Abstract: Stratified double extreme ranked set sampling
(SDERSS) method is introduced and considered for estimating the
population mean. The SDERSS is compared with the simple random
sampling (SRS), stratified ranked set sampling (SRSS) and stratified
simple set sampling (SSRS). It is shown that the SDERSS estimator
is an unbiased of the population mean and more efficient than the
estimators using SRS, SRSS and SSRS when the underlying
distribution of the variable of interest is symmetric or asymmetric.
Abstract: The main aim of the presented experiments is to
improve behaviour of sandwich structures under dynamic loading,
such as crash or explosion. This paper describes experimental
investigation on the response of new advanced materials to low and
high velocity load. Blast wave energy absorbers were designed using
two types of porous lightweight raw particle materials based on
expanded glass and ceramics with dimensions of 0.5-1 mm,
combined with polymeric binder. The effect of binder amount on the
static and dynamic properties of designed materials was observed.
Prism shaped specimens were prepared and loaded to obtain physicomechanical
parameters – bulk density, compressive and flexural
strength under quasistatic load, the dynamic response was determined
using Split Hopkinson Pressure bar apparatus. Numerical
investigation of the material behaviour in sandwich structure was
performed using implicit/explicit solver LS-Dyna. As the last step,
the developed material was used as the interlayer of blast resistant
litter bin, and it´s functionality was verified by real field blast tests.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: Human leukocyte antigen (HLA) typing from next
generation sequencing (NGS) data has the potential for applications in
clinical laboratories and population genetic studies. Here we introduce
a novel technique for HLA typing from NGS data based on
read-mapping using a comprehensive reference panel containing all
known HLA alleles and de novo assembly of the gene-specific short
reads. An accurate HLA typing at high-digit resolution was achieved
when it was tested on publicly available NGS data, outperforming
other newly-developed tools such as HLAminer and PHLAT.
Abstract: Organic Rankine Cycle (ORC) is the most commonly used method for recovering energy from small sources of heat. The investigation of the ORC in supercritical condition is a new research area as it has a potential to generate high power and thermal efficiency in a waste heat recovery system. This paper presents a steady state ORC model in supercritical condition and its simulations with a real engine’s exhaust data. The key component of ORC, evaporator, is modelled using finite volume method, modelling of all other components of the waste heat recovery system such as pump, expander and condenser are also presented. The aim of this paper is to investigate the effects of mass flow rate and evaporator outlet temperature on the efficiency of the waste heat recovery process. Additionally, the necessity of maintaining an optimum evaporator outlet temperature is also investigated. Simulation results show that modification of mass flow rate is the key to changing the operating temperature at the evaporator outlet.
Abstract: Currently, there is excessively growing information
about places on Facebook, which is the largest social network but
such information is not explicitly organized and ranked. Therefore
users cannot exploit such data to recommend places conveniently and
quickly. This paper proposes a Facebook application and an Android
application that recommend places based on the number of check-ins
of those places, the distance of those places from the current location,
the number of people who like Facebook page of those places, and
the number of talking about of those places. Related Facebook data is
gathered via Facebook API requests. The experimental results of the
developed applications show that the applications can recommend
places and rank interesting places from the most to the least. We have
found that the average satisfied score of the proposed Facebook
application is 4.8 out of 5. The users’ satisfaction can increase by
adding the app features that support personalization in terms of
interests and preferences.
Abstract: Image search engines rely on the surrounding textual
keywords for the retrieval of images. It is a tedious work for the
search engines like Google and Bing to interpret the user’s search
intention and to provide the desired results. The recent researches
also state that the Google image search engines do not work well on
all the images. Consequently, this leads to the emergence of efficient
image retrieval technique, which interprets the user’s search intention
and shows the desired results. In order to accomplish this task, an
efficient image re-ranking framework is required. Sequentially, to
provide best image retrieval, the new image re-ranking framework is
experimented in this paper. The implemented new image re-ranking
framework provides best image retrieval from the image dataset by
making use of re-ranking of retrieved images that is based on the
user’s desired images. This is experimented in two sections. One is
offline section and other is online section. In offline section, the reranking
framework studies differently (reference classes or Semantic
Spaces) for diverse user query keywords. The semantic signatures get
generated by combining the textual and visual features of the images.
In the online section, images are re-ranked by comparing the
semantic signatures that are obtained from the reference classes with
the user specified image query keywords. This re-ranking
methodology will increases the retrieval image efficiency and the
result will be effective to the user.
Abstract: Solenoid operated electromagnetic control valve
(ECV) playing an important role for car’s air conditioning control
system. ECV is used in external variable displacement swash plate
type compressor and controls the entire air conditioning system by
means of a pulse width modulation (PWM) input signal supplying
from an external source (controller). Complete form of ECV contains
number of internal features like valve body, core, valve guide,
plunger, guide pin, plunger spring, bellows etc. While designing the
ECV; dimensions of different internal items must meet the standard
requirements as it is quite challenging. In this research paper,
especially the dimensioning of ECV body and its three pressure ports
through which the air/refrigerant passes are considered. Here internal
leakage test analysis of ECV body is being carried out from its
discharge port (Pd) to crankcase port (Pc) when the guide valve is
placed inside it. The experiments have made both in ordinary and
digital system using different assumptions and thereafter compare the
results.
Abstract: To date, one of the few comprehensive indicators for
the measurement of food security is the Global Food Security Index
(GFSI). This index is a dynamic quantitative and qualitative
benchmarking model, constructed from 28 unique indicators, that
measures drivers of food security across both developing and
developed countries. Whereas the GFSI has been calculated across a
set of 109 countries, in this paper we aim to present and compare, for
the Middle East and North Africa (MENA), 1) the Food Security
Index scores achieved and 2) the data available on affordability,
availability, and quality of food. The data for this work was taken
from the latest available report published by the creators of the GFSI,
which in turn used information from national and international
statistical sources. MENA countries rank from place 17/109 (Israel,
although with resent political turmoil this is likely to have changed)
to place 91/109 (Yemen) with household expenditure spent in food
ranging from 15.5% (Israel) to 60% (Egypt). Lower spending on food
as a share of household consumption in most countries and better
food safety net programs in the MENA have contributed to a notable
increase in food affordability. The region has also, however,
experienced a decline in food availability, owing to more limited
food supplies and higher volatility of agricultural production. In
terms of food quality and safety the MENA has the top ranking
country (Israel). The most frequent challenges faced by the countries
of the MENA include public expenditure on agricultural research and
development as well as volatility of agricultural production. Food
security is a complex phenomenon that interacts with many other
indicators of a country’s wellbeing; in the MENA it is slowly but
markedly improving.
Abstract: This paper investigates the activity of the rectus
femoris (RF) and biceps femoris (BF) in healthy subjects during salat
(prostration) and specific exercise (squat exercise) using
electromyography (EMG). A group of undergraduates aged between
19 to 25 years voluntarily participated in this study. The myoelectric
activity of the muscles were recorded and analyzed. The finding
indicated that there were contractions of the muscles during the salat
and exercise with almost same EMG’s level. From the result,
Wilcoxon’s Rank Sum test showed significant difference between
prostration and squat exercise (p
Abstract: Cloud computing (CC) has already gained overall
appreciation in research and practice. Whereas the willingness to
integrate cloud services in various IT environments is still unbroken,
the previous CC procurement processes run mostly in an unorganized
and non-standardized way. In practice, a sufficiently specific, yet
applicable business process for the important acquisition phase is
often lacking. And research does not appropriately remedy this
deficiency yet. Therefore, this paper introduces a field-tested
approach for CC procurement. Based on an extensive literature
review and augmented by expert interviews, we designed a model
that is validated and further refined through an in-depth real-life case
study. For the detailed process description, we apply the event-driven
process chain notation (EPC). The gained valuable insights into the
case study may help CC research to shift to a more socio-technical
area. For practice, next to giving useful organizational instructions
we will provide extended checklists and lessons learned.
Abstract: There is decagram of strategic decisions of operations
and production/service management (POSM) within operational
research (OR) which must collate, namely: design, inventory, quality,
location, process and capacity, layout, scheduling, maintain ace, and
supply chain. This paper presents an architectural configuration
conceptual framework of a decagram of sets decisions in a form of
mathematical complete graph and abelian graph.
Mathematically, a complete graph is undirected (UDG), and
directed (DG) a relationship where every pair of vertices is
connected, collated, confluent, and holomorphic.
There has not been any study conducted which, however,
prioritizes the holomorphic sets which of POMS within OR field of
study. The study utilizes OR structured technique known as The
Analytic Hierarchy Process (AHP) analysis for organizing, sorting
and prioritizing(ranking) the sets within the decagram of POMS
according to their attribution (propensity), and provides an analysis
how the prioritization has real-world application within the 21st
century.
Abstract: This paper investigates the joint effect of the
interconnected (n,k)-star network topology and Multi-Agent
automated control on restoration and reconfiguration of power
systems. With the increasing trend in development in Multi-Agent
control technologies applied to power system reconfiguration
in presence of faulty components or nodes. Fault tolerance is
becoming an important challenge in the design processes of the
distributed power system topology. Since the reconfiguration of a
power system is performed by agent communication, the (n,k)-star
interconnected network topology is studied and modeled in this
paper to optimize the process of power reconfiguration. In this paper,
we discuss the recently proposed (n,k)-star topology and examine its
properties and advantages as compared to the traditional multi-bus
power topologies. We design and simulate the topology model for
distributed power system test cases. A related lemma based on the
fault tolerance and conditional diagnosability properties is presented
and proved both theoretically and practically. The conclusion is
reached that (n,k)-star topology model has measurable advantages
compared to standard bus power systems while exhibiting fault
tolerance properties in power restoration, as well as showing
efficiency when applied to power system route discovery.
Abstract: Search engine plays an important role in internet, to
retrieve the relevant documents among the huge number of web
pages. However, it retrieves more number of documents, which are
all relevant to your search topics. To retrieve the most meaningful
documents related to search topics, ranking algorithm is used in
information retrieval technique. One of the issues in data miming is
ranking the retrieved document. In information retrieval the ranking
is one of the practical problems. This paper includes various Page
Ranking algorithms, page segmentation algorithms and compares
those algorithms used for Information Retrieval. Diverse Page Rank
based algorithms like Page Rank (PR), Weighted Page Rank (WPR),
Weight Page Content Rank (WPCR), Hyperlink Induced Topic
Selection (HITS), Distance Rank, Eigen Rumor, Distance Rank Time
Rank, Tag Rank, Relational Based Page Rank and Query Dependent
Ranking algorithms are discussed and compared.
Abstract: Key frame extraction methods select the most
representative frames of a video, which can be used in different areas
of video processing such as video retrieval, video summary, and video
indexing. In this paper we present a novel approach for extracting key
frames from video sequences. The frame is characterized uniquely by
his contours which are represented by the dominant blocks. These
dominant blocks are located on the contours and its near textures.
When the video frames have a noticeable changement, its dominant
blocks changed, then we can extracte a key frame. The dominant
blocks of every frame is computed, and then feature vectors are
extracted from the dominant blocks image of each frame and arranged
in a feature matrix. Singular Value Decomposition is used to calculate
sliding windows ranks of those matrices. Finally the computed ranks
are traced and then we are able to extract key frames of a video.
Experimental results show that the proposed approach is robust
against a large range of digital effects used during shot transition.
Abstract: India holds 17.5% of the world’s population but has
only 2% of the total geographical area of the world where 27.35% of
the area is categorized as wasteland due to lack of or less
groundwater. So there is a demand for excessive groundwater for
agricultural and non agricultural activities to balance its growth rate.
With this in mind, an attempt is made to find the groundwater
potential zone in Gomukhi Nadhi sub basin of Vellar River basin,
TamilNadu, India covering an area of 1146.6 Sq.Km consists of 9
blocks from Peddanaickanpalayam to Virudhachalam in the sub
basin. The thematic maps such as Geology, Geomorphology,
Lineament, Landuse and Landcover and Drainage are prepared for
the study area using IRS P6 data. The collateral data includes rainfall,
water level, soil map are collected for analysis and inference. The
digital elevation model (DEM) is generated using Shuttle Radar
Topographic Mission (SRTM) and the slope of the study area is
obtained. ArcGIS 10.1 acts as a powerful spatial analysis tool to find
out the ground water potential zones in the study area by means of
weighted overlay analysis. Each individual parameter of the thematic
maps are ranked and weighted in accordance with their influence to
increase the water level in the ground. The potential zones in the
study area are classified viz., Very Good, Good, Moderate, Poor with
its aerial extent of 15.67, 381.06, 575.38, 174.49 Sq.Km respectively.
Abstract: Climate warming would increase rainfall by shifting
precipitation falling form from snow to rain, and would accelerate
snow cover disappearing by increasing snowpack. Using temperature
and precipitation data in the temperature-index snowmelt model, we
evaluated variability of snowfall and continuous snow cover duration
(CSCD) during 1944-2010 over Pelso, central Finland. Mann-
Kendall non-parametric test determined that annual precipitation
increased by 2.69 (mm/year, p
Abstract: Self-service technologies (SSTs) make an important
contribution to the daily life of people nowadays. However, the
introduction of SST does not lead to its usage. Thereby, this paper
was an attempt on discovery of the most preferred SST in the
customers’ point of view. To fulfill this aim, the Analytical Hierarchy
Process (AHP) was applied based on Saaty’s questionnaire which
was administered to the customers of e-banking services located in
Golestan providence, northern Iran. This study used qualitative
factors in association with the intention of consumers’ usage of SSTs
to rank three SSTs: ATM, mobile banking and internet banking. The
results showed that mobile banking get the highest weight in
consumers’ point of view. This research can be useful both for
managers and service providers and also for customers who intend to
use e-banking.
Abstract: Microarray technology is universally used in the study
of disease diagnosis using gene expression levels. The main
shortcoming of gene expression data is that it includes thousands of
genes and a small number of samples. Abundant methods and
techniques have been proposed for tumor classification using
microarray gene expression data. Feature or gene selection methods
can be used to mine the genes that directly involve in the
classification and to eliminate irrelevant genes. In this paper
statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR)
and F-Statistics are used to rank the genes. The ranked genes are used
for further classification. Particle Swarm Optimization (PSO)
algorithm and Shuffled Frog Leaping (SFL) algorithm are used to
find the significant genes from the top-m ranked genes. The Naïve
Bayes Classifier (NBC) is used to classify the samples based on the
significant genes. The proposed work is applied on Lung and Ovarian
datasets. The experimental results show that the proposed method
achieves 100% accuracy in all the three datasets and the results are
compared with previous works.