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The accuracy of a positioning system is usually expressed as its average position error in an experiment. However, when the ground truth is no longer available, it would still be useful to know the reliability of a position estimate based on a single measurement. To obtain a reliability metric, we hypothesize that there is a relation between the uncertainty in a position's posterior probability distribution,...
Currently, the air quality monitoring becomes important things for knowing the value of air pollution especially in the cities. In our previous research, we built the air quality monitoring system using wireless sensor network (WSN). Each sensor nodes will transmit all of the air quality data to the base station controller (BSC). This data consists of weather condition, temperature, humidity, carbon...
We report several experiments on using Recurrent Neural Networks (RNNs) for sentence binary classification task. In terms of sentence classification, RNNs have an important advantage compared to well-known traditional machine learning models (e.g. SVM and Maximum Entropy), in which it can naturally take into account neighboring information between contiguous words. In addition, to perform binary classification...
Architectural distortion is the third most common sign of breast cancer in mammograms. The accurate recognition is important for computer aided diagnosis of breast cancer. However, due to the subtle symptom and complex structures in the mammogram images, it is difficult to recognize whether a region of interest (ROI) is truly an architectural distortion. In this paper, we proposed a new method for...
Multi-disciplinary study of human computer interaction has provided significant impact in the fields of neural engineering, cognitive neuroscience, rehabilitation and brain-computer interaction. This paper evaluates the impact of neurofeedback in the context of a simple computer game controlled by attention based brain signals. The designed game protocol requires the player to memorize a set of numbers...
In real time continuous activity recognition systems, utilization of a data segmentation stage increases the dependency of success ratio on the size of activity set as well as activity type, duration and sensor sampling rate. In this study, we analyzed if iterative K-Nearest Neighbour based knowledge discovery performed on acceleration data can substitute for the segmentation stage to reduce these...
In view of the nonlinear and non-stationary characteristics of vibration signals for rolling bearings, a fault classification method of rolling bearing based on local mean decomposition (LMD)-sample entropy and Least Squares Support Vector Machines (LS-SVM) was proposed. LMD method was employed to decompose vibration signals of rolling bearings into several product function components, and sample...
Application of latent Dirichlet allocation (LDA) in text analysis has received much attention because it is capable of characterizing the hidden topics of the documents within the Bayesian framework. In this paper, we train the LDA model with financial research reports to predict the most correlated industries of the financial news among the 24 first-level industries of Chinese market. Since the topics...
Decision trees are common algorithms in machine learning. Traditionally, these algorithms make trees recursively and at each step, they inspect data to induce the part of the tree. However decision trees are famous for their instability and high variance in error. In this paper a solution which adds error correction rule to a traditional decision tree algorithm is examined. In fact an algorithm which...
Since text mining saves a large amount of information in text format, it has a very high potential application. One of the main applications of text mining is to classify texts in subject order. In this paper, we tried to propose a aarianew method in order to increase classification accuracy and efficiency, by considering different methods of Persian text classification. We used a number of 5330 news...
As a machine learning method under sparse Bayesian framework, classical Relevance Vector Machine (RVM) applies kernel methods to construct Radial Basis Function(RBF) networks using a least number of relevant basis functions. Compared to the well-known Support Vector Machine (SVM), the RVM provides a better sparsity, and an automatic estimation of hyperparameters. However, the performance of the original...
Plants are related to human. Recognize an unfamiliar plant correctly without any expert understanding is big task. Due to Improvement in image processing, it is likely to know leaf image rapidly from which species it is. Pulse coupled neural network is a helpful tool for feature extraction. Entropy sequence is key feature which is obtained from pulse-coupled neural network. Along with entropy sequence...
We present a method for automatically learning object and state models, which can be used for recognition in an augmented reality task guidance system. We assume that the task involves objects whose appearance is fairly consistent, but the background may vary. The novelty of our approach is that the system can be automatically constructed from examples of experts performing the task. As a result,...
In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term that explicitly forces the classifier's prediction for multiple classes to be mutually-exclusive...
Low-level feature encoding combined with Spatial Pyramid Matching (SPM) is widely adopted in the image classification system nowadays to extract features, which are usually high-dimensional. This not only makes the classification problem computationally prohibitive, but also raises other issues, such as the “curse of dimensionality”. In this paper we present supervised dimensionality reduction (DR)...
In this paper, we propose a robust probability based sparse method to solve single sample face recognition, which harvests the advantages of both local and global representation. Different from previous sparse representation methods that generate sparse coefficients by 𝑙1, we produce sparse class probability distribution by proposing a multi-phase sparse probability (MSP) framework. To create class...
In many human activity recognition systems the size of the unlabeled training data may be significantly large due to expensive human effort required for data annotation. Moreover, the insufficient data collection process from heterogenous sources may cause dissimilarities between training and testing data. To address these limitations, a novel probabilistic approach that combines learning using privileged...
The huge amount of time required to construct a set of labeled images to train a classifier has led researchers to develop algorithms which can identify the most informative training images, such that labelling those will be sufficient to achieve a considerable classification accuracy. In this paper we focus on choosing a subset of the most informative and diverse images based on which the classification...
Learning attribute models for applications like Zero-Shot Learning (ZSL) and image search is challenging because they require attribute classifiers to generalize to test data that may be very different from the training data. A typical scenario is when the notion of an attribute may differ from one user to another, e.g. one user may find a shoe formal whereas another user may not. In this case, the...
Computer vision is widely used at present. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of complexity and overlap. In this paper, a fruit recognition algorithm based on convolution neural network(CNN) is proposed. At first the image regions are extracted using selective search algorithm, then the regions have been selected by means of an entropy of...
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