Abstract: Data mining techniques have been used in medical
research for many years and have been known to be effective. In order
to solve such problems as long-waiting time, congestion, and delayed
patient care, faced by emergency departments, this study concentrates
on building a hybrid methodology, combining data mining techniques
such as association rules and classification trees. The methodology is
applied to real-world emergency data collected from a hospital and is
evaluated by comparing with other techniques. The methodology is
expected to help physicians to make a faster and more accurate
classification of chest pain diseases.
Abstract: In this article, models based on quantitative analysis,
physical geometry and regression analysis are established, by using
analytic hierarchy process analysis, fuzzy cluster analysis, fuzzy
photographic and data fitting. The reasons of various leaf shapes
among different species and the differences between the leaf shapes on
same tree have been solved by using software, such as Eviews, VB and
Matlab. We also successfully estimate the leaf mass of a tree and the
correlation with the tree profile.
Abstract: The use of a Geographic Information System (GIS) in
roadway lighting to show the state of street-lighting and nighttime
accident is demonstrated. Geographical maps were generated
showing colored streets based on how much of the street's length is
illuminated. The night to daytime accidents ratio at intersections
were found along with the state of lighting at those intersections.
The result is a method to show the state of street-lighting at roads and
intersections and a quick guide for decision makers to implement
strategies for better street-lighting to reduce night time traffic
accidents in a particular district.
Abstract: This paper proposes a fast tree join scheme to provide
seamless multicast handover in the mobile networks based on the Fast
Mobile IPv6 (FMIPv6). In the existing FMIPv6-based multicast
handover scheme, the bi-directional tunnelling or the remote
subscription is employed with the packet forwarding from the previous
access router (AR) to the new AR. In general, the remote subscription
approach is preferred to the bi-directional tunnelling one, since in the
remote subscription scheme we can exploit an optimized multicast
path from a multicast source to many mobile receivers. However, in
the remote subscription scheme, if the tree joining operation takes a
long time, the amount of data packets to be forwarded and buffered for
multicast handover will increase, and thus the corresponding buffer
may overflow, which results in severe packet losses. In order to reduce
these costs associated with packet forwarding and buffering, this paper
proposes the fast join to multicast tree, in which the new AR will join
the multicast tree as fast as possible, so that the new multicast data
packets can also arrive at the new AR, by which the packet forwarding
and buffering costs can be reduced. From numerical analysis, it is
shown that the proposed scheme can give better performance than the
existing FMIPv6-based multicast handover schemes in terms of the
multicast packet delivery costs.
Abstract: Methods for organizing web data into groups in order
to analyze web-based hypertext data and facilitate data availability
are very important in terms of the number of documents available
online. Thereby, the task of clustering web-based document structures
has many applications, e.g., improving information retrieval on the
web, better understanding of user navigation behavior, improving web
users requests servicing, and increasing web information accessibility.
In this paper we investigate a new approach for clustering web-based
hypertexts on the basis of their graph structures. The hypertexts will
be represented as so called generalized trees which are more general
than usual directed rooted trees, e.g., DOM-Trees. As a important
preprocessing step we measure the structural similarity between the
generalized trees on the basis of a similarity measure d. Then,
we apply agglomerative clustering to the obtained similarity matrix
in order to create clusters of hypertext graph patterns representing
navigation structures. In the present paper we will run our approach
on a data set of hypertext structures and obtain good results in
Web Structure Mining. Furthermore we outline the application of
our approach in Web Usage Mining as future work.
Abstract: Image fusion aims to enhance the perception
of a scene by combining important information captured by
different sensors. Dual-Tree Complex Wavelet (DT-CWT) has been
thouroughly investigated for image fusion, since it takes advantages
of approximate shift invariance and direction selectivity. But it can
only handle limited direction information. To allow a more flexible
directional expansion for images, we propose a novel fusion scheme,
referred to as complex contourlet transform (CCT). It successfully
incorporates directional filter banks (DFB) into DT-CWT. As a result
it efficiently deal with images containing contours and textures,
whereas it retains the property of shift invariance. Experimental
results demonstrated that the method features high quality fusion
performance and can facilitate many image processing applications.
Abstract: Particulate matter (PM) in ambient air is responsible
for adverse health effects in adults and children. Relatively little is
known about the concentrations, sources and health effects of PM in
indoor air. A monitoring study was conducted in Ankara by three
campaigns in order to measure PM levels in indoor and outdoor
environments to identify and quantify associations between sources
and concentrations. Approximately 82 homes (1st campaign for 42,
2nd campaign for 12, and 3rd campaign for 28), three rooms (living
room, baby-s room and living room used as a baby-s room) and
outdoor ambient at each home were sampled with Grimm
Environmental Dust Monitoring (EDM) 107, during different
seasonal periods of 2011 and 2012. In this study, the relationship
between indoor and outdoor PM levels for particulate matter less than
10 micrometer (.m) (PM10), particulate matter less than 2.5.m
(PM2.5) and particulate matter less than 1.0.m (PM1) were
investigated. The mean concentration of PM10, PM2.5, and PM1.0 at
living room used as baby-s room is higher than living and baby-s
room (or bedroom) for three sampling campaigns. It is concluded that
the household activities and environmental conditions are very
important for PM concentrations in the indoor environments during
the sampling periods. The amount of smokers, being near a main
street and/or construction activities increased the PM concentration.
This study is based on the assessment the relationship between indoor
and outdoor PM levels and the household activities and
environmental conditions
Abstract: The inherent flexibilities of XML in both structure
and semantics makes mining from XML data a complex task with
more challenges compared to traditional association rule mining in
relational databases. In this paper, we propose a new model for the
effective extraction of generalized association rules form a XML
document collection. We directly use frequent subtree mining
techniques in the discovery process and do not ignore the tree
structure of data in the final rules. The frequent subtrees based on the
user provided support are split to complement subtrees to form the
rules. We explain our model within multi-steps from data preparation
to rule generation.
Abstract: Traffic enforcement units (the Police) are partly
responsible for the severity and frequency of the traffic accidents via
the effectiveness of their safety measures. The Police claims that the
reductions in accidents and their severities occur largely by their
timely interventions at the black spots, through traffic management
or temporary changes in the road design (guiding, reducing speeds
and eliminating sight obstructions, etc.). Yet, some other external
factors than the Police measures may intervene into which such
claims require a statistical confirmation. In order to test the net
impact of the Police contribution in the reduction of the number of
crashes, Chi square test was applied for 25 spots (streets and
intersections) and an average evaluation was achieved for general
conclusion in the case study of Izmir city. Separately, the net impact
of economic crisis in the reduction of crashes is assessed by the
trend analysis for the case of the economic crisis with the probable
reduction effects on the trip generation or modal choice. Finally, it
was proven that the Police measures were effective to some degree as
they claimed, though the economic crisis might have only negligible
contribution to the reductions in the same period observed.
Abstract: Wavelet transforms are multiresolution
decompositions that can be used to analyze signals and images.
Image compression is one of major applications of wavelet
transforms in image processing. It is considered as one of the most
powerful methods that provides a high compression ratio. However,
its implementation is very time-consuming. At the other hand,
parallel computing technologies are an efficient method for image
compression using wavelets. In this paper, we propose a parallel
wavelet compression algorithm based on quadtrees. We implement
the algorithm using MatlabMPI (a parallel, message passing version
of Matlab), and compute its isoefficiency function, and show that it is
scalable. Our experimental results confirm the efficiency of the
algorithm also.
Abstract: The latest Geographic Information System (GIS)
technology makes it possible to administer the spatial components of
daily “business object," in the corporate database, and apply suitable
geographic analysis efficiently in a desktop-focused application. We
can use wireless internet technology for transfer process in spatial
data from server to client or vice versa. However, the problem in
wireless Internet is system bottlenecks that can make the process of
transferring data not efficient. The reason is large amount of spatial
data. Optimization in the process of transferring and retrieving data,
however, is an essential issue that must be considered. Appropriate
decision to choose between R-tree and Quadtree spatial data indexing
method can optimize the process. With the rapid proliferation of
these databases in the past decade, extensive research has been
conducted on the design of efficient data structures to enable fast
spatial searching. Commercial database vendors like Oracle have also
started implementing these spatial indexing to cater to the large and
diverse GIS. This paper focuses on the decisions to choose R-tree
and quadtree spatial indexing using Oracle spatial database in mobile
GIS application. From our research condition, the result of using
Quadtree and R-tree spatial data indexing method in one single
spatial database can save the time until 42.5%.
Abstract: Multiplication algorithms have considerable effect on
processors performance. A new high-speed, low-power
multiplication algorithm has been presented using modified Dadda
tree structure. Three important modifications have been implemented
in inner product generation step, inner product reduction step and
final addition step. Optimized algorithms have to be used into basic
computation components, such as multiplication algorithms. In this
paper, we proposed a new algorithm to reduce power, delay, and
transistor count of a multiplication algorithm implemented using low
power modified counter. This work presents a novel design for
Dadda multiplication algorithms. The proposed multiplication
algorithm includes structured parts, which have important effect on
inner product reduction tree. In this paper, a 1.3V, 64-bit carry hybrid
adder is presented for fast, low voltage applications. The new 64-bit
adder uses a new circuit to implement the proposed carry hybrid
adder. The new adder using 80 nm CMOS technology has been
implemented on 700 MHz clock frequency. The proposed
multiplication algorithm has achieved 14 percent improvement in
transistor count, 13 percent reduction in delay and 12 percent
modification in power consumption in compared with conventional
designs.
Abstract: Scene interpretation systems need to match (often ambiguous)
low-level input data to concepts from a high-level ontology.
In many domains, these decisions are uncertain and benefit greatly
from proper context. This paper demonstrates the use of decision
trees for estimating class probabilities for regions described by feature
vectors, and shows how context can be introduced in order to improve
the matching performance.
Abstract: In literature, there are metrics for identifying the
quality of reusable components but the framework that makes use of
these metrics to precisely predict reusability of software components
is still need to be worked out. These reusability metrics if identified
in the design phase or even in the coding phase can help us to reduce
the rework by improving quality of reuse of the software component
and hence improve the productivity due to probabilistic increase in
the reuse level. As CK metric suit is most widely used metrics for
extraction of structural features of an object oriented (OO) software;
So, in this study, tuned CK metric suit i.e. WMC, DIT, NOC, CBO
and LCOM, is used to obtain the structural analysis of OO-based
software components. An algorithm has been proposed in which the
inputs can be given to K-Means Clustering system in form of
tuned values of the OO software component and decision tree is
formed for the 10-fold cross validation of data to evaluate the in
terms of linguistic reusability value of the component. The developed
reusability model has produced high precision results as desired.
Abstract: A new target detection technique is presented in this
paper for the identification of small boats in coastal surveillance. The
proposed technique employs an adaptive progressive thresholding (APT) scheme to first process the given input scene to separate any
objects present in the scene from the background. The preprocessing
step results in an image having only the foreground objects, such as
boats, trees and other cluttered regions, and hence reduces the search
region for the correlation step significantly. The processed image is then fed to the shifted phase-encoded fringe-adjusted joint transform
correlator (SPFJTC) technique which produces single and delta-like
correlation peak for a potential target present in the input scene. A
post-processing step involves using a peak-to-clutter ratio (PCR) to determine whether the boat in the input scene is authorized or unauthorized. Simulation results are presented to show that the
proposed technique can successfully determine the presence of an authorized boat and identify any intruding boat present in the given input scene.
Abstract: Traffic management in an urban area is highly facilitated by the knowledge of the traffic conditions in every street or highway involved in the vehicular mobility system. Aim of the paper is to propose a neuro-fuzzy approach able to compute the main parameters of a traffic system, i.e., car density, velocity and flow, by using the images collected by the web-cams located at the crossroads of the traffic network. The performances of this approach encourage its application when the traffic system is far from the saturation. A fuzzy model is also outlined to evaluate when it is suitable to use more accurate, even if more time consuming, algorithms for measuring traffic conditions near to saturation.
Abstract: Pattern matching based on regular tree grammars have been widely used in many areas of computer science. In this paper, we propose a pattern matcher within the framework of code generation, based on a generic and a formalized approach. According to this approach, parsers for regular tree grammars are adapted to a general pattern matching solution, rather than adapting the pattern matching according to their parsing behavior. Hence, we first formalize the construction of the pattern matches respective to input trees drawn from a regular tree grammar in a form of the so-called match trees. Then, we adopt a recently developed generic parser and tightly couple its parsing behavior with such construction. In addition to its generality, the resulting pattern matcher is characterized by its soundness and efficient implementation. This is demonstrated by the proposed theory and by the derived algorithms for its implementation. A comparison with similar and well-known approaches, such as the ones based on tree automata and LR parsers, has shown that our pattern matcher can be applied to a broader class of grammars, and achieves better approximation of pattern matches in one pass. Furthermore, its use as a machine code selector is characterized by a minimized overhead, due to the balanced distribution of the cost computations into static ones, during parser generation time, and into dynamic ones, during parsing time.
Abstract: The drug discovery process starts with protein
identification because proteins are responsible for many functions
required for maintenance of life. Protein identification further needs
determination of protein function. Proposed method develops a
classifier for human protein function prediction. The model uses
decision tree for classification process. The protein function is
predicted on the basis of matched sequence derived features per each
protein function. The research work includes the development of a
tool which determines sequence derived features by analyzing
different parameters. The other sequence derived features are
determined using various web based tools.
Abstract: The Globally Asynchronous Locally Synchronous Network on Chip (GALS NoC) is the most efficient solution that provides low latency transfers and power efficient System on Chip (SoC) interconnect. This study presents a GALS and generic NoC architecture based on a configurable router. This router integrates a sophisticated dynamic arbiter, the wormhole routing technique and can be configured in a manner that allows it to be used in many possible NoC topologies such as Mesh 2-D, Tree and Polygon architectures. This makes it possible to improve the quality of service (QoS) required by the proposed NoC. A comparative performances study of the proposed NoC architecture, Tore architecture and of the most used Mesh 2D architecture is performed. This study shows that Spidergon architecture is characterised by the lower latency and the later saturation. It is also shown that no matter what the number of used links is raised; the Links×Diameter product permitted by the Spidergon architecture remains always the lower. The only limitation of this architecture comes from it-s over cost in term of silicon area.
Abstract: Keystroke authentication is a new access control system
to identify legitimate users via their typing behavior. In this paper,
machine learning techniques are adapted for keystroke authentication.
Seven learning methods are used to build models to differentiate user
keystroke patterns. The selected classification methods are Decision
Tree, Naive Bayesian, Instance Based Learning, Decision Table, One
Rule, Random Tree and K-star. Among these methods, three of them
are studied in more details. The results show that machine learning
is a feasible alternative for keystroke authentication. Compared to
the conventional Nearest Neighbour method in the recent research,
learning methods especially Decision Tree can be more accurate. In
addition, the experiment results reveal that 3-Grams is more accurate
than 2-Grams and 4-Grams for feature extraction. Also, combination
of attributes tend to result higher accuracy.