Abstract: Neural networks are well known for their ability to
model non linear functions, but as statistical methods usually does,
they use a no parametric approach thus, a priori knowledge is not
obvious to be taken into account no more than the a posteriori
knowledge. In order to deal with these problematics, an original way
to encode the knowledge inside the architecture is proposed. This
method is applied to the problem of the evapotranspiration inside
karstic aquifer which is a problem of huge utility in order to deal
with water resource.
Abstract: In this study, any possible differences between mathematics beliefs and anxiety of prospective elementary mathematics teachers have been investigated according to their gender. In this purpose, 1st, 2nd, 3rd and 4th grade students from a Government University in Turkey were selected as a sample. Mathematics Teaching Anxiety Scale (MATAS) and Beliefs About Mathematics Survey (BAMS) has been used as data collection tools. As a result of the study, it has been observed that prospective male teachers have more instrumentalist approach in learning mathematics than females according to their mathematical beliefs. On the other hand, females have more mathematics teaching anxiety than males especially, for subject knowledge in mathematics and selfconfidence.
Abstract: In this paper, a new learning algorithm based on a
hybrid metaheuristic integrating Differential Evolution (DE) and
Reduced Variable Neighborhood Search (RVNS) is introduced to train
the classification method PROAFTN. To apply PROAFTN, values of
several parameters need to be determined prior to classification. These
parameters include boundaries of intervals and relative weights for
each attribute. Based on these requirements, the hybrid approach,
named DEPRO-RVNS, is presented in this study. In some cases, the
major problem when applying DE to some classification problems
was the premature convergence of some individuals to local optima.
To eliminate this shortcoming and to improve the exploration and
exploitation capabilities of DE, such individuals were set to iteratively
re-explored using RVNS. Based on the generated results on
both training and testing data, it is shown that the performance of
PROAFTN is significantly improved. Furthermore, the experimental
study shows that DEPRO-RVNS outperforms well-known machine
learning classifiers in a variety of problems.
Abstract: Postgraduate education is generally aimed at providing in-depth knowledge and understanding that include general philosophy in the world sciences, management, technologies, applications and other elements closely related to specific areas. In most universities, besides core and non-core subjects, a thesis is one of the requirements for the postgraduate student to accomplish before graduating. This paper reports on the empirical investigation into attributes that are associated with the obstacles to thesis accomplishment among postgraduate students. Using the quantitative approach the experiences of postgraduate students were tapped. Findings clearly revealed that information seeking, writing skills and other factors which refer to supervisor and time management, in particular, are recognized as contributory factors which positively or negatively influence postgraduates’ thesis accomplishment. Among these, writing skills dimensions were found to be the most difficult process in thesis accomplishment compared to information seeking and other factors. This pessimistic indication has provided some implications not only for the students but supervisors and institutions as a whole.
Abstract: In this paper, we present symbolic recognition models to extract knowledge characterized by document structures. Focussing on the extraction and the meticulous exploitation of the semantic structure of documents, we obtain a meaningful contextual tagging corresponding to different unit types (title, chapter, section, enumeration, etc.).
Abstract: This paper investigates the effects of knowledge-based acceleration feedback control integrated with Automatic Generation Control (AGC) to enhance the quality of frequency control of governing system. The Intelligent Acceleration Feedback Controller (IAFC) is proposed to counter the over and under frequency occurrences due to major load change in power system network. Therefore, generator tripping and load shedding operations can be reduced. Meanwhile, the integration of IAFC with AGC, a well known Load-Frequency Control (LFC) is essential to ensure the system frequency is restored to the nominal value. Computer simulations of frequency response of governing system are used to optimize the parameters of IAFC. As a result, there is substantial improvement on the LFC of governing system that employing the proposed control strategy.
Abstract: Creativity is often based on an unorthodox
recombination of knowledge; in fact: 80% of all innovations use
given knowledge and put it into a new combination. Cross-industry
innovations follow this way of thinking and bring together problems
and solution ideas from different industries. Therefore analogies and
search strategies have to be developed. Taking this path, the
questions where to search, what to search and how to search have to
be answered. Afterwards, the gathered information can be used
within a planned search process. Identified solution ideas have to be
assessed and analyzed in detail for the success promising adaption
planning.
Abstract: One main drawback of intrusion detection system is the
inability of detecting new attacks which do not have known
signatures. In this paper we discuss an intrusion detection method
that proposes independent component analysis (ICA) based feature
selection heuristics and using rough fuzzy for clustering data. ICA is
to separate these independent components (ICs) from the monitored
variables. Rough set has to decrease the amount of data and get rid of
redundancy and Fuzzy methods allow objects to belong to several
clusters simultaneously, with different degrees of membership. Our
approach allows us to recognize not only known attacks but also to
detect activity that may be the result of a new, unknown attack. The
experimental results on Knowledge Discovery and Data Mining-
(KDDCup 1999) dataset.
Abstract: The Application of e-health solutions has brought superb advancements in the health care industry. E-health solutions have already been embraced in the industrialized countries. In an effort to catch up with the growth, the developing countries have strived to revolutionize the healthcare industry by use of Information technology in different ways. Based on a technology assessment carried out in Kenya – one of the developing countries – and using multiple case studies in Nyanza Province, this work focuses on an investigation on how five rural hospitals are adapting to the technology shift. The issues examined include the ICT infrastructure and e-health technologies in place, the knowledge of participants in terms of benefits gained through the use of ICT and the challenges posing barriers to the use of ICT technologies in these hospitals. The results reveal that the ICT infrastructure in place is inadequate for e-health implementations as a result to various challenges that exist. Consequently, suggestions on how to tackle the various challenges have been addressed in this paper.
Abstract: In non destructive testing by radiography, a perfect
knowledge of the weld defect shape is an essential step to
appreciate the quality of the weld and make decision on its
acceptability or rejection. Because of the complex nature of the
considered images, and in order that the detected defect region
represents the most accurately possible the real defect, the choice
of thresholding methods must be done judiciously. In this paper,
performance criteria are used to conduct a comparative study of
four non parametric histogram thresholding methods for automatic
extraction of weld defect in radiographic images.
Abstract: Contact centres have been exemplars of scientific management in the discipline of operations management for more than a decade now. With the movement of industries from a resource based economy to knowledge based economy businesses have started to realize the customer eccentricity being the key to sustainability amidst high velocity of the market. However, as technologies have converged and advanced, so have the contact centres. Contact Centres have redirected the supply chains and the concept of retailing is highly diminished due to over exaggeration of cost reduction strategies. In conditions of high environmental velocity together with services featuring considerable information intensity contact centres will require up to date and enlightened agents to satisfy the demands placed upon them by those requesting their services. In this paper we examine salient factors such as Power Distance, Knowledge structures and the dynamics of job specialisation and enlargement to suggest critical success factors in the domain of contact centres.
Abstract: We introduce an extended resource leveling model that abstracts real life projects that consider specific work ranges for each resource. Contrary to traditional resource leveling problems this model considers scarce resources and multiple objectives: the minimization of the project makespan and the leveling of each resource usage over time. We formulate this model as a multiobjective optimization problem and we propose a multiobjective genetic algorithm-based solver to optimize it. This solver consists in a two-stage process: a main stage where we obtain non-dominated solutions for all the objectives, and a postprocessing stage where we seek to specifically improve the resource leveling of these solutions. We propose an intelligent encoding for the solver that allows including domain specific knowledge in the solving mechanism. The chosen encoding proves to be effective to solve leveling problems with scarce resources and multiple objectives. The outcome of the proposed solvers represent optimized trade-offs (alternatives) that can be later evaluated by a decision maker, this multi-solution approach represents an advantage over the traditional single solution approach. We compare the proposed solver with state-of-art resource leveling methods and we report competitive and performing results.
Abstract: Modeling of complex dynamic systems, which are
very complicated to establish mathematical models, requires new and
modern methodologies that will exploit the existing expert
knowledge, human experience and historical data. Fuzzy cognitive
maps are very suitable, simple, and powerful tools for simulation and
analysis of these kinds of dynamic systems. However, human experts
are subjective and can handle only relatively simple fuzzy cognitive
maps; therefore, there is a need of developing new approaches for an
automated generation of fuzzy cognitive maps using historical data.
In this study, a new learning algorithm, which is called Big Bang-Big
Crunch, is proposed for the first time in literature for an automated
generation of fuzzy cognitive maps from data. Two real-world
examples; namely a process control system and radiation therapy
process, and one synthetic model are used to emphasize the
effectiveness and usefulness of the proposed methodology.
Abstract: Text similarity measurement is a fundamental issue in
many textual applications such as document clustering, classification,
summarization and question answering. However, prevailing approaches
based on Vector Space Model (VSM) more or less suffer
from the limitation of Bag of Words (BOW), which ignores the semantic
relationship among words. Enriching document representation
with background knowledge from Wikipedia is proven to be an effective
way to solve this problem, but most existing methods still
cannot avoid similar flaws of BOW in a new vector space. In this
paper, we propose a novel text similarity measurement which goes
beyond VSM and can find semantic affinity between documents.
Specifically, it is a unified graph model that exploits Wikipedia as
background knowledge and synthesizes both document representation
and similarity computation. The experimental results on two different
datasets show that our approach significantly improves VSM-based
methods in both text clustering and classification.
Abstract: An important structuring mechanism for knowledge bases is building clusters based on the content of their knowledge objects. The objects are clustered based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. In this paper, a set of related HPRs is called a cluster and is represented by a HPR-tree. This paper discusses an algorithm based on cumulative learning scenario for dynamic structuring of clusters. The proposed scheme incrementally incorporates new knowledge into the set of clusters from the previous episodes and also maintains summary of clusters as Synopsis to be used in the future episodes. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested incremental structuring of clusters would be useful in mining data streams.
Abstract: A learning content management system (LCMS) is an
environment to support web-based learning content development.
Primary function of the system is to manage the learning process as
well as to generate content customized to meet a unique requirement
of each learner. Among the available supporting tools offered by
several vendors, we propose to enhance the LCMS functionality to
individualize the presented content with the induction ability. Our
induction technique is based on rough set theory. The induced rules
are intended to be the supportive knowledge for guiding the content
flow planning. They can also be used as decision rules to help
content developers on managing content delivered to individual
learner.
Abstract: Discrete Cosine Transform (DCT) based transform coding is very popular in image, video and speech compression due to its good energy compaction and decorrelating properties. However, at low bit rates, the reconstructed images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. Lapped transform was proposed as an alternative to the DCT with reduced blocking artifacts and increased coding gain. Lapped transforms are popular for their good performance, robustness against oversmoothing and availability of fast implementation algorithms. However, there is no proper study reported in the literature regarding the statistical distributions of block Lapped Orthogonal Transform (LOT) and Lapped Biorthogonal Transform (LBT) coefficients. This study performs two goodness-of-fit tests, the Kolmogorov-Smirnov (KS) test and the 2- test, to determine the distribution that best fits the LOT and LBT coefficients. The experimental results show that the distribution of a majority of the significant AC coefficients can be modeled by the Generalized Gaussian distribution. The knowledge of the statistical distribution of transform coefficients greatly helps in the design of optimal quantizers that may lead to minimum distortion and hence achieve optimal coding efficiency.
Abstract: Knowledge and these notions have become more and
more important and we speak about a knowledge based society
today. A lot of small and big companies have reacted upon these new
challenges. But there is a deep abyss about knowledge conception
and practice between the professional researchers and company - life.
The question of this research was: How can small and mediumsized
companies be equal to the demands of new economy?
Questionnaires were used in this research and a special segment of
the native knowledge based on economy was focused on.
Researchers would have liked to know what the sources of success
are and how they can be in connection with questions of knowledge
acquisition, knowledge transfer, knowledge utilization in small and
medium-sized companies. These companies know that they have to
change their behaviour and thinking, but they are not on the suitable
level that they can compete with bigger or multinational companies.
Abstract: Information and Communications Technologies (ICT) has been integrated in education in many developing and developed countries alike, but the use of ICT in Tanzanian schools is dismal. Many Tanzanian secondary schools have no computers. The few schools with computers use them primarily for secretarial services and computer literacy training. The Tanzanian education system at other levels like secondary school level has to undergo substantial transformation, underscored by the growing application of new information and communication technology. This paper presents the e-readiness survey result from secondary schools in Tanzania. The paper also suggests how Tanzania can make use of the few present ICT resources to support and improve teaching and learning functions to improve performance and acquisition of knowledge by using e-Learning Management System (e-LMS).
Abstract: The objectives of this research were to explore factors
influencing knowledge management process in the manufacturing
industry and develop a model to support knowledge management
processes. The studied factors were technology infrastructure, human
resource, knowledge sharing, and the culture of the organization. The
knowledge management processes included discovery, capture,
sharing, and application. Data were collected through questionnaires
and analyzed using multiple linear regression and multiple
correlation. The results found that technology infrastructure, human
resource, knowledge sharing, and culture of the organization
influenced the discovery and capture processes. However, knowledge
sharing had no influence in sharing and application processes. A
model to support knowledge management processes was developed,
which indicated that sharing knowledge needed further improvement
in the organization.