Results 141 to 150 of about 62,652 (233)

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System. [PDF]

Healthc Inform Res, 2018
Maharlou H, Niakan Kalhori SR, Shahbazi S, Ravangard R.   +3 more
europepmc   +1 more source

Multi‐Modal Deep Feature Extraction and Classifier‐Level Integration for Brain Tumour Classification Using CT and MRI Image

Artificial Intelligence for Engineering, Volume 2, Issue 1, Page 18-37, March 2026.
This multi‐modal deep learning framework classifies brain tumours using multi‐backbone deep feature extraction and classifier‐level fusion from CT and MRI images. In extensive experiments, fused features from SqueezeNet, ResNet, and MobileNet combined with a neural network classifier outperform single‐modality and single‐model baselines and show that ...
N. Shyamala, S. Mahaboob Basha
wiley   +1 more source

Reframing the Chipped Edge: Combining Materiality, Ontology, and Embodiment to Rethink Stone Tool‐Making and Human Conscious Behavior in the Paleolithic Past

Anthropology of Consciousness, Volume 37, Issue 1, Spring 2026.
ABSTRACT Combining different theoretical frameworks can lead to new insights into the role of material things in shaping human experience in the Paleolithic period. This paper first presents a historical review of three theoretical approaches in archaeology, anthropology, and the philosophy of mind: Material culture and materiality studies, the ...
Bar Efrati
wiley   +1 more source

AOSNP‐ADAPTR resource level‐based recommendations on practical diagnostic strategies for WHO CNS5 adult‐type diffuse gliomas

Brain Pathology, Volume 36, Issue 2, March 2026.
ADAPTR recommendations for Adult‐type Diffuse Gliomas in Resource‐restrained settings. Abstract The fifth edition of the WHO classification of CNS Tumors (WHO CNS5) has revised the diagnostic and grading criteria for Adult‐type Diffuse Gliomas (ADGs) by integrating molecular parameters with histologic features.
Vani Santosh, Chitra Sarkar, Shilpa Rao, Ho‐Keung Ng, Junji Shibahara, Maysa Al‐Hussaini, Michael E. Buckland, Sung‐Hye Park, Tarik Tihan, Pieter Wesseling, David N. Louis, Takashi Komori   +11 more
wiley   +1 more source

A neuro-fuzzy system for characterization of arm movements. [PDF]

Sensors (Basel), 2013
Balbinot A, Favieiro G.
europepmc   +1 more source

Enhancing Urban Flood Loss Mapping by Integrating ANFIS Classifier With a Two‐Dimensional Hydrodynamic Model

Journal of Flood Risk Management, Volume 19, Issue 1, March 2026.
ABSTRACT Flood loss mapping is one of the essential prerequisites for urban flood assessment studies to identify areas vulnerable to floods and to make cities safe and resilient. This study develops a neuro‐fuzzy loss model to generate flood loss maps, classifying loss levels into several categories ranging from no loss to severe loss.
Mahdi Sedighkia, Roslyn Prinsley, Barry Croke   +2 more
wiley   +1 more source

GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism. [PDF]

Heliyon
Gülmez B.
europepmc   +1 more source

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays. [PDF]

Sci Adv
Shi T, Zhang H, Cui S, Liu J, Gu Z, Wang Z, Yan X, Liu Q.   +7 more
europepmc   +1 more source

NF-MORL: a neuro-fuzzy multi-objective reinforcement learning framework for task scheduling in fog computing environments. [PDF]

Sci Rep
Yu X, Tang L, Mi J, Long L, Qin X, Li X, Mo Q.   +6 more
europepmc   +1 more source

A neuro-fuzzy multi-topology adaptive routing framework for QoS-aware healthcare IoT communications. [PDF]

Sci Rep
Parveen MS, Bhuvaneswari PTV.
europepmc   +1 more source