Abstract: Texture classification is a trendy and a catchy
technology in the field of texture analysis. Textures, the repeated
patterns, have different frequency components along different
orientations. Our work is based on Texture Classification and its
applications. It finds its applications in various fields like Medical
Image Classification, Computer Vision, Remote Sensing,
Agricultural Field, and Textile Industry. Weed control has a major
effect on agriculture. A large amount of herbicide has been used for
controlling weeds in agriculture fields, lawns, golf courses, sport
fields, etc. Random spraying of herbicides does not meet the exact
requirement of the field. Certain areas in field have more weed
patches than estimated. So, we need a visual system that can
discriminate weeds from the field image which will reduce or even
eliminate the amount of herbicide used. This would allow farmers to
not use any herbicides or only apply them where they are needed. A
machine vision precision automated weed control system could
reduce the usage of chemicals in crop fields. In this paper, an
intelligent system for automatic weeding strategy Multi Resolution
Combined Statistical & spatial Frequency is used to discriminate the
weeds from the crops and to classify them as narrow, little and broad
weeds.
Abstract: This research aims at development of the Multiple
Intelligences Measurement of Elementary Students. The structural
accuracy test and normality establishment are based on the Multiple
Intelligences Theory of Gardner. This theory consists of eight aspects
namely linguistics, logic and mathematics, visual-spatial relations,
body and movement, music, human relations, self-realization/selfunderstanding
and nature. The sample used in this research consists
of elementary school students (aged between 5-11 years). The size of
the sample group was determined by Yamane Table. The group has
2,504 students. Multistage Sampling was used. Basic statistical
analysis and construct validity testing were done using confirmatory
factor analysis. The research can be summarized as follows; 1.
Multiple Intelligences Measurement consisting of 120 items is
content-accurate. Internal consistent reliability according to the
method of Kuder-Richardson of the whole Multiple Intelligences
Measurement equals .91. The difficulty of the measurement test is
between .39-.83. Discrimination is between .21-.85. 2). The Multiple
Intelligences Measurement has construct validity in a good range,
that is 8 components and all 120 test items have statistical
significance level at .01. Chi-square value equals 4357.7; p=.00 at the
degree of freedom of 244 and Goodness of Fit Index equals 1.00.
Adjusted Goodness of Fit Index equals .92. Comparative Fit Index
(CFI) equals .68. Root Mean Squared Residual (RMR) equals 0.064
and Root Mean Square Error of Approximation equals 0.82. 3). The
normality of the Multiple Intelligences Measurement is categorized
into 3 levels. Those with high intelligence are those with percentiles
of more than 78. Those with moderate/medium intelligence are those
with percentiles between 24 and 77.9. Those with low intelligence
are those with percentiles from 23.9 downwards.
Abstract: Although so far, many methods for ranking fuzzy numbers
have been discussed broadly, most of them contained some shortcomings,
such as requirement of complicated calculations, inconsistency
with human intuition and indiscrimination. The motivation of
this study is to develop a model for ranking fuzzy numbers based
on the lexicographical ordering which provides decision-makers with
a simple and efficient algorithm to generate an ordering founded on
a precedence. The main emphasis here is put on the ease of use
and reliability. The effectiveness of the proposed method is finally
demonstrated by including a comprehensive comparing different
ranking methods with the present one.
Abstract: An improved processing description to be employed in biosonar signal processing in a cochlea model is proposed and examined. It is compared to conventional models using a modified discrimination analysis and both are tested. Their performances are evaluated with echo data captured from natural targets (trees).Results indicate that the phase characteristics of low-pass filters employed in the echo processing have a significant effect on class separability for this data.
Abstract: Analyses carried out on examples of detected defects
echoes showed clearly that one can describe these detected forms according to a whole of characteristic parameters in order to be able to make discrimination between a planar defect and a volumic defect.
This work answers to a problem of ultrasonics NDT like Identification of the defects. The problems as well as the objective of
this realized work, are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect - the second part is devoted to principal components analysis
(PCA) for optimization of the attributes vector. And finally to establish the algorithm of classification (SVM, Support Vector Machine) which allows discrimination between a plane defect and a
volumic defect. We have completed this work by a conclusion where we draw up a summary of the completed works, as well as the robustness of the
various algorithms proposed in this study.
Abstract: The automatic discrimination of seismic signals is an important practical goal for the earth-science observatories due to the large amount of information that they receive continuously. An essential discrimination task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present new techniques for seismic signals classification: local, regional and global discrimination. These techniques were tested on seismic signals from the data base of the National Geophysical Institute of the Centre National pour la Recherche Scientifique et Technique (Morocco) by using the Moroccan software for seismic signals analysis.
Abstract: This paper presents a new approach for automatic
document categorization. Exploiting the logical structure of the
document, our approach assigns a HTML document to one or more
categories (thesis, paper, call for papers, email, ...). Using a set of
training documents, our approach generates a set of rules used to
categorize new documents. The approach flexibility is carried out
with rule weight association representing your importance in the
discrimination between possible categories. This weight is
dynamically modified at each new document categorization. The
experimentation of the proposed approach provides satisfactory
results.
Abstract: Medical Surgical Nursing is one of the major subjects
in nursing. This study examined the validity and reliability of the
achievement examination utilizing the Classical Test Theory and
Item Response Theory. The study answered the following objectives
specifically : ( a) To establish the validity and reliability of the
achievement examination utilizing Classical Test Theory and Item
Response Theory ; ( b ) To determine the dimensionality measure of
items and ( c ) to compare the item difficulty and item discrimination
of the Medical Surgical Nursing Achievement examination using
Classical Test Theory ( CTT ) and Item Response Theory ( IRT ).
The developed instrument was administered to fourth year nursing
students (N= 136) of a private university in Manila. The findings
yielded the following results: The achievement examination is
reliable both using CTT and IRT. The findings indicate person and
item statistics from two frameworks are quite alike. The achievement
examination formed a unidimensional construct.
Abstract: This paper describes a new supervised fusion (hybrid)
electrocardiogram (ECG) classification solution consisting of a new
QRS complex geometrical feature extraction as well as a new version
of the learning vector quantization (LVQ) classification algorithm
aimed for overcoming the stability-plasticity dilemma. Toward this
objective, after detection and delineation of the major events of ECG
signal via an appropriate algorithm, each QRS region and also its
corresponding discrete wavelet transform (DWT) are supposed as
virtual images and each of them is divided into eight polar sectors.
Then, the curve length of each excerpted segment is calculated
and is used as the element of the feature space. To increase the
robustness of the proposed classification algorithm versus noise,
artifacts and arrhythmic outliers, a fusion structure consisting of
five different classifiers namely as Support Vector Machine (SVM),
Modified Learning Vector Quantization (MLVQ) and three Multi
Layer Perceptron-Back Propagation (MLP–BP) neural networks with
different topologies were designed and implemented. The new proposed
algorithm was applied to all 48 MIT–BIH Arrhythmia Database
records (within–record analysis) and the discrimination power of the
classifier in isolation of different beat types of each record was
assessed and as the result, the average accuracy value Acc=98.51%
was obtained. Also, the proposed method was applied to 6 number
of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging
to 20 different records of the aforementioned database (between–
record analysis) and the average value of Acc=95.6% was achieved.
To evaluate performance quality of the new proposed hybrid learning
machine, the obtained results were compared with similar peer–
reviewed studies in this area.
Abstract: Endemic Artemia franciscana populations can be found throughout the American continent and also as an introduced specie in several country all over the world, such as in the Mediterranean region where Artemia franciscana was identified as an invasive specie replacing native Artemia parthenogenetica and Artemia salina. In the present study, the characterization of the new invasive Artemia franciscana reported from Sabkhet Halk El-Menzel (Tunisia) was done based on the cysts biometry, nauplii instar-I length, Adult sexual dimorphism and fatty acid profile. The mean value of the diameter of non-decapsulated and decapsulated cysts, chorion thickness and naupliar length is 235.8, 226.3, 4.75 and 426.8 μm, respectively. Sexual dimorphism for adults specimen showed that maximal distance between compound eyes, diameter for compound eyes, length of first antenna and the abdomen length compared to the total body length ratio, are the most important variables for males and females discrimination with a total contribution of 62.39 %. The analysis of fatty acid methyl esters profile of decapsulated cysts resulted in low levels of linolenic acid (LLA, C18:3n-3) and high levels of eicosapentaenoic acid (EPA, C20:5n-3) with 3.11 and 11.10 %, respectively. Low quantity of docosahexaenoic acid (DHA, 22:6n-3) was also observed with 0.17 mg.g-1 dry weight.
Abstract: Using 1km grid datasets representing monthly mean
precipitation, monthly mean temperature, and dry matter production
(DMP), we considered the regional plant production ability in
Southeast and South Asia, and also employed pixel-by-pixel
correlation analysis to assess the intensity of relation between climate
factors and plant production. While annual DMP in South Asia was
approximately less than 2,000kg, the one in most part of Southeast
Asia exceeded 2,500 - 3,000kg. It suggested that plant production in
Southeast Asia was superior to South Asia, however, Rain-Use
Efficiency (RUE) representing dry matter production per 1mm
precipitation showed that inland of Indochina Peninsula and India
were higher than islands in Southeast Asia. By the results of
correlation analysis between climate factors and DMP, while the area
in most parts of Indochina Peninsula indicated negative correlation
coefficients between DMP and precipitation or temperature, the area
in Malay Peninsula and islands showed negative correlation to
precipitation and positive one to temperature, and most part of India
dominating South Asia showed positive to precipitation and negative
to temperature. In addition, the areas where the correlation coefficients
exceeded |0.8| were regarded as “susceptible" to climate factors, and
the areas smaller than |0.2| were “insusceptible". By following the
discrimination, the map implying expected impacts by climate change
was provided.
Abstract: Discrimination in employment has its wider social and
economic consequences other than mere violating a basic human
right. Discrimination involves treating people differently because of
certain grounds such as race, color, or sex, which results in the
impairment of equality of opportunity and treatment. As an essential
part of promoting decent work, combating discrimination through the
principle of non-discrimination has been established by the
International Labor Organization (ILO) through the Declaration on
Fundamental Principles and Rights at Work 1998. Considering
elimination of discrimination in employment as a core labor standard,
member states are expected to respect, promote and implement it to
their national laws and policies. Being a member state, Malaysia has
to position herself align with this international requirement. The
author discusses the related convention together with Malaysia-s
responses on the matter. At the closing stage, the prospect of
Malaysia is presumed taking into account of the current positions and
reports submitted to the ILO.
Abstract: We have developed a database for membrane protein functions, which has more than 3000 experimental data on functionally important amino acid residues in membrane proteins along with sequence, structure and literature information. Further, we have proposed different methods for identifying membrane proteins based on their functions: (i) discrimination of membrane transport proteins from other globular and membrane proteins and classifying them into channels/pores, electrochemical and active transporters, and (ii) β-signal for the insertion of mitochondrial β-barrel outer membrane proteins and potential targets. Our method showed an accuracy of 82% in discriminating transport proteins and 68% to classify them into three different transporters. In addition, we have identified a motif for targeting β-signal and potential candidates for mitochondrial β-barrel membrane proteins. Our methods can be used as effective tools for genome-wide annotations.
Abstract: This study was carried out in Ankara, the capital city of Turkey, in order to determine how people living in the slums of Ankara benefit from educational equality. Within the scope of the research, interviews were made with 64 families whose children have been getting education from the primary schools of these parts and the data of the study was collected by the researcher. The results of the research demonstrate that the children getting education in the slums of Ankara can not experience educational equality and justice. The results of this study show that the opportunities of the schools in the slums of Ankara are very limited, so the individuals in these districts can not equally benefit from the education. The families are aware of the problem they are faced with. KeywordsDiscrimination, inequality, primary education, slums of Turkey.
Abstract: Image processing for capsule endoscopy requires large
memory and it takes hours for diagnosis since operation time is
normally more than 8 hours. A real-time analysis algorithm of capsule
images can be clinically very useful. It can differentiate abnormal
tissue from health structure and provide with correlation information
among the images. Bleeding is our interest in this regard and we
propose a method of detecting frames with potential bleeding in
real-time. Our detection algorithm is based on statistical analysis and
the shapes of bleeding spots. We tested our algorithm with 30 cases of
capsule endoscopy in the digestive track. Results were excellent where
a sensitivity of 99% and a specificity of 97% were achieved in
detecting the image frames with bleeding spots.
Abstract: This study carried out in order to investigate the
effects of salinity on carbon isotope discrimination (Δ) of shoots and
roots of four sugar beet cultivars (cv) including Madison (British
origin) and three Iranian culivars (7233-P12, 7233-P21 and 7233-P29).
Plants were grown in sand culture medium in greenhouse conditions.
Plants irrigated with saline water (tap water as control, 50 mM, 150
mM, 250 mM and 350 mM of NaCl + CaCl2 in 5 to 1 molar ratio)
from 4 leaves stage for 16 weeks. Carbon isotope discrimination
significantly decreased with increasing salinity. Significant
differences of Δ between shoot and root were observed in all cvs and
all levels of salinity. Madison cv showed lower Δ in shoot and root
than other three cvs at all levels of salinity expect control, but cv
7233-P29 had significantly higher Δ values at saline conditions of 150
mM and above. Therefore, Δ might be applicable, as a useful tool, for
study of salinity tolerance of sugar beet genotypes.
Abstract: Certain tRNA synthetases have developed highly accurate molecular machinery to discriminate their cognate amino acids. Those aaRSs achieve their goal via editing reaction in the Connective Polypeptide 1 (CP1). Recently mutagenesis studies have revealed the critical importance of residues in the CP1 domain for editing activity and X-ray structures have shown binding mode of noncognate amino acids in the editing domain. To pursue molecular mechanism for amino acid discrimination, molecular modeling studies were performed. Our results suggest that aaRS bind the noncognate amino acid more tightly than the cognate one. Finally, by comparing binding conformations of the amino acids in three systems, the amino acid binding mode was elucidated and a discrimination mechanism proposed. The results strongly reveal that the conserved threonines are responsible for amino acid discrimination. This is achieved through side chain interactions between T252 and T247/T248 as well as between those threonines and the incoming amino acids.
Abstract: The basis of examines is survey of 500 in the years
2002-2010, which was selected according to homogeneity of land
cover and where 1090 revenues were evaluated. For achieved yields
of winter wheat is obtained multicriterial regression function
depending on the major factors influencing the consumption of
nitrogen. The coefficient of discrimination of the established model is
0.722. The increase in efficiency of fertilization is involved in supply
of organic nutrients, tillage, soil pH, past weather, the humus content
in the subsoil and grain content to 0.001 mm. The decrease in
efficiency was mainly influenced by the total dose of mineral
nitrogen, although it was divided into multiple doses, the proportion
loamy particles up to 0.01 mm, rainy, or conversely dry weather
during the vegetation. The efficiency of nitrogen was found to be the
smallest on undeveloped soils and the highest on chernozem and
alluvial soils.
Abstract: The rate of production of main products of the Fischer-Tropsch reactions over Fe/HZSM5 bifunctional catalyst in a fixed bed reactor is investigated at a broad range of temperature, pressure, space velocity, H2/CO feed molar ratio and CO2, CH4 and water flow rates. Model discrimination and parameter estimation were performed according to the integral method of kinetic analysis. Due to lack of mechanism development for Fisher – Tropsch Synthesis on bifunctional catalysts, 26 different models were tested and the best model is selected. Comprehensive one and two dimensional heterogeneous reactor models are developed to simulate the performance of fixed-bed Fischer – Tropsch reactors. To reduce computational time for optimization purposes, an Artificial Feed Forward Neural Network (AFFNN) has been used to describe intra particle mass and heat transfer diffusion in the catalyst pellet. It is seen that products' reaction rates have direct relation with H2 partial pressure and reverse relation with CO partial pressure. The results show that the hybrid model has good agreement with rigorous mechanistic model, favoring that the hybrid model is about 25-30 times faster.
Abstract: This paper describes a new approach of classification
using genetic programming. The proposed technique consists of
genetically coevolving a population of non-linear transformations on
the input data to be classified, and map them to a new space with a
reduced dimension, in order to get a maximum inter-classes
discrimination. The classification of new samples is then performed
on the transformed data, and so become much easier. Contrary to the
existing GP-classification techniques, the proposed one use a
dynamic repartition of the transformed data in separated intervals, the
efficacy of a given intervals repartition is handled by the fitness
criterion, with a maximum classes discrimination. Experiments were
first performed using the Fisher-s Iris dataset, and then, the KDD-99
Cup dataset was used to study the intrusion detection and
classification problem. Obtained results demonstrate that the
proposed genetic approach outperform the existing GP-classification
methods [1],[2] and [3], and give a very accepted results compared to
other existing techniques proposed in [4],[5],[6],[7] and [8].