scholarly journals Research on Financial Data Query and Distribution Scheme Based on SQL Database

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xiaolan Fu
Keyword(s):  
Intrusion Detection ◽  
Weighted Average ◽  
Average Density ◽  
Financial Data ◽  
Experimental Results ◽  
Education Management ◽  
Data Query ◽  
Sql Database ◽  
Distribution Scheme ◽  
Kdd Cup 99

With the advance of optimization and merger colleges and universities, a university often contains more than one campus. The traditional centralized education management system has been unable to meet the needs of use. The model detects the intrusion by dividing the clusters in the clustering result into normal clusters and abnormal clusters and analyzing the weighted average density of object x to be detected in each cluster and the weighted overlapping distance of x and each centre point. We verified the intrusion detection performance of the model on the KDD Cup 99 dataset. The experimental results show that the model established in this paper has certain theoretical value.

scholarly journals K-Modes Clustering Algorithm Based on Weighted Overlap Distance and Its Application in Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yawen Dai ◽  
Guanghui Yuan ◽  
Zhaoyuan Yang ◽  
Bin Wang
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Detection System ◽  
Weighted Average ◽  
Average Density ◽  
Selection Algorithm ◽  
Detection Model ◽  
Kdd Cup 99

In order to better apply the K-modes algorithm to intrusion detection, this paper overcomes the problems of the existing K-modes algorithm based on rough set theory. Firstly, for the problem of K-modes clustering in the initial class center selection, an initial class center selection algorithm Ini_Weight based on weighted density and weighted overlap distance is proposed. Secondly, based on the Ini_Weight algorithm, a new K-modes clustering algorithm WODKM based on weighted overlap distance is proposed. Thirdly, the WODKM clustering algorithm is applied to intrusion detection to obtain a new unsupervised intrusion detection model. The model detects the intrusion by dividing the clusters in the clustering result into normal clusters and abnormal clusters and analyzing the weighted average density of the object x to be detected in each cluster and the weighted overlapping distance of x and each center point. We verified the intrusion detection performance of the model on the KDD Cup 99 dataset. The experimental results of the current study show that the proposed intrusion detection model achieves efficient results and solves the problems existing in the present-day intrusion detection system to some extent.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Optimization Method of Massive Data Query

2014 ◽  
Vol 602-605 ◽  
pp. 3247-3250
Author(s):  
Yu Ming Chen
Keyword(s):  
Optimization Method ◽  
Experimental Results ◽  
Massive Data ◽  
Information Complexity ◽  
Apriori Algorithm ◽  
Amount Of Information ◽  
Data Query ◽  
Target Data

Optimization method ofmassive dataquery is researched in this paper.In the modernInternet environment,the datahas the characteristics oflarge amount of information, complexity, disorder, andchaosassociation. Using traditionalqueried methodsoftenrequirea lot oflimitedconditions, witha lot of drawbacks such as time-consuming data query, moreineffective queryand low efficiency.To this end, anoptimizationmethod of massive data query based onparallel Apriori algorithm is proposed in this paper.The massive dataare made simplification processing andredundant data are deleted to providedata foundation for fast and accuratedataquery.Effectiveassociation rulesof the massive data are calculated, in order to obtain the relevantof the target data. Based onAprioriparallel algorithm,massivedata are processedto achieveaccurate query. Experimental results show thatthe use ofoptimization algorithm for massive dataquerycan improvethe query speedof target data and it has a strong superiority.

scholarly journals Constraints on the density distribution of type Ia supernovae ejecta inferred from late-time light-curve flattening

2020 ◽  
Vol 493 (4) ◽  
pp. 5617-5624
Author(s):  
Doron Kushnir ◽  
Eli Waxman
Keyword(s):  
Light Curve ◽  
Weighted Average ◽  
Far Infrared ◽  
Average Density ◽  
Light Curves ◽  
Type Ia Supernovae ◽  
Late Time ◽  
Simple Method ◽  
Type Ia ◽  
Ia Supernovae

ABSTRACT The finite time, τdep, over which positrons from β+ decays of 56Co deposit energy in type Ia supernovae ejecta lead, in case the positrons are trapped, to a slower decay of the bolometric luminosity compared to an exponential decline. Significant light-curve flattening is obtained when the ejecta density drops below the value for which τdep equals the 56Co lifetime. We provide a simple method to accurately describe this ‘delayed deposition’ effect, which is straightforward to use for analysis of observed light curves. We find that the ejecta heating is dominated by delayed deposition typically from 600 to 1200 d, and only later by longer lived isotopes 57Co and 55Fe decay (assuming solar abundance). For the relatively narrow 56Ni velocity distributions of commonly studied explosion models, the modification of the light curve depends mainly on the 56Ni mass-weighted average density, 〈ρ〉t3. Accurate late-time bolometric light curves, which may be obtained with JWST far-infrared (far-IR) measurements, will thus enable to discriminate between explosion models by determining 〈ρ〉t3 (and the 57Co and 55Fe abundances). The flattening of light curves inferred from recent observations, which is uncertain due to the lack of far-IR data, is readily explained by delayed deposition in models with $\langle \rho \rangle t^{3} \approx 0.2\, \mathrm{M}_{\odot }\, (10^{4}\, \textrm{km}\, \textrm{s}^{-1})^{-3}$, and does not imply supersolar 57Co and 55Fe abundances.

scholarly journals Hybrid Encryption Scheme for Hospital Financial Data Based on Noekeon Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fei Yao
Keyword(s):  
Success Rate ◽  
Hybrid Algorithm ◽  
Research Study ◽  
Financial Data ◽  
Experimental Results ◽  
Encryption Scheme ◽  
Basic Principles ◽  
Hybrid Encryption ◽  
Rsa Algorithm ◽  
Comprehensive Performance

The previous encryption methods of hospital financial data have the problem of overburden. Therefore, a research study on hybrid encryption of hospital financial data based on Noekeon algorithm is proposed. From the basic principles of the Noekeon algorithm and the application and implementation of the Noekeon algorithm, a hybrid encryption scheme for hospital financial data based on the Noekeon algorithm is designed. In order to improve the security of the encryption system, the RSA algorithm is used to encrypt the encrypted content twice. The hybrid algorithm realizes the hybrid encryption of the hospital's financial data. Finally, a hybrid encryption system for hospital financial data based on Noekeon algorithm is designed. Experimental results show that this method has a higher success rate and better comprehensive performance. It not only improves the encryption efficiency of hospital financial data but also enhances the security of hospital financial data, which has greater application value.

scholarly journals Intrusion Detection Method Based on Adaptive Clonal Genetic Algorithm and Backpropagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Lu ◽  
Menghan Liu ◽  
Jie Zhou ◽  
Zhigang Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Poor Performance ◽  
Data Sets ◽  
Backpropagation Neural Network ◽  
External Threats ◽  
Kdd Cup 99

Intrusion Detection System (IDS) is an important part of ensuring network security. When the system faces network attacks, it can identify the source of threats in a timely and accurate manner and adjust strategies to prevent hackers from intruding. Efficient IDS can identify external threats well, but traditional IDS has poor performance and low recognition accuracy. To improve the detection rate and accuracy of IDS, this paper proposes a novel ACGA-BPNN method based on adaptive clonal genetic algorithm (ACGA) and backpropagation neural network (BPNN). ACGA-BPNN is simulated on the KDD-CUP’99 and UNSW-NB15 data sets. The simulation results indicate that, in contrast to the methods based on simulated annealing (SA) and genetic algorithm (GA), the detection rate and accuracy of ACGA-BPNN are much higher than of GA-BPNN and SA-BPNN. In the classification results of KDD-CUP’99, the classification accuracy of ACGA-BPNN is 11% higher than GA-BPNN and 24.2% higher than SA-BPNN, and F-score reaches 99.0%. In addition, ACGA-BPNN has good global searchability and its convergence speed is higher than that of GA-BPNN and SA-BPNN. Furthermore, ACGA-BPNN significantly improves the overall detection performance of IDS.

Building Repository Banking and Finance (ReBaF)

2013 ◽  
pp. 234-241
Author(s):  
Munawaroh
Keyword(s):  
Information System ◽  
Open Source ◽  
Programming Language ◽  
Digital Library ◽  
Open Source Software ◽  
Financial Data ◽  
Library Services ◽  
Banking And Finance ◽  
Sql Database ◽  
Digital Library Services

Repository Banking and Finance (ReBaf) is one of the digital library services developed by STIE Perbanas Surabaya in an effort to extend the collection of banking and financial data organized by the Library of STIE Perbanas Surabaya. The ReBaf SISFO (information system) applies open-source software basis with PHP SQL programming language and Postgre SQL database.

Methods for the Experimental Evaluation of True Stress-Strain Curves After Necking of Conventional Tensile Specimens: Exploratory Investigation and Proposals

2012 ◽  
Author(s):  
Grace Kelly Q. Ganharul ◽  
Nick de Brangança Azevedo ◽  
Gustavo Henrique B. Donato
Keyword(s):  
Real Time ◽  
Weighted Average ◽  
Tensile Tests ◽  
Plastic Instability ◽  
True Stress ◽  
Experimental Results ◽  
Uniaxial Tensile ◽  
High Definition ◽  
Stress Strain

Numerical elastic-plastic simulations have undergone significant expansion during the last decades (e.g. refined fracture mechanics finite element models including ductile tearing). However, one limitation to increase the accuracy of such models is the reliable experimental characterization of true stress-strain curves from conventional uniaxial tensile tests after necking (plastic instability), which complicates the direct assessment of the true stress-strain curves until failure. As a step in this direction, this work presents four key activities: i) first, existing correction methods are presented, including Bridgman, power law, weighted average and others; ii) second, selected metals are tested to experimentally characterize loads and the geometric evolution of necking. High-definition images are used to obtain real-time measurements following a proposed methodology; iii) third, refined non-linear FEM models are developed to reproduce necking and assess stresses as a function of normalized neck geometry; iv) finally, existing correction methods are critically compared to experimental results and FEM predictions in terms of potential and accuracy. The experimental results evaluated using high-definition images presented an excellent geometrical characterization of instability. FEM models were able to describe stress-strain-displacement fields after necking, supporting the exploratory validations and proposals of this work. Classical methodologies could be adapted based on experiments to provide accurate stress-strain curves up to failure with less need for real-time measurements, thus giving further support to the determination of true material properties considering severe plasticity.

scholarly journals Building attack detection system base on machine learning

2021 ◽  
Vol 6 (2) ◽  
pp. 018-032
Author(s):  
Rasha Thamer Shawe ◽  
Kawther Thabt Saleh ◽  
Farah Neamah Abbas
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector ◽  
Data Set ◽  
Kdd Cup 99

These days, security threats detection, generally discussed to as intrusion, has befitted actual significant and serious problem in network, information and data security. Thus, an intrusion detection system (IDS) has befitted actual important element in computer or network security. Avoidance of such intrusions wholly bases on detection ability of Intrusion Detection System (IDS) which productions necessary job in network security such it identifies different kinds of attacks in network. Moreover, the data mining has been playing an important job in the different disciplines of technologies and sciences. For computer security, data mining are presented for serving intrusion detection System (IDS) to detect intruders accurately. One of the vital techniques of data mining is characteristic, so we suggest Intrusion Detection System utilizing data mining approach: SVM (Support Vector Machine). In suggest system, the classification will be through by employing SVM and realization concerning the suggested system efficiency will be accomplish by executing a number of experiments employing KDD Cup’99 dataset. SVM (Support Vector Machine) is one of the best distinguished classification techniques in the data mining region. KDD Cup’99 data set is utilized to execute several investigates in our suggested system. The experimental results illustration that we can decrease wide time is taken to construct SVM model by accomplishment suitable data set pre-processing. False Positive Rate (FPR) is decrease and Attack detection rate of SVM is increased .applied with classification algorithm gives the accuracy highest result. Implementation Environment Intrusion detection system is implemented using Mat lab 2015 programming language, and the examinations have been implemented in the environment of Windows-7 operating system mat lab R2015a, the processor: Core i7- Duo CPU 2670, 2.5 GHz, and (8GB) RAM.

Solar- and Sky-Radiation Integrator 1

1961 ◽  
Vol 42 (5) ◽  
pp. 325-333 ◽  
Author(s):  
D. P. Brown ◽  
R. A. Harvey
Keyword(s):  
Solar Radiation ◽  
Radiant Energy ◽  
Weighted Average ◽  
Graphical Analysis ◽  
Experimental Results ◽  
Previous Period ◽  
Passive Networks ◽  
Theoretical Considerations

An integrator, which automatically and periodically records the weighted average solar radiation from the previous period, has been developed. The instrument is used to obtain a measure of the average solar radiant energy per day. This measurement was previously obtained by manual—and often tedious—graphical analysis. Theoretical considerations involving averaging by passive networks are presented with experimental results which show the accuracy of the instrument.

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