For many practical applications, a high computational cost of inference over deep network
architectures might be unacceptable. A small degradation in the overall inference accuracy …

At this current time, data stream classification plays a key role in big data analytics due to its
enormous growth. Most of the existing classification methods used ensemble learning …

With the onset of the digital age, people all around the world have come to heavily rely on
the use of digital modes of payment, credit card payments being the most dominant …

Shill Bidding (SB) is still a predominant auction fraud because it is the toughest to identify
due to its resemblance to the standard bidding behavior. To reduce losses on the buyers' …

Abstract “Boxology” is the graphical representation of patterns that are commonly observed
in hybrid learning and reasoning systems. Since some hybrid systems also involve humans …

Federated learning (FL) is a distributed machine learning method in which client nodes train
deep neural network models locally using their own training data and then send that trained …

Recent advancements in sensor technology offer opportunities to manage business
processes in a proactive manner. To enable an effective and real-time monitoring, sensor …

Recent works in the domain of deep learning for object recognition on common image
classification benchmarks often address the representation learning problem under the …

Traditional pattern matching and classification algorithms in machine learning field build
models by extracting patterns from whole data at once. It means once a model is learned …

We present a modelling framework for the investigation of prototype-based classifiers in non-
stationary environments. Specifically, we study Learning Vector Quantization (LVQ) systems …