Abstract: Human genome is not only the evolutionary
summation of all advantageous events, but also houses lesions of
deleterious foot prints. A single gene mutation sometimes may
express multiple consequences in numerous tissues and a linear
relationship of the genotype and the phenotype may often be obscure.
ß Thalassemia minor, a transfusion independent mild anaemia,
coupled with environment among other factors may articulate into
phenotypic pleotropy with Hypocholesterolemia, Vitamin D
deficiency, Tissue hypoxia, Hyper-parathyroidism and Psychological
alterations. Occurrence of Pancreatic insufficiency, resultant
steatorrhoea, Vitamin-D (25-OH) deficiency (13.86 ngm/ml) with
Hypocholesterolemia (85mg/dl) in a 30 years old male ß Thal-minor
patient (Hemoglobin 11mg/dl with Fetal Hemoglobin 2.10%, Hb A2
4.60% and Hb Adult 84.80% and altered Hemogram) with increased
Para thyroid hormone (62 pg/ml) & moderate Serum Ca+2
(9.5mg/ml) indicate towards a cascade of phenotypic pleotropy
where the ß Thalassemia mutation ,be it in the 5’ cap site of the
mRNA , differential splicing etc in heterozygous state is effecting
several metabolic pathways. Compensatory extramedulary
hematopoiesis may not coped up well with the stressful life style of
the young individual and increased erythropoietic stress with high
demand for cholesterol for RBC membrane synthesis may have
resulted in Hypocholesterolemia.Oxidative stress and tissue hypoxia
may have caused the pancreatic insufficiency, leading to Vitamin D
deficiency. This may in turn have caused the secondary
hyperparathyroidism to sustain serum Calcium level. Irritability and
stress intolerance of the patient was a cumulative effect of the vicious
cycle of metabolic compromises. From these findings we propose
that the metabolic deficiencies in the ß Thalassemia mutations may
be considered as the phenotypic display of the pleotropy to explain
the genetic epidemiology.
According to the recommendations from the NIH Workshop on
Gene-Environment Interplay in Common Complex Diseases: Forging
an Integrative Model, study design of observations should be
informed by gene-environment hypotheses and results of a study
(genetic diseases) should be published to inform future hypotheses.
Variety of approaches is needed to capture data on all possible
aspects, each of which is likely to contribute to the etiology of
disease. Speakers also agreed that there is a need for development of
new statistical methods and measurement tools to appraise
information that may be missed out by conventional method where
large sample size is needed to segregate considerable effect.
A meta analytic cohort study in future may bring about significant
insight on to the title comment.
Abstract: This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
Abstract: Caching was suggested as a solution for reducing bandwidth utilization and minimizing query latency in mobile environments. Over the years, different caching approaches have been proposed, some relying on the server to broadcast reports periodically informing of the updated data while others allowed the clients to request for the data whenever needed. Until recently a hybrid cache consistency scheme Scalable Asynchronous Cache Consistency Scheme SACCS was proposed, which combined the two different approaches benefits- and is proved to be more efficient and scalable. Nevertheless, caching has its limitations too, due to the limited cache size and the limited bandwidth, which makes the implementation of cache replacement strategy an important aspect for improving the cache consistency algorithms. In this thesis, we proposed a new cache replacement strategy, the Least Unified Value strategy (LUV) to replace the Least Recently Used (LRU) that SACCS was based on. This paper studies the advantages and the drawbacks of the new proposed strategy, comparing it with different categories of cache replacement strategies.
Abstract: This paper explores the scalability issues associated
with solving the Named Entity Recognition (NER) problem using
Support Vector Machines (SVM) and high-dimensional features. The
performance results of a set of experiments conducted using binary
and multi-class SVM with increasing training data sizes are
examined. The NER domain chosen for these experiments is the
biomedical publications domain, especially selected due to its
importance and inherent challenges. A simple machine learning
approach is used that eliminates prior language knowledge such as
part-of-speech or noun phrase tagging thereby allowing for its
applicability across languages. No domain-specific knowledge is
included. The accuracy measures achieved are comparable to those
obtained using more complex approaches, which constitutes a
motivation to investigate ways to improve the scalability of multiclass
SVM in order to make the solution more practical and useable.
Improving training time of multi-class SVM would make support
vector machines a more viable and practical machine learning
solution for real-world problems with large datasets. An initial
prototype results in great improvement of the training time at the
expense of memory requirements.
Abstract: This research focuses on micro-enterprise of
Malaysian Malays that are involved in very small-scaled business
activities. Among them include food stall and burger stall operators,
night market hawkers, grocery store operators as well as construction
and small service activities works. The study seeks to explore why
some micro-entrepreneurs still lag in entrepreneurship and what
needs to be rectified. This quantitative study is conducted on 173
Malay micro-enterprise owners (MEOs) and 58 Malay failed microenterprise
owners (FMEOs) involved in all range of businesses
throughout the state of Perak, Malaysia. The main aims are to
identify the gaps between the failed micro-enterprise owners
(FMEOs) and existing micro-enterprise owners (MEOs) and the
problems faced among FMEOs. The results reveal that the MEOs had
strong motivations and better marketing approaches as compared to
FMEOs. Furthermore, the FMEOs failed in the business ventures
mainly due to lack of management, sales and marketing skills and
poor competitive abilities to keep up with rivals.
Abstract: A Decision Support System/Expert System for stock
portfolio selection presented where at first step, both technical and
fundamental data used to estimate technical and fundamental return
and risk (1st phase); Then, the estimated values are aggregated with
the investor preferences (2nd phase) to produce convenient stock
portfolio.
In the 1st phase, there are two expert systems, each of which is
responsible for technical or fundamental estimation. In the technical
expert system, for each stock, twenty seven candidates are identified
and with using rough sets-based clustering method (RC) the effective
variables have been selected. Next, for each stock two fuzzy rulebases
are developed with fuzzy C-Mean method and Takai-Sugeno-
Kang (TSK) approach; one for return estimation and the other for
risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation
method. In parallel, for fundamental expert systems,
fuzzy rule-bases have been identified in the form of “IF-THEN" rules
through brainstorming with the stock market experts and the input
data have been derived from financial statements; as a result two
fuzzy rule-bases have been generated for all the stocks, one for return
and the other for risk.
In the 2nd phase, user preferences represented by four criteria and
are obtained by questionnaire. Using an expert system, four estimated
values of return and risk have been aggregated with the respective
values of user preference. At last, a fuzzy rule base having four rules,
treats these values and produce a ranking score for each stock which
will lead to a satisfactory portfolio for the user.
The stocks of six manufacturing companies and the period of
2003-2006 selected for data gathering.
Abstract: Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.
Abstract: Performance of any continuous speech recognition system is highly dependent on performance of the acoustic models. Generally, development of the robust spoken language technology relies on the availability of large amounts of data. Common way to cope with little data for training each state of Markov models is treebased state tying. This tying method applies contextual questions to tie states. Manual procedure for question generation suffers from human errors and is time consuming. Various automatically generated questions are used to construct decision tree. There are three approaches to generate questions to construct HMMs based on decision tree. One approach is based on misrecognized phonemes, another approach basically uses feature table and the other is based on state distributions corresponding to context-independent subword units. In this paper, all these methods of automatic question generation are applied to the decision tree on FARSDAT corpus in Persian language and their results are compared with those of manually generated questions. The results show that automatically generated questions yield much better results and can replace manually generated questions in Persian language.
Abstract: This study investigated the effect of cross sectional
geometry on sediment transport rate. The processes of sediment
transport are generally associated to environmental management,
such as pollution caused by the forming of suspended sediment in the
channel network of a watershed and preserving physical habitats and
native vegetations, and engineering applications, such as the
influence of sediment transport on hydraulic structures and flood
control design. Many equations have been proposed for computing
the sediment transport, the influence of many variables on sediment
transport has been understood; however, the effect of other variables
still requires further research. For open channel flow, sediment
transport capacity is recognized to be a function of friction slope,
flow velocity, grain size, grain roughness and form roughness, the
hydraulic radius of the bed section and the type and quantity of
vegetation cover. The effect of cross sectional geometry of the
channel on sediment transport is one of the variables that need
additional investigation. The width-depth ratio (W/d) is a
comparative indicator of the channel shape. The width is the total
distance across the channel and the depth is the mean depth of the
channel. The mean depth is best calculated as total cross-sectional
area divided by the top width. Channels with high W/d ratios tend to
be shallow and wide, while channels with low (W/d) ratios tend to be
narrow and deep. In this study, the effects of the width-depth ratio on
sediment transport was demonstrated theoretically by inserting the
shape factor in sediment continuity equation and analytically by
utilizing the field data sets for Yalobusha River. It was found by
utilizing the two approaches as a width-depth ratio increases the
sediment transport decreases.
Abstract: Signalized intersections on high-volume arterials are
often congested during peak hours, causing a decrease in through
movement efficiency on the arterial. Much of the vehicle delay
incurred at conventional intersections is caused by high left-turn
demand. Unconventional intersection designs attempt to reduce
intersection delay and travel time by rerouting left-turns away from
the main intersection and replacing it with right-turn followed by Uturn.
The proposed new type of U-turn intersection is geometrically
designed with a raised island which provides a protected U-turn
movement. In this study several scenarios based on different
distances between U-turn and main intersection, traffic volume of
major/minor approaches and percentage of left-turn volumes were
simulated by use of AIMSUN, a type of traffic microsimulation
software. Subsequently some models are proposed in order to
compute travel time of each movement. Eventually by correlating
these equations to some in-field collected data of some implemented
U-turn facilities, the reliability of the proposed models are approved.
With these models it would be possible to calculate travel time of
each movement under any kind of geometric and traffic condition. By
comparing travel time of a conventional signalized intersection with
U-turn intersection travel time, it would be possible to decide on
converting signalized intersections into this new kind of U-turn
facility or not. However comparison of travel time is not part of the
scope of this research. In this paper only travel time of this innovative
U-turn facility would be predicted. According to some before and
after study about the traffic performance of some executed U-turn
facilities, it is found that commonly, this new type of U-turn facility
produces lower travel time. Thus, evaluation of using this type of
unconventional intersection should be seriously considered.
Abstract: Normally business changes are made in order to
change a level of activity in some way, whether it is sales, cash flow,
productivity, or product portfolio. When attempts are made to make
such changes, too often the business reverts to the old levels of
activity as soon as management attention is diverted. Risk
management is a field of growing interest to project managers as well
as in general business and organizational management. There are
several approaches used to manage risk in projects and this paper is a
brief outline of some that you might encounter, with an indication of
their strengths and weaknesses.
Abstract: The various applications of VLSI circuits in highperformance
computing, telecommunications, and consumer
electronics has been expanding progressively, and at a very hasty
pace. This paper describes a new model for partitioning a circuit
using DBSCAN and fuzzy ARTMAP neural network. The first step
is concerned with feature extraction, where we had make use
DBSCAN algorithm. The second step is the classification and is
composed of a fuzzy ARTMAP neural network. The performance of
both approaches is compared using benchmark data provided by
MCNC standard cell placement benchmark netlists. Analysis of the
investigational results proved that the fuzzy ARTMAP with
DBSCAN model achieves greater performance then only fuzzy
ARTMAP in recognizing sub-circuits with lowest amount of
interconnections between them The recognition rate using fuzzy
ARTMAP with DBSCAN is 97.7% compared to only fuzzy
ARTMAP.
Abstract: Ability of accurate and reliable location estimation in
indoor environment is the key issue in developing great number of
context aware applications and Location Based Services (LBS).
Today, the most viable solution for localization is the Received
Signal Strength (RSS) fingerprinting based approach using wireless
local area network (WLAN). This paper presents two RSS
fingerprinting based approaches – first we employ widely used
WLAN based positioning as a reference system and then investigate
the possibility of using GSM signals for positioning. To compare
them, we developed a positioning system in real world environment,
where realistic RSS measurements were collected. Multi-Layer
Perceptron (MLP) neural network was used as the approximation
function that maps RSS fingerprints and locations. Experimental
results indicate advantage of WLAN based approach in the sense of
lower localization error compared to GSM based approach, but GSM
signal coverage by far outreaches WLAN coverage and for some
LBS services requiring less precise accuracy our results indicate that
GSM positioning can also be a viable solution.
Abstract: In this paper we propose a method which improves the efficiency of video coding. Our method combines an adaptive GOP (group of pictures) structure and the shot cut detection. We have analyzed different approaches for shot cut detection with aim to choose the most appropriate one. The next step is to situate N frames to the positions of detected cuts during the process of video encoding. Finally the efficiency of the proposed method is confirmed by simulations and the obtained results are compared with fixed GOP structures of sizes 4, 8, 12, 16, 32, 64, 128 and GOP structure with length of entire video. Proposed method achieved the gain in bit rate from 0.37% to 50.59%, while providing PSNR (Peak Signal-to-Noise Ratio) gain from 1.33% to 0.26% in comparison to simulated fixed GOP structures.
Abstract: Aspheric optical components are an alternative to the use of conventional lenses in the implementation of imaging systems for the visible range. Spherical lenses are capable of producing aberrations. Therefore, they are not able to focus all the light into a single point. Instead, aspherical lenses correct aberrations and provide better resolution even with compact lenses incorporating a small number of lenses.
Metrology of these components is very difficult especially when the resolution requirements increase and insufficient or complexity of conventional tools requires the development of specific approaches to characterization.
This work is part of the problem existed because the objectives are the study and comparison of different methods used to measure surface rays hybrid aspherical lenses.
Abstract: Optimal design of structure has a main role in reduction of material usage which leads to deduction in the final cost of construction projects. Evolutionary approaches are found to be more successful techniques for solving size and shape structural optimization problem since it uses a stochastic random search instead of a gradient search. By reviewing the recent literature works the problem found was the optimization of weight. A new meta-heuristic algorithm called as Cuckoo Search (CS) Algorithm has used for the optimization of the total weight of the truss structures. This paper has used set of 10 bars and 25 bars trusses for the testing purpose. The main objective of this work is to reduce the number of iterations, weight and the total time consumption. In order to demonstrate the effectiveness of the present method, minimum weight design of truss structures is performed and the results of the CS are compared with other algorithms.
Abstract: SIP (Session Initiation Protocol), using HTML based
call control messaging which is quite simple and efficient, is being
replaced for VoIP networks recently. As for authentication and
authorization purposes there are many approaches and considerations
for securing SIP to eliminate forgery on the integrity of SIP
messages. On the other hand Elliptic Curve Cryptography has
significant advantages like smaller key sizes, faster computations on
behalf of other Public Key Cryptography (PKC) systems that obtain
data transmission more secure and efficient. In this work a new
approach is proposed for secure SIP authentication by using a public
key exchange mechanism using ECC. Total execution times and
memory requirements of proposed scheme have been improved in
comparison with non-elliptic approaches by adopting elliptic-based
key exchange mechanism.
Abstract: This article first summarizes reasons why current approaches supporting Open Learning and Distance Education need to be complemented by tools permitting lecturers, researchers and students to cooperatively organize the semantic content of Learning related materials (courses, discussions, etc.) into a fine-grained shared semantic network. This first part of the article also quickly describes the approach adopted to permit such a collaborative work. Then, examples of such semantic networks are presented. Finally, an evaluation of the approach by students is provided and analyzed.
Abstract: User-Centered Design (UCD), Usability Engineering (UE) and Participatory Design (PD) are the common Human- Computer Interaction (HCI) approaches that are practiced in the software development process, focusing towards issues and matters concerning user involvement. It overlooks the organizational perspective of HCI integration within the software development organization. The Management Information Systems (MIS) perspective of HCI takes a managerial and organizational context to view the effectiveness of integrating HCI in the software development process. The Human-Centered Design (HCD) which encompasses all of the human aspects including aesthetic and ergonomic, is claimed as to provide a better approach in strengthening the HCI approaches to strengthen the software development process. In determining the effectiveness of HCD in the software development process, this paper presents the findings of a content analysis of HCI approaches by viewing those approaches as a technology which integrates user requirements, ranging from the top management to other stake holder in the software development process. The findings obtained show that HCD approach is a technology that emphasizes on human, tools and knowledge in strengthening the HCI approaches to strengthen the software development process in the quest to produce a sustainable, usable and useful software product.
Abstract: Optical flow is a research topic of interest for many
years. It has, until recently, been largely inapplicable to real-time
applications due to its computationally expensive nature. This paper
presents a new reliable flow technique which is combined with a
motion detection algorithm, from stationary camera image streams,
to allow flow-based analyses of moving entities, such as rigidity, in
real-time. The combination of the optical flow analysis with motion
detection technique greatly reduces the expensive computation of
flow vectors as compared with standard approaches, rendering the
method to be applicable in real-time implementation. This paper
describes also the hardware implementation of a proposed pipelined
system to estimate the flow vectors from image sequences in real
time. This design can process 768 x 576 images at a very high frame
rate that reaches to 156 fps in a single low cost FPGA chip, which is
adequate for most real-time vision applications.