Data classification in presence of noise can lead to much worse results than expected for
pure patterns. In this paper we investigate this problem in the case of deep convolutional …

Due to the advances made in recent years, methods based on deep neural networks have
been able to achieve a state-of-the-art performance in various computer vision problems. In …

Convolutional neural network (CNN) based image classifiers always take input as an image,
automatically learn its feature and classify into predefined output class. If input image …

The automatic detection and classification of vehicles in traffic sequences is a typical task
which is carried out in many practical video surveillance systems. The advent of deep …

Surrogate-assisted evolutionary algorithms (SAEAs) have shown promising performance for
solving expensive optimization problems (EOPs) whose true evaluations are …

It is essential to develop infrared (IR) thermogram identification technologies to establish
automatic diagnosis systems in power substations. The convolutional neural network (CNN) …

Many real world problems can be modeled as large-scale global optimization (LSGO)
problems which are very challenging due to their high nonlinearity, high dimensionality and …

Surrogate-assisted meta-heuristic algorithms (SAMAs) have been increasingly popular in
recent years for solving challenging optimization problems. However, the majority of recent …

The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up
urban environments is a persistent and serious concern for governments in the developing …

Accurate and real-time detection of road objects is a necessary component that self-driving
cars must be equipped with to drive as safely as humans do. This task is critical since it is …