Naive Bayesian Network Research Articles

A novel data mining approach for detection of brain disorder diseases using an integrated wavelet transform technique

An integrated form of wavelet transform technique (IDWT) has been explored and established as a feature extraction strategy to detect the brain disorder from brain MRI images is this study. There are different orientations of human brain images such as; sagittal, coronal and transaxial plane. Here, transaxial plane human brain images are collected and the advantages of Haar and Bi-orthogonal 1.3 wavelet functions are used to extract the relevant features in two-dimensional space. After features are extracted from two different integrated wavelet functions, then the entire combined feature matrix has been provided as an input to Support Vector Machine (SVM) with linear and polynomial kernel to classify different brain disorder diseases. This proposed integrated feature extraction strategy has been compared with the multi-layer probabilistic neural network (MLPNN), Naive Bayesian network and Logistic Regression as well as existing techniques studied during the literature survey and it has been observed that proposed DWT with SVM polynomial kernel is giving 100% result, additionally, the proposed model has been validated using accuracy, precision, recall and F1-score.

Improving smart home security; integrating behaviour prediction into smart home

The paper highlights various security issues in existing smart home technology and its inhabitant behaviour prediction techniques and proposes a novel behaviour prediction algorithm to improve home security. The algorithm proposed in this work identifies legitimate user behaviour and distinguishes it from attack behaviour. The work also identifies the parameters necessary to predict user behaviour during the seven week learning period. The paper identified three factors namely time parameter, light parameter, user's key placement behaviour to successfully predict user behaviour. The algorithm learned normal and suspicious user behaviours during the seven week training period and naive Bayesian network was designed based on the knowledge. The newly developed security algorithm was implemented in a studio apartment for a period of two weeks which was accessed 24 times generating two warnings and one alarm.

A Novel Selection Method of Network Intrusion Optimal Route Detection based on Naive Bayesian

In order to improve the network security performance and resist the increasingly complex and diversified network intrusion, and reduce the false alarm rate of network intrusion and improve the detection efficiency, this paper proposes the selection method of the network intrusion optimal route detection based on naive Bayesian. We selected the feature subset of network route data by the principal component analysis and accordingly processed the network route detection sample set, getting the input characteristics of network route detection. The research selected the new low dimensional feature of network route data through linear or nonlinear transformation, and used the naive Bayesian network structure to classify the new network route data set. Simulation results show that the proposed method can improve the detection rate of network intrusion optimal route and reduce the false alarm rate, getting a more perfect result of network intrusion detection.

SECURE INTERNET OF THINGS-BASED CLOUD FRAMEWORK TO CONTROL ZIKA VIRUS OUTBREAK.

Zika virus (ZikaV) is currently one of the most important emerging viruses in the world which has caused outbreaks and epidemics and has also been associated with severe clinical manifestations and congenital malformations. Traditional approaches to combat the ZikaV outbreak are not effective for detection and control. The aim of this study is to propose a cloud-based system to prevent and control the spread of Zika virus disease using integration of mobile phones and Internet of Things (IoT). A Naive Bayesian Network (NBN) is used to diagnose the possibly infected users, and Google Maps Web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each ZikaV infected user, mosquito-dense sites, and breeding sites on the Google map that helps the government healthcare authorities to control such risk-prone areas effectively and efficiently. The performance and accuracy of the proposed system are evaluated using dataset for 2 million users. Our system provides high accuracy for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment. The cloud-based proposed system contributed to the accurate NBN-based classification of infected users and accurate identification of risk-prone areas using Google Maps.

The effectiveness of classification algorithms on IPv6 IID construction

This study assessed the effectiveness of classifying IPv6 interface identifier (IID) address construction using machine learning algorithms. It was observed that IID construction can be reliably determined through the usage of assisted machine learning algorithms such as the naive Bayesian classifiers (NBC) or artificial neural networks (ANNs). It was also observed that the NBC classification, whilst more efficient, was less accurate than the use of ANN for classifying interface identifiers. Training times for an unoptimised ANN were seen to be far greater than NBC, which may be a considerable limitation to its effectiveness in real world applications (such as log or traffic analysis). Future research will continue to improve the classification training times for ANN situations, potentially involving general-purpose computing on graphics processing units (GPGPU) systems, as well as applying the techniques to real world applications such as IPv6 IDS sensors, honeypots or honeynets.

Basic Health Screening by Exploiting Data Mining Techniques

This study aimed at proposing a basic health screening system based on data mining techniques in order to help related personnel on basic health screening and to facilitate citizens on self-examining health conditions. The research comprised of two steps. The first step was to create a model by using classification techniques that are Bayesian methods (Naive Bayes, Bayesian networks, and Naive Bayesian Updateable) and decision tree methods (C4.5, ID3, Partial Rule) to find important attributes causing the disease. In this step, the accuracy of each method was compared to the other methods to select the most efficient model as an input for the next step. The second step was to develop a basic health screening system by exploiting rules from the model developed in the first step as the second step’s inputs were to classify from a citizen’s health profile whether a given citizen is in a normal group, risk group or sick group. Research findings revealed two important attributes directly contributing to diabetes: Blood pressure (BP) and docetaxel (DTX). Furthermore, C4.5 algorithm provided the most accuracy with accuracy of 99.7969%, precision of 99.8%, recall of 99.8% and F-measure of 99.8%.

A Comparative Study on Efficiency of Classification Techniques with Zone Level Gabor Features towards Handwritten Telugu Character Recognition

Achieving high accuracies in recognition of handwritten text is a challenging research problem and never exhausting. The factors that instill challenges in handwritten character recognition include high degree of variability in writing, script type and the type of documents etc. In this paper, we focus on recognition of handwritten Telugu text commonly found in document images. The character set include all the vowels, consonants and single level vowel consonant clusters chosen in accordance with the commonly used terminology employed in composition pre-printed documents such as admission forms. In this paper, an algorithm is devised that performs zone-based feature extraction of the segmented character images. In the proposed work, the Gabor features are extracted from the character image at zone level and its efficiency is evaluated individually on two different zone representations and entire image at various scales and orientations. The classification and recognition performance is analyzed using nearest neighbor classifier, Naive Bayesian, multi-class SVM and probabilistic neural networks classifiers. The efficiency of the classifiers are also tested with statistical, Histogram of Gradients and Hu moments‟ feature extraction methods and the best accuracy of the system is found to be 84.8% for Gabor features with zone representation 2 and with multi-class SVM classifier.

Comparing the Machine Ability to Recognize Hand-Written Hindu and Arabic Digits

AbstractThe main aim of this work is to compare Hindu and Arabic digits with respect to a machine’s ability to recognize them. This comparison is done on the raw representation (images) of the digits and on their features extracted using two feature selection methods. Three learning algorithms with different inductive biases were used in the comparison performed using the raw representation; two of them were also used to compare the digits using their extracted features. All classifiers gave better results for Hindu digits in both cases; when raw representation was used and when the selected features where used. The experiments also show that Hindu digits can be classified with better accuracy, higher confidence and using fewer features than Arabic digits. These results indicate that hand-written Hindu digits are actually easier to recognize than hand-written Arabic digits. The machine learning methods used in this work are instance based learning (the kNN algorithm), Naive Bayesian and neural networks. T...

REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

Abstract. Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.

Research on an optimal selection method for sensor network node under high-speed mobile environment

The traditional selection methods for the selection of sensor network node under high-speed mobile environment is randomness, which cannot effectively use the multiple attributes of nodes, resulting in the irregular distribution of cluster head of nodes, and high energy consumption. An optimal selection method for sensor network node under the high-speed mobile environment based on EERNFS and Naive Bayesian Networks is proposed. By using EERNFS algorithm within each network intercept / sleep cycle, the sensor network node is made processing under high-speed mobile environment, to ensure the stable local connectivity and consistent collaboration intercepts, and reduces energy consumption. Naive Bayes algorithm is used to make optimization of general Bayesian classification method, and set the parameters. In the two-dimensional area, several sensor nodes are randomly placed, and the known two-dimensional area is divided. A certain node is selected arbitrarily in different regions to constitute the original set of Naive Bayes algorithm, and compute the training results and thresholds in the Bayesian system of node set at this moment. On the basis of the threshold, the optimal selection of sensor network node is achieved. Simulation results show that the proposed method not only has higher node coverage, but also the operating efficiency and energy consumption are better than the genetic method.

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review.

Introduction:Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction.Objective:This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms.Methods:PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study.Result:In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms.Discussion:This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy.Conclusion:The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Business Cycle Forecasting Model using Fuzzy Interactive Naive Bayesian Network

This study presents a business cycle forecasting model using economic indicators based on Fuzzy Interactive Naive Bayesian (FINB) network. FINB classifier is a modified model to enhance the classification capacity by weakening the conditional independence of naive Bayesian network. In particular, it facilitates the construction of an interaction network map consisting of leading indexes, thereby clarifying the degree of influence of a certain index to and from other indexes. The experimental results of the final interaction network map provide valuable information hidden in the prediction mechanism. Particularly, under the changing economic environment in the wake of the global financial crisis of 2008, future economic situations are hard to predict owing to the complexity of financial systems in which increased variables and the heightened interdependencies between them are interwoven within the system. These factors in turn aggravated the fragility of the financial system as a whole, resulting in increased uncertainty in predicting the outlook of the world economy. The proposed interaction network map described here will provide new insight into how the financial mechanism operates, new information pinpointing the main factors of the business cycle.

Lung Cancer Classification using Fuzzy Interactive Naïve Bayesian Network

Lung cancer is the most serious disease in the world and millions of people die of it every year. Because of the limitations of current treatment processes, it is difficult to cure lung cancer if the patient is no longer in the early stages. Therefore, it is necessary to diagnose lung cancer as early as possible, thereby increasing the chances to cure it. The Fuzzy Interactive Naive Bayesian (FINB) network is a new Bayes network that can be used to classify lung cancer by using microarray data sets. The FINB network is an interactive network and every attribution has an interactive parent and with a weight on the relationship that shows the interaction of the attribution in the data set. In our experiments, we use the gene expression profiles from the Affymetrix Human Genome U133 Plus 2.0 microarray. We use the Neural Network with a Weighted Fuzzy Membership Function (NEWFM) to train the data set and reconstruct the Fuzzy Interactive Naive Bayesian network. Then we compare the results with Tree augment naive Bayesian (TAN) network. We conclude that the FINB network performs better than the TAN network.

Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

A Method for Classification Using Data Mining Technique for Diabetes: A Study of Health Care Information System

Many researchers in the health information system field have been attracted to develop computer applications that help in the diagnosis process. Imperatively, data mining algorithms address the vital role in all of these applications. Many contributions were made in this area. There has always been a debate on the algorithm that gives the best classifier, the parameters to be used, the dataset pre-processing steps, etc. In this paper, the author largely emphasizes that the best way to build a predictive model with relatively high classification accuracy is to build several predictive models and to choose the model that gives the best results through parameters optimization. Diagnosing diabetes mellitus has gained considerable attention in the last few decades due to the increased severity of the disease. In this research, the author reviews four predictive data mining approaches that are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset; k-nearest neighbour, support vector machine, multilayer perceptron neural network, and naive bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

A Parallel Naive Bayesian Network Public Opinion Fast Classification Algorithm Based on Hadoop Platform

A Parallel Naive Bayesian Network Public Opinion Fast Classification Algorithm Based on Hadoop Platform

Reproducibility of parameter learning with missing observations in naive Wnt Bayesian network trained on colorectal cancer samples and doxycycline-treated cell lines.

In this manuscript the reproducibility of parameter learning with missing observations in a naive Bayesian network and its effect on the prediction results for Wnt signaling activation in colorectal cancer is tested. The training of the network is carried out separately on doxycycline-treated LS174T cell lines (GSE18560) as well as normal and adenoma samples (GSE8671). A computational framework to test the reproducibility of the parameters is designed in order check the veracity of the prediction results. Detailed experimental analysis suggests that the prediction results are accurate and reproducible with negligible deviations. Anomalies in estimated parameters are accounted for due to the representation issues of the Bayesian network model. High prediction accuracies are reported for normal (N) and colon-related adenomas (AD), colorectal cancer (CRC), carcinomas (C), adenocarcinomas (ADC) and replication error colorectal cancer (RER CRC) test samples. Test samples from inflammatory bowel diseases (IBD) do not fare well in the prediction test. Also, an interesting case regarding hypothesis testing came up while proving the statistical significance of the different design setups of the Bayesian network model. It was found that hypothesis testing may not be the correct way to check the significance between design setups, especially when the structure of the model is the same, given that the model is trained on a single piece of test data. The significance test does have value when the datasets are independent. Finally, in comparison to the biologically inspired models, the naive Bayesian model may give accurate results, but this accuracy comes at the cost of a loss of crucial biological knowledge which might help reveal hidden relations among intra/extracellular factors affecting the Wnt pathway.