Abstract: At present, application of the extension of soft set theory in decision making problems in day to day life is progressing rapidly. The concepts of fuzzy soft set and its properties have been evolved as an area of interest for the researchers. The generalization of the concepts recently got importance and a rapid growth in the research in this area witnessed its vital-ness. In this paper, an application of the concept of generalized fuzzy soft set to make decision in a social problem is presented. Further, this paper also highlights some of the key issues of the related areas.
Abstract: Nowadays, ontologies are used for achieving a
common understanding within a user community and for sharing
domain knowledge. However, the de-centralized nature of the web
makes indeed inevitable that small communities will use their own
ontologies to describe their data and to index their own resources.
Certainly, accessing to resources from various ontologies created
independently is an important challenge for answering end user
queries. Ontology mapping is thus required for combining ontologies.
However, mapping complete ontologies at run time is a
computationally expensive task. This paper proposes a system in
which mappings between concepts may be generated dynamically as
the concepts are encountered during user queries. In this way, the
interaction itself defines the context in which small and relevant
portions of ontologies are mapped. We illustrate application of the
proposed system in the context of Technology Enhanced Learning
(TEL) where learners need to access to learning resources covering
specific concepts.
Abstract: Augmented and Virtual Realties is quickly becoming
a hotbed of activity with millions of dollars being spent on R & D
and companies such as Google and Microsoft rushing to stake their
claim. Augmented reality (AR) is however marching ahead due to the
spread of the ideal AR device – the smartphone. Despite its potential,
there remains a deep digital divide between the Developed and
Developing Countries. The Technological Acceptance Model (TAM)
and Hofstede cultural dimensions also predict the behaviour intention
to uptake AR in India will be large. This paper takes a quantified
approach by collecting 340 survey responses to AR scenarios and
analyzing them through statistics. The Survey responses show that
the Intention to Use, Perceived Usefulness and Perceived Enjoyment
dimensions are high among the urban population in India. This along
with the exponential smartphone indicates that India is on the cusp of
a boom in the AR sector.
Abstract: In this paper, we will discuss about the data interpolation by using the rational cubic Ball curve. To generate a curve with a better and satisfactory smoothness, the curve segments must be connected with a certain amount of continuity. The continuity that we will consider is of type G1 continuity. The conditions considered are known as the G1 Hermite condition. A simple application of the proposed method is to generate an Arabic font satisfying the required continuity.
Abstract: Natural gas sweetening process is a controlled process that must be done at maximum efficiency and with the highest quality. In this work, due to complexity and non-linearity of the process, the H2S gas separation and the intelligent fuzzy controller, which is used to enhance the process, are simulated in MATLAB – Simulink. New design of fuzzy control for Gas Separator is discussed in this paper. The design is based on the utilization of linear state-estimation to generate the internal knowledge-base that stores input-output pairs. The obtained input/output pairs are then used to design a feedback fuzzy controller. The proposed closed-loop fuzzy control system maintains the system asymptotically-stability while it enhances the system time response to achieve better control of the concentration of the output gas from the tower. Simulation studies are carried out to illustrate the Gas Separator system performance.
Abstract: Automatic License plate recognition (ALPR) is a technology which recognizes the registration plate or number plate or License plate of a vehicle. In this paper, an Indian vehicle number plate is mined and the characters are predicted in efficient manner. ALPR involves four major technique i) Pre-processing ii) License Plate Location Identification iii) Individual Character Segmentation iv) Character Recognition. The opening phase, named pre-processing helps to remove noises and enhances the quality of the image using the conception of Morphological Operation and Image subtraction. The second phase, the most puzzling stage ascertain the location of license plate using the protocol Canny Edge detection, dilation and erosion. In the third phase, each characters characterized by Connected Component Approach (CCA) and in the ending phase, each segmented characters are conceptualized using cross correlation template matching- a scheme specifically appropriate for fixed format. Major application of ALPR is Tolling collection, Border Control, Parking, Stolen cars, Enforcement, Access Control, Traffic control. The database consists of 500 car images taken under dissimilar lighting condition is used. The efficiency of the system is 97%. Our future focus is Indian Vehicle License Plate Validation (Whether License plate of a vehicle is as per Road transport and highway standard).
Abstract: AmI proposes a new way of thinking about computers, which follows the ideas of the Ubiquitous Computing vision of Mark Weiser. In these, there is what is known as a Disappearing Computer Initiative, with users immersed in intelligent environments. Hence, technologies need to be adapted so that they are capable of replacing the traditional inputs to the system by embedding these in every-day artifacts. In this work, we present an approach, which uses Radiofrequency Identification (RFID) and Near Field Communication (NFC) technologies. In the latter, a new form of interaction appears by contact. We compare both technologies by analyzing their requirements and advantages. In addition, we propose using a combination of RFID and NFC.
Abstract: Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.
Abstract: Social networks have recently gained a growing
interest on the web. Traditional formalisms for representing social
networks are static and suffer from the lack of semantics. In this
paper, we will show how semantic web technologies can be used to
model social data. The SemTemp ontology aligns and extends
existing ontologies such as FOAF, SIOC, SKOS and OWL-Time to
provide a temporal and semantically rich description of social data.
We also present a modeling scenario to illustrate how our ontology
can be used to model social 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.
Abstract: Sentiment analysis (SA) has received growing
attention in Arabic language research. However, few studies have yet
to directly apply SA to Arabic due to lack of a publicly available
dataset for this language. This paper partially bridges this gap due to
its focus on one of the Arabic dialects which is the Saudi dialect. This
paper presents annotated data set of 4700 for Saudi dialect sentiment
analysis with (K= 0.807). Our next work is to extend this corpus and
creation a large-scale lexicon for Saudi dialect from the corpus.
Abstract: This paper discusses the intake of combining multi-criteria
decision analysis (MCDA) with OLAP systems, to generate
an integrated analysis process dealing with complex multi-criteria
decision-making situations. In this context, a multi-agent modeling is
presented for decision support systems by combining multi-criteria
decision analysis (MCDA) with OLAP systems. The proposed
modeling which consists in performing the multi-agent system
(MAS) architecture, procedure and protocol of the negotiation model
is elaborated as a decision support tool for complex decision-making
environments. Our objective is to take advantage from the multi-agent
system which distributes resources and computational
capabilities across interconnected agents, and provide a problem
modeling in terms of autonomous interacting component-agents.
Thus, the identification and evaluation of criteria as well as the
evaluation and ranking of alternatives in a decision support situation
will be performed by organizing tasks and user preferences between
different agents in order to reach the right decision. At the end, an
illustrative example is conducted to demonstrate the function and
effectiveness of our MAS modeling.
Abstract: Cloud computing is a business model which provides
an easier management of computing resources. Cloud users can
request virtual machine and install additional softwares and configure
them if needed. However, user can also request virtual appliance
which provides a better solution to deploy application in much faster
time, as it is ready-built image of operating system with necessary
softwares installed and configured. Large numbers of virtual
appliances are available in different image format. User can
download available appliances from public marketplace and start
using it. However, information published about the virtual appliance
differs from each providers leading to the difficulty in choosing
required virtual appliance as it is composed of specific OS with
standard software version. However, even if user choses the
appliance from respective providers, user doesn’t have any flexibility
to choose their own set of softwares with required OS and
application. In this paper, we propose a referenced architecture for
dynamically customizing virtual appliance and provision them in an
easier manner. We also add our experience in integrating our
proposed architecture with public marketplace and Mi-Cloud, a cloud
management software.
Abstract: Recently developed cooperative diversity scheme
enables a terminal to get transmit diversity through the support of other
terminals. However, most of the introduced cooperative schemes have
a common fault of decreased transmission rate because the destination
should receive the decodable compositions of symbols from the source
and the relay. In order to achieve high data rate, we propose a
cooperative scheme that employs hierarchical modulation. This
scheme is free from the rate loss and allows seamless cooperative
communication.
Abstract: Nowadays, food safety is a great public concern;
therefore, robust and effective techniques are required for detecting
the safety situation of goods. Hyperspectral Imaging (HSI) is an
attractive material for researchers to inspect food quality and safety
estimation such as meat quality assessment, automated poultry
carcass inspection, quality evaluation of fish, bruise detection of
apples, quality analysis and grading of citrus fruits, bruise detection
of strawberry, visualization of sugar distribution of melons,
measuring ripening of tomatoes, defect detection of pickling
cucumber, and classification of wheat kernels. HSI can be used to
concurrently collect large amounts of spatial and spectral data on the
objects being observed. This technique yields with exceptional
detection skills, which otherwise cannot be achieved with either
imaging or spectroscopy alone. This paper presents a nonlinear
technique based on kernel Fukunaga-Koontz transform (KFKT) for
detection of fat content in ground meat using HSI. The KFKT which
is the nonlinear version of FKT is one of the most effective
techniques for solving problems involving two-pattern nature. The
conventional FKT method has been improved with kernel machines
for increasing the nonlinear discrimination ability and capturing
higher order of statistics of data. The proposed approach in this paper
aims to segment the fat content of the ground meat by regarding the
fat as target class which is tried to be separated from the remaining
classes (as clutter). We have applied the KFKT on visible and nearinfrared
(VNIR) hyperspectral images of ground meat to determine
fat percentage. The experimental studies indicate that the proposed
technique produces high detection performance for fat ratio in ground
meat.
Abstract: We present an approach to triangle mesh simplification
designed to be executed on the GPU. We use a quadric error metric
to calculate an error value for each vertex of the mesh and order all
vertices based on this value. This step is followed by the parallel
removal of a number of vertices with the lowest calculated error
values. To allow for the parallel removal of multiple vertices we use
a set of per-vertex boundaries that prevent mesh foldovers even when
simplification operations are performed on neighbouring vertices. We
execute multiple iterations of the calculation of the vertex errors,
ordering of the error values and removal of vertices until either a
desired number of vertices remains in the mesh or a minimum error
value is reached. This parallel approach is used to speed up the
simplification process while maintaining mesh topology and avoiding
foldovers at every step of the simplification.
Abstract: Cloud computing can reduce the start-up expenses of implementing EHR (Electronic Health Records). However, many of the healthcare institutions are yet to implement cloud computing due to the associated privacy and security issues. In this paper, we analyze the challenges and opportunities of implementing cloud computing in healthcare. We also analyze data of over 5000 US hospitals that use Telemedicine applications. This analysis helps to understand the importance of smart phones over the desktop systems in different departments of the healthcare institutions. The wide usage of smartphones and cloud computing allows ubiquitous and affordable access to the health data by authorized persons, including patients and doctors. Cloud computing will prove to be beneficial to a majority of the departments in healthcare. Through this analysis, we attempt to understand the different healthcare departments that may benefit significantly from the implementation of cloud computing.
Abstract: Wireless sensors, also known as wireless sensor nodes,
have been making a significant impact on human daily life. The
Radio Frequency Identification (RFID) and Wireless Sensor Network
(WSN) are two complementary technologies; hence, an integrated
implementation of these technologies expands the overall
functionality in obtaining long-range and real-time information on the
location and properties of objects and people. An approach for
integrating ZigBee and RFID networks is proposed in this paper, to
create an energy-efficient network improved by the benefits of
combining ZigBee and RFID architecture. Furthermore, the
compatibility and requirements of the ZigBee device and
communication links in the typical RFID system which is presented
with the real world experiment on the capabilities of the proposed
RFID system.
Abstract: E-Learning enables the users to learn at anywhere at
any time. In E-Learning systems, authenticating the E-Learning user
has security issues. The usage of appropriate communication
networks for providing the internet connectivity for E-learning is
another challenge. WiMAX networks provide Broadband Wireless
Access through the Multicast Broadcast Service so these networks
can be most suitable for E-Learning applications. The authentication
of E-Learning user is vulnerable to session hijacking problems. The
repeated authentication of users can be done to overcome these
issues. In this paper, session based Profile Caching Authentication is
proposed. In this scheme, the credentials of E-Learning users can be
cached at authentication server during the initial authentication
through the appropriate subscriber station. The proposed cache based
authentication scheme performs fast authentication by using cached
user profile. Thus, the proposed authentication protocol reduces the
delay in repeated authentication to enhance the security in ELearning.
Abstract: Growth and remodeling of biological structures have
gained lots of attention over the past decades. Determining the
response of living tissues to mechanical loads is necessary for a wide
range of developing fields such as prosthetics design or computerassisted
surgical interventions. It is a well-known fact that biological
structures are never stress-free, even when externally unloaded. The
exact origin of these residual stresses is not clear, but theoretically,
growth is one of the main sources. Extracting body organ’s shapes
from medical imaging does not produce any information regarding
the existing residual stresses in that organ. The simplest cause of such
stresses is gravity since an organ grows under its influence from
birth. Ignoring such residual stresses might cause erroneous results in
numerical simulations. Accounting for residual stresses due to tissue
growth can improve the accuracy of mechanical analysis results. This
paper presents an original computational framework based on gradual
growth to determine the residual stresses due to growth. To illustrate
the method, we apply it to a finite element model of a healthy human
face reconstructed from medical images. The distribution of residual
stress in facial tissues is computed, which can overcome the effect of
gravity and maintain tissues firmness. Our assumption is that tissue
wrinkles caused by aging could be a consequence of decreasing
residual stress and thus not counteracting gravity. Taking into
account these stresses seems therefore extremely important in
maxillofacial surgery. It would indeed help surgeons to estimate
tissues changes after surgery.