Abstract: Recognition of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. . In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.
Abstract: Along with the progress of our information society,
various risks are becoming increasingly common, causing multiple social problems. For this reason, risk communications for
establishing consensus among stakeholders who have different
priorities have become important. However, it is not always easy for the decision makers to agree on measures to reduce risks based on
opposing concepts, such as security, privacy and cost. Therefore, we previously developed and proposed the “Multiple Risk Communicator" (MRC) with the following functions: (1) modeling
the support role of the risk specialist, (2) an optimization engine, and (3) displaying the computed results. In this paper, MRC program
version 1.0 is applied to the personal information leakage problem. The application process and validation of the results are discussed.
Abstract: Due to the tremendous amount of information provided
by the World Wide Web (WWW) developing methods for mining
the structure of web-based documents is of considerable interest. In
this paper we present a similarity measure for graphs representing
web-based hypertext structures. Our similarity measure is mainly
based on a novel representation of a graph as linear integer strings,
whose components represent structural properties of the graph. The
similarity of two graphs is then defined as the optimal alignment of
the underlying property strings. In this paper we apply the well known
technique of sequence alignments for solving a novel and challenging
problem: Measuring the structural similarity of generalized trees.
In other words: We first transform our graphs considered as high
dimensional objects in linear structures. Then we derive similarity
values from the alignments of the property strings in order to
measure the structural similarity of generalized trees. Hence, we
transform a graph similarity problem to a string similarity problem for
developing a efficient graph similarity measure. We demonstrate that
our similarity measure captures important structural information by
applying it to two different test sets consisting of graphs representing
web-based document structures.
Abstract: The paper describes a knowledge based system for
analysis of microscopic wear particles. Wear particles contained in
lubricating oil carry important information concerning machine
condition, in particular the state of wear. Experts (Tribologists) in the
field extract this information to monitor the operation of the machine
and ensure safety, efficiency, quality, productivity, and economy of
operation. This procedure is not always objective and it can also be
expensive. The aim is to classify these particles according to their
morphological attributes of size, shape, edge detail, thickness ratio,
color, and texture, and by using this classification thereby predict
wear failure modes in engines and other machinery. The attribute
knowledge links human expertise to the devised Knowledge Based
Wear Particle Analysis System (KBWPAS). The system provides an
automated and systematic approach to wear particle identification
which is linked directly to wear processes and modes that occur in
machinery. This brings consistency in wear judgment prediction
which leads to standardization and also less dependence on
Tribologists.
Abstract: In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.
Abstract: A lot of research has been done in the past decade in the field of audio content analysis for extracting various information from audio signal. One such significant information is the "perceived mood" or the "emotions" related to a music or audio clip. This information is extremely useful in applications like creating or adapting the play-list based on the mood of the listener. This information could also be helpful in better classification of the music database. In this paper we have presented a method to classify music not just based on the meta-data of the audio clip but also include the "mood" factor to help improve the music classification. We propose an automated and efficient way of classifying music samples based on the mood detection from the audio data. We in particular try to classify the music based on mood for Indian bollywood music. The proposed method tries to address the following problem statement: Genre information (usually part of the audio meta-data) alone does not help in better music classification. For example the acoustic version of the song "nothing else matters by Metallica" can be classified as melody music and thereby a person in relaxing or chill out mood might want to listen to this track. But more often than not this track is associated with metal / heavy rock genre and if a listener classified his play-list based on the genre information alone for his current mood, the user shall miss out on listening to this track. Currently methods exist to detect mood in western or similar kind of music. Our paper tries to solve the issue for Indian bollywood music from an Indian cultural context
Abstract: The expectation of network performance from the
early days of ARPANET until now has been changed significantly.
Every day, new advancement in technological infrastructure opens
the doors for better quality of service and accordingly level of
perceived quality of network services have been increased over the
time. Nowadays for many applications, late information has no value
or even may result in financial or catastrophic loss, on the other hand,
demands for some level of guarantee in providing and maintaining
quality of service are ever increasing. Based on this history, having a
QoS aware routing system which is able to provide today's required
level of quality of service in the networks and effectively adapt to the
future needs, seems as a key requirement for future Internet. In this
work we have extended the traditional AntNet routing system to
support QoS with multiple metrics such as bandwidth and delay
which is named Q-Net. This novel scalable QoS routing system aims
to provide different types of services in the network simultaneously.
Each type of service can be provided for a period of time in the
network and network nodes do not need to have any previous
knowledge about it. When a type of quality of service is requested,
Q-Net will allocate required resources for the service and will
guarantee QoS requirement of the service, based on target objectives.
Abstract: In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentation-recognition using HMMs of unique design trained using online features most of which are novel. The HMM output characters boundaries represent the proposed segmentation points (PSP) which are then validated by rules-based post stage without any contextual information help to solve different segmentation errors. The HMM has been designed and tested using a self collected dataset (OHASD) [1]. Most errors cases are cured and remarkable segmentation enhancement is achieved. Very promising word and character segmentation rates are obtained regarding the unconstrained Arabic handwriting difficulty and not using context help.
Abstract: We compare three categorical data clustering
algorithms with respect to the problem of classifying cultural data
related to the aesthetic judgment of comics artists. Such a
classification is very important in Comics Art theory since the
determination of any classes of similarities in such kind of data will
provide to art-historians very fruitful information of Comics Art-s
evolution. To establish this, we use a categorical data set and we
study it by employing three categorical data clustering algorithms.
The performances of these algorithms are compared each other,
while interpretations of the clustering results are also given.
Abstract: Since Software testing becomes an important part of
Software development in order to improve the quality of software,
many automation tools are created to help testing functionality of
software. There are a few issues about usability of these tools, one is
that the result log which is generated from tools contains useless
information that the tester cannot use result log to communicate
efficiently, or the result log needs to use a specific application to open.
This paper introduces a new method, SBTAR that improves usability
of automated test tools in a part of a result log. The practice will use
the capability of tools named as IBM Rational Robot to create a
customized function, the function would generate new format of a
result log which contains useful information faster and easier to
understand than using the original result log which was generated
from the tools. This result log also increases flexibility by Microsoft
Word or WordPad to make them readable.
Abstract: This paper presents an efficient approach to feeder
reconfiguration for power loss reduction and voltage profile
imprvement in unbalanced radial distribution systems (URDS). In
this paper Genetic Algorithm (GA) is used to obtain solution for
reconfiguration of radial distribution systems to minimize the losses.
A forward and backward algorithm is used to calculate load flows in
unbalanced distribution systems. By simulating the survival of the
fittest among the strings, the optimum string is searched by
randomized information exchange between strings by performing
crossover and mutation. Results have shown that proposed algorithm
has advantages over previous algorithms The proposed method is
effectively tested on 19 node and 25 node unbalanced radial
distribution systems.
Abstract: Multiple-input multiple-output (MIMO) systems are
widely in use to improve quality, reliability of wireless transmission
and increase the spectral efficiency. However in MIMO systems,
multiple copies of data are received after experiencing various
channel effects. The limitations on account of complexity due to
number of antennas in case of conventional decoding techniques have
been looked into. Accordingly we propose a modified sphere decoder
(MSD-1) algorithm with lower complexity and give rise to system
with high spectral efficiency. With the aim to increase signal
diversity we apply rotated quadrature amplitude modulation (QAM)
constellation in multi dimensional space. Finally, we propose a new
architecture involving space time trellis code (STTC) concatenated
with space time block code (STBC) using MSD-1 at the receiver for
improving system performance. The system gains have been verified
with channel state information (CSI) errors.
Abstract: Supplement use is common in athletes. Besides their cost, they may have side effects on health and performance. 250 questionnaires were distributed among female athletes (mean age 27.08 years). The questionnaire aimed to explore the frequency, type, believes, attitudes and knowledge regarding dietary supplements. Knowledge was good in 30.3%, fair in 60.2%, and poor in 9.1% of respondents. 65.3% of athletes did not use supplements regularly. The most widely used supplements were vitamins (48.4%), minerals (42.9%), energy supplements (21.3%), and herbals (20.9%). 68.9% of athletes believed in their efficacy. 34.4% experienced performance enhancement and 6.8% of reported side effects. 68.2% reported little knowledge and 60.9% were eager to learn more. In conclusion, many of the female athletes believe in the efficacy of supplements and think they are an unavoidable part of competitive sports. However, their information is not sufficient. We have to stress on education, consulting sessions, and rational prescription.
Abstract: Load balancing in distributed computer systems is the
process of redistributing the work load among processors in the
system to improve system performance. Most of previous research in
using fuzzy logic for the purpose of load balancing has only
concentrated in utilizing fuzzy logic concepts in describing
processors load and tasks execution length. The responsibility of the
fuzzy-based load balancing process itself, however, has not been
discussed and in most reported work is assumed to be performed in a
distributed fashion by all nodes in the network. This paper proposes a
new fuzzy dynamic load balancing algorithm for homogenous
distributed systems. The proposed algorithm utilizes fuzzy logic in
dealing with inaccurate load information, making load distribution
decisions, and maintaining overall system stability. In terms of
control, we propose a new approach that specifies how, when, and by
which node the load balancing is implemented. Our approach is
called Centralized-But-Distributed (CBD).
Abstract: This article deals with the popularity of candidates for the president of the United States of America. The popularity is assessed according to public comments on the Web 2.0. Social networking, blogging and online forums (collectively Web 2.0) are for common Internet users the easiest way to share their personal opinions, thoughts, and ideas with the entire world. However, the web content diversity, variety of technologies and website structure differences, all of these make the Web 2.0 a network of heterogeneous data, where things are difficult to find for common users. The introductory part of the article describes methodology for gathering and processing data from Web 2.0. The next part of the article is focused on the evaluation and content analysis of obtained information, which write about presidential candidates.
Abstract: In this paper, we use Generalized Hamiltonian systems approach to synchronize a modified sixth-order Chua's circuit, which generates hyperchaotic dynamics. Synchronization is obtained between the master and slave dynamics with the slave being given by an observer. We apply this approach to transmit private information (analog and binary), while the encoding remains potentially secure.
Abstract: Rapid progress in audio compression technology has contributed to the explosive growth of music available in digital form today. In a reversal of ideas, this work makes use of a recently proposed efficient audio compression scheme to develop three important applications in the context of Music Information Retrieval (MIR) for the effective manipulation of large music databases, namely automatic music recommendation (AMR), digital rights management (DRM) and audio finger-printing for song identification. The performance of these three applications has been evaluated with respect to a database of songs collected from a diverse set of genres.
Abstract: This paper presents an innovative computer system
that contributes to sustainable development of the enterprise. The
research refers to a rethinking of traditional systems of collaboration
and risk assessment, present in any organization, leading to a
sustainable enterprise. This concept integrates emerging tools that
allow the implementation and exploitation of the collective
intelligence of the enterprise, allowing the exchange of contextual,
agile and simplified information, and collaboration with networks of
customers and partners in an environment where risks are controlled.
Risk assessment is done in a systemic way: the enterprise as the
system compared to the contained departments and the enterprise as a
subsystem compared to: families of international standards and
sustainability-s responsibilities. The enterprise, in this systemic
vision, responds to the requirements that any existing system to
operate continuously in an indefinite future without reaching key
resource depletion. The research is done by integrating collaborative
science, engineering, management, psychology, obtaining thus a
cornerstone of sustainable development of the enterprise.
Abstract: Removing noise from the any processed images is very important. Noise should be removed in such a way that important information of image should be preserved. A decisionbased nonlinear algorithm for elimination of band lines, drop lines, mark, band lost and impulses in images is presented in this paper. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and evaluation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. However, the restricted window size renders median operation less effective whenever noise is excessive in that case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of Mean Square Error [MSE], Peak-Signal-to-Noise Ratio [PSNR], Signal-to-Noise Ratio Improved [SNRI], Percentage Of Noise Attenuated [PONA], and Percentage Of Spoiled Pixels [POSP]. This is compared with standard algorithms already in use and improved performance of the proposed algorithm is presented. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms which are required for removal of different artifacts.
Abstract: The underground shopping mall has the constructional
problem of the fire evacuation. Also, the people sometimes lose their
direction and information of current time in the mall. If the
emergencies such as terrorist explosions or gas explosions are
happened, they have to go out soon. Under such circumstances, inside
of the mall has high risk for life. In this research, the authors propose a
way that he/she can go out from the underground shopping mall
quickly. If the narrow exits are discovered by using active RFID
(Radio Frequency Identification) tags and using cellular phones, they
can evacuate as soon as possible. To verify this hypothesis, the authors
design the model and carry out the agent-based simulation. They treat,
as a case study, the Tenjin mall in Fukuoka Prefecture in Japan. The
result of the simulation is that the case of the pedestrian with using
active RFID tags and cellular phones reduced the amount of time to
spend on the evacuation. Even if the diffusion of RFID tags and
cellular phones was not perfect, they could show the effectiveness of
reducing the time of evacuation.