Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: Speech Segmentation is the measure of the change
point detection for partitioning an input speech signal into regions
each of which accords to only one speaker. In this paper, we apply
two features based on multi-scale product (MP) of the clean speech,
namely the spectral centroid of MP, and the zero crossings rate of
MP. We focus on multi-scale product analysis as an important tool
for segmentation extraction. The MP is based on making the product
of the speech wavelet transform coefficients (WTC). We have
estimated our method on the Keele database. The results show the
effectiveness of our method. It indicates that the two features can find
word boundaries, and extracted the segments of the clean speech.
Abstract: Modelling of the earth's surface and evaluation of
urban environment, with 3D models, is an important research topic.
New stereo capabilities of high resolution optical satellites images,
such as the tri-stereo mode of Pleiades, combined with new image
matching algorithms, are now available and can be applied in urban
area analysis. In addition, photogrammetry software packages gained
new, more efficient matching algorithms, such as SGM, as well as
improved filters to deal with shadow areas, can achieve more dense
and more precise results.
This paper describes a comparison between 3D data extracted
from tri-stereo and dual stereo satellite images, combined with pixel
based matching and Wallis filter. The aim was to improve the
accuracy of 3D models especially in urban areas, in order to assess if
satellite images are appropriate for a rapid evaluation of urban
environments.
The results showed that 3D models achieved by Pleiades tri-stereo
outperformed, both in terms of accuracy and detail, the result
obtained from a Geo-eye pair. The assessment was made with
reference digital surface models derived from high resolution aerial
photography. This could mean that tri-stereo images can be
successfully used for the proposed urban change analyses.
Abstract: One image is worth more than thousand words.
Images if analyzed can reveal useful information. Low level image
processing deals with the extraction of specific feature from a single
image. Now the question arises: What technique should be used to
extract patterns of very large and detailed image database? The
answer of the question is: “Image Mining”. Image Mining deals with
the extraction of image data relationship, implicit knowledge, and
another pattern from the collection of images or image database. It is
nothing but the extension of Data Mining. In the following paper, not
only we are going to scrutinize the current techniques of image
mining but also present a new technique for mining images using
Genetic Algorithm.
Abstract: Fast changing knowledge systems on the Internet can
be accessed more efficiently with the help of automatic document
summarization and updating techniques. The aim of multi-document
update summary generation is to construct a summary unfolding the
mainstream of data from a collection of documents based on the
hypothesis that the user has already read a set of previous documents.
In order to provide a lot of semantic information from the documents,
deeper linguistic or semantic analysis of the source documents were
used instead of relying only on document word frequencies to select
important concepts. In order to produce a responsive summary,
meaning oriented structural analysis is needed. To address this issue,
the proposed system presents a document summarization approach
based on sentence annotation with aspects, prepositions and named
entities. Semantic element extraction strategy is used to select
important concepts from documents which are used to generate
enhanced semantic summary.
Abstract: Text mining techniques are generally applied for
classifying the text, finding fuzzy relations and structures in data
sets. This research provides plenty text mining capabilities. One
common application is text classification and event extraction,
which encompass deducing specific knowledge concerning incidents
referred to in texts. The main contribution of this paper is the
clarification of a concept graph generation mechanism, which is based
on a text classification and optimal fuzzy relationship extraction.
Furthermore, the work presented in this paper explains the application
of fuzzy relationship extraction and branch and bound (BB) method
to simplify the texts.
Abstract: Leukaemia is a blood cancer disease that contributes
to the increment of mortality rate in Malaysia each year. There are
two main categories for leukaemia, which are acute and chronic
leukaemia. The production and development of acute leukaemia cells
occurs rapidly and uncontrollable. Therefore, if the identification of
acute leukaemia cells could be done fast and effectively, proper
treatment and medicine could be delivered. Due to the requirement of
prompt and accurate diagnosis of leukaemia, the current study has
proposed unsupervised pixel segmentation based on clustering
algorithm in order to obtain a fully segmented abnormal white blood
cell (blast) in acute leukaemia image. In order to obtain the
segmented blast, the current study proposed three clustering
algorithms which are k-means, fuzzy c-means and moving k-means
algorithms have been applied on the saturation component image.
Then, median filter and seeded region growing area extraction
algorithms have been applied, to smooth the region of segmented
blast and to remove the large unwanted regions from the image,
respectively. Comparisons among the three clustering algorithms are
made in order to measure the performance of each clustering
algorithm on segmenting the blast area. Based on the good sensitivity
value that has been obtained, the results indicate that moving kmeans
clustering algorithm has successfully produced the fully
segmented blast region in acute leukaemia image. Hence, indicating
that the resultant images could be helpful to haematologists for
further analysis of acute leukaemia.
Abstract: A thermosyphon system is a heat transfer loop which
operates on the basis of gravity and buoyancy forces. It guarantees a
good reliability and low maintenance cost as it does not involve any
mechanical pump. Therefore, it can be used in many industrial
applications such as refrigeration and air conditioning, electronic
cooling, nuclear reactors, geothermal heat extraction, etc. But flow
instabilities and loop configuration are the major problems in this
system. Several previous researchers studied that stabilities can be
suppressed by using nanofluids as loop fluid. In the present study a
rectangular thermosyphon loop with end heat exchangers are
considered for the study. This configuration is more appropriate for
many practical applications such as solar water heater, geothermal
heat extraction, etc. In the present work, steady-state analysis is
carried out on thermosyphon loop with parallel flow coaxial heat
exchangers at heat source and heat sink. In this loop nanofluid is
considered as the loop fluid and water is considered as the external
fluid in both hot and cold heat exchangers. For this analysis onedimensional
homogeneous model is developed. In this model,
conservation equations like conservation of mass, momentum, energy
are discretized using finite difference method. A computer code is
written in MATLAB to simulate the flow in thermosyphon loop. A
comparison in terms of heat transfer is made between water and
nanofluid as working fluids in the loop.
Abstract: In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation,
style, illumination, and can suffer from perspective distortion.
Pre-processing is performed to make the characters scale and
rotation invariant. Since text degradations can not be appropriately
defined using well-known geometric transformations such
as translation, rotation, affine transformation and shearing, we
use the whole character black pixels as our feature vector.
Classification is performed with minimum distance classifier
using the maximum likelihood criterion, which delivers very
promising Character Recognition Rate (CRR) of 89%. We
achieve considerably higher Word Recognition Rate (WRR) of
99% when using lower level linguistic knowledge about product
words during the recognition process.
Abstract: In this study, we are interested in a species of the
family of Asteraceae (Tagetes erecta). This family is considered as a
source of antimicrobial extracts with strong capacity. The extraction
of the flavonoids is carried out by the method of liquid/liquid with the
use of successive solvents. Afterwards, we evaluated the biological
activity of the flavonoids on five pathogenic bacterial stocks such as
Escherichia coli, Bacillus subtilis, Klebsiella pneumoniae,
Pseudomonas aeruginosa and Staphylococcus aureus and two stocks
of yeasts to knowing Candida albicans) and Saccharomyces
cerevisiae, by employing the method of the aromatogramme starting
from a solid disc. The result of the antimicrobial activity shows an
action and a variable degree of sensitivity according to bacterial
stocks tested. It will be noted that the flavonoids have an inhibiting
effect on E. coli, B. subtilis, K. pneumoniae and S. aureus. But a
resistance with respect to the extract by P. aeruginosa, C. albicans
and S. cerevisiae is to be mentioned.
Abstract: In this paper, Fuzzy C-Means clustering with
Expectation Maximization-Gaussian Mixture Model based hybrid
modeling algorithm is proposed for Continuous Tamil Speech
Recognition. The speech sentences from various speakers are used
for training and testing phase and objective measures are between the
proposed and existing Continuous Speech Recognition algorithms.
From the simulated results, it is observed that the proposed algorithm
improves the recognition accuracy and F-measure up to 3% as
compared to that of the existing algorithms for the speech signal from
various speakers. In addition, it reduces the Word Error Rate, Error
Rate and Error up to 4% as compared to that of the existing
algorithms. In all aspects, the proposed hybrid modeling for Tamil
speech recognition provides the significant improvements for speechto-
text conversion in various applications.
Abstract: Brown seaweeds are abundant in Portuguese coastline
and represent an almost unexploited marine economic resource. One
of the most common species, easily available for harvesting in the
northwest coast, is Saccorhiza polyschides grows in the lowest shore
and costal rocky reefs. It is almost exclusively used by local farmers
as natural fertilizer, but contains a substantial amount of valuable
compounds, particularly alginates, natural biopolymers of high
interest for many industrial applications.
Alginates are natural polysaccharides present in cell walls of
brown seaweed, highly biocompatible, with particular properties that
make them of high interest for the food, biotechnology, cosmetics
and pharmaceutical industries. Conventional extraction processes are
based on thermal treatment. They are lengthy and consume high
amounts of energy and solvents. In recent years, microwave-assisted
extraction (MAE) has shown enormous potential to overcome major
drawbacks that outcome from conventional plant material extraction
(thermal and/or solvent based) techniques, being also successfully
applied to the extraction of agar, fucoidans and alginates. In the
present study, acid pretreatment of brown seaweed Saccorhiza
polyschides for subsequent microwave-assisted extraction (MAE) of
alginate was optimized. Seaweeds were collected in Northwest
Portuguese coastal waters of the Atlantic Ocean between May and
August, 2014. Experimental design was used to assess the effect of
temperature and acid pretreatment time in alginate extraction.
Response surface methodology allowed the determination of the
optimum MAE conditions: 40 mL of HCl 0.1 M per g of dried
seaweed with constant stirring at 20ºC during 14h. Optimal acid
pretreatment conditions have enhanced significantly MAE of
alginates from Saccorhiza polyschides, thus contributing for the
development of a viable, more environmental friendly alternative to
conventional processes.
Abstract: Creating a database scheme is essentially a manual
process. From a requirement specification the information contained
within has to be analyzed and reduced into a set of tables, attributes
and relationships. This is a time consuming process that has to go
through several stages before an acceptable database schema is
achieved. The purpose of this paper is to implement a Natural
Language Processing (NLP) based tool to produce a relational
database from a requirement specification. The Stanford CoreNLP
version 3.3.1 and the Java programming were used to implement the
proposed model. The outcome of this study indicates that a first draft
of a relational database schema can be extracted from a requirement
specification by using NLP tools and techniques with minimum user
intervention. Therefore this method is a step forward in finding a
solution that requires little or no user intervention.
Abstract: Mammography has been one of the most reliable
methods for early detection of breast cancer. There are different
lesions which are breast cancer characteristic such as
microcalcifications, masses, architectural distortions and bilateral
asymmetry. One of the major challenges of analysing digital
mammogram is how to extract efficient features from it for accurate
cancer classification. In this paper we proposed a hybrid feature
extraction method to detect and classify all four signs of breast
cancer. The proposed method is based on multiscale surrounding
region dependence method, Gabor filters, multi fractal analysis,
directional and morphological analysis. The extracted features are
input to self adaptive resource allocation network (SRAN) classifier
for classification. The validity of our approach is extensively
demonstrated using the two benchmark data sets Mammographic
Image Analysis Society (MIAS) and Digital Database for Screening
Mammograph (DDSM) and the results have been proved to be
progressive.
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: The efficiency of wood vinegar mixed with each
individual of three plants extract such as: citronella grass
(Cymbopogon nardus), neem seed (Azadirachta indica A. Juss), and
yam bean seed (Pachyrhizus erosus Urb.) were tested against the
second instar larvae of housefly (Musca domestica L.). Steam
distillation was used for extraction of the citronella grass while neem
and yam bean were simple extracted by fermentation with ethyl
alcohol. Toxicity test was evaluated in laboratory based on two
methods of larvicidal bioassay: topical application method (contact
poison) and feeding method (stomach poison). Larval mortality was
observed daily and larval survivability was recorded until the
survived larvae developed to pupae and adults. The study resulted
that treatment of wood vinegar mixed with citronella grass showed
the highest larval mortality by topical application method (50.0%)
and by feeding method (80.0%). However, treatment of mixed wood
vinegar and neem seed showed the longest pupal duration to 25 day
and 32 days for topical application method and feeding method
respectively. Additional, larval duration on treated M. domestica
larvae was extended to 13 days for topical application method and 11
days for feeding method. Thus, the feeding method gave higher
efficiency compared with the topical application method.
Abstract: Essential oils are expensive phytochemicals produced
and extracted from specific species belonging to particular families in
the plant kingdom. In the United Arab Emirates country (UAE), is
located in the arid region of the world, nine species, from the
Lamiaceae family, having the capability to produce therapeutic grade
essential oils. These species include; Mentha spicata, Ocimum
forskolei, Salvia macrosiphon, Salvia aegyptiaca, Salvia macilenta,
Salvia spinosa, Teucrium polium, Teucrium stocksianum and Zataria
multiflora. Although, such potential species are indigenous to the
UAE, however, there are almost no studies available to investigate
the chemical composition and the quality of the extracted essential
oils under the UAE climatological conditions. Therefore, great
attention has to be given to such valuable natural resources, through
conducting highly supported research projects, tailored to the UAE
conditions, and investigating different extraction techniques,
including the application of the latest available technologies, such as
superficial fluid CO2. This is crucially needed; in order to accomplish
the greatest possibilities in the medicinal field, specifically in the
discovery of new therapeutic chemotypes, as well as, to achieve the
sustainability of this natural resource in the country.
Abstract: Due to the large amount of information in the World
Wide Web (WWW, web) and the lengthy and usually linearly
ordered result lists of web search engines that do not indicate
semantic relationships between their entries, the search for topically
similar and related documents can become a tedious task. Especially,
the process of formulating queries with proper terms representing
specific information needs requires much effort from the user. This
problem gets even bigger when the user's knowledge on a subject and
its technical terms is not sufficient enough to do so. This article
presents the new and interactive search application DocAnalyser that
addresses this problem by enabling users to find similar and related
web documents based on automatic query formulation and state-ofthe-
art search word extraction. Additionally, this tool can be used to
track topics across semantically connected web documents.
Abstract: Natural gas, as one of the most important sources of
energy for many of the industrial and domestic users all over the
world, has a complex, huge supply chain which is in need of heavy
investments in all the phases of exploration, extraction, production,
transportation, storage and distribution. The main purpose of supply
chain is to meet customers’ need efficiently and with minimum cost.
In this study, with the aim of minimizing economic costs, different
levels of natural gas supply chain in the form of a multi-echelon,
multi-period fuzzy linear programming have been modeled. In this
model, different constraints including constraints on demand
satisfaction, capacity, input/output balance and presence/absence of a
path have been defined. The obtained results suggest efficiency of the
recommended model in optimal allocation and reduction of supply
chain costs.
Abstract: Red River Gum (Eucalyptus camaldulensis) is a tree
of the genus Eucalyptus widely distributed in Algeria and in the
world. The value of its aromatic secondary metabolites offers new
perspectives in the pharmaceutical industry. This strategy can
contribute to the sustainable development of our country. Preliminary
tests performed on the essential oil of Eucalyptus camendulensis
showed that this oil has antibacterial activity vis-à-vis the bacterial
strains (Enterococcus feacalis, Enterobacter cloaceai, Proteus
microsilis, Escherichia coli, Klebsiella pneumonia, and Pseudomonas
aeruginosa) and antifungic (Fusarium sporotrichioide and Fusarium
graminearum). The culture medium used was nutrient broth Muller
Hinton. The interaction between the bacteria and the essential oil is
expressed by a zone of inhibition with diameters of MIC indirectly
expression of. And we used the PDA medium to determine the fungal
activity. The extraction of the aromatic fraction (essentially oilhydrolat)
of the fresh aerian part of the Eucalyptus camendulensis
was performed by hydrodistillation. The average essential oil yield is
0.99%. The antimicrobial and fungal study of the essential oil and
hydrosol showed a high inhibitory effect on the growth of pathogens.