Results 51 to 60 of about 66,291 (256)

Coincidence Detection Using Spiking Neurons with Application to Face Recognition

Journal of Applied Mathematics, 2015
We elucidate the practical implementation of Spiking Neural Network (SNN) as local ensembles of classifiers. Synaptic time constant τs is used as learning parameter in representing the variations learned from a set of training data at classifier level ...
Fadhlan Kamaruzaman, Amir Akramin Shafie, Yasir M. Mustafah   +2 more
doaj   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Intrusion Detection Systems Using Adaptive Regression Splines [PDF]

, 2003
Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable intrusion detection systems.
Abraham, Ajith, Mukkamala, Srinivas, Ramos, Vitorino, Sung, Andrew H.   +3 more
core   +4 more sources

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

, 2019
Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from
Pedrycz, Witold, Pratama, Mahardhika, Webb, Geoffrey I.   +2 more
core   +1 more source

Schooling Trajectories and the Development of Brain Dynamics: A Comparative Study of Montessori and Traditional Education

Advanced Science, EarlyView.
We investigate whether Montessori and traditional schooling systems shape the developmental trajectory of large‐scale brain dynamics in different ways. We quantify the arrow of time (“non‐reversibility”) in neural activity during resting state and movie‐watching, revealing distinct maturational patterns.
Elvira del Agua, Anira Escrichs, Yonatan Sanz Perl, Morten L. Kringelbach, Adele Diamond, Solange Denervaud, Gustavo Deco   +6 more
wiley   +1 more source

Multivariate Neural Network Operators: Simultaneous Approximation and Voronovskaja‐Type Theorem

Mathematical Methods in the Applied Sciences
ABSTRACTIn this paper, the simultaneous approximation and a Voronoskaja‐type theorem for the multivariate neural network operators of the Kantorovich type have been proved. In order to establish such results, a suitable multivariate Strang–Fix type condition has been assumed.
Cantarini M., Costarelli D.
openaire   +3 more sources

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source

Multivariate Perturbed Hyperbolic Tangent-Activated Singular Integral Approximation

Mathematics
Here we study the quantitative multivariate approximation of perturbed hyperbolic tangent-activated singular integral operators to the unit operator. The engaged neural network activation function is both parametrized and deformed, and the related kernel
George A. Anastassiou
doaj   +1 more source

Multivariate Fuzzy Perturbed Neural Network Operators Approximation

Journal of Nonlinear Sciences and Applications, 2014
This article studies the determination of the rate of convergence to the unit of each of three newly introduced here multivariate fuzzy perturbed normalized neural network operators of one hidden layer. These are given through the multivariate fuzzy modulus of continuity of the involved multivariate fuzzy number valued function or its high order fuzzy ...
openaire   +2 more sources