
This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies, allowing the model to fully exploit the ...
Si-Yu Han, Lihan Jia, Lanzhe Guo
openaire   +2 more sources

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

open access: yesMolecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim   +3 more
wiley   +1 more source

Intelligent Tutoring Systems for Adult Learning in STEM Disciplines

open access: yesNew Directions for Adult and Continuing Education, EarlyView.
ABSTRACT Intelligent tutoring systems (ITS) are reshaping adult learning in STEM by providing adaptive, data‐driven instruction across classrooms, workplaces, and informal environments. In the context of ITS, this article compares generative AI, which creates personalized explanations and practice materials, with explainable AI, which focuses on ...
Jill Zarestky, Amanda R. Lager Gleason
wiley   +1 more source

Predecting power transformer health index and life expectation based on digital twins and multitask LSTM-GRU model

open access: yesScientific Reports
Power transformers play a crucial role in enabling the integration of renewable energy sources and improving the overall efficiency and reliability of smart grid systems.
Nora El-Rashidy   +2 more
doaj   +1 more source

The Potential of SoC FPAAs for Emerging Ultra-Low-Power Machine Learning

open access: yesJournal of Low Power Electronics and Applications, 2022
Large-scale field-programmable analog arrays (FPAA) have the potential to handle machine inference and learning applications with significantly low energy requirements, potentially alleviating the high cost of these processes today, even in cloud-based ...
Jennifer Hasler
doaj   +1 more source

Artificial Intelligence and Mental Well‐Being in Adult Education: Implications for Practice and Professional Responsibility

open access: yesNew Directions for Adult and Continuing Education, EarlyView.
ABSTRACT Mental well‐being is central to adult learner success, yet many adult education institutions lack capacity to provide timely and accessible support. This article examines how artificial intelligence (AI) can strengthen mental health–adjacent supports in adult and continuing higher education, with attention to professional practice and ...
Adam L. McClain, Thomas Wade
wiley   +1 more source

Closed-Loop Neural Interfaces with Embedded Machine Learning [PDF]

open access: yes2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2020
Neural interfaces capable of multi-site electrical recording, on-site signal classification, and closed-loop therapy are critical for the diagnosis and treatment of neurological disorders. However, deploying machine learning algorithms on low-power neural devices is challenging, given the tight constraints on computational and memory resources for such
Bingzhao Zhu, Uisub Shin, Mahsa Shoaran
openaire   +2 more sources

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