Abstract: Nowadays, education cannot be imagined without digital technologies. It broadens the horizons of teaching learning processes. Several universities are offering online courses. For evaluation purpose, e-examination systems are being widely adopted in academic environments. Multiple-choice tests are extremely popular. Moving away from traditional examinations to e-examination, Moodle as Learning Management Systems (LMS) is being used. Moodle logs every click that students make for attempting and navigational purposes in e-examination. Data mining has been applied in various domains including retail sales, bioinformatics. In recent years, there has been increasing interest in the use of data mining in e-learning environment. It has been applied to discover, extract, and evaluate parameters related to student’s learning performance. The combination of data mining and e-learning is still in its babyhood. Log data generated by the students during online examination can be used to discover knowledge with the help of data mining techniques. In web based applications, number of right and wrong answers of the test result is not sufficient to assess and evaluate the student’s performance. So, assessment techniques must be intelligent enough. If student cannot answer the question asked by the instructor then some easier question can be asked. Otherwise, more difficult question can be post on similar topic. To do so, it is necessary to identify difficulty level of the questions. Proposed work concentrate on the same issue. Data mining techniques in specific clustering is used in this work. This method decide difficulty levels of the question and categories them as tough, easy or moderate and later this will be served to the desire students based on their performance. Proposed experiment categories the question set and also group the students based on their performance in examination. This will help the instructor to guide the students more specifically. In short mined knowledge helps to support, guide, facilitate and enhance learning as a whole.
Abstract: With the strengthened regulation on the mandatory use
of recycled aggregate, development of construction materials using
recycled aggregate has recently increased. This study aimed to secure
the performance of asphalt concrete mixture by developing
recycled-modified asphalt using recycled basalt aggregate from the
Jeju area. The strength of the basalt aggregate from the Jeju area used
in this study was similar to that of general aggregate, while the specific
surface area was larger due to the development of pores. Modified
asphalt was developed using a general aggregate-recycled aggregate
ratio of 7:3, and the results indicated that the Marshall stability
increased by 27% compared to that of asphalt concrete mixture using
only general aggregate, and the flow values showed similar levels.
Also, the indirect tensile strength increased by 79%, and the toughness
increased by more than 100%. In addition, the TSR for examining
moisture resistance was 0.95 indicating that the reduction in the
indirect tensile strength due to moisture was very low (5% level), and
the developed recycled-modified asphalt could satisfy all the quality
standards of asphalt concrete mixture.
Abstract: Energy consumption data, in particular those involving
public buildings, are impacted by many factors: the building structure,
climate/environmental parameters, construction, system operating
condition, and user behavior patterns. Traditional methods for data
analysis are insufficient. This paper delves into the data mining
technology to determine its application in the analysis of building
energy consumption data including energy consumption prediction,
fault diagnosis, and optimal operation. Recent literature are reviewed
and summarized, the problems faced by data mining technology in the
area of energy consumption data analysis are enumerated, and research
points for future studies are given.
Abstract: Scripts are one of the basic text resources to understand
broadcasting contents. Topic modeling is the method to get the
summary of the broadcasting contents from its scripts. Generally,
scripts represent contents descriptively with directions and speeches,
and provide scene segments that can be seen as semantic units.
Therefore, a script can be topic modeled by treating a scene segment
as a document. Because scene segments consist of speeches mainly,
however, relatively small co-occurrences among words in the scene
segments are observed. This causes inevitably the bad quality of
topics by statistical learning method. To tackle this problem, we
propose a method to improve topic quality with additional word
co-occurrence information obtained using scene similarities. The
main idea of improving topic quality is that the information that
two or more texts are topically related can be useful to learn high
quality of topics. In addition, more accurate topical representations
lead to get information more accurate whether two texts are related
or not. In this paper, we regard two scene segments are related
if their topical similarity is high enough. We also consider that
words are co-occurred if they are in topically related scene segments
together. By iteratively inferring topics and determining semantically
neighborhood scene segments, we draw a topic space represents
broadcasting contents well. In the experiments, we showed the
proposed method generates a higher quality of topics from Korean
drama scripts than the baselines.
Abstract: The early-stage damage detection in offshore
structures requires continuous structural health monitoring and for the
large area the position of sensors will also plays an important role in
the efficient damage detection. Determining the dynamic behavior of
offshore structures requires dense deployment of sensors. The wired
Structural Health Monitoring (SHM) systems are highly expensive
and always needs larger installation space to deploy. Wireless sensor
networks can enhance the SHM system by deployment of scalable
sensor network, which consumes lesser space. This paper presents the
results of wireless sensor network based Structural Health Monitoring
method applied to a scaled experimental model of offshore structure
that underwent wave loading. This method determines the
serviceability of the offshore structure which is subjected to various
environment loads. Wired and wireless sensors were installed in the
model and the response of the scaled BLSRP model under wave
loading was recorded. The wireless system discussed in this study is
the Raspberry pi board with Arm V6 processor which is programmed
to transmit the data acquired by the sensor to the server using Wi-Fi
adapter, the data is then hosted in the webpage. The data acquired
from the wireless and wired SHM systems were compared and the
design of the wireless system is verified.
Abstract: In this research, we propose to conduct diagnostic and
predictive analysis about the key factors and consequences of urban
population relocation. To achieve this goal, urban simulation models
extract the urban development trends as land use change patterns from
a variety of data sources. The results are treated as part of urban big
data with other information such as population change and economic
conditions. Multiple data mining methods are deployed on this data to
analyze nonlinear relationships between parameters. The result
determines the driving force of population relocation with respect to
urban sprawl and urban sustainability and their related parameters.
This work sets the stage for developing a comprehensive urban
simulation model for catering to specific questions by targeted users. It
contributes towards achieving sustainability as a whole.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: Test automation allows performing difficult and time
consuming manual software testing tasks efficiently, quickly and
repeatedly. However, development and maintenance of automated
tests is expensive, so it needs a proper prioritization what to automate
first. This paper describes a simple yet efficient approach for such
prioritization of test cases based on the effort needed for both manual
execution and software test automation. The suggested approach is
very flexible because it allows working with a variety of assessment
methods, and adding or removing new candidates at any time. The
theoretical ideas presented in this article have been successfully
applied in real world situations in several software companies by the
authors and their colleagues including testing of real estate websites,
cryptographic and authentication solutions, OSGi-based middleware
framework that has been applied in various systems for smart homes,
connected cars, production plants, sensors, home appliances, car head
units and engine control units (ECU), vending machines, medical
devices, industry equipment and other devices that either contain or
are connected to an embedded service gateway.
Abstract: Issues relating to the destructive phenomena that can
damage people and goods have returned to the centre of debate in
Italy with the increase in catastrophic episodes in recent years in a
country which is highly vulnerable to hydrological risk.
Environmental factors and geological and geomorphological
territorial characteristics play an important role in determining the
level of vulnerability and the natural tendency to risk. However, a
territory has also been subjected to the requirements of and
transformations of society and this brings other relevant factors. The
reasons for the increase in destructive phenomena are often to be
found in the territorial development models adopted. Stewardship of
the landscape and management of risk are related issues. This study aims to summarize the most relevant elements about
this connection and at the same time to clarify the role of
environmental risk assessment as a tool to aid in the sustainable
management of landscape. Finally, the study reflects on how regional
and urban planners deal with environmental risk and which aspects
should be monitored in order to adopt responsible and useful
interventions.
Abstract: Web Usage Mining is the application of data mining
techniques to find usage patterns from web log data, so as to grasp
required patterns and serve the requirements of Web-based
applications. User’s expertise on the internet may be improved by
minimizing user’s web access latency. This may be done by
predicting the future search page earlier and the same may be prefetched
and cached. Therefore, to enhance the standard of web
services, it is needed topic to research the user web navigation
behavior. Analysis of user’s web navigation behavior is achieved
through modeling web navigation history. We propose this technique
which cluster’s the user sessions, based on the K-medoids technique.
Abstract: A myriad of environmental issues face the Nigerian
industrial region, resulting from; oil and gas production, mining,
manufacturing and domestic wastes. Amidst these, much effort has
been directed by stakeholders in the Nigerian oil producing regions,
because of the impacts of the region on the wider Nigerian economy.
Although collaborative environmental management has been noted as
an effective approach in managing environmental issues, little
attention has been given to the roles and practices of stakeholders in
effecting a collaborative environmental management framework for
the Nigerian oil-producing region. This paper produces a framework
to expand and deepen knowledge relating to stakeholders aspects of
collaborative roles in managing environmental issues in the Nigeria
oil-producing region. The knowledge is derived from analysis of
stakeholders’ practices – studied through multiple case studies using
document analysis. Selected documents of key stakeholders –
Nigerian government agencies, multi-national oil companies and host
communities, were analyzed. Open and selective coding was
employed manually during document analysis of data collected from
the offices and websites of the stakeholders. The findings showed
that the stakeholders have a range of roles, practices, interests, drivers
and barriers regarding their collaborative roles in managing
environmental issues. While they have interests for efficient resource
use, compliance to standards, sharing of responsibilities, generating
of new solutions, and shared objectives; there is evidence of major
barriers and these include resource allocation, disjointed policy,
ineffective monitoring, diverse socio- economic interests, lack of
stakeholders’ commitment and limited knowledge sharing. However,
host communities hold deep concerns over the collaborative roles of
stakeholders for economic interests, particularly, where government
agencies and multi-national oil companies are involved. With these
barriers and concerns, a genuine stakeholders’ collaboration is found
to be limited, and as a result, optimal environmental management
practices and policies have not been successfully implemented in the
Nigeria oil-producing region. A framework is produced that describes
practices that characterize collaborative environmental management
might be employed to satisfy the stakeholders’ interests. The
framework recommends critical factors, based on the findings, which
may guide a collaborative environmental management in the oil
producing regions. The recommendations are designed to re-define
the practices of stakeholders in managing environmental issues in the
oil producing regions, not as something wholly new, but as an
approach essential for implementing a sustainable environmental
policy. This research outcome may clarify areas for future research as
well as to contribute to industry guidance in the area of collaborative
environmental management.
Abstract: Recently, Job Recommender Systems have gained
much attention in industries since they solve the problem of
information overload on the recruiting website. Therefore, we
proposed Extended Personalized Job System that has the capability of
providing the appropriate jobs for job seeker and recommending
some suitable information for them using Data Mining Techniques
and Dynamic User Profile. On the other hands, company can also
interact to the system for publishing and updating job information.
This system have emerged and supported various platforms such as
web application and android mobile application. In this paper, User
profiles, Implicit User Action, User Feedback, and Clustering
Techniques in WEKA libraries were applied and implemented. In
additions, open source tools like Yii Web Application Framework,
Bootstrap Front End Framework and Android Mobile Technology
were also applied.
Abstract: The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
Abstract: The article deals with the personality of military
professionals (commanders) and their way of leading and
commanding today and in historical context. The first part focuses on
the leadership skills of Alexander the Great, who introduced strategic
innovations and even from today's perspective; he excelled in
efficient work with people. This paper focuses on the way which he
achieved his goals. Further attention is paid to approaches to
commander´s personality by other great generals. The paper is also
focused on personality traits of military professionals necessary for
successful management and leadership in today's variable and
challenging environment. Finally, attention is paid to the effective
and ineffective ways of behavior of commanders and determining
what styles of leadership is appropriate for a given situation, whether
in peacetime or on deployment.
Abstract: A seizure prediction method is proposed by extracting
global features using phase correlation between adjacent epochs for
detecting relative changes and local features using fluctuation/
deviation within an epoch for determining fine changes of different
EEG signals. A classifier and a regularization technique are applied
for the reduction of false alarms and improvement of the overall
prediction accuracy. The experiments show that the proposed method
outperforms the state-of-the-art methods and provides high prediction
accuracy (i.e., 97.70%) with low false alarm using EEG signals in
different brain locations from a benchmark data set.
Abstract: A model to predict the plastic zone size for material
under plane stress condition has been developed and verified
experimentally. The developed model is a function of crack size,
crack angle and material property (dislocation density). Simulation
and validation results show that the model developed show good
agreement with experimental results. Samples of low carbon steel
(0.035%C) with included surface crack angles of 45o, 50o, 60o, 70o
and 90o and crack depths of 2mm and 4mm were subjected to low
strain rate between 0.48 x 10-3 s-1 – 2.38 x 10-3 s-1. The mechanical
properties studied were ductility, tensile strength, modulus of
elasticity, yield strength, yield strain, stress at fracture and fracture
toughness. The experimental study shows that strain rate has no
appreciable effect on the size of plastic zone while crack depth and
crack angle plays an imperative role in determining the size of the
plastic zone of mild steel materials.
Abstract: The underutilization of biomass resources in the
Philippines, combined with its growing population and the rise in
fossil fuel prices confirms demand for alternative energy sources. The
goal of this paper is to provide a comparison of MODIS-based and
Landsat-based agricultural land cover maps when used in the
estimation of rice hull’s available energy potential. Biomass resource
assessment was done using mathematical models and remote sensing
techniques employed in a GIS platform.
Abstract: This research work presents the surface
thermodynamics approach to M-TB/HIV-Human sputum
interactions. This involved the use of the Hamaker coefficient
concept as a surface energetics tool in determining the interaction
processes, with the surface interfacial energies explained using van
der Waals concept of particle interactions. The Lifshitz derivation for
van der Waals forces was applied as an alternative to the contact
angle approach which has been widely used in other biological
systems. The methodology involved taking sputum samples from
twenty infected persons and from twenty uninfected persons for
absorbance measurement using a digital Ultraviolet visible
Spectrophotometer. The variables required for the computations with
the Lifshitz formula were derived from the absorbance data. The
Matlab software tools were used in the mathematical analysis of the
data produced from the experiments (absorbance values). The
Hamaker constants and the combined Hamaker coefficients were
obtained using the values of the dielectric constant together with the
Lifshitz Equation. The absolute combined Hamaker coefficients
A132abs and A131abs on both infected and uninfected sputum samples
gave the values of A132abs = 0.21631x10-21Joule for M-TB infected
sputum and Ã132abs = 0.18825x10-21Joule for M-TB/HIV infected
sputum. The significance of this result is the positive value of the
absolute combined Hamaker coefficient which suggests the existence
of net positive van der waals forces demonstrating an attraction
between the bacteria and the macrophage. This however, implies that
infection can occur. It was also shown that in the presence of HIV,
the interaction energy is reduced by 13% conforming adverse effects
observed in HIV patients suffering from tuberculosis.
Abstract: The post-rain puddles problem that occurs in the first
yard of Prambanan Temple are often disturbing visitor activity. A
poodle layer and a drainage system had ever built to avoid such a
problem, but puddles still did not stop appearing after rain.
Permeability parameter needs to be determined by using a simpler
procedure to find exact method of solution. The instrument modelling
was proposed according to the development of field permeability
testing instrument. This experiment used a proposed Constant
Discharge method. Constant Discharge method used a tube poured
with constant water flow from unsaturated until saturated soil
condition. Volumetric water content (θ) were monitored by soil
moisture measurement device. The results were correlations between
k and θ which were drawn by numerical approach from Van
Genutchen model. Parameters θr optimum value obtained from the
test was at very dry soil. Coefficient of permeability with a density of
19.8 kN/m3 for unsaturated conditions was in range of 3 x 10-6
cm/sec (Sr=68%) until 9.98 x 10-4 cm/sec (Sr=82%). The equipment
and testing procedure developed in this research was quite effective,
simple and easy to be implemented on determining field soil
permeability coefficient value of sandy soil. Using constant discharge
method in proposed permeability test, value of permeability
coefficient under unsaturated condition can be obtained without
establish soil water characteristic curve.
Abstract: The increase of technogenic and natural accidents,
accompanied by air pollution, for example, by combustion products,
leads to the necessity of respiratory protection. This work is devoted to the development of a calorimetric method
and a device which allows investigating quickly the kinetics of
carbon dioxide sorption by chemisorbents on the base of potassium
superoxide in order to assess the protective properties of respiratory
protective closed circuit apparatus. The features of the traditional approach for determining the
sorption properties in a thin layer of chemisorbent are described, as
well as methods and devices, which can be used for the sorption
kinetics study. The authors developed an approach (as opposed to the traditional
approach) based on the power measurement of internal heat sources
in the chemisorbent layer. The emergence of the heat sources is a
result of exothermic reaction of carbon dioxide sorption. This
approach eliminates the necessity of chemical analysis of samples
and can significantly reduce the time and material expenses during
chemisorbents testing. Error of determining the volume fraction of adsorbed carbon
dioxide by the developed method does not exceed 12%. Taking into
account the efficiency of the method, we consider that it is a good
alternative to traditional methods of chemical analysis under the
assessment of the protection sorbents quality.