The development of deep convolutional neural network architecture is critical to the improvement image classification task performance. Many studies use and focus on modifying structure improve Conversely, our study focuses loss function design. Cross-entropy Loss (CEL) has been widely used for training multi-class classification. Although CEL successfully implemented in several tasks, it only ...

Feature selection is an important task in data-driven control applications to identify relevant features and remove non-informative ones, for example residual fault diagnosis. For multi-class data, the objective find a minimal set of that can distinguish data from all different classes. A distributed feature algorithm derived using convex optimization Alternating Direction Method Multipliers. T...

Colorectal image classification is a novel application area in medical processing. images are one of the most prevalent malignant tumour disease type world. However, due to complexity histopathological imaging, accurate and effective still needs be addressed. In this work we proposed architecture convolution neural network with deep learning models for multiclass histopathology images. We achie...

human action recognition is an important problem in computer vision. one of the methods that are recently used is sparse coding. conventional sparse coding algorithms learn dictionaries and codes in an unsupervised manner and neglect class information that is available in the training set. but in this paper for solving this problem, we use a discriminative sparse code based on multi-manifolds. ...

Many existing approaches employ one-vs-rest method to decompose a multi-label classification problem into a set of 2class classification problems, one for each class. This method is valid in traditional single-label classification, it, however, incurs training inconsistency in multi-label classification, because in the latter a data point could belong to more than one class. In order to deal wi...

Using hierarchies of classes is one the standard methods to solve multi-class classification problems. In literature, selecting right hierarchy considered play a key role in improving performance. Although different have been proposed, there still lack understanding what makes good and method extract perform better or worse. To this effect, we analyze compare some most popular approaches extrac...

Real-time disease prediction has emerged as the main focus of study in field computerized medicine. Intelligent identification framework can assist medical practitioners diagnosing a way that is reliable, consistent, and timely, successfully lowering mortality rates, particularly during endemics pandemics. To prevent this pandemic’s rapid widespread, it vital to quickly identify, confine, treat...

A spectral feature selection scheme is proposed for multi-class automated rock recognition from real world drilling data using Gaussian Process classification. This work is part of a larger project aimed at surface mine automation. The motivation for this research is to investigate which combination of drilling data measurements is most relevant for rock recognition. We conduct feature selectio...