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The rapid growth of computer networks has led to massive flow of data every second. Some of these data flow is a malicious activity and cannot detect by anti-malware and firewalls. Therefore, an intrusion detection system is an urgent issue aims to distinguish between non relevant and relevant data in order to maintain data availability and integrity. Due to this, a hybrid intrusion detection system is proposed in this study based on machine learning techniques to tackle various challenging issues in data set such as mixed type data, incomplete, and uncertain data. The proposed system has achieved its objectives by supporting: Firstly, the density based clustering approach due to its robustness to noise removal. Secondly, the K means and K-nearest neighbour algorithms to transform n dimensional data into one dimensional data in order to deal with mixed type data and minimize the running time. Finally, a special type of dissimilarity measure has been supported to tackle the problem of missing data. The experimental results illustrate that the proposed classifier has better or similar classification accuracy (92.9%) as compared with support vector machine (91.5%), CANN(92.2%), Random forest(93.3%), and EIDS-ACC- OD(91.9%) classifiers in KDD Cup99 data set. However, the proposed classifier has the best performance (92.2%) and (91.2%) when randomly removing 5% and 10% of the KDD Cup99 data set in spite of decreasing the overall accuracy classification for all the classifiers. [ABSTRACT FROM AUTHOR]

We consider the problem of guaranteed estimation of unknown right-hand sides of the equations entering the statement of the exterior Neumann problems for the Helmholtz equation from indirect observations of their solutions. A method is developed for the determination of guaranteed a posteriori estimates of this right-hand sides which are compatible with measurement data. It is shown that such estimates can be expressed via solutions of a uniquely solvable system of the Helmholtz equations. [ABSTRACT FROM AUTHOR]

As well-known machine learning methods, decision trees are widely applied in classification and recognition areas. In this paper, with the uncertainty of labels handled by belief functions, a new decision tree method based on belief entropy is proposed and then extended to random forest. With the Gaussian mixture model, this tree method is able to deal with continuous attribute values directly, without pretreatment of discretization. Specifically, the tree method adopts belief entropy, a kind of uncertainty measurement based on the basic belief assignment, as a new attribute selection tool. To improve the classification performance, we constructed a random forest based on the basic trees and discuss different prediction combination strategies. Some numerical experiments on UCI machine learning data set were conducted, which indicate the good classification accuracy of the proposed method in different situations, especially on data with huge uncertainty.

Featured Application: The classification problem is one of the problems often used in medical applications. The proposed method can be used to quantify the influence of the classification attributes on result of the classification. Health and safety is a problem that is intensively discussed nowadays. The failures in healthcare are called medical errors: if the patient's condition worsens or he/she contracts an illness, then the actions that led to this are interpreted as medical errors. Medical errors can be the result of new procedures, extremes of age, complex or urgent care, improper documentation, illegible hand-writing, or patient actions. One of the ways to reduce medical error is an evaluation of its possibility, and then using the result of this evaluation to improve the medical organization units and processes in patient diagnosis, treatment, and care. This evaluation is possible based on methods of reliability engineering. The reliability engineering methods allow evaluating of different systems' reliability and the influence of external and internal factors on system reliability. These methods' application needs the system to be investigated or objective interpretation in terms of reliability engineering. Therefore, such a system in healthcare, for the diagnosis of disease, a patient's treatment, the influence of different factors on a patient's condition, and others, should be presented according to the rules and demands of reliability engineering. The first step is development of the mathematical representation of the investigated system or object according to the demands of the reliability analysis. One of the often-used mathematical representations in the reliability analysis of a system is the structure function. However, this mathematical representation needs completely specified initial data. The initial data from the healthcare domain for medical error analysis is uncertain and incompletely specified. Therefore, the development of this mathematical representation needs special methods. In this paper, a new method for the mathematical representation of system development based on uncertain and incompletely specified data is proposed. The system evaluation based on the structure function allows computing of many reliability indices and measures used in reliability engineering. The approbation of this method is considered based on an example of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Uncertain data objects are objects that can be characterized by either a probability density function (PDF) or with multiple points. Because of existing levels of uncertainty for uncertain data objects, the scatter of this type of objects might be very different than the scatter of certain data objects. Measures of scatter for uncertain objects have not been defined before. In this paper, we define covariance matrix, within scatter matrix, and between scatter matrix as the measures of scatter for uncertain data objects. Also, in this paper, we extend the idea of Fisher linear discriminant analysis for uncertain objects (UFLDA). We also develop kernel Fisher discriminant analysis for uncertain objects (UKFDA). The developed uncertain kernel Fisher discriminants are for two cases: 1) when the uncertain objects are given with PDF and 2) when the uncertain objects are given with multiple points. We compare the performance of our developed uncertain Fisher discriminants (UFLDA and UKFDA) with a few other methods in classification of uncertain data objects through several examples on both simulated and real-world data. We will show in the experiments that our developed uncertain Fisher discriminants outperform other methods in classifying uncertain data objects. [ABSTRACT FROM AUTHOR]

In recent years, numerous applications have been continuously generating large amounts of uncertain data. The advanced analysis queries such as skyline operators are essential topics to extract interesting objects from the vast uncertain dataset. Recently, the MapReduce system has been widely used in the area of big data analysis. Although the probabilistic skyline query is not decomposable, it does not make sense to implement the probabilistic skyline query in the MapReduce framework. This paper proposes an effective parallel method called parallel computation of probabilistic skyline query (PCPS) that can measure the probabilistic skyline set in one MapReduce computation pass. The proposed method takes into account the critical sections and detects data with a high probability of existence through a proposed smart sampling algorithm. PCPS implements a new approach to the fair allocation of input data. The experimental results indicate that our proposed approach can not only reduce the processing time of the probabilistic skyline queries, but also achieve fair precision with varying dimensionality degrees. [ABSTRACT FROM AUTHOR]

Quantitative demonstrating of organic frameworks has turned into an essential computational methodology in the configuration of novel and investigation of existing natural frameworks. Be that as it may, active information that portrays the framework's elements should be known keeping in mind the end goal to get pertinent results with the routine displaying strategies. This information is frequently robust or even difficult to get. Here, we exhibit a model of quantitative fuzzy rational demonstrating approach that can adapt to obscure motor information and hence deliver applicable results despite the fact that dynamic information is fragmented or just dubiously characterized. Besides, the methodology can be utilized as a part of the blend with the current cutting edge quantitative demonstrating strategies just in specific parts of the framework, i.e., where the data are absent. The contextual analysis of the methodology suggested in this paper is performed on the model of nine-quality genes. We propose a kind of FPN model in light of fuzzy sets to manage the quantitative modeling of biological systems. The tests of our model appear that the model is practical and entirely powerful for information impersonation and thinking of fuzzy expert frameworks.

In this article, we consider vector optimization problems with uncertain data. We first formulate optimistic counterparts of the reference problems and propose concepts of efficient solutions to such counterparts. We then introduce concepts of pointwise and global well-posedness for optimistic counterparts. Using the generalized Gerstewitz's function and properties of elements in the image space, we establish the relationships between well-posedness properties for the reference problems and that for scalar optimization ones. Based on such relations, we have studied sufficient conditions of these well-posedness properties for the considered problems via the corresponding scalar problems. Finally, by virtue of a forcing function, the characterizations of the two concepts of well-posedness for such problems are presented. [ABSTRACT FROM AUTHOR]

To solve the problem of Top-k query algorithm for widely used uncertain data without enough efficiency, a new parameterized Top-k query algorithm (ETK) was proposed based on the analysis of possible world model. The data probability and the score were constrained by the algorithm, and the top k data with the highest Top-k probability and the score were returned to comprehensively consider the two attributes of data probability and score. To improve the algorithm efficiency, the pruning methods were proposed based on data score, data existence probability and data dominance relationship. The proposed algorithm was compared with the previous algorithms, and the experiments were carried out for different parameters. The results show that the proposed algorithm has improved time consumption performance for dealing with uncertain data. [ABSTRACT FROM AUTHOR]

In the case of current technology, most of the measurements are focused on geometric distance, and the distribution of data is not considered. In order to compensate for this shortcoming of geometric distance measurement, this paper uses the KL distance as the similarity measurement standard for uncertain data, and the DOUD_C algorithm and COUD_C algorithm are proposed respectively in the discrete domain and continuous domain. In order to solve the problem of efficient clustering of the high dimensional data, this paper considers the data structure of grid, and BROUD_C algorithm is proposed. According to the adjacency characteristic of the grid, the cluster process is extended continuously, the algorithm can find clusters of arbitrary shapes, and we can filter a large number of isolated points, it solves the uncertain data clustering problem effectively in the obstacle space. The experimental results show that compared to the OBS_UK_means with VPA and SDA pruning algorithm and FOPTICS algorithm, the clustering performance of BROUD_C algorithm is more significant and CPU has less execution time in the obstacle space. [ABSTRACT FROM AUTHOR]

Uncertain data are data with uncertainty information, which exist widely in database applications. In recent years, uncertainty in data has brought challenges in almost all database management areas such as data modeling, query representation, query processing, and data mining. There is no doubt that uncertain data management has become a hot research topic in the field of data management. In this study, we explore problems in managing uncertain data, present state-of-the-art solutions, and provide future research directions in this area. The discussed uncertain data management techniques include data modeling, query processing, and data mining in uncertain data in the forms of relational, XML, graph, and stream. [ABSTRACT FROM AUTHOR]

Data discovery is a broad discipline that comprises a various of techniques for extracting meaningful information. In practical, there are various reasons for uncertain data such as error devices of data collection, data transferring in interference environment, statistic or survey data, predict information and so on. Therefore, studying and discovering meaningful information on uncertain data have been more challenges. Some researches have been studied definitions and methods for uncertain data to solve specified scenarios according to satisfying criteria on semantics. In this paper, we first review and classify types of uncertain data in specific scenario. Secondly, some cases of queries processing to demonstrate the semantics on uncertain data are analysed based on their answers. Thirdly, we review almost probabilities of queries that has been used to evaluate the performing queries process according to semantics of uncertain. Lastly, we mention some problems of semantics and their answers which have been argued in doing uncertain data research. [ABSTRACT FROM AUTHOR]

The purpose of the article is to develop a fuzzy model of assessment of risks of activities of oil and gas production enterprises. Methodology. Due to a large number of factors, influencing the probability of risk occurrence, and in order to obtain a comprehensive indicator during the research, we have applied a fuzzy cascade model of the Mamdani type. Research results. In the conditions of instability and constant uncertainty of oil and gas production processes, identification and forecasting of the occurrence of risks of operations of oil and gas production enterprises by traditional mathematical methods of modelling provide no required reliability and accuracy of forecasting. In this regard, we propose an integral assessment and application of the fuzzy logic methodology for obtaining the required results for the adoption of effective managerial decisions. Despite the complexity of the mathematical apparatus, risk assessment on the basis of the theory of fuzzy sets makes it possible to create a sufficiently flexible model, which will operate with a large number of input arguments and give as a resultant variable a value, which can be considered to be objective with some degree of approximation. Practical importance. The step-by-step addition of each group of risk factors to the model allows obtaining reliable results of the probability of occurrence of risk events on a real-time basis, which significantly reduces the company’s losses. Value/originality. According to the results of the research, the Mamdani-type fuzzy cascade model of the assessment of risks of the activities of the oil and gas production enterprises is developed for the first time.