Academic Articles & Research Papers

Backpropagation neural networks

Artificial Neural Networks

Abstract Artificial neural networks are useful tools that have been applied successfully in a broad range of signal‐processing problems. From a signal‐processing perspective, their ability to adapt continuously to new data allows them to tr...

Neural Networks: Tricks of the Trade

An Introduction to Convolutional Neural Networks

The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of art...

Neural Networks

Neural Networks: A Comprehensive Foundation

From the Publisher: This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emer...

Artificial Neural Networks

An artificial neural network (ANN) is a functional abstraction of the biologic neural structures of the central nervous system (Aleksander and Morton 1993; Rudomin et al. 1993; Arbib 1995; Anderson 1995). It is essentially a simple mathemat...

On Calibration of Modern Neural Networks

Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike thos...

Connectomic insights into the impact of 1p/19q co-deletion in dominant hemisphere insular glioma patients

ObjectivesThis study aimed to elucidate the influences of 1p/19q co-deletion on structural connectivity alterations in patients with dominant hemisphere insular diffuse gliomas.MethodsWe incorporated 32 cases of left insular gliomas and 20 ...

Tailoring Mission Effectiveness and Efficiency of a Ground Vehicle Using Exergy-Based Model Predictive Control (MPC)

To ensure dominance over a multi-domain battlespace, energy and power utilization must be accurately characterized for the dissimilar operational conditions. Using MATLAB/Simulink in combination with multiple neural networks, we created a m...

A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across...

Monthly Evapotranspiration Modeling in the Absence of Climatic Data Using the Soft Computing Methods in West and Northwest of Iran

Evapotranspiration (ET0), a major component of the hydrologic cycle, is important in water resources development and irrigation planning. The ET0 for west and northwest of Iran was estimated using the FAO Penman-Montieth method (FAO-56) as ...

Spatial analysis and risk mapping of Fasciola hepatica infection in dairy cattle at the Peruvian central highlands

This study aimed to develop maps for Fasciola hepatica infection occurrence in dairy cattle in the districts of Matahuasi and Baños in the Peruvian central highlands. For this, a model based on the correlation between environmental variable...

Dynamic Elliptical Shaping Control for Swarm Robots

Solving the robotic swarm coverage problem for an elliptical area has various applications for exploring novel environments. Solutions for this problem should cover a specified ellipse and seamlessly adapt to changing numbers of robots. Pre...

Application of Artificial Neural Networks for Corrosion Behavior of Ni–Zn Electrophosphate Coating on Galvanized Steel and Gene Expression Programming Models

Zn–Ni electrophosphate coating is one of the most commonly used materials in industrial applications. The corrosion resistance of this coating is very important in order to achieve the minimum corrosion current of the Zn–Ni electrophosphate...

Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training.

This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different le...

ACTIVATION FUNCTIONS IN NEURAL NETWORKS

Artificial Neural Networks are inspired from the human brain and the network of neurons present in the brain.The information is processed and passed on from one neuron to another through neuro synaptic junctions.Similarly, in artificial neu...

Fundamentals of Artificial Neural Networks

From the Publisher: As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Funda...

Neural Networks

Artificial neural networks

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, ...