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This paper investigates the Malay speaker identification using Neural Networks. Speech database was developed with five speakers as trainers and five speakers as imposters. The speech training set included 30 vowel sounds of five trainer speakers. The test set included 30 vowel sounds from the five trainers and 30 vowel sounds from five imposters. The speech sounds were sampled at 20 kHz with 16 bit...
In this paper, we study the potentiality of discrimination between vulnerabilities given by CVSS framework. CVSSis an op en framework which assess the intrinsic characteristics of vulnerabilities and gives a severity score for each one. We study the distribution of CVSS metrics (in particular base metrics)in the NVD database. We then focus on the environmental part of CVSS framework, which allows...
In this paper, we present a novel dynamic scheme to estimate the execution time of stored procedures inside the Database Management Systems (DBMS). Our estimation model can provide users an accurate estimation of the execution time of stored procedures. The proposed estimation model adopts the basic idea of Radial Basis Function (RBF) network to accurately predict the execution time of the stored...
With the development of database technology and the popularity of Internet, the database applications have been increasingly widespread; we must face on some serious database security. The database system is the aggregates of the message, and it is the core components of the computer message system. The security of database is very important; it is related to the success of enterprise and national...
The growing structured Web databases on the web, making large-scale Deep Web data integration faces enormous challenges. Organizing such structured web databases into a hierarchy directory tree is one of critical step towards the large-scale integration of Deep Web. In this paper, a method for automatic classification of Web database is addressed. Firstly, the method for calculating the semantic similarities...
The development of database technology has solved the memory and retrieval of substantive data, but the biomedicine database existing the phenomenon of “data rich, information poor”. In order to solve the problem of Knowledge Discovery in Database, great importance has been continuously attached to the data mining. In this paper, we elaborate the Particularities and Key issues of data mining in biomedicine,...
This work presents an unconstrained offline hand-written line recognition system based on hybrid HMM (Hidden Markov Model)/ANN (Artificial Neural Network) models. The particularity of the system lies in the use of an ensemble of connectionist/statistical character n-gram language models. These language models are trained with a text corpus at character level; therefore, no explicit lexicon is used...
This work presents an automatic method for identification of residential load profile in the Smart Grid context. Hence, in this research were used client/server software interfaces to transmit and receive data through the Internet. In this case, the residential consumers and utility were represented by client and server software, respectively. However, to consider all the stages of this method, a...
Triggers and stored procedures have an irreplaceable importance in the teaching process about the course of database application, as them can realized a complex program logic and can test student's comprehensive application ability to SQL languages. At present, all kinds of management platforms of DBMS (such as the Enterprise Management) are still some inconvenient aspects: when the users want to...
One of the most popular machine learning algorithms, ANN (Artificial Neural Network) has been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. Data mining has extensive and significant applications in a large variety of areas. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks...
The way of collecting sensor data will face a revolution when newly developing technology of sensor network will become fully functional. The program/stack memory and the battery life of sensor nodes are not suitable for complex data mining in runtime. Effective data mining can be implemented on the central base station, where the computational power is not generally constrained. Real-world sensor...
When designing a neural or fuzzy system, a careful preprocessing of the database is of utmost importance in order to produce a trustable system. In function approximation applications, when a functional relationship between input and output variables is supposed to exist, the presence of data where the similar set of input variables is associated to very different values of the output is not always...
Our goal is to predict the performance of multi-node systems consisting of identical processing nodes based on single node profiles. The performance of multi-node systems significantly depends on the amount of inter-node communication. Therefore, we built an analytical model of the communication amount, i.e., the number of transfers of cached copies, on multi-node systems with coherence mechanisms...
Many speech and language related techniques employ models that are trained using text data. In this paper, we introduce a novel method for selecting optimized training sets from text databases. The coverage of the subset selected for training is optimized using hierarchical clustering and the generalized Levenshtein distance. The validity of the proposed subset optimization technique is verified in...
In the paper, a transformer dissolved gas analysis in oil (DGA) fault integrated diagnosis system based on Oracle is developed, and its modules are introduced. Fault diagnosis of transformer is based on three-ratio-method, grey relational entropy, fuzzy clustering, Artificial Neural Network, featured by its analytic hierarchy process to integrated analysis, so as to perfect existing diagnosis methods...
This paper has analysed the a priori algorithm performance, and has pointed out performance bottleneck question of the a priori algorithm. Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy respectively. There is a paradox between efficiency and accuracy. In order to resolve to this conflict, a novel algorithm based on probability estimate...
The database correlation method (DCM) is a network based positioning technology which has shown superior in terms of accuracy. DCM is based on a pre-measured database of a location dependent variable such as received signal strength (RSS). Even though the technique has good potential, the practical difficulty in forming the database (fingerprints) using field measurements has become the major challenge...
Intrusion detection system (IDS) is an effective tool that can help to prevent unauthorized access to network resources. A good intrusion detection system should have higher detection rate and lower false positive. A new classification system using Jordan/Elman (J/L) neural network for ID is proposed to detect intrusions from normal connections with satisfactory detection rate and false positive....
The databases of real world contains a huge volume of data and among them there are hidden piles of interesting relations that are actually very hard to find out. The knowledge discovery databases (KDD) appear as a possible solution to find out such relations aiming at converting information into knowledge. However, not a data presented in the bases are useful to a KDD. Usually, data are processed...
In this paper, an online fault diagnosis scheme for nonlinear systems is derived from fuzzy rules extracted from the neurofuzzy network that models the residuals of the system. As the neurofuzzy network is updated online by the recursive least squares method, the proposed technique is able to diagnose faults online. To initiate the fault diagnosis scheme, a binary or real-valued fault database is...
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