Abstract: Brain functional networks based on resting-state EEG
data were compared between patients with mild Alzheimer’s disease
(mAD) and matched patients with amnestic subtype of mild cognitive
impairment (aMCI). We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions and the network analysis based
on graph theory to further investigate the alterations of functional
networks in mAD compared with aMCI group. We aimed at
investigating the changes of network integrity, local clustering,
information processing efficiency, and fault tolerance in mAD brain
networks for different frequency bands based on several topological
properties, including degree, strength, clustering coefficient, shortest
path length, and efficiency. Results showed that the disruptions of
network integrity and reductions of network efficiency in mAD
characterized by lower degree, decreased clustering coefficient, higher
shortest path length, and reduced global and local efficiencies in the
delta, theta, beta2, and gamma bands were evident. The significant
changes in network organization can be used in assisting
discrimination of mAD from aMCI in clinical.
Abstract: The work proposes a decision support methodology
for the credit risk minimization in selection of investment projects.
The methodology provides two stages of projects’ evaluation.
Preliminary selection of projects with minor credit risks is made
using the Expertons Method. The second stage makes ranking of
chosen projects using the Possibilistic Discrimination Analysis
Method. The latter is a new modification of a well-known Method of
Fuzzy Discrimination Analysis.
Abstract: Employment right is linked to the people’s livelihood
in our society. As a most important and representative part in the labor
market, the employment of public servants is always taking much
attention. But the discrimination in the employment of public servants
has always existed and, to become a controversy in our society. The
paper try to discuss this problem from four parts as follows: First, the
employment of public servants has a representative status in our labor
market. The second part is about the discrimination in the employment
of public servants. The third part is about the right of equality and its
significance. The last part is to analysis the legal predicament about
discrimination in the employment of public servants in China.
Abstract: PPARs function as regulators of lipid and lipoprotein metabolism. The aim of the study was to compare the lipid profile between two phases of fasting and to examine the frequency and relationship of peroxisome proliferator-activated receptor, PPARα gene polymorphisms to lipid profile in fasting respondents. We conducted a case-control study protocol, which included 21 healthy volunteers without gender discrimination at the age of 18 years old. 3 ml of blood sample was drawn before the fasting phase and during the fasting phase (in Ramadhan month). 1ml of serum for the lipid profile was analyzed by using the automated chemistry analyser (Olympus, AU 400) and the data were analysed using the Paired T-Test (SPSS ver.20). DNA was extracted and PCR was conducted utilising 6 sets of primer. Primers were designed within 6 exons of interest in PPARα gene. Genetic and metabolic characteristics of fasting respondents and controls were estimated and compared. Fasting respondents were significantly have lowered the LDL levels (p=0.03). There were no polymorphisms detected except in exon 1 with 5% of this population study respectively. The polymorphisms in exon 1 of the PPARα gene were found in low frequency. Regarding the 1375G/T and 1386G/T polymorphisms in the exon 1 of the PPARα gene, the T-allele in fasting phase had no association with the decreased LDL levels (Fisher Exact Test). However this association is more promising when the sample size is larger in order to elucidate the precise impact of the polymorphisms on lipid profile in the population. In conclusion, the PPARα gene polymorphisms do not appear to affect the LDL of fasting respondents.
Abstract: Events discrimination and decision maker in sport field are the subject of many interesting studies in computer vision and artificial intelligence. A large volume of research has been conducted for automatic semantic event detection and summarization of sports videos. Indeed the results of these researches have a very significant contribution, as well to television broadcasts as to the football teams, since the result of sporting event can be reflected on the economic field. In this paper, we propose a novel fuzzy sequential technique which lead to discriminate events and specify the technico-tactics on going the game, nor the fuzzy system or the sequential one, may be able to respond to the asked question, in fact fuzzy process is not sufficient, it does not respect the chronological order according the time of various events, similarly the sequential process needs flexibility about the parameters used in this study, it may affect a membership degree of each parameter on the one hand and respect the sequencing of events for each frame on the other hand. Indeed this technique describes special events such as dribbling, headings, short sprints, rapid acceleration or deceleration, turning, jumping, kicking, ball occupation, and tackling according velocity vectors of the two players and the ball direction.
Abstract: Research has shown that a recruitment policy devoid of gender discrimination enhances employee performance in an organization. Previous studies in Nigeria show that gender discrimination against men and women based on their ethnic, religious and geographical identity is common. This survey, however, focuses on discrimination against women on the basis of gender and performance in government universities in Lagos State, Nigeria. The model used for this study was developed and tested in which one hundred and eighty seven copies of the questionnaire that were administered to respondents as completed by the academic staff of government universities in Lagos State were retrieved. Pearson correlation and regression were utilized for the analysis of the study, and the result showed that managerial roles based on gender discrimination against women in government universities in Lagos State have affected employee job performance negatively. The study concludes that for as long as gender discrimination rather than merit remains the basis for staff employment into positions of authority in Nigerian Universities, enhanced performance is more likely to elude employees and the educational sector in general.
Abstract: Throughout its history, Capital has established a decisive form of discrimination that has effectively strengthened its power against Labor: discrimination between an endogenous labor force (integrated, with certain guarantees and rights in the capitalist nexus) and an exogenous labor force (yet to be incorporated or incorporated as ‘heterochthonous’, without such guarantees and rights). We refer to the historical incorporation of the exogenous population from the non-capitalist to the capitalist nexus (with the consequent replaceability of the endogenous labor force) as absolute mobility.
The more possibilities Capital has of accessing a population in the non-capitalist nexus and of being able to incorporate it through absolute mobility into the capitalist nexus, the greater its unilaterality or class domination. In contrast, when these possibilities run dry, Capital is more inclined towards reformism or negotiation.
However, this absolute mobility has historically been combined with relative mobility of the labor force, which includes various processes of which labor force migration is a fundamental component.
This paper holds that both types of mobility are at the core of class struggles.
Abstract: This paper presents a novel method for inferring the
odor based on neural activities observed from rats- main olfactory
bulbs. Multi-channel extra-cellular single unit recordings were done
by micro-wire electrodes (tungsten, 50μm, 32 channels) implanted in
the mitral/tufted cell layers of the main olfactory bulb of anesthetized
rats to obtain neural responses to various odors. Neural response
as a key feature was measured by substraction of neural firing rate
before stimulus from after. For odor inference, we have developed a
decoding method based on the maximum likelihood (ML) estimation.
The results have shown that the average decoding accuracy is about
100.0%, 96.0%, 84.0%, and 100.0% with four rats, respectively. This
work has profound implications for a novel brain-machine interface
system for odor inference.
Abstract: In this paper, Wavelet based ANFIS for finding inter
turn fault of generator is proposed. The detector uniquely responds to
the winding inter turn fault with remarkably high sensitivity.
Discrimination of different percentage of winding affected by inter
turn fault is provided via ANFIS having an Eight dimensional input
vector. This input vector is obtained from features extracted from
DWT of inter turn faulty current leaving the generator phase
winding. Training data for ANFIS are generated via a simulation of
generator with inter turn fault using MATLAB. The proposed
algorithm using ANFIS is giving satisfied performance than ANN
with selected statistical data of decomposed levels of faulty current.
Abstract: Fingerprint based identification system; one of a well
known biometric system in the area of pattern recognition and has
always been under study through its important role in forensic
science that could help government criminal justice community. In
this paper, we proposed an identification framework of individuals by
means of fingerprint. Different from the most conventional
fingerprint identification frameworks the extracted Geometrical
element features (GEFs) will go through a Discretization process.
The intention of Discretization in this study is to attain individual
unique features that could reflect the individual varianceness in order
to discriminate one person from another. Previously, Discretization
has been shown a particularly efficient identification on English
handwriting with accuracy of 99.9% and on discrimination of twins-
handwriting with accuracy of 98%. Due to its high discriminative
power, this method is adopted into this framework as an independent
based method to seek for the accuracy of fingerprint identification.
Finally the experimental result shows that the accuracy rate of
identification of the proposed system using Discretization is 100%
for FVC2000, 93% for FVC2002 and 89.7% for FVC2004 which is
much better than the conventional or the existing fingerprint
identification system (72% for FVC2000, 26% for FVC2002 and
32.8% for FVC2004). The result indicates that Discretization
approach manages to boost up the classification effectively, and
therefore prove to be suitable for other biometric features besides
handwriting and fingerprint.
Abstract: This paper presents a new strategy of identification
and classification of pathological voices using the hybrid method
based on wavelet transform and neural networks. After speech
acquisition from a patient, the speech signal is analysed in order to
extract the acoustic parameters such as the pitch, the formants, Jitter,
and shimmer. Obtained results will be compared to those normal and
standard values thanks to a programmable database. Sounds are
collected from normal people and patients, and then classified into
two different categories. Speech data base is consists of several
pathological and normal voices collected from the national hospital
“Rabta-Tunis". Speech processing algorithm is conducted in a
supervised mode for discrimination of normal and pathology voices
and then for classification between neural and vocal pathologies
(Parkinson, Alzheimer, laryngeal, dyslexia...). Several simulation
results will be presented in function of the disease and will be
compared with the clinical diagnosis in order to have an objective
evaluation of the developed tool.
Abstract: The frontal area in the brain is known to be involved in
behavioral judgement. Because a Kanji character can be discriminated
visually and linguistically from other characters, in Kanji character
discrimination, we hypothesized that frontal event-related potential
(ERP) waveforms reflect two discrimination processes in separate
time periods: one based on visual analysis and the other based
on lexcical access. To examine this hypothesis, we recorded ERPs
while performing a Kanji lexical decision task. In this task, either a
known Kanji character, an unknown Kanji character or a symbol was
presented and the subject had to report if the presented character was
a known Kanji character for the subject or not. The same response
was required for unknown Kanji trials and symbol trials. As a preprocessing
of signals, we examined the performance of a method
using independent component analysis for artifact rejection and found
it was effective. Therefore we used it. In the ERP results, there
were two time periods in which the frontal ERP wavefoms were
significantly different betweeen the unknown Kanji trials and the
symbol trials: around 170ms and around 300ms after stimulus onset.
This result supported our hypothesis. In addition, the result suggests
that Kanji character lexical access may be fully completed by around
260ms after stimulus onset.
Abstract: The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the type of the fault. MATLAB/Simulink is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed scheme for fault diagnosis is able to classify all the faults on the parallel transmission line rapidly and correctly.
Abstract: The purposes of the paper are to know and improve
the legal awareness of Surakarta local government officer to Law
No.12/2006 on Citizenship in vanishing civic discrimination to
Chinese Indonesians. Some issues of the Chinese Indonesian are the
definitions of Indonesian citizens and native Indonesian people or
“warga negara Indonesia (WNI) asli" and their obligation to show
Indonesian Citizenship Certificate (SBKRI) in processing civil
documents. Legal awareness of Surakarta local government officer
can be categorized as “legal knowledge" only. They know the laws
but they do not implement it yet. Nevertheless, at least this research
has given the new awareness in citizenship law for the officers.
Abstract: Linearization of graph embedding has been emerged
as an effective dimensionality reduction technique in pattern
recognition. However, it may not be optimal for nonlinearly
distributed real world data, such as face, due to its linear nature. So, a
kernelization of graph embedding is proposed as a dimensionality
reduction technique in face recognition. In order to further boost the
recognition capability of the proposed technique, the Fisher-s
criterion is opted in the objective function for better data
discrimination. The proposed technique is able to characterize the
underlying intra-class structure as well as the inter-class separability.
Experimental results on FRGC database validate the effectiveness of
the proposed technique as a feature descriptor.
Abstract: In this paper, a second order autoregressive (AR)
model is proposed to discriminate alcoholics using single trial
gamma band Visual Evoked Potential (VEP) signals using 3 different
classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN),
Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear
Discriminant (LD). Electroencephalogram (EEG) signals were
recorded from alcoholic and control subjects during the presentation
of visuals from Snodgrass and Vanderwart picture set. Single trial
VEP signals were extracted from EEG signals using Elliptic filtering
in the gamma band spectral range. A second order AR model was
used as gamma band VEP exhibits pseudo-periodic behaviour and
second order AR is optimal to represent this behaviour. This
circumvents the requirement of having to use some criteria to choose
the correct order. The averaged discrimination errors of 2.6%, 2.8%
and 11.9% were given by LD, MLP-BP and SFA classifiers. The
high LD discrimination results show the validity of the proposed
method to discriminate between alcoholic subjects.
Abstract: In this study, the problem of discriminating between interictal epileptic and non- epileptic pathological EEG cases, which present episodic loss of consciousness, investigated. We verify the accuracy of the feature extraction method of autocross-correlated coefficients which extracted and studied in previous study. For this purpose we used in one hand a suitable constructed artificial supervised LVQ1 neural network and in other a cross-correlation technique. To enforce the above verification we used a statistical procedure which based on a chi- square control. The classification and the statistical results showed that the proposed feature extraction is a significant accurate method for diagnostic discrimination cases between interictal and non-interictal EEG events and specifically the classification procedure showed that the LVQ neural method is superior than the cross-correlation one.
Abstract: In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.
Abstract: For identifying the discriminative sequence features between exons and introns, a new paradigm, rescaled-range frameshift analysis (RRFA), was proposed. By RRFA, two new
sequence features, the frameshift sensitivity (FS) and the accumulative
penta-mer complexity (APC), were discovered which
were further integrated into a new feature of larger scale, the persistency in anti-mutation (PAM). The feature-validation experiments
were performed on six model organisms to test the power
of discrimination. All the experimental results highly support that FS, APC and PAM were all distinguishing features between exons
and introns. These identified new sequence features provide new insights into the sequence composition of genes and they have
great potentials of forming a new basis for recognizing the exonintron boundaries in gene sequences.
Abstract: We propose a method for discrimination and
classification of ovarian with benign, malignant and normal tissue
using independent component analysis and neural networks. The
method was tested for a proteomic patters set from A database, and
radial basis functions neural networks. The best performance was
obtained with probabilistic neural networks, resulting I 99% success
rate, with 98% of specificity e 100% of sensitivity.