Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Beyond energy, the growing number of defects in physical substrates is becoming another
major constraint that affects the design of computing devices and systems. As the underlying …

Graphics processing units (GPUs) are playing a critical role in convolutional neural networks
(CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments …

It has been over a decade since neural networks were first applied to solve combinatorial
optimization problems. During this period, enthusiasm has been erratic as new approaches …

This paper provides an overview of all current issues that are connected to the presence of
nitrosamines in water technology. N-nitrosodimethylamine (NDMA) is the most frequently …

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …

A method is proposed to estimate the fault tolerance (FT) of feedforward artificial neural nets
(ANNs) and synthesize robust nets. The fault model abstracts a variety of failure modes for …

Programmiersprachen und-systeme zur Simulation neuronaler Netze eingegangen. Der
Schwerpunkt des Buches liegt damit im Zusammenfassen und Ordnen einer Breite von …

Neural Networks: Computational Models and Applications covers a wealth of important
theoretical and practical issues in neural networks, including the learning algorithms of feed …