Academic Articles & Research Papers

An Introduction to Neural Networks

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain funct...

Neural networks

article Free Access Share on Neural networks: applications in industry, business and science Authors: Bernard Widrow Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile , David E. Rumelhart Stanford Univ., Stanford, CA Sta...

Neural networks and their applications

Neural networks provide a range of powerful new techniques for solving problems in pattern recognition, data analysis, and control. They have several notable features including high processing speeds and the ability to learn the solution to...

Neural Networks

Neural Networks

On neural networks.

JIŘÍ BENEŠTypical neural network models representing a broad survey of actual trends are considered in an unifying way as Complexes subject to specific control systems, called Formators which may have a two-level arrangement and incorporate...

Dual-Channel Dynamic Gated Spatio-Temporal Graph for Traffic Flow Forecasting

Traffic flow forecasting is a critical and essential technology in the field of Intelligent Transportation Systems (ITS), as it plays a pivotal role in optimizing traffic management, improving road safety, and enhancing the overall efficien...

Fast Fourier Transform Networks for Object Tracking Based on Correlation Filter

In this paper, we propose fast Fourier transform networks for object tracking, called FFTNet. FFTNet is a correlation filter (CF)-based tracker that integrates two main components of CF, i.e., auto correlation and cross correlation between ...

3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples

Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well know...

Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation

Relative permeability is a substantial parameter for estimating multi-phase fluid flow in porous rocks. It is a complex physical property that is influenced by the behavior and interactions between the fluid and rock phases. Relative permea...

Optimization of Communication Schemes for DMA-Controlled Accelerators

The hardware accelerator controlled by direct memory access (DMA) is greatly influenced by the communication bandwidth from/to DRAM through on-chip buses. This paper proposes a novel performance estimation algorithm to optimize the communic...

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models ...

Stock market forecasting research based on GA-WOA-LSTM.

With the increasing complexity and prosperity of global financial markets, stock market forecasting plays a critical role in investment decision-making, market regulation, and economic planning. This study proposes a hybrid prediction model...

CL-BPUWM: continuous learning with Bayesian parameter updating and weight memory

Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the pa...

Deep learning in nano-photonics: inverse design and beyond

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. Many of the recent works on machine-learning inverse design are highly specific, and the...

Deep Learning in Image Cytometry: A Review

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and ho...

Deep learning in computational mechanics: a review

Abstract The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies w...

On optimization methods for deep learning

The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to dev...

Deep Learning

This book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising....

Face recognition with Bayesian convolutional networks for robust surveillance systems

Abstract Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method st...