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Most of us hear music to experience emotions. Music can soothe your bad mood. Music systems available at present times allow you to play selected songs and recommends songs in genres based on your tastes or tastes of users similar to others. Since such music systems are not designed with the emotions evoked in mind, music listeners cannot completely rely on such systems and do not enjoy listening...
With the rapid development of the information technology, more and more high-speed networks came out. The 4G LTE network as a recently emerging network has gradually entered the mainstream of the communication network. This paper proposed an effective content-based information filtering based on the 4G LTE high-speed network by combing the content-based filter and traditional simple filter. Firstly,...
As an initial step in protecting different plant species from extinction, establishment of database for plant becomes necessary in order to catalogue various plant diversities. Therefore, automatic and accurate recognition and classification system of plants is important. Thus, the research aims to study plant classification using naïve Bayes (NB) method. Leaf shape and texture serves as input features...
Nonparametric Bayesian approach is considered for learning appropriate dictionaries in sparse image representations. However, for images from multiple separate sources, existing methods have two issues that potentially limit their practical implements: first, learning one unified dictionary is not optimal for representing image samples in different subspaces; second, the required number of dictionaries...
Fabric defect inspection is the pivotal part in the production of textile products. Since manual inspection is tedious and erroneous, automated fabric inspection has been topic of research for past years. Automation of fabric inspection involves two major aspects: defect detection and defect classification. We focused on classifying defects based on geometric features of defects. The features are...
Tax fraud includes a large spectrum of methods to deny the facts and realities, claiming wrong information, and accomplishing financial businesses regardless of what the legal frameworks are. Nowadays, with the development tax systems and the large volume of the data stored in them, need is felt for a tool by which we can process the stored data and provide users with the information obtained from...
Social media including Twitter create a space for expression and dissemination of thoughts and opinions on various topics and various events and they have created opportunity to apply theories and technology leading to search and explore the trends. Mining stream data needs to be balanced in three different branches: accuracy, time and memory. The optimal accuracy rate has obtained in the stream data...
We aim to dispel the blind faith in theoretical criteria for optimisation of the edited nearest neighbour classifier and its version called the Voronoi classifier. Three criteria from past and recent literature are considered: two bounds using Vapnik-Chervonenkis (VC) dimension and a probabilistic criterion derived by a Bayesian approach. We demonstrate the shortcomings of these criteria for selecting...
News has, in this day and age, transformed primarily into a digital format with leading newspapers and news agencies having a significant online presence. The speed at which news reaches the reader notwithstanding, the proliferation of blogs and microblogs to deliver specialized content has become the order of the day. Even highly engaged users tend to disengage with a website when the content they...
Binary relevance (BR) is a well-known framework for multi-label classification. It decomposes multi-label classification into binary (one-vs-rest) classification subproblems, one for each label. The BR approach is a simple and straightforward way for multi-label classification, but it still has several drawbacks. First, it does not consider label correlations. Second, each binary classifier may suffer...
Efficient performance modeling of today's analog and mixed-signal (AMS) circuits is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Co-Learning Bayesian Model Fusion (CL-BMF). The key idea of CL-BMF is to take advantage of the additional information collected from simulation and/or measurement to reduce the performance modeling...
In today's digital world use of computer systems, computer networks and internet are increasing rapidly. Due to this, information is processed digitally. So, providing effective security to such digital data is crucial task. There are some tools and systems are available for the security of digital information. From these tools and systems intrusion detection system is an important method. It is necessary...
In airline service industry, it is difficult to collect data about customers' feedback by questionnaires, but Twitter provides a sound data source for them to do customer sentiment analysis. However, little research has been done in the domain of Twitter sentiment classification about airline services. In this paper, an ensemble sentiment classification strategy was applied based on Majority Vote...
We study the two stage classification approach using Hidden Markov Models and Bayesian Network to natural hand gesture classification. We analyze 22 natural gestures with three sets of sensors (finger bend, accelerometers and pitch/roll), classifying each of them separately, and then combining the results using Bayesian classifier. This method achieves significant improvement over single stage classifier...
This paper studies the statistical channel state information (CSI) acquisition in single-cell for massive multiuser multiple-input multiple-output (MIMO) system in the beam domain. Compared with the Bayesian channel estimation which models the channels by an independent and identically Gaussian-mixture (GM) distribution, we model the channels by an independent and non-identically Gaussian distribution...
Network intrusion detection research work that employed KDDCup 99 dataset often encounters challenges in creating classifiers that could handle unequal distributed attack categories. In such cases, classifier could not effectively learn the characteristics of rare categories, which will lead to a poor detection rate of rare categories. The efficiency of intrusion detection is mainly determined by...
As the present fusing strategies cannot utilize the correlation of different detection results for image steganography effectively, a steganalysis method is proposed based on fusing SVM classifiers. Firstly, different feature subsets are used for the training of SVM classifiers. Secondly, the detection results of multi-classifiers are utilized to train a fusing classifier, the fusing classifier can...
In this paper, we propose a new approach that is to combine a model of time series with Bayesian networks to create a new forecasting model to predict the occurrence of harmful pests of rice. Ability of forecast system knows when immigrant brown plant hopper (BPH) peaks to make a calendar of sowing rice to avoid them. This is indeed helpful for experts as well as farmers to sow rice of seeds actively...
Toward the goal of improved representation learning, we propose a novel deep architecture for unsupervised feature learning based on a recursive multilayered union of subspaces (UoS) model. The model is able to accurately generate recursive nested signal segments at increasing fields of view as we progress from one layer to the next. The local subspace dimension (latent space) grows linearly while...
We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables. The network is the Discrete counterpart of Independent Component Analysis (DICA) and it is manipulated in a factor graph form for inference and learning. A full set of simulations is reported for character images from the MNIST dataset. The results show that the...
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