Academic Articles & Research Papers

Evaluation of noise reduction methods in digital images: Classical filtering vs adaptive learning-based models

Digital images acquired through imaging systems often lose their original fidelity due to various types of noise introduced during acquisition, storage, or transmission, consequently reducing clarity and adversely affecting subsequent image...

Humanized substitutions of Vmat1 in mice alter amygdala-dependent behaviors associated with the evolution of anxiety

Summary: The human vesicular monoamine transporter 1 (VMAT1) harbors unique substitutions (Asn136Thr/Ile) that affect monoamine uptake into synaptic vesicles. These substitutions are absent in all known mammals, suggesting their contributio...

How structure determines correlations in neuronal networks.

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, ...

A role for neural modeling in the study of brain disorders: Comment on From intracerebral EEG signals to brain connectivity: identification of epileptogenic networks in partial epilepsy by F. Wendling et al.

Digital Local Return Services and Purchase Intention in Cross-Border E-Commerce: A Risk–Trust Perspective

Cross-border e-commerce offers consumers broader product access, yet uncertainty surrounding returns continues to suppress online purchase decisions. This study conceptualizes digital local return services as a digital assurance mechanism i...

The Handbook of brain theory and neural networks

From the Publisher: Dramatically updating and extending the first edition, published in 1995, the second edition of The Handbook of Brain Theory and Neural Networks presents the enormous progress made in recent years in the many subfields ...

Practical neural network recipes in C

This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to prod...

Models of Neural Networks

An Introduction to Neural Networks

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counte...

Introduction to the Artificial Neural Networks

An Artificial Neural Network (ANN) is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. Basic building block of every artificial neural network is artificial neuron, that is, a simp...

Neural Networks in Computer Intelligence

From the Publisher: Neural Networks in Computer Intelligence provides basic concepts,algorithms,and analysis of important neural network models developed to date,with emphasis on the importance of knowledge in intelligent system design. Th...

Neural networks for nonlinear programming

The dynamics of the modified canonical nonlinear programming circuit are studied and how to guarantee the stability of the network's solution. By considering the total cocontent function, the solution of the canonical nonlinear programming ...

Overview of Artificial Neural Networks

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to ...

Shear banding and flow instabilities in wormlike micelles: Modelling and mechanisms – A review

Worm-like micelles (WLMs) are dynamic, self-assembling supramolecular structures that exhibit complex viscoelastic behaviour due to their ability to undergo reversible scission, fusion, branching, and sequence rearrangement. This review pro...

Neural Networks in Chemistry

Abstract The capabilities of the human brain have always fascinated scientists and led them to investigate its inner workings. Over the past 50 years a number of models have been developed which have attempted to replicate the brain's vario...

Memristor-based neural networks

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent p...

Neural Networks in Materials Science.

There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach o...

neuralnet: Training of Neural Networks

Artificial neural networks are applied in many situations.neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables.Thus...

Elements of Artificial Neural Networks

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have ...

Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents

Abstract Background Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast...