This paper focuses on the active interpretability for deep learning-based speech emotion
recognition (SER). To achieve this, we propose an explicit feature constrained model, the …

Since the declaration of COVID‐19 as a pandemic, the world stock markets have suffered
huge losses prompting investors to limit or avoid these losses. The stock market was one of …

In this paper, we present AnalogVNN, a simulation framework built on PyTorch that can
simulate the effects of optoelectronic noise, limited precision, and signal normalization …

Medical image segmentation has gained greater attention over the past decade, especially
in the field of image-guided surgery. Here, robust, accurate and fast segmentation tools are …

In machine learning algorithms, parameters and hy-perparameters are important properties
in the training process. Parameters are modified through machine learning algorithms while …

Deep learning is more extensively used in image analysis-based classification of wounds
with an aim to facilitate the monitoring of wound prognosis in preventive treatments. In this …

Convolutional neural networks are composed of many hidden layers and each layer has its
properties and may require some parameters. Such training models train on the weights …

In today's interconnected world, diverse sensor types are critical for powering various
applications and services. The limited energy resources of these sensors present a …

Recent models for image processing are using the Convolutional neural network (CNN)
which requires a pixel per pixel analysis of the input image. This method works well …