International Science Index

41
10008701
Regional Low Gravity Anomalies Influencing High Concentrations of Heavy Minerals on Placer Deposits
Abstract:

Regions of low gravity and gravity anomalies both influence heavy mineral concentrations on placer deposits. Economically imported heavy minerals are likely to have higher levels of deposition in low gravity regions of placer deposits. This can be found in coastal regions of Southern Asia, particularly in Sri Lanka and Peninsula India and areas located in the lowest gravity region of the world. The area about 70 kilometers of the east coast of Sri Lanka is covered by a high percentage of ilmenite deposits, and the southwest coast of the island consists of Monazite placer deposit. These deposits are one of the largest placer deposits in the world. In India, the heavy mineral industry has a good market. On the other hand, based on the coastal placer deposits recorded, the high gravity region located around Papua New Guinea, has no such heavy mineral deposits. In low gravity regions, with the help of other depositional environmental factors, the grains have more time and space to float in the sea, this helps bring high concentrations of heavy mineral deposits to the coast. The effect of low and high gravity can be demonstrated by using heavy mineral separation devices.  The Wilfley heavy mineral separating table is one of these; it is extensively used in industries and in laboratories for heavy mineral separation. The horizontally oscillating Wilfley table helps to separate heavy and light mineral grains in to deferent fractions, with the use of water. In this experiment, the low and high angle of the Wilfley table are representing low and high gravity respectively. A sample mixture of grain size <0.85 mm of heavy and light mineral grains has been used for this experiment. The high and low angle of the table was 60 and 20 respectively for this experiment. The separated fractions from the table are again separated into heavy and light minerals, with the use of heavy liquid, which consists of a specific gravity of 2.85. The fractions of separated heavy and light minerals have been used for drawing the two-dimensional graphs. The graphs show that the low gravity stage has a high percentage of heavy minerals collected in the upper area of the table than in the high gravity stage. The results of the experiment can be used for the comparison of regional low gravity and high gravity levels of heavy minerals. If there are any heavy mineral deposits in the high gravity regions, these deposits will take place far away from the coast, within the continental shelf.

Paper Detail
133
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40
10008549
Imposter Detection Based on Location in Vehicular Ad-Hoc Network
Abstract:

Vehicular Ad hoc Network is basically the solution of several problems associated while vehicles are plying on the road. In this paper, we have focused on the detection of imposter node while it has stolen the ID's of the authenticated vehicle in the network. The purpose is to harm the network through imposter messages. Here, we have proposed a protocol namely Imposter Detection based on Location (IDBL), which will store the location coordinate of the each vehicle as the key of the authenticity of the message so that imposter node can be detected. The imposter nodes send messages from a stolen ID and show that it is from an authentic node ID. So, to detect this anomaly, the first location is checked and observed different from original vehicle location. This node is known as imposter node. We have implemented the algorithm through JAVA and tested various types of node distribution and observed the detection probability of imposter node.

Paper Detail
53
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39
10005310
A Framework for SQL Learning: Linking Learning Taxonomy, Cognitive Model and Cross Cutting Factors
Abstract:

Databases comprise the foundation of most software systems. System developers inevitably write code to query these databases. The de facto language for querying is SQL and this, consequently, is the default language taught by higher education institutions. There is evidence that learners find it hard to master SQL, harder than mastering other programming languages such as Java. Educators do not agree about explanations for this seeming anomaly. Further investigation may well reveal the reasons. In this paper, we report on our investigations into how novices learn SQL, the actual problems they experience when writing SQL, as well as the differences between expert and novice SQL query writers. We conclude by presenting a model of SQL learning that should inform the instructional material design process better to support the SQL learning process.

Keywords:
Paper Detail
501
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38
10005192
Effect of Weathering on the Mineralogy and Geochemistry of Sediments of the Hyper Saline Urmia Salt Lake, Iran
Abstract:
Urmia Salt Lake (USL) is a hypersaline lake in the northwest of Iran. It contains halite as main dissolved and precipitated mineral and the major mineral mixed with lake bed sediments. Other detrital minerals such as calcite, aragonite, dolomite, quartz, feldspars, augite are forming lake sediments. This study examined the impact of weathering of this sediments collected from 1.5 meters depth and augite placers. The study indicated that weathering of tephritic and adakite rocks of the Islamic Island at the immediate boundary of the lake play a main control of lake bed sediments and has produced a large volume of augite placer along the lake bank. Weathering increases from south to toward north with increasing distance from Islamic Island. Geochemistry of lake sediments demonstrated the enrichment of MgO, CaO, Sr with an elevated anomaly of Eu, possibly due to surface absorbance of Mn and Fe associated Sr elevation originating from adakite volcanic rocks in the vicinity of the lake basin. The study shows the local geology is the major factor in origin of lake sediments than chemical and biochemical produced mineral during diagenetic processes.
Paper Detail
597
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37
10003926
Application of Building Information Modeling in Energy Management of Individual Departments Occupying University Facilities
Abstract:
To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.
Paper Detail
1123
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36
10003512
Anomaly Detection with ANN and SVM for Telemedicine Networks
Abstract:
In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.
Paper Detail
1093
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35
10002689
Outdoor Anomaly Detection with a Spectroscopic Line Detector
Abstract:
One of the tasks of optical surveillance is to detect anomalies in large amounts of image data. However, if the size of the anomaly is very small, limited information is available to distinguish it from the surrounding environment. Spectral detection provides a useful source of additional information and may help to detect anomalies with a size of a few pixels or less. Unfortunately, spectral cameras are expensive because of the difficulty of separating two spatial in addition to one spectral dimension. We investigate the possibility of modifying a simple spectral line detector for outdoor detection. This may be especially useful if the area of interest forms a line, such as the horizon. We use a monochrome CCD that also enables detection into the near infrared. A simple camera is attached to the setup to determine which part of the environment is spectrally imaged. Our preliminary results indicate that sensitive detection of very small targets is indeed possible. Spectra could be taken from the various targets by averaging columns in the line image. By imaging a set of lines of various widths we found narrow lines that could not be seen in the color image but remained visible in the spectral line image. A simultaneous analysis of the entire spectra can produce better results than visual inspection of the line spectral image. We are presently developing calibration targets for spatial and spectral focusing and alignment with the spatial camera. This will present improved results and more use in outdoor application.
Paper Detail
1011
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34
10002768
Space Telemetry Anomaly Detection Based on Statistical PCA Algorithm
Abstract:
The critical concern of satellite operations is to ensure the health and safety of satellites. The worst case in this perspective is probably the loss of a mission, but the more common interruption of satellite functionality can result in compromised mission objectives. All the data acquiring from the spacecraft are known as Telemetry (TM), which contains the wealth information related to the health of all its subsystems. Each single item of information is contained in a telemetry parameter, which represents a time-variant property (i.e. a status or a measurement) to be checked. As a consequence, there is a continuous improvement of TM monitoring systems to reduce the time required to respond to changes in a satellite's state of health. A fast conception of the current state of the satellite is thus very important to respond to occurring failures. Statistical multivariate latent techniques are one of the vital learning tools that are used to tackle the problem above coherently. Information extraction from such rich data sources using advanced statistical methodologies is a challenging task due to the massive volume of data. To solve this problem, in this paper, we present a proposed unsupervised learning algorithm based on Principle Component Analysis (PCA) technique. The algorithm is particularly applied on an actual remote sensing spacecraft. Data from the Attitude Determination and Control System (ADCS) was acquired under two operation conditions: normal and faulty states. The models were built and tested under these conditions, and the results show that the algorithm could successfully differentiate between these operations conditions. Furthermore, the algorithm provides competent information in prediction as well as adding more insight and physical interpretation to the ADCS operation.
Paper Detail
1131
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33
10000714
Lithium Oxide Effect on the Thermal and Physical Properties of the Ternary System Glasses (Li2O3-B2O3-Al2O3)
Abstract:

The borate glasses are known by their structural characterized by existence of unit’s structural composed by triangles and tetrahedrons boron in different configurations depending on the percentage of B2O3 in the glass chemical composition. In this paper, effect of lithium oxide addition on the thermal and physical properties of an alumina borate glass, was investigated. It was found that the boron abnormality has a significant effect in the change of glass properties according to the addition rate of lithium oxide.

Paper Detail
1915
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32
10000187
EUDIS-An Encryption Scheme for User-Data Security in Public Networks
Abstract:

The method of introducing the proxy interpretation for sending and receiving requests increase the capability of the server and our approach UDIV (User-Data Identity Security) to solve the data and user authentication without extending size of the data makes better than hybrid IDS (Intrusion Detection System). And at the same time all the security stages we have framed have to pass through less through that minimize the response time of the request. Even though an anomaly detected, before rejecting it the proxy extracts its identity to prevent it to enter into system. In case of false anomalies, the request will be reshaped and transformed into legitimate request for further response. Finally we are holding the normal and abnormal requests in two different queues with own priorities.

Paper Detail
1239
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31
9999476
On Algebraic Structure of Improved Gauss-Seidel Iteration
Abstract:

Analysis of real life problems often results in linear systems of equations for which solutions are sought. The method to employ depends, to some extent, on the properties of the coefficient matrix. It is not always feasible to solve linear systems of equations by direct methods, as such the need to use an iterative method becomes imperative. Before an iterative method can be employed to solve a linear system of equations there must be a guaranty that the process of solution will converge. This guaranty, which must be determined apriori, involve the use of some criterion expressible in terms of the entries of the coefficient matrix. It is, therefore, logical that the convergence criterion should depend implicitly on the algebraic structure of such a method. However, in deference to this view is the practice of conducting convergence analysis for Gauss- Seidel iteration on a criterion formulated based on the algebraic structure of Jacobi iteration. To remedy this anomaly, the Gauss- Seidel iteration was studied for its algebraic structure and contrary to the usual assumption, it was discovered that some property of the iteration matrix of Gauss-Seidel method is only diagonally dominant in its first row while the other rows do not satisfy diagonal dominance. With the aid of this structure we herein fashion out an improved version of Gauss-Seidel iteration with the prospect of enhancing convergence and robustness of the method. A numerical section is included to demonstrate the validity of the theoretical results obtained for the improved Gauss-Seidel method.

Paper Detail
1967
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30
9997753
Relay Node Selection Algorithm for Cooperative Communications in Wireless Networks
Authors:
Abstract:

IEEE 802.11a/b/g standards support multiple transmission rates. Even though the use of multiple transmission rates increase the WLAN capacity, this feature leads to the performance anomaly problem. Cooperative communication was introduced to relieve the performance anomaly problem. Data packets are delivered to the destination much faster through a relay node with high rate than through direct transmission to the destination at low rate. In the legacy cooperative protocols, a source node chooses a relay node only based on the transmission rate. Therefore, they are not so feasible in multi-flow environments since they do not consider the effect of other flows. To alleviate the effect, we propose a new relay node selection algorithm based on the transmission rate and channel contention level. Performance evaluation is conducted using simulation, and shows that the proposed protocol significantly outperforms the previous protocol in terms of throughput and delay.

Paper Detail
2232
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29
9996847
A Study on Abnormal Behavior Detection in BYOD Environment
Abstract:

Advancement of communication technologies and smart devices in the recent times is leading to changes into the integrated wired and wireless communication environments. Since early days, businesses had started introducing environments for mobile device application to their operations in order to improve productivity (efficiency) and the closed corporate environment gradually shifted to an open structure. Recently, individual user's interest in working environment using mobile devices has increased and a new corporate working environment under the concept of BYOD is drawing attention. BYOD (bring your own device) is a concept where individuals bring in and use their own devices in business activities. Through BYOD, businesses can anticipate improved productivity (efficiency) and also a reduction in the cost of purchasing devices. However, as a result of security threats caused by frequent loss and theft of personal devices and corporate data leaks due to low security, companies are reluctant about adopting BYOD system. In addition, without considerations to diverse devices and connection environments, there are limitations in detecting abnormal behaviors, such as information leaks, using the existing network-based security equipment. This study suggests a method to detect abnormal behaviors according to individual behavioral patterns, rather than the existing signature-based malicious behavior detection, and discusses applications of this method in BYOD environment.

Paper Detail
1484
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28
9996779
Evaluating Performance of an Anomaly Detection Module with Artificial Neural Network Implementation
Abstract:

Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Paper Detail
1449
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27
16382
Autonomously Determining the Parameters for SVDD with RBF Kernel from a One-Class Training Set
Abstract:

The one-class support vector machine “support vector data description” (SVDD) is an ideal approach for anomaly or outlier detection. However, for the applicability of SVDD in real-world applications, the ease of use is crucial. The results of SVDD are massively determined by the choice of the regularisation parameter C and the kernel parameter  of the widely used RBF kernel. While for two-class SVMs the parameters can be tuned using cross-validation based on the confusion matrix, for a one-class SVM this is not possible, because only true positives and false negatives can occur during training. This paper proposes an approach to find the optimal set of parameters for SVDD solely based on a training set from one class and without any user parameterisation. Results on artificial and real data sets are presented, underpinning the usefulness of the approach.

Paper Detail
2074
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26
16383
An Anomaly Detection Approach to Detect Unexpected Faults in Recordings from Test Drives
Abstract:

In the automotive industry test drives are being conducted during the development of new vehicle models or as a part of quality assurance of series-production vehicles. The communication on the in-vehicle network, data from external sensors, or internal data from the electronic control units is recorded by automotive data loggers during the test drives. The recordings are used for fault analysis. Since the resulting data volume is tremendous, manually analysing each recording in great detail is not feasible. This paper proposes to use machine learning to support domainexperts by preventing them from contemplating irrelevant data and rather pointing them to the relevant parts in the recordings. The underlying idea is to learn the normal behaviour from available recordings, i.e. a training set, and then to autonomously detect unexpected deviations and report them as anomalies. The one-class support vector machine “support vector data description” is utilised to calculate distances of feature vectors. SVDDSUBSEQ is proposed as a novel approach, allowing to classify subsequences in multivariate time series data. The approach allows to detect unexpected faults without modelling effort as is shown with experimental results on recordings from test drives.

Paper Detail
1688
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25
16422
Underpricing of IPOs during Hot and Cold Market Periods on the South African Stock Exchange (JSE)
Abstract:

Underpricing is one anomaly in initial public offerings (IPO) literature that has been widely observed across different stock markets with different trends emerging over different time periods. This study seeks to determine how IPOs on the JSE performed on the first day, first week and first month over the period of 1996-2011. Underpricing trends are documented for both hot and cold market periods in terms of four main sectors (cyclical, defensive, growth stock and interest rate sensitive stocks). Using a sample of 360 listed companies on the JSE, the empirical findings established that IPOs on the JSE are significantly underpriced with an average market adjusted first day return of 62.9%. It is also established that hot market IPOs on the JSE are more underpriced than the cold market IPOs. Also observed is the fact that as the offer price per share increases above the median price for any given period, the level of underpricing decreases substantially. While significant differences exist in the level of underpricing of IPOs in the four different sectors in the hot and cold market periods, interest rates sensitive stocks showed a different trend from the other sectors and thus require further investigation to uncover this pattern.

Paper Detail
3179
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24
13220
Intrusion Detection Using a New Particle Swarm Method and Support Vector Machines
Abstract:
Intrusion detection is a mechanism used to protect a system and analyse and predict the behaviours of system users. An ideal intrusion detection system is hard to achieve due to nonlinearity, and irrelevant or redundant features. This study introduces a new anomaly-based intrusion detection model. The suggested model is based on particle swarm optimisation and nonlinear, multi-class and multi-kernel support vector machines. Particle swarm optimisation is used for feature selection by applying a new formula to update the position and the velocity of a particle; the support vector machine is used as a classifier. The proposed model is tested and compared with the other methods using the KDD CUP 1999 dataset. The results indicate that this new method achieves better accuracy rates than previous methods.
Paper Detail
1400
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23
7003
Research on Hybrid Neural Network in Intrusion Detection System
Abstract:

This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.

Paper Detail
1593
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22
95
Anomaly Based On Frequent-Outlier for Outbreak Detection in Public Health Surveillance
Abstract:

Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.

Paper Detail
1528
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21
17408
Anomalous Thermal Behavior of CuxMg1-xNb2O6 (x=0,0.4,0.6,1) for LTCC Substrate
Abstract:

LTCC (Low Temperature Co-fired Ceramics) being the most advantageous technology towards the multilayer substrates for various applications, demands an extensive study of its raw materials. In the present work, a series of CuxMg1-xNb2O6 (x=0,0.4,0.6,1) has been prepared using sol-gel synthesis route and sintered at a temperature of 900°C to study its applicability for LTCC technology as the firing temperature is 900°C in this technology. The phase formation has been confirmed using X-ray Diffraction. Thermal properties like thermal conductivity and thermal expansion being very important aspect as the former defines the heat flow to avoid thermal instability in layers and the later provides the dimensional congruency of the dielectric material and the conductors, are studied here over high temperature up to the firing temperature. Although the values are quite satisfactory from substrate requirement point view, results have shown anomaly over temperature. The anomalous thermal behavior has been further analyzed using TG-DTA.

Paper Detail
1053
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20
11549
Influenza Pattern Analysis System through Mining Weblogs
Abstract:

Weblogs are resource of social structure to discover and track the various type of information written by blogger. In this paper, we proposed to use mining weblogs technique for identifying the trends of influenza where blogger had disseminated their opinion for the anomaly disease. In order to identify the trends, web crawler is applied to perform a search and generated a list of visited links based on a set of influenza keywords. This information is used to implement the analytics report system for monitoring and analyzing the pattern and trends of influenza (H1N1). Statistical and graphical analysis reports are generated. Both types of the report have shown satisfactory reports that reflect the awareness of Malaysian on the issue of influenza outbreak through blogs.

Paper Detail
1938
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19
15875
Behavioral Signature Generation using Shadow Honeypot
Abstract:
A novel behavioral detection framework is proposed to detect zero day buffer overflow vulnerabilities (based on network behavioral signatures) using zero-day exploits, instead of the signature-based or anomaly-based detection solutions currently available for IDPS techniques. At first we present the detection model that uses shadow honeypot. Our system is used for the online processing of network attacks and generating a behavior detection profile. The detection profile represents the dataset of 112 types of metrics describing the exact behavior of malware in the network. In this paper we present the examples of generating behavioral signatures for two attacks – a buffer overflow exploit on FTP server and well known Conficker worm. We demonstrated the visualization of important aspects by showing the differences between valid behavior and the attacks. Based on these metrics we can detect attacks with a very high probability of success, the process of detection is however very expensive.
Paper Detail
1418
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18
8179
Biological Diagnosis and Physiopathology of von Willebrand-s Disease in a Part of the Algerian Population in the East and the South
Abstract:
Von Willebrand-s disease is the most common inherited bleeding disorder in humans, it caused by qualitative abnormalities of the von Willebrand factor (vWF). Our objective is to determine the prevalence of this disease at part of the Algerian population in the East and the South by a biological diagnosis based on specific biological tests (automated platelet count, the bleeding time (TS), the time of cephalin + activator (TCA), measure of the prothrombin rate (TP), vWF rate and factor VIII rate, Molecular electrophoresis of vWF multimers in agarose gel in the presence of SDS). Four patients of type III or severe Willebrand-s disease were found on 200 suspect cases. All cases are showed a deficit in vWF rate (< 5%), and factor VIII (P
Paper Detail
1205
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17
311
Petrology and Geochemistry of Granitic Rocks in South Sulawesi, Indonesia: Implication for Origin of Magma and Geodynamic Setting
Abstract:
Petrology and geochemical characteristics of granitic rocks from South Sulawesi, especially from Polewaliand Masamba area are presented in order to elucidate their origin of magma and geodynamic setting. The granitic rocks in these areas are dominated by granodiorite and granite in composition. Quartz, K-feldspar and plagioclase occur as major phases with hornblende and biotite as major ferromagnesian minerals. All of the samples were plotted in calc-alkaline field, show metaluminous affinity and typical of I-type granitic rock. Harker diagram indicates that granitic rocks experienced fractional crystallization during magmatic evolution. Both groups displayed an extreme enrichment of LILE, LREE and a slight negative Eu anomaly which resemble upper continental crust affinity. They were produced from partial melting of upper continental crust and have close relationship of sources composition within a suite. The geochemical characteristics explained the arc related subduction environment which later give an evidence of continent-continent collision between Australia-derived microcontinent and Sundalandto form continental arc environment.
Paper Detail
2623
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16
14026
Increase of Heat Index over Bangladesh: Impact of Climate Change
Abstract:
Heat Index describes the combined effect of temperature and humidity on human body. This combined effect is causing a serious threat to the health of people because of the changing climate. With climate change, climate variability and thus the occurrence of heat waves is likely to increase. Evidence is emerging from the analysis of long-term climate records of an increase in the frequency and duration of extreme temperature events in all over Bangladesh particularly during summer. Summer season has prolonged while winters have become short in Bangladesh. Summers have become hotter and thus affecting the lives of the people engaged in outdoor activities during scorching sun hours. In 2003 around 62 people died due to heat wave across the country. In this paper Bangladesh is divided in four regions and heat index has been calculated from 1960 to 2010 in these regions of the country. The aim of this paper is to identify the spots most vulnerable to heat strokes and heat waves due to high heat index. The results show upward trend of heat index in almost all the regions of Bangladesh. The highest increase in heat index value has been observed in areas of South-west region and North-west Region. The highest change in average heat index has been found in Jessore by almost 5.50C.
Paper Detail
1931
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15
12761
Soft Computing based Retrieval System for Medical Applications
Abstract:
With increasing data in medical databases, medical data retrieval is growing in popularity. Some of this analysis including inducing propositional rules from databases using many soft techniques, and then using these rules in an expert system. Diagnostic rules and information on features are extracted from clinical databases on diseases of congenital anomaly. This paper explain the latest soft computing techniques and some of the adaptive techniques encompasses an extensive group of methods that have been applied in the medical domain and that are used for the discovery of data dependencies, importance of features, patterns in sample data, and feature space dimensionality reduction. These approaches pave the way for new and interesting avenues of research in medical imaging and represent an important challenge for researchers.
Keywords:
Paper Detail
1156
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14
1750
Scaling up Detection Rates and Reducing False Positives in Intrusion Detection using NBTree
Abstract:
In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and dynamic large intrusion detection dataset, the detection accuracy of naïve Bayesian classifier does not scale up as well as decision tree. It has been successfully tested in other problem domains that naïve Bayesian tree improves the classification rates in large dataset. In naïve Bayesian tree nodes contain and split as regular decision-trees, but the leaves contain naïve Bayesian classifiers. The experimental results on KDD99 benchmark network intrusion detection dataset demonstrate that this new approach scales up the detection rates for different attack types and reduces false positives in network intrusion detection.
Paper Detail
1650
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13
13186
Genetic-based Anomaly Detection in Logs of Process Aware Systems
Abstract:
Nowaday-s, many organizations use systems that support business process as a whole or partially. However, in some application domains, like software development and health care processes, a normative Process Aware System (PAS) is not suitable, because a flexible support is needed to respond rapidly to new process models. On the other hand, a flexible Process Aware System may be vulnerable to undesirable and fraudulent executions, which imposes a tradeoff between flexibility and security. In order to make this tradeoff available, a genetic-based anomaly detection model for logs of Process Aware Systems is presented in this paper. The detection of an anomalous trace is based on discovering an appropriate process model by using genetic process mining and detecting traces that do not fit the appropriate model as anomalous trace; therefore, when used in PAS, this model is an automated solution that can support coexistence of flexibility and security.
Paper Detail
1135
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12
5652
Attacks Classification in Adaptive Intrusion Detection using Decision Tree
Abstract:
Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious network-based or host-based attacks by using traditional statistical methods to new data mining approaches in last decades. However, today's commercially available intrusion detection systems are signature-based that are not capable of detecting unknown attacks. In this paper, we present a new learning algorithm for anomaly based network intrusion detection system using decision tree algorithm that distinguishes attacks from normal behaviors and identifies different types of intrusions. Experimental results on the KDD99 benchmark network intrusion detection dataset demonstrate that the proposed learning algorithm achieved 98% detection rate (DR) in comparison with other existing methods.
Paper Detail
2553
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