Academic Articles & Research Papers

Neural Networks in Applied Statistics

Artificial neural networks are computer algorithms or computer programs derived in part from attempts to model the activity of nerve cells. They have been applied to pattern recognition, classification, and optimization problems in the phys...

Understanding Neural Networks as Statistical Tools

Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally be...

Neural networks in the capital markets

PART ONE: NEURAL NETWORKS: Introduction Design Considerations Methods for Optimal Network Design Data Modelling Considerations Testing Strategies and Metrics PART TWO: EQUITY APPLICATIONS: Modelling Stock Returns in the Framework of APT: A ...

Neural Networks for Chemical Engineers

Part 1 Theoretical issues: an introduction to artificial neural networks, D.T. Pham unsupervised neural learning, E. Oja back-propagation and its variations, D. Tsaptsinos the general approximation problem for feed-forward neural networks, ...

Intriguing properties of neural networks

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpr...

Hamiltonian Neural Networks

Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models th...

A neural network model for survival data

Neural networks have received considerable attention recently, mostly by non-statisticians. They are considered by many to be very promising tools for classification and prediction. In this paper we present an approach to modelling censored...

Neural networks for optimization and signal processing

From the Publisher: Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs pro...

Applications of Neural Networks in Electromagnetics

From the Book: Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...

An Introduction to Convolutional Neural Networks

Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous for...

Artificial Intelligence: Principles and Applications

Part 1. Principles of artificial intelligence, John Campbell. Part 2 Tools and techniques: artificial intelligence programming environments and the POPLOG system, John Gibson LISP, lists and pattern-matching, Tony Hasemer. Part 3 Applicatio...

EdgeAIGC: Model caching and resource allocation for edge artificial intelligence generated content

With the rapid development of generative artificial intelligence technology, the traditional cloud-based centralized model training and inference face significant limitations due to high transmission latency and costs, which restrict user-s...

Challenges in automotive software engineering

The amount of software in cars grows exponentially. Driving forces of this development are cheaper and more powerful hardware and the demand for innovations by new functions. The rapid increase of software and software based functionality b...

Default Detection Rate-Dependent Software Reliability Model with Imperfect Debugging

From the perspective of FDR (fault detection rate), which is an indispensable component in reliability modeling, this paper proposes two kinds of reliability models under imperfect debugging. This model is a relatively flexible and unified ...

Essentials of Software Engineering

"The basic concepts and theories of software engineering have stabilized considerably from the early days of thirty to forty years ago. Nevertheless, the technology and tools continue to evolve, expand and improve every four to five years. ...

Models of motivation in software engineering

Software engineering economics

The field of software economics seeks to develop technical theories, guidelines, and practices of software development based on sound, established, and emerging models of value and value-creation---adapted to the domain of software developm...

Software engineering for mobility

Mobility is redefining the hardware and software fabric of distributed systems. Wireless communication allows network hosts to participate in a distributed computation while on the move. Novel middleware technologies allow software componen...

Skin-on-a-Chip for Cosmetic Safety Assessment: From Reconstructed Skin Models to Regulatory Translation.

On the Dual Nature of Atmospheric Escape

Planetary atmospheres cannot remain hydrostatic at all altitudes because they approach finite density at infinite radius, implying infinite mass. Classical treatments address this in two directions: either retain a hydrostatic structure whi...