Evolution of a Hybrid Model for an Effective Perimeter Security Device

2015 ◽  
Vol 65 (6) ◽  
pp. 466
Author(s):  
S. Selvakumar ◽  
A.R. Vasudevan
Keyword(s):  
Hybrid Model ◽  
Computational Efficiency ◽  
Dirichlet Process ◽  
Hybrid Methods ◽  
A Priori ◽  
Number Of Clusters ◽  
Training Time ◽  
Bayesian Mixture ◽  
Clustering And Classification ◽  
Security Device

Clustering and classification models, or hybrid models are the most widely used models that can handle the diverse nature of NIDS dataset. Dirichlet process clustering technique is a non-parametric Bayesian mixture model that considers the data distribution of the dataset for the formation of distinct clusters. The number of clusters is not known a priori and it differs across different datasets. Determining the number of clusters based on the distribution of data instances can increase the performance of the model. Naive Bayes model, a supervised learning classification technique, maintains a better computational efficiency, by reducing the training time. In this paper, we propose a hybrid model to exploit the positive aspect of proper clustering of data instances and the computational efficiency in building a NIDS. RIPPER algorithm is used to extract rules from the traffic description for updation of the rule database. Experiments were conducted in the KDD CUP’99 and SSENet-2011 datasets to study the performance of the proposed model. Also, a comparison of three hybrid methods with the proposed hybrid model was carried out. The results showed that the proposed hybrid model is superior in building a robust perimeter security device.

App2Vec: Context-Aware Application Usage Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Huandong Wang ◽  
Yong Li ◽  
Mu Du ◽  
Zhenhui Li ◽  
Depeng Jin
Keyword(s):  
Dirichlet Process ◽  
Service Providers ◽  
State Of The Art ◽  
Representation Learning ◽  
Context Aware ◽  
Challenging Problem ◽  
Performance Gap ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

Decentralized Fixed-Interval Smoothing Algorithms

1986 ◽  
Vol 108 (1) ◽  
pp. 86-89 ◽  
Author(s):  
Keigo Watanabe
Keyword(s):  
Computational Efficiency ◽  
A Priori ◽  
Superposition Principle ◽  
Fixed Interval ◽  
Central Processor ◽  
Decentralized Estimation ◽  
Information Filter

The Weineret-Desai smoother formula is applied to derive new decentralized fixed-interval smoothing algorithms for a decentralized estimation structure consisting of a central processor and of M local processors. Such algorithms are based on decentralizing the estimates of global backward information filter and obtained from the use of the superposition principle in scattering framework. The smoothing update problem is also investigated to illustrate the application of the proposed algorithms. The emphasis is on computational efficiency, independence of local a priori statistics, and flexibility of implementation.

scholarly journals ABOUT PARAMETRIZATION OF SELECTION OF SIGNIFICANT CLUSTERS

2019 ◽  
Vol 4 (1) ◽  
pp. 64-67
Author(s):  
Pavel Kim
Keyword(s):  
Cluster Analysis ◽  
A Priori ◽  
Multidimensional Data ◽  
Number Of Clusters ◽  
Clustering Model ◽  
Measure Of Similarity ◽  
Selection Of

One of the fundamental tasks of cluster analysis is the partitioning of multidimensional data samples into groups of clusters – objects, which are closed in the sense of some given measure of similarity. In a some of problems, the number of clusters is set a priori, but more often it is required to determine them in the course of solving clustering. With a large number of clusters, especially if the data is “noisy,” the task becomes difficult for analyzing by experts, so it is artificially reduces the number of consideration clusters. The formal means of merging the “neighboring” clusters are considered, creating the basis for parameterizing the number of significant clusters in the “natural” clustering model [1].

scholarly journals Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2399 ◽  
Author(s):  
Cunwei Sun ◽  
Yuxin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fangqing Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Hybrid Model ◽  
Data Augmentation ◽  
Small Sample ◽  
Restricted Boltzmann Machine ◽  
Small Samples ◽  
Boltzmann Machine ◽  
Training Time

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

Simultaneous Clustering and Classification of Function Recovery Patterns of Ischemic Stroke

2020 ◽  
Vol 10 (6) ◽  
pp. 1401-1407
Author(s):  
Hyungtai Kim ◽  
Minhee Lee ◽  
Min Kyun Sohn ◽  
Jongmin Lee ◽  
Deog Yung Kim ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Classification Performance ◽  
Optimal Number ◽  
Clinical Test ◽  
Data Set ◽  
Number Of Clusters ◽  
Function Recovery ◽  
Clustering And Classification ◽  
Class Labels

This paper shows the simultaneous clustering and classification that is done in order to discover internal grouping on an unlabeled data set. Moreover, it simultaneously classifies the data using clusters discovered as class labels. During the simultaneous clustering and classification, silhouette and F1 scores were calculated for clustering and classification, respectively, according to the number of clusters in order to find an optimal number of clusters that guarantee the desired level of classification performance. In this study, we applied this approach to the data set of Ischemic stroke patients in order to discover function recovery patterns where clear diagnoses do not exist. In addition, we have developed a classifier that predicts the type of function recovery for new patients with early clinical test scores in clinically meaningful levels of accuracy. This classifier can be a helpful tool for clinicians in the rehabilitation field.

COMPENSATION COMPETITIVE LEARNING

2001 ◽  
Vol 01 (03) ◽  
pp. 303-322
Author(s):  
ZHI-QIANG LIU ◽  
YAJUN ZHANG
Keyword(s):  
Input Data ◽  
Learning Algorithm ◽  
A Priori ◽  
Competitive Learning ◽  
Experimental Results ◽  
Initial Number ◽  
Number Of Clusters ◽  
And Performance ◽  
Competitive Algorithms

In general, in competitive learning the requirement for the initial number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. The behavior and performance of the competitive algorithms are very sensitive to the initial locations and number of the prototypes. In this paper after investigating several important competitive learning paradigms, we present compensation techniques for overcoming the problems in competitive learning. Our experimental results show that competition with compensation can improve the performance of the learning algorithm.

scholarly journals Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yisu Lu ◽  
Jun Jiang ◽  
Wei Yang ◽  
Qianjin Feng ◽  
Wufan Chen
Keyword(s):  
Brain Tumor ◽  
Random Field ◽  
Real Time ◽  
Markov Random Field ◽  
Dirichlet Process ◽  
Anisotropic Diffusion ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Number Of Clusters ◽  
Markov Random

Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.

scholarly journals An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends

2020 ◽  
Author(s):  
Muhammad Haseeb Arshad ◽  
M. A. Abido
Keyword(s):  
Neural Networks ◽  
Computational Efficiency ◽  
Learning Algorithms ◽  
Neural Nets ◽  
Sequential Learning ◽  
Future Trends ◽  
Feed Forward ◽  
Training Time ◽  
Feed Forward Neural Networks ◽  
Hidden Layer

This paper serves as an overview for sequential learning algorithms for single hidden layer neural nets. Cite as: M. H. Arshad, M. A. Abido. An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends. Abstract: In this paper, a brief survey of the commonly used sequential-learning algorithms used with single hidden layer feed-forward neural networks is presented. A glimpse at the different kinds that are available in the literature up until now, how they have developed throughout the years, and their relative execution is summarized. Most important things to take note of during the designing phase of neural networks are its complexity, computational efficiency, maximum training time, and ability to generalize the under-study problem. The comparison of different sequential learning algorithms in regard to these merits for single hidden layer neural networks is drawn.

Estimating the Number of Clusters in Multivariate Data by Self-Organizing Maps

1999 ◽  
Vol 09 (03) ◽  
pp. 195-202 ◽  
Author(s):  
JOSÉ ALFREDO FERREIRA COSTA ◽  
MÁRCIO LUIZ DE ANDRADE NETTO
Keyword(s):  
A Priori ◽  
Multivariate Data ◽  
Feature Space ◽  
Search Space ◽  
Data Sets ◽  
Self Organizing Maps ◽  
Data Set ◽  
Number Of Clusters ◽  
Using Data ◽  
Self Organizing

Determining the structure of data without prior knowledge of the number of clusters or any information about their composition is a problem of interest in many fields, such as image analysis, astrophysics, biology, etc. Partitioning a set of n patterns in a p-dimensional feature space must be done such that those in a given cluster are more similar to each other than the rest. As there are approximately [Formula: see text] possible ways of partitioning the patterns among K clusters, finding the best solution is very hard when n is large. The search space is increased when we have no a priori number of partitions. Although the self-organizing feature map (SOM) can be used to visualize clusters, the automation of knowledge discovery by SOM is a difficult task. This paper proposes region-based image processing methods to post-processing the U-matrix obtained after the unsupervised learning performed by SOM. Mathematical morphology is applied to identify regions of neurons that are similar. The number of regions and their labels are automatically found and they are related to the number of clusters in a multivariate data set. New data can be classified by labeling it according to the best match neuron. Simulations using data sets drawn from finite mixtures of p-variate normal densities are presented as well as related advantages and drawbacks of the method.

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