Abstract: Performance appraisal of employee is important in
managing the human resource of an organization. With the change
towards knowledge-based capitalism, maintaining talented
knowledge workers is critical. However, management classification
of “outstanding", “poor" and “average" performance may not be an
easy decision. Besides that, superior might also tend to judge the
work performance of their subordinates informally and arbitrarily
especially without the existence of a system of appraisal. In this
paper, we propose a performance appraisal system using
multifactorial evaluation model in dealing with appraisal grades
which are often express vaguely in linguistic terms. The proposed
model is for evaluating staff performance based on specific
performance appraisal criteria. The project was collaboration with
one of the Information and Communication Technology company in
Malaysia with reference to its performance appraisal process.
Abstract: In the era of great competition, understanding and satisfying
customers- requirements are the critical tasks for a company
to make a profits. Customer relationship management (CRM) thus
becomes an important business issue at present. With the help of
the data mining techniques, the manager can explore and analyze
from a large quantity of data to discover meaningful patterns and
rules. Among all methods, well-known association rule is most
commonly seen. This paper is based on Apriori algorithm and uses
genetic algorithms combining a data mining method to discover fuzzy
classification rules. The mined results can be applied in CRM to
help decision marker make correct business decisions for marketing
strategies.
Abstract: Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.
Abstract: The paper discusses the mathematics of pattern
indexing and its applications to recognition of visual patterns that are
found in video clips. It is shown that (a) pattern indexes can be
represented by collections of inverted patterns, (b) solutions to
pattern classification problems can be found as intersections and
histograms of inverted patterns and, thus, matching of original
patterns avoided.
Abstract: The new idea of this research is application of a new fault detection and isolation (FDI) technique for supervision of sensor networks in transportation system. In measurement systems, it is necessary to detect all types of faults and failures, based on predefined algorithm. Last improvements in artificial neural network studies (ANN) led to using them for some FDI purposes. In this paper, application of new probabilistic neural network features for data approximation and data classification are considered for plausibility check in temperature measurement. For this purpose, two-phase FDI mechanism was considered for residual generation and evaluation.
Abstract: Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.
Abstract: Emerging Bio-engineering fields such as Brain
Computer Interfaces, neuroprothesis devices and modeling and
simulation of neural networks have led to increased research activity
in algorithms for the detection, isolation and classification of Action
Potentials (AP) from noisy data trains. Current techniques in the field
of 'unsupervised no-prior knowledge' biosignal processing include
energy operators, wavelet detection and adaptive thresholding. These
tend to bias towards larger AP waveforms, AP may be missed due to
deviations in spike shape and frequency and correlated noise
spectrums can cause false detection. Also, such algorithms tend to
suffer from large computational expense.
A new signal detection technique based upon the ideas of phasespace
diagrams and trajectories is proposed based upon the use of a
delayed copy of the AP to highlight discontinuities relative to
background noise. This idea has been used to create algorithms that
are computationally inexpensive and address the above problems.
Distinct AP have been picked out and manually classified from
real physiological data recorded from a cockroach. To facilitate
testing of the new technique, an Auto Regressive Moving Average
(ARMA) noise model has been constructed bases upon background
noise of the recordings. Along with the AP classification means this
model enables generation of realistic neuronal data sets at arbitrary
signal to noise ratio (SNR).
Abstract: Modern manufacturing facilities are large scale,
highly complex, and operate with large number of variables under
closed loop control. Early and accurate fault detection and diagnosis
for these plants can minimise down time, increase the safety of plant
operations, and reduce manufacturing costs. Fault detection and
isolation is more complex particularly in the case of the faulty analog
control systems. Analog control systems are not equipped with
monitoring function where the process parameters are continually
visualised. In this situation, It is very difficult to find the relationship
between the fault importance and its consequences on the product
failure. We consider in this paper an approach to fault detection and
analysis of its effect on the production quality using an adaptive
centring and scaling in the pickling process in cold rolling. The fault
appeared on one of the power unit driving a rotary machine, this
machine can not track a reference speed given by another machine.
The length of metal loop is then in continuous oscillation, this affects
the product quality. Using a computerised data acquisition system,
the main machine parameters have been monitored. The fault has
been detected and isolated on basis of analysis of monitored data.
Normal and faulty situation have been obtained by an artificial neural
network (ANN) model which is implemented to simulate the normal
and faulty status of rotary machine. Correlation between the product
quality defined by an index and the residual is used to quality
classification.
Abstract: Segmentation is an important step in medical image
analysis and classification for radiological evaluation or computer
aided diagnosis. The CAD (Computer Aided Diagnosis ) of lung CT
generally first segment the area of interest (lung) and then analyze
the separately obtained area for nodule detection in order to
diagnosis the disease. For normal lung, segmentation can be
performed by making use of excellent contrast between air and
surrounding tissues. However this approach fails when lung is
affected by high density pathology. Dense pathologies are present in
approximately a fifth of clinical scans, and for computer analysis
such as detection and quantification of abnormal areas it is vital that
the entire and perfectly lung part of the image is provided and no
part, as present in the original image be eradicated. In this paper we
have proposed a lung segmentation technique which accurately
segment the lung parenchyma from lung CT Scan images. The
algorithm was tested against the 25 datasets of different patients
received from Ackron Univeristy, USA and AGA Khan Medical
University, Karachi, Pakistan.
Abstract: Monitoring lightning electromagnetic pulses (sferics) and other terrestrial as well as extraterrestrial transient radiation signals is of considerable interest for practical and theoretical purposes in astro- and geophysics as well as meteorology. Managing a continuous flow of data, automation of the analysis and classification process is important. Features based on a combination of wavelet and statistical methods proved efficient for this task and serve as input into a radial basis function network that is trained to discriminate transient shapes from pulse like to wave like. We concentrate on signals in the Very Low Frequency (VLF, 3 -30 kHz) range in this paper, but the developed methods are independent of this specific choice.
Abstract: In this paper, a novel method for recognition of musical
instruments in a polyphonic music is presented by using an
embedded hidden Markov model (EHMM). EHMM is a doubly
embedded HMM structure where each state of the external HMM
is an independent HMM. The classification is accomplished for
two different internal HMM structures where GMMs are used as
likelihood estimators for the internal HMMs. The results are compared
to those achieved by an artificial neural network with two
hidden layers. Appropriate classification accuracies were achieved
both for solo instrument performance and instrument combinations
which demonstrates that the new approach outperforms the similar
classification methods by means of the dynamic of the signal.
Abstract: Computer languages are usually lumped together
into broad -paradigms-, leaving us in want of a finer classification
of kinds of language. Theories distinguishing between -genuine
differences- in language has been called for, and we propose that
such differences can be observed through a notion of expressive mode.
We outline this concept, propose how it could be operationalized and
indicate a possible context for the development of a corresponding
theory. Finally we consider a possible application in connection
with evaluation of language revision. We illustrate this with a case,
investigating possible revisions of the relational algebra in order to
overcome weaknesses of the division operator in connection with
universal queries.
Abstract: As the Internet continues to grow at a rapid pace as
the primary medium for communications and commerce and as
telecommunication networks and systems continue to expand their
global reach, digital information has become the most popular and
important information resource and our dependence upon the
underlying cyber infrastructure has been increasing significantly.
Unfortunately, as our dependency has grown, so has the threat to the
cyber infrastructure from spammers, attackers and criminal
enterprises. In this paper, we propose a new machine learning based
network intrusion detection framework for cyber security. The
detection process of the framework consists of two stages: model
construction and intrusion detection. In the model construction stage,
a semi-supervised machine learning algorithm is applied to a
collected set of network audit data to generate a profile of normal
network behavior and in the intrusion detection stage, input network
events are analyzed and compared with the patterns gathered in the
profile, and some of them are then flagged as anomalies should these
events are sufficiently far from the expected normal behavior. The
proposed framework is particularly applicable to the situations where
there is only a small amount of labeled network training data
available, which is very typical in real world network environments.
Abstract: Feature selection study is gaining importance due to its contribution to save classification cost in terms of time and computation load. In search of essential features, one of the methods to search the features is via the decision tree. Decision tree act as an intermediate feature space inducer in order to choose essential features. In decision tree-based feature selection, some studies used decision tree as a feature ranker with a direct threshold measure, while others remain the decision tree but utilized pruning condition that act as a threshold mechanism to choose features. This paper proposed threshold measure using Manhattan Hierarchical Cluster distance to be utilized in feature ranking in order to choose relevant features as part of the feature selection process. The result is promising, and this method can be improved in the future by including test cases of a higher number of attributes.
Abstract: Classification of video sequences based on their contents is a vital process for adaptation techniques. It helps decide which adaptation technique best fits the resource reduction requested by the client. In this paper we used the principal feature analysis algorithm to select a reduced subset of video features. The main idea is to select only one feature from each class based on the similarities between the features within that class. Our results showed that using this feature reduction technique the source video features can be completely omitted from future classification of video sequences.
Abstract: The purpose of planned islanding is to construct a
power island during system disturbances which are commonly
formed for maintenance purpose. However, in most of the cases
island mode operation is not allowed. Therefore distributed
generators (DGs) must sense the unplanned disconnection from the
main grid. Passive technique is the most commonly used method for
this purpose. However, it needs improvement in order to identify the
islanding condition. In this paper an effective method for
identification of islanding condition based on phase space and neural
network techniques has been developed. The captured voltage
waveforms at the coupling points of DGs are processed to extract the
required features. For this purposed a method known as the phase
space techniques is used. Based on extracted features, two neural
network configuration namely radial basis function and probabilistic
neural networks are trained to recognize the waveform class.
According to the test result, the investigated technique can provide
satisfactory identification of the islanding condition in the
distribution system.
Abstract: From an economic standpoint the current and future
road traffic situation in urban areas is a cost factor. Traffic jams and
congestion prolong journey times and tie up resources in trucks and
personnel. Many discussions about imposing charges or tolls for
cities in Europe in order to reduce traffic congestion are currently in
progress. Both of these effects lead – directly or indirectly - to
additional costs for the urban distribution systems in retail
companies. One approach towards improving the efficiency of retail
distribution systems, and thus towards avoiding negative
environmental factors in urban areas, is horizontal collaboration for
deliveries to retail outlets – Urban Retail Logistics. This paper
presents a classification system to help reveal where cooperation
between retail companies is possible and makes sense for deliveries
to retail outlets in urban areas.
Abstract: This paper presents a new classification algorithm using colour and texture for obstacle detection. Colour information is computationally cheap to learn and process. However in many cases, colour alone does not provide enough information for classification. Texture information can improve classification performance but usually comes at an expensive cost. Our algorithm uses both colour and texture features but texture is only needed when colour is unreliable. During the training stage, texture features are learned specifically to improve the performance of a colour classifier. The algorithm learns a set of simple texture features and only the most effective features are used in the classification stage. Therefore our algorithm has a very good classification rate while is still fast enough to run on a limited computer platform. The proposed algorithm was tested with a challenging outdoor image set. Test result shows the algorithm achieves a much better trade-off between classification performance and efficiency than a typical colour classifier.
Abstract: The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.
Abstract: For the characterization of the weld defect region in the radiographic image, looking for features which are invariant regarding the geometrical transformations (rotation, translation and scaling) proves to be necessary because the same defect can be seen from several angles according to the orientation and the distance from the welded framework to the radiation source. Thus, panoply of geometrical attributes satisfying the above conditions is proposed and which result from the calculation of the geometrical parameters (surface, perimeter, etc.) on the one hand and the calculation of the different order moments, on the other hand. Because the large range in values of the raw features and taking into account other considerations imposed by some classifiers, the scaling of these values to lie between 0 and 1 is indispensable. The principal component analysis technique is used in order to reduce the number of the attribute variables in the aim to give better performance to the further defect classification.