Abstract: Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form: Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. This paper focuses on the issue of mining Quantified rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses Quantified production rules as initial individuals of GP and discovers hierarchical structure. In proposed approach rules are quantified by using Dempster Shafer theory. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Quantified Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy, using Dempster Shafer theory. Experimental results are presented to demonstrate the performance of the proposed algorithm.
Abstract: Pattern recognition is the research area of Artificial
Intelligence that studies the operation and design of systems that
recognize patterns in the data. Important application areas are image
analysis, character recognition, fingerprint classification, speech
analysis, DNA sequence identification, man and machine
diagnostics, person identification and industrial inspection. The
interest in improving the classification systems of data analysis is
independent from the context of applications. In fact, in many
studies it is often the case to have to recognize and to distinguish
groups of various objects, which requires the need for valid
instruments capable to perform this task. The objective of this article
is to show several methodologies of Artificial Intelligence for data
classification applied to biomedical patterns. In particular, this work
deals with the realization of a Computer-Aided Detection system
(CADe) that is able to assist the radiologist in identifying types of
mammary tumor lesions. As an additional biomedical application of
the classification systems, we present a study conducted on blood
samples which shows how these methods may help to distinguish
between carriers of Thalassemia (or Mediterranean Anaemia) and
healthy subjects.
Abstract: The one of best robust search technique on large scale
search area is heuristic and meta heuristic approaches. Especially in
issue that the exploitation of combinatorial status in the large scale
search area prevents the solution of the problem via classical
calculating methods, so such problems is NP-complete. in this
research, the problem of winner determination in combinatorial
auctions have been formulated and by assessing older heuristic
functions, we solve the problem by using of genetic algorithm and
would show that this new method would result in better performance
in comparison to other heuristic function such as simulated annealing
greedy approach.
Abstract: In recent years image watermarking has become an
important research area in data security, confidentiality and image
integrity. Many watermarking techniques were proposed for medical
images. However, medical images, unlike most of images, require
extreme care when embedding additional data within them because
the additional information must not affect the image quality and
readability. Also the medical records, electronic or not, are linked to
the medical secrecy, for that reason, the records must be confidential.
To fulfill those requirements, this paper presents a lossless
watermarking scheme for DICOM images. The proposed a fragile
scheme combines two reversible techniques based on difference
expansion for patient's data hiding and protecting the region of
interest (ROI) with tamper detection and recovery capability.
Patient's data are embedded into ROI, while recovery data are
embedded into region of non-interest (RONI). The experimental
results show that the original image can be exactly extracted from the
watermarked one in case of no tampering. In case of tampered ROI,
tampered area can be localized and recovered with a high quality
version of the original area.
Abstract: Automated discovery of hierarchical structures in
large data sets has been an active research area in the recent past.
This paper focuses on the issue of mining generalized rules with crisp
hierarchical structure using Genetic Programming (GP) approach to
knowledge discovery. The post-processing scheme presented in this
work uses flat rules as initial individuals of GP and discovers
hierarchical structure. Suitable genetic operators are proposed for the
suggested encoding. Based on the Subsumption Matrix(SM), an
appropriate fitness function is suggested. Finally, Hierarchical
Production Rules (HPRs) are generated from the discovered
hierarchy. Experimental results are presented to demonstrate the
performance of the proposed algorithm.
Abstract: Motion capturing technology has been used for quite a
while and several research has been done within this area. Nevertheless,
we discovered open issues within current motion capturing
environments. In this paper we provide a state-of-the-art overview of
the addressed research areas and show issues with current motion
capturing environments. Observations, interviews and questionnaires
have been used to reveal the challenges actors are currently facing in
a motion capturing environment. Furthermore, the idea to create a
more immersive motion capturing environment to improve the acting
performances and motion capturing outcomes as a potential solution
is introduced. It is hereby the goal to explain the found open issues
and the developed ideas which shall serve for further research as a
basis. Moreover, a methodology to address the interaction and
systems design issues is proposed. A future outcome could be that
motion capture actors are able to perform more naturally, especially
if using a non-body-worn solution.
Abstract: Problem solving has traditionally been one of the principal research areas for artificial intelligence. Yet, although artificial intelligence reasoning techniques have been employed in several product support systems, the benefit of integrating product support, knowledge engineering, and problem solving, is still unclear. This paper studies the synergy of these areas and proposes a knowledge engineering framework that integrates product support systems and artificial intelligence techniques. The framework includes four spaces; the data, problem, hypothesis, and solution ones. The data space incorporates the knowledge needed for structured reasoning to take place, the problem space contains representations of problems, and the hypothesis space utilizes a multimodal reasoning approach to produce appropriate solutions in the form of virtual documents. The solution space is used as the gateway between the system and the user. The proposed framework enables the development of product support systems in terms of smaller, more manageable steps while the combination of different reasoning techniques provides a way to overcome the lack of documentation resources.
Abstract: Over the past decades, automatic face recognition has become a highly active research area, mainly due to the countless application possibilities in both the private as well as the public sector. Numerous algorithms have been proposed in the literature to cope with the problem of face recognition, nevertheless, a group of methods commonly referred to as appearance based have emerged as the dominant solution to the face recognition problem. Many comparative studies concerned with the performance of appearance based methods have already been presented in the literature, not rarely with inconclusive and often with contradictory results. No consent has been reached within the scientific community regarding the relative ranking of the efficiency of appearance based methods for the face recognition task, let alone regarding their susceptibility to appearance changes induced by various environmental factors. To tackle these open issues, this paper assess the performance of the three dominant appearance based methods: principal component analysis, linear discriminant analysis and independent component analysis, and compares them on equal footing (i.e., with the same preprocessing procedure, with optimized parameters for the best possible performance, etc.) in face verification experiments on the publicly available XM2VTS database. In addition to the comparative analysis on the XM2VTS database, ten degraded versions of the database are also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in "real-life" operating conditions. Our experimental results suggest that linear discriminant analysis ensures the most consistent verification rates across the tested databases.
Abstract: The field research was carried out at the Látókép AGTC KIT research area of the University of Debrecen in Eastern-Hungary, on the area of the aeolain loess of the Hajdúság. We examined the effects of the sowing time on the phytopathogenic characteristics and yield production by applying various fertilizer treatments on two different sunflower genotypes (NK Ferti, PR64H42) in 2012 and 2013. We applied three different sowing times (early, optimal, late) and two different treatment levels of fungicides (control = no fungicides applied, double fungicide protection).
During our investigations, the studied cropyears were of different sowing time optimum in terms of yield amount (2012: early, 2013: average). By Pearson’s correlation analysis, we have found that delaying the sowing time pronouncedly decreased the extent of infection in both crop years (Diaporthe: r=0.663**, r=0.681**, Sclerotinia: r=0.465**, r=0.622**). The fungicide treatment not only decreased the extent of infection, but had yield increasing effect too (2012: r=0.498**, 2013: r=0.603**). In 2012, delaying of the sowing time increased (r=0.600**), but in 2013, it decreased (r= 0.356*) the yield amount.
Abstract: Multimedia distributed systems deal with heterogeneous
data, such as texts, images, graphics, video and audio. The specification
of temporal relations among different data types and distributed
sources is an open research area. This paper proposes a fully
distributed synchronization model to be used in multimedia systems.
One original aspect of the model is that it avoids the use of a common
reference (e.g. wall clock and shared memory). To achieve this, all
possible multimedia temporal relations are specified according to
their causal dependencies.
Abstract: Grid computing is a form of distributed computing
that involves coordinating and sharing computational power, data
storage and network resources across dynamic and geographically
dispersed organizations. Scheduling onto the Grid is NP-complete,
so there is no best scheduling algorithm for all grid computing
systems. An alternative is to select an appropriate scheduling
algorithm to use in a given grid environment because of the
characteristics of the tasks, machines and network connectivity. Job
and resource scheduling is one of the key research area in grid
computing. The goal of scheduling is to achieve highest possible
system throughput and to match the application need with the
available computing resources. Motivation of the survey is to
encourage the amateur researcher in the field of grid computing, so
that they can understand easily the concept of scheduling and can
contribute in developing more efficient scheduling algorithm. This
will benefit interested researchers to carry out further work in this
thrust area of research.
Abstract: This paper describes a platform that faces the main
research areas for e-learning educational contents. Reusability tackles
the possibility to use contents in different courses reducing costs and
exploiting available data from repositories. In our approach the
production of educational material is based on templates to reuse
learning objects. In terms of interoperability the main challenge lays
on reaching the audience through different platforms. E-learning
solution must track social consumption evolution where nowadays
lots of multimedia contents are accessed through the social networks.
Our work faces it by implementing a platform for generation of
multimedia presentations focused on the new paradigm related to
social media. The system produces videos-courses on top of web
standard SMIL (Synchronized Multimedia Integration Language)
ready to be published and shared. Regarding interfaces it is
mandatory to satisfy user needs and ease communication. To
overcome it the platform deploys virtual teachers that provide natural
interfaces while multimodal features remove barriers to pupils with
disabilities.
Abstract: The inherent iterative nature of product design and development poses significant challenge to reduce the product design and development time (PD). In order to shorten the time to market, organizations have adopted concurrent development where multiple specialized tasks and design activities are carried out in parallel. Iterative nature of work coupled with the overlap of activities can result in unpredictable time to completion and significant rework. Many of the products have missed the time to market window due to unanticipated or rather unplanned iteration and rework. The iterative and often overlapped processes introduce greater amounts of ambiguity in design and development, where the traditional methods and tools of project management provide less value. In this context, identifying critical metrics to understand the iteration probability is an open research area where significant contribution can be made given that iteration has been the key driver of cost and schedule risk in PD projects. Two important questions that the proposed study attempts to address are: Can we predict and identify the number of iterations in a product development flow? Can we provide managerial insights for a better control over iteration? The proposal introduces the concept of decision points and using this concept intends to develop metrics that can provide managerial insights into iteration predictability. By characterizing the product development flow as a network of decision points, the proposed research intends to delve further into iteration probability and attempts to provide more clarity.
Abstract: This paper proposes an analytical method for the
dynamics of generating firms- alliance networks along with business
phases. Dynamics in network developments have previously been
discussed in the research areas of organizational strategy rather than in
the areas of regional cluster, where the static properties of the
networks are often discussed. The analytical method introduces the
concept of business phases into innovation processes and uses
relationships called prior experiences; this idea was developed in
organizational strategy to investigate the state of networks from the
viewpoints of tradeoffs between link stabilization and node
exploration. This paper also discusses the results of the analytical
method using five cases of the network developments of firms. The
idea of Embeddedness helps interpret the backgrounds of the
analytical results. The analytical method is useful for policymakers of
regional clusters to establish concrete evaluation targets and a
viewpoint for comparisons of policy programs.
Abstract: Protein 3D structure prediction has always been an
important research area in bioinformatics. In particular, the
prediction of secondary structure has been a well-studied research
topic. Despite the recent breakthrough of combining multiple
sequence alignment information and artificial intelligence algorithms
to predict protein secondary structure, the Q3 accuracy of various
computational prediction algorithms rarely has exceeded 75%. In a
previous paper [1], this research team presented a rule-based method
called RT-RICO (Relaxed Threshold Rule Induction from Coverings)
to predict protein secondary structure. The average Q3 accuracy on
the sample datasets using RT-RICO was 80.3%, an improvement
over comparable computational methods. Although this demonstrated
that RT-RICO might be a promising approach for predicting
secondary structure, the algorithm-s computational complexity and
program running time limited its use. Herein a parallelized
implementation of a slightly modified RT-RICO approach is
presented. This new version of the algorithm facilitated the testing of
a much larger dataset of 396 protein domains [2]. Parallelized RTRICO
achieved a Q3 score of 74.6%, which is higher than the
consensus prediction accuracy of 72.9% that was achieved for the
same test dataset by a combination of four secondary structure
prediction methods [2].
Abstract: this paper presented a survey analysis subjected on
network bandwidth management from published papers referred in
IEEE Explorer database in three years from 2009 to 2011. Network
Bandwidth Management is discussed in today-s issues for computer
engineering applications and systems. Detailed comparison is
presented between published papers to look further in the IP based
network critical research area for network bandwidth management.
Important information such as the network focus area, a few
modeling in the IP Based Network and filtering or scheduling used in
the network applications layer is presented. Many researches on
bandwidth management have been done in the broad network area
but fewer are done in IP Based network specifically at the
applications network layer. A few researches has contributed new
scheme or enhanced modeling but still the issue of bandwidth
management still arise at the applications network layer. This survey
is taken as a basic research towards implementations of network
bandwidth management technique, new framework model and
scheduling scheme or algorithm in an IP Based network which will
focus in a control bandwidth mechanism in prioritizing the network
traffic the applications layer.
Abstract: Proper management of residues originated from
industrial activities is considered as one of the serious challenges
faced by industrial societies due to their potential hazards to the
environment. Common disposal methods for industrial solid wastes
(ISWs) encompass various combinations of solely management
options, i.e. recycling, incineration, composting, and sanitary
landfilling. Indeed, the procedure used to evaluate and nominate the
best practical methods should be based on environmental, technical,
economical, and social assessments. In this paper an environmentaltechnical
assessment model is developed using analytical network
process (ANP) to facilitate the decision making practice for ISWs
generated at Gilan province, Iran. Using the results of performed
surveys on industrial units located at Gilan, the various groups of
solid wastes in the research area were characterized, and four
different ISW management scenarios were studied. The evaluation
process was conducted using the above-mentioned model in the
Super Decisions software (version 2.0.8) environment. The results
indicates that the best ISW management scenario for Gilan province
is consist of recycling the metal industries residues, composting the
putrescible portion of ISWs, combustion of paper, wood, fabric and
polymeric wastes as well as energy extraction in the incineration
plant, and finally landfilling the rest of the waste stream in addition
with rejected materials from recycling and compost production plants
and ashes from the incineration unit.
Abstract: The systematic manipulations of shapes and sizes of
inorganic compounds greatly benefit the various application fields
including optics, magnetic, electronics, catalysis and medicine.
However shape control has been much more difficult to achieve.
Hence exploration of novel method for the preparation of differently
shaped nanoparticles is challenging research area. II-VI group of
semiconductor cadmium sulphide (CdS) nanostructure with different
morphologies (such as, acicular like, mesoporous, spherical shapes)
and of crystallite sizes vary from 11 to 16 nm were successfully
synthesized by chemical aqueous precipitation of Cd2+ ions with
homogeneously released S2- ions from decomposition of cadmium
sulphate (CdSO4) and thioacetamide (CH3CSNH2) by annealing at
different radiations (microwave, ultrasonic and sunlight) with matter
and systematic research has been done for various factors affecting
the controlled growth rate of CdS nanoparticles. The obtained
nanomaterials have been characterized by X-ray Diffraction (XRD),
Fourier Transform Infrared Spectroscopy (FTIR),
Thermogravometric (DSC-TGA) analysis and Scanning Electron
Microscopy (SEM). The result indicates that on increasing the
reaction time particle size increases but on increasing the molar ratios
grain size decreases.
Abstract: Few decades ago, electronic and sensor technologies
are merged into vehicles as the Advanced Driver Assistance
System(ADAS). However, sensor-based ADASs have limitations
about weather interference and a line-of-sight nature problem. In our
project, we investigate a Relative Position based ADAS(RP-ADAS).
We divide the RP-ADAS into four main research areas: GNSS,
VANET, Security/Privacy, and Application. In this paper, we research
the GNSS technologies and determine the most appropriate one. With
the performance evaluation, we figure out that the C/A code based
GPS technologies are inappropriate for 'which lane-level' application.
However, they can be used as a 'which road-level' application.
Abstract: With the advantage of wireless network technology,
there are a variety of mobile applications which make the issue of
wireless sensor networks as a popular research area in recent years.
As the wireless sensor network nodes move arbitrarily with the
topology fast change feature, mobile nodes are often confronted with
the void issue which will initiate packet losing, retransmitting,
rerouting, additional transmission cost and power consumption.
When transmitting packets, we would not predict void problem
occurring in advance. Thus, how to improve geographic routing with
void avoidance in wireless networks becomes an important issue. In
this paper, we proposed a greedy geographical void routing algorithm
to solve the void problem for wireless sensor networks. We use the
information of source node and void area to draw two tangents to
form a fan range of the existence void which can announce voidavoiding
message. Then we use source and destination nodes to draw
a line with an angle of the fan range to select the next forwarding
neighbor node for routing. In a dynamic wireless sensor network
environment, the proposed greedy void avoiding algorithm can be
more time-saving and more efficient to forward packets, and improve
current geographical void problem of wireless sensor networks.