Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

Abstract Facial emotion recognition (FER) is a topic that has gained interest over the years for its role in bridging gap between Human and Machine interactions. This study explores potential of real time FER modelling, to be integrated closed loop system, help treatment children suffering from Autism Spectrum Disorder (ASD). The aim this show differences implementing Traditional machine learni...

Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most existing methods makes it difficult to directly search architectures on large-scale tasks. The typical way conducting large scale for architectural building block a small dataset (either using proxy set from or completely different...

We propose a novel method for discovering shape regions that strongly correlate with user-prescribed tags. For example, given a collection of chairs tagged as either “has armrest” or “lacks armrest”, our system correctly highlights the armrest regions as the main distinctive parts between the two chair types. To obtain point-wise predictions from shape-wise tags we develop a novel neural networ...

For decades, in diffusion cloud chambers, different types of subatomic particle tracks from radioactive sources or cosmic radiation had to be identified with the naked eye which limited amount data that could processed. In order allow these classical detectors enter digital era, we successfully developed a neuro-explicit artificial intelligence model that, given an image chamber, automatically ...

We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a significantly larger neural net. During real-world workflows, one often trains very many different neural networks during the experimentation and design process. This is a wasteful process in which each new model is trained from s...

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields ...

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention (MRA-NET) is appropriate for classification. The suggested technique comprises multi-staged ar...