Vol 16 No 2 - July-December 2016

ISSN: 1694-2507 (Print)
ISSN: 1694-2108 (Online)
International Journal of Computer Science
and Business Informatics
(IJCSBI.ORG)
VOL 16, NO 2
JULY-DECEMBER 2016

Table of Contents VOL 16, NO 2 JULY-DECEMBER 2016
Scalable Rough C-Means clustering using Firefly algorithm..................................................................1
Abhilash Namdev and B.K. Tripathy
Significance of Embedded Systems to IoT................................................................................................. 15
P. R. S. M. Lakshmi, P. Lakshmi Narayanamma and K. Santhi Sri
Cognitive Abilities, Information Literacy Knowledge and Retrieval Skills of Undergraduates: A
Comparison of Public and Private Universities in Nigeria ........................................................................ 24
Janet O. Adekannbi and Testimony Morenike Oluwayinka
Risk Assessment in Constructing Horseshoe Vault Tunnels using Fuzzy Technique................................ 48
Erfan Shafaghat and Mostafa Yousefi Rad
Evaluating the Adoption of Deductive Database Technology in Augmenting Criminal Intelligence in
Zimbabwe: Case of Zimbabwe Republic Police......................................................................................... 68
Mahlangu Gilbert, Furusa Samuel Simbarashe, Chikonye Musafare and Mugoniwa Beauty
Analysis of Petrol Pumps Reachability in Anand District of Gujarat ....................................................... 77
Nidhi Arora
IJCSBI.ORG

International Journal of Computer Science and Business Informatics
IJCSBI.ORG
Scalable Rough C-Means clustering
using Firefly algorithm
Abhilash Namdev
School of Computer Science and Engineering
VIT University, Vellore - 632014, India
E-mail: abhilash.namdev8@gmail.com
B.K. Tripathy
School of Computer Science and Engineering
VIT University, Vellore - 632014, India
E-mail: tripathybk@vit.ac.in
ABSTRACT
Our main interest is in dealing with the disadvantages of old clustering algorithms and
coming up with a method that can generate clusters which produce optimal results when
compared with previous approaches. Firstly, our focus is on analyzing the limitations of
most widely used clustering algorithm. Here we choose K means clustering algorithm for
the purpose. To provide the optimal results from the initial stage of algorithm we use firefly
algorithm. The bioinspired algorithm that generates optimal minimum or maximum values
based on certain parameters. To avoid the strictness on the boundary area in k means
algorithm, we choose Rough C means algorithm, which provide some flexibility during the
clustering process. Our proposed method provides most efficiency both in terms of time
and space. We used efficient data structures which help us to avoid waste of memory while
computation and also our algorithm utilizes maximum resources of the machine to make
the execution rate as fast as possible.
Keywords
Clustering, datasets, firefly, threads, k-means, rough c mean
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1. INTRODUCTION
The process of clustering is the organization of patterns into sensible
groups which helps us to find the similarities and dissimilarities among the
patterns and to derive conclusion about them. We can easily find such type
of grouping in the fields of medical science, geography, biology,
engineering and sociology. This process of grouping comes under the
unsupervised learning. The basic steps involve in this grouping task starts
with feature selection, and followed by proximity measures, clustering
criterion, clustering algorithm, then validation of results and interpretation
of results come under the process. There are many directions where cluster
analysis is in use, some of them are data reduction, hypothesis generation,
hypothesis testing and prediction based on cluster.
To understand the fundamentals of any clustering process, let’s take
any dataset named X. Now k-clustering defines that the partition of dataset
X into k groups 1 2, ,... kG G G such that the following conditions must be
satisfy
• ; 1,2,...iG i kφ≠ =
•
1
k
i
i
G X
=
=
• ; , 1,2,...i jG G i j i j kφ= ≠ ∧ =
This type of clustering is sometimes called hard or crisp. The alternative
of above clustering are fuzzy and rough type of clustering, where element
from the dataset X can belong to more than one clusters. One of the most
popular and well known examples of hard clustering algorithmic scheme is
k means or c means algorithm. Here squared Euclidian distance is used to
find the dissimilarity among the elements of dataset. However, the results
of this version of algorithm highly depend on the order in which initial
centroids are chosen. (McCullock John, 2012) The main advantage of c-means
or k-means algorithm is its simplicity in computing the process. But k
means is not suitable for the categorical data i.e., it results well only with
continuous data valued.
Lingras proposed a clustering approach which was based on the
concept of rough set theory, known as Rough c means algorithm. This
algorithm describes the group by the value of its centroids as well as upper
and lower approximation. Rough c means algorithm solve the problem of k
means algorithm to some extent by allowing element to be the part of either
one cluster or between two clusters. Elements in the lower approximation
are considered to be completely belonging to that class of elements.
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The problem with both the above discussed algorithm is that they
both are sensitive to the initial cluster-centroids. Since initial centroids are
chosen randomly, many times the algorithm doesn’t produce optimal
results. Therefore, in the proposed system, we have used firefly algorithm to
determine the values for the initial class centroids. Then these optimal
values are applied to rough c-means algorithm, which ultimately increase
the accuracy of cluster. The firefly algorithm is implemented on both these
clustering approaches and the results are comparatively analyzed. The idea
behind the firefly algorithm work around the flashing behavior of the
fireflies. Each firefly has its degree of attractiveness and intensity of light.
Based on these parameters the firefly is attracted toward other firefly in the
working space. On the basis of this brightness parameter, the firefly with
low brightness takes a moment towards the firefly high brightness value at
each of the iterations. And the values of this parameters update on usual
period. The best position of the firefly is selected after running for the
specific period of time. These optimal values are considered as the centroid
values for our algorithm.
The system is designed in java technology, which is one of the most
efficient and widely used high-level object oriented language of the world.
The implementation in java is very easy, manageable and understandable by
the programmer. To make the system efficient and flexible many dynamic
approaches has been adopted in the side of data structure. Data structures
are the arrangement of the data inside the computer’s memory. And
algorithm ultimately modifies this structure in various ways. Data structures
are chosen to make the system efficient in terms of both space and time;
Like Arraylist acts as dynamic array which can grow or shrink as per the
requirement. This ultimately avoids the wastage of memory as compared to
normal array concept, where size of the array is fixed in advance. Which
lead to the waste of memory when the part of memory doesn’t even used
during the computations. Another measure adopted in order to utilize the
waste of time during the user interaction, which is the concept of
multithreading. Java provides the built in support for the multithreaded
programming. The multithreaded program contains more the one part that
can work simultaneously. (Singh Chaitanya, 2016) Multithreading provide
us the facility to write efficient program that utilize the maximum
processing power. Since Multithreading is important for interactive systems,
we have used it in our proposed system. To time spent by the user to
interact with the system, this time is in background utilize to run the basic
operations of clustering. The task performed in the background is usually
not dependent on the activity of user.
The system is tested over different datasets collected from the dataset
repository available on the internet. At the present stage all the dataset used
for the experiments are in the excel format, since Microsoft excel is the one
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which is most widely used for storing data across the world. The datasets
consist of the large scale and high dimensional data. The number of
instances and attributes of the datasets are varied from 5 to 5000 in
numbers.
2. REVIEW OF LITERATURE
In 1967, J.B MacQueen has first proposed the K means algorithm
(MacQueen, 1967), which is now the most widely used clustering algorithm
in the field of data mining. (Mathew et al, 2014a) have introduced the
concept of parallel clustering approach of K-means algorithm. They found
in (Mathew et al, 2014a) that average number of iterations the program
takes was lower than the old approaches. In (Swamy et al, 2015) it is
concluded that as the size of the data increases, the time taken by K-means
algorithms also increases with high rate. They (Swamy et al, 2015)
suggested the concept of parallel processing was introduced to decrease the
execution time during the clustering process. Then Yang (Yang, 2010) has
introduced the concept of firefly in 2008. According to them (Swamy et al,
2015) firefly algorithm is the swarm intelligence algorithm, inspired by
insects with their unique property of flashing. In (Raja et al, 2013) the
various randomization parameters are analyzed and the conclusion that the
value which is best suited on respective condition are provided. In (Lohrer
et al, 2013) it is suggested that firefly algorithm is a very efficient algorithm
that achieves optimal results when compared with Particle swarm
optimization (PSO). Also time taken to execute this algorithm is very less as
compared to other approaches. They (Lohrer et al, 2013) are still working
on the area to develop firefly on the hardware of system, which leads to
even lesser time as compared to current approach.
In (Zhang et al, 2006) the concept of dynamic load balancing is
introduced. They also suggested the use of parallel programming to
implement k means algorithm and step up the efficiency by using load
balancing between the core of the machine. They have adopted the strategy
of master/slave design model. (Karinor et al, 2015) tried two different
approaches for k means algorithm. One is for small scale dataset and other
is for large scale data using both serial and MapReduce implementation of
parallel algorithm. And concluded that with the increase in iterations of
algorithm, the computation overload also increases, i.e., they both are
directly proportional. In (Aamir et al, 2014) it is explained as how to use
parallel programming in java. This is the fact that if we want to enhance the
execution rate of our program then we must have to learn the concept of
parallel programming. They have explained very well as how parallel
programming tools and techniques are used in both shared and distributed
environment.
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(Chen et al, 2011) studied the performance issues of java program
using multithreading on multicore machine. They examined the tuning of
JVM in their research. To utilize the maximum benefits of multithreading
concept we must have to efficiently utilize the cache memory. Firefly
algorithm can give its best results only when the best parameters and
objective functions are selected. (MO et al, 2013) closely examined the
effect of each parameter in firefly algorithm. They suggested certain
guidelines in determining the values of these parameters. They used
different functions that test the effect on fireflies by parameter change.
Since proper start is important for K-means algorithm to achieve optimal
results, (Xu et al, 2014) have proposed K-means++ algorithm. This
algorithm increases the productivity of standard k means approach by using
Map reduce technique. They successfully tested their approach on both real
and synthetic data.
FIREFLY ALGORITHM
Inspired by the flashing property of fireflies, in (Yang, 2010) the firefly
algorithm is proposed. Every firefly is having its unique property with
respect to light and fitness. Attractiveness is directly proportional to light
intensity.
a. Fireflies are also known as agents and let xi be the position of the th
i
firefly in d dimension.
b. Initial fitness values for all the fireflies can be set by using
F(X) = � X2D
i=0
; X  xi
c. The distance ijr between the two fireflies, say the th
i and jth
ones can
be calculated as
2
1
( )
D
ij id jd
d
r x x
=
= −∑
d. The firefly with lower brightness values is attracted towards the
firefly with higher brightness value.
e. In that case the position update of firefly can be calculated by
Where β is degree of attractiveness and α is a random parameter.
f. After that, light intensity is updated using the formula
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2
0( ) d
I r I e γ−
=
Where I0 is initial intensity, and γ is absorption coefficient.
g. Arrange the firefly in order and find the best firefly.
The position of theses optimal fireflies is considered to be the optimal
values. These values are selected as the optimal centroid values for our
system.
3. PROPOSED METHOD
After studying the previous approaches, we got the first thing to
design good clustering algorithm is to select the optimal centroid values at
the start. Also we have seen that the firefly algorithm is best suited when
there is the need of optimal values. Hence, in our proposed method, we used
the firefly algorithm to get the optimal initial centroid values at the early
stage of the algorithm. To provide some sort of flexibility in the process of
clustering, we implemented firefly algorithm on Rough C Means
algorithms. Since we studied that K means algorithm is crisp or hard in the
boundary region, we tried some new approach which is not tested yet.
Rough C means algorithm allow the element to become the part of either
completely in one class or in between two classes. This approach is used to
make the clustering flexible.
Our proposed method is to implement Rough C means algorithm
and provide the optimal result to it by adding the firefly algorithm. The
results of this approach are tested on different set of data. The experiment
was performed on a dataset whose number of instances varies from 5 to
5000. Mostly the dataset used for the experiment is the real numbered data,
because it is quite easy to perform and handle real data for the purpose of
experiment. The dataset is collected from the online repository available on
the internet. The system is developed in JAVA technology because it is easy
to program and highly manageable as compared to other programming
language. (Oracle-Java-Documentation, 2015) The efficient data structures like
Arraylist and concept of multithreading is used that allow the system to
avoid wastage of time and space. The approach has been followed to make
the code run on multiple processors. This can make the system to run faster
than other approaches. The proposed method is then compared with existing
method in terms of execution time.
Pseudo-code for proposed method
1. Start
2. Ask user to enter number of clusters needed  noc
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3. Ask user to view his dataset
4. If yes, then display the dataset file
5. Call store() method, to store the data in temporary storage, to avoid
accidental modification
6. Call firefly() method to initialize the value of centroids
a. set the parameters α, β, ϒ
b. calculate initial fitness values of each firefly  I[i]
c. calculate distance between each two fireflies  r[i][j]
d. if (I[i] < I[j])
e. then firefly i move toward firefly j
f. Update β and fitness value I
g. Repeat until maxGen < noc
h. Sort the fireflies and select best positions
7. Calculate the minimum distance of element from centroid of cluster
8. Calculate next to minimum distance of element from another
centroid
9. Check if the difference between both the distances is less the
threshold
10. If yes, the element belongs to boundary
11. Else, Assign elements to respective cluster
12. Recalculate mean based of each cluster and boundary elements
13. Repeat from step 7 until new centroid values are not equal to old
centroid values
14. Display the final results by printing elements with respective cluster
15. Display, number of iterations algorithm took
The important function during the program execution carried out
from the calculation of Euclidean distances in each iteration. The distance
of the element from every centroid is computed. Then the minimum and
second to minimum distance is collected for the use. If the difference
between these two distances is less than some threshold value, then that
particular element is considered to be the part of boundary between those
two clusters. And other are considered being the part of lower
approximation of that cluster. The system diagram of proposed method is
shown next.
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Fig.1. Model for proposed system
The figure clearly shows the interaction of User with the system.
User is allowed to ask for his required number of clusters and he can also
see his dataset on the console screen of the system. The diagram also
projects the interaction of system with the secondary storage. User has to
specify the address of his dataset in advance before performing clustering
operation. The data from the file is collected in some temporary storage, and
all the further operations are carried out from that data. This is done to avoid
the accidental modification on original dataset during the operation. At this
stage the system can only able to read the datasets from excel file. Since
most of the datasets use Microsoft excel to store and manage data, hence
our system in its early state is developed to work with only excel
worksheets.
4. EXPERIMENTAL RESULTS
We have implemented our proposed system in java technology,
because program in java is easy to manage. The execution of code is done
on Intel(R) core(TM) i3 CPU with 2.53 GHz processor, installed memory
(RAM) is 4.00 GB, while the system has 64-bit operating system (Windows
10).
Dataset description
Dataset 1: The voter dataset is used which has two attributes. The one
represents age and another represents sex of the candidate. It has 439
numbers of instances. The data in the file is of type integer. While the age
with value 1 represents female candidates and value 2 represents male
candidate. Data with NULL values are left blank which is ignored by the
algorithm.
Dataset 2: The weather dataset, which has 4 attributes named by city name,
Year, Lowest temperature and highest temperature. The dataset has 3000
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number of tuples or instances. The type of the data in the dataset is integer.
In this dataset Null values are treated as zero.
Dataset 3: The third dataset is also weather dataset but with different size.
Here the number of attributes is 9 named by order country, city, year, low T,
high T, warm, cold, and average. This dataset has 5000 instances. This is
the largest dataset we have tested on our system.
In addition to the technology, we refer certain tools in the process of
development of the system. The program is developed in NetBeans IDE
8.0.2 because using NetBeans IDE is we can easily develop java desktop,
mobile and web applications. It provides fast and smart coding environment
for the developer. Another tool used for analyzing the outcomes and
comparing with proposed work is WEKA. The WEKA is the tool for
performing experiments with various algorithms from the data mining field.
It is an open source software. It provides various datasets of different size
for performing experiments. It also provides various visualization
techniques for better understanding the results like graphs, tree etc.
To make it easily understandable, we are presenting the screen of
outcomes produced while experimenting. Here we took the dataset with 5
instance and 2 attributes.
Fig.2. Sample dataset for demonstration
Optimal Centroid values collected using the firefly algorithm
Fig.3. Results of Firefly algorithm
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There are certain parameters, which are very carefully selected from
previous researches. The sphere optimization function is adopted in firefly
algorithm, because it easy to implement and mostly used for research. The
other parameters like α, β, ϒ are put as constant values. Final results
collected after running on Rough C means algorithm
Fig.4. Final Results after Clustering
On each run, the system will show the elapsed time. For example,
the elapsed time of the above sample experiment is 16 milliseconds or 0.016
seconds. As given on the above results, the output of the algorithm appears
in two categories. One with the elements belongs to lower approximation of
the cluster and another with the element which are the part of boundary
region. The performance of the system is also measured on the CPU scale.
The number of cycles processor takes to execute the algorithm is also the
point of concern. The frequency of our machine is about 2.48 GHz. The
next chart taken during the time of execution, which primarily shows the
basic details about the machine include cache size, memory, core and speed
of processor. With the fundamental details, the chart clearly shows the
amount of CPU utilization during the time of execution. We have calculated
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97% of CPU utilization during the process of execution. The chart also
displays the number of processes as well as number of threads running on
the system.
The performance noted by task manage is shown below
Fig.4. CPU utilization graph
The CPU usage in the above graph shows that processing power is
utilizes up to 97 %. As discussed early, the experiment is carried out on
different set of data collected from the repository available on Internet. The
readings of the experiment are shown below by the table. The datasets are
varied from different size in terms of both attribute and instances. The table
below shows the execution time of different datasets when size of the
dataset varies. And we found that as the size of the dataset increases, the
execution time also increases. The execution time is measured in
milliseconds. The first dataset covered in the table is same as the one shown
previously.
Table 1: Comparison of execution time
Dataset No. of
attributes
No. of
instances
Execution time
(milliseconds)
Sample 2 5 16
Voter 2 439 832
Weather_1 4 3000 7953
Weather_2 7 5000 39298
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The graph is plotted to understand the results with the mark. The
points covered in the graph are execution time, number of instances and
number of attributes. The execution time is measured with respect to
different number of attributes and instances.
Fig.5. Execution time chart
The above chart shows that execution time is directly proportional to
the increase in number of instances and attributes. The vertical axis is taken
for time and horizontal axis is for number of attributes.
The selection of different parameters plays a vital role in the final
results. Parameters like the threshold, alpha (α), beta (β), Gama (γ ) are
mostly taken from the previous researches performed on the selection of
optimal parameters. Out of the above mentioned parameters, the threshold
value of rough c-means generally varies with different dataset and user’s
requirements.
5. CONCLUSIONS
We observed that although the K-means algorithm for clustering is
easy to implement and is the most widely used one, there are many
drawbacks in this algorithm which pulls it away from resulting optimal
output. Couple of main gaps in the algorithm are, first it is sensitive to the
selection of initial centers for its cluster and another, that it doesn’t permit
any element to take a part of more than one cluster group. This motivated us
to design something to solve this problem. So, we used the firefly algorithm,
to get better start to the proposed clustering algorithm. After this we used
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the rough c-means algorithm instead of the K means clustering, which
provides more flexibility to the clustering phase by allowing elements to
belong to more than one cluster. Our proposed method is successfully tested
on datasets of different sizes. It is proposed to apply this approach by
replacing the rough c-means algorithm with other algorithms and perform
comparative analysis to find out the most suitable combination of firefly
algorithm with clustering algorithm in this direction.
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Xu, Y. Qu, W. Li, Z. Min, G. Li, K. and Liu, Z. (2014). Efficient k-Means++
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This paper may be cited as:
Namdev, A. and Tripathy, B.K. 2016. Scalable Rough C-Means clustering
using Firefly algorithm. International Journal of Computer Science and
Business Informatics, Vol. 16, No. 2, pp. 1-14.
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Significance of Embedded Systems
to IoT
P. R. S. M. Lakshmi
Assistant Professor, Department of IT,
VFSTR University
Guntur
P. Lakshmi Narayanamma
Assistant Professor, Department of MS,
VFSTR University
Guntur
K. Santhi Sri
Associate Professor, Department of IT
VFSTR University
Guntur
ABSTRACT
Internet of Things is the concoction of solely assured computing devices which are
embedded inside the accessible Internet Infrastructure. So, Internet of Things essentially is
connecting Embedded Systems toward Internet. Hence, before going to IoT, first
understand the concept of Embedded Systems. In this paper, first section describes about
Embedded Systems and their boards, importance of Arduino. Second section explains the
concept of basic architecture of IoT and devices of IoT. Third section consists of various
platforms of IoT in detail.
Keywords
IoT, internet, systems
1. INTRODUCTION
Embedded controllers like DEC Alpha, MIPS, SuperH and so on are the
spirit of Embedded systems. Mainly those make a distinction of embedded
controllers through Microprocessors is their interior memory of read and
write. Thus anyone be capable of developing light weight programs using
Embedded C or Assembly language among application resembling Keil and
blaze the application addicted to the hardware. This application keeps track
of runs on a loop [1][2]. The majority of the ES contains a solitary
application is blaze through numerous sequence of program instructions.
Hence, distinct users of Personal Computers, embedded controller devices
in an Embedded System run on a single application considerably. The users
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are able to attach numerous IO devices through these embedded controllers
are either I/O Mapped or Memory mapped. These easy components of
hardware devices include Printers, Key boards [3][5], LCD monitors and so
on. By using Analog to Digital convertor interfaces the user can attach
various sensors.
These strategies preserve the organize of high voltage, power and present
ranking strategies such as Bulbs using drive devices, motors, fans like
Relayoptocoupler etc. From simple DIY Robots to additional business
systems such as ovens, Washing Machines, Cars, AC Controllers, hand held
devices, Railway and Bus Bill printers Embedded System is throughout the
world[7][8].
Internet of Things (IoT) is a contemporary message sending exemplar that
envisions a in the vicinity of future, in which the objects of daily life resolve
and outfitted amid Micro-controllers, Transceivers used for digital
statement, and appropriate set of rules load with the purpose of making
them to Communicate with one to one and with the specific users, to turn
into a primary component of the Web. Internet of Things aims by building
the Internet flat further immersive and omnipresent additionally, through
enable simple right of entry and dealings with a extensive range of devices
like, for example, Appliances of house, Supervision Cameras, Monitoring
Sensors, Actuators, Displays, Vehicles etc.,
Internet of Things force cultivate the expansion of a quantity of applications
to make an utilization of the potential gigantic quantity and mixture of
generated data by such things for providing innovative services to society,
companies and public administrations. The archetype undeniably finds
appliance in several dissimilar domains, like Automations of homes and
industries, medicinal aids, healthcare on mobiles, old aid, intellectual vigor
administration and Smart grids, Auto-motive, Traffic Management and so
on. To expand skills to archetype Mobile enabled products use state of the
art techniques. Develop a software and hardware environment to guide users
through the IoT.
IoT is connection through internet of embedded systems and smart objects
further than those wireless phones can be attached through external devices
of hardware and personal computers, tablets, wireless phones and laptops
are remotely access core of IoT[5][6]. To analyze the importance of
Embedded Systems in the Internet of Things, this paper is organized into
three Sections. In this paper, first section describes about Embedded
Systems. Second section explains the concept of basic architecture of IoT
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and devices of IoT. Third section consists of various platforms of IoT in
detail.
2. EMBEDDED SYSTEMS AND ITS BOARDS
Embedded Systems are most attractive because the following are the some
factors such as autonomous, less cost, less space and less power etc.
According to the autonomous factor users build specific systems to a
meticulous application. Regular peripherals of a program can be able to
move embedded controller unit into new smart controllers such as oven
controller and washing machine controllers etc. Embedded systems can be
built these type of smart devices based on their requirement. The distinct
users can’t work without monitors like that an embedded system can’t
specifically need output devices [9][8].
As per the factor of less expenditure, the expenditure of the embedded
controller unit is lesser than the developed one.
Along with less space, apparently Embedded System takes very minute
space compared to other mini laptops.
In relation with many embedded controllers are admired options for
embedded systems access at same voltages and same voltage power
supplies. Here voltage regulators rectify through filters.
Other factors like input and output devices cost and speed, consumption of
energy per instructions and so on.
DragonBoard™ 410c is a Single Board Computer which is proficient of
consecutively a diversity of dissimilar Operating Systems, to facilitate
individual told the users can be open to select the Operating System and the
user thinks it is most excellent fit for users. Now, users know that it is a
varied user support for this DragonBoard. Whether Windows user or a Mac
user, or prefer Android over Ubuntu, select an Operating System to greatest
agile to user requirements, download, installation process and flashing pro
all presently offered Operating Systems. As the varied scenery of this board
follow multiple ways to flash users new Operating System and grant an
advantages and disadvantages for the different methods. By the end
switching between Operating System can be simple and exhilarating to
users of each and every level of skills.
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2.1. Common Embedded Boards for ES
These embedded boards are supported for various embedded controllers and
can be developed by standard software and hardware units. The following
table shows the various sample boards can be used for embedded system
applications [16].
Table.1: Common Embedded Boards for Embedded Systems
CatalystBT Ultra low power CoM based on Intel
CatalystCV Ultra stumpy clout and multicore CPU based on latest
Intel processor
CatalystTC Small form factor of Intel
CatalystLP stumpy power and high concert solution based on
Intel
CatalystFX Stretchy elucidation operational with Intel for multi-
media and IO applications.
Vector stumpy power, EPIC SBC in Intel
ALUDRA solitary embark computer with grain processor
CatalystEC EPIC solitary embark computer with grain processor
ISIS XL Central processing unit module with extensive
warmth Intel atom stumpy power
Catalyst Module
XL
solitary embark computer based on Intel processor
Integrated Development Environment can be developed for easily accessing
of develop, burn and debug software for the production [11][12]. Managing
hardware in the chips users can use compact kits for most complex
applications instead of embedded controllers.
Arduino is the truly evolved embedded kit by a DIY .It will give knowledge
to understand the boards of embedded systems also used in Internet of
Things [14]. This architecture is the combination of Atmel embedded
controller family through particular hardware towards a board which
contains built-in boot loader for plugs and run embedded applications. This
Arduino software helps to write, debug and burn applications into Arduino
through an IDE [16]. This IDE may get the serial data from the board
through serial communication. This software is common to embedded
systems and Internet of Things. Users can use communication devices such
as Bluetooth, CDMA, USB, Wi-Fi and so on. Embedded system can be
remotely reconfiguring one or more Embedded Systems as middle wares on
a remote terminal like computers, Laptops, Tablets and so on. As per
module unit control and Embedded System category will be checkered, it
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offers the support for easier managing of multifaceted System. Among
these strategy to persuade interoperability, ensure security and defend
privacy and possessions rights, the Internet of Things will starts to get total
potential particularly hold close decision making of data driven.
3. BASIC ARCHITECTURE OF IOT AND ITS DEVICES
Internet of Things and its devices are mix with smart things and embedded
systems through internet with different IP addresses those were discover
and communicate. IoT are basically Embedded Systems and elegant
substance coupled to Internet with exceptional IP address which can be
naked and communicated on Internet. These comprise Sensors external
peripheral devices and actuators external peripheral devices.
Coming to day to day activities cell headset is principally an Embedded
System through a CPU by the innermost component has flaunt on output
devices. These sustain broad diversity of sensors like accelerometer,
ambient light sensors, gyroscope etc. These are associated to internet cell
phones get unique IP addresses, know how to access internet. Further it can
be fit virtually on each and every IoT descriptions. Smart objects are the
objects that are not only have a state, have specific data which is associated
through a state but an object and also verify the character, duration and
protocols of connectivity[10][15]. Some techniques like Near Field
Communication, Bluetooth Low Energy and Radio Frequency Identification
create it the usage of cell phones.
Radio frequency identification, Blue-tooth stumpy oomph and Near Field
Communication craft it doable to exploit our headset as a whilst of readers.
Tap the devices with specific objects users can be capable of extracting the
information [9]. RFID and NFC tags have not any type of embedded
systems [1]. However, the data may be from internet with the help of
readers. This type of objects is called as smart objects. The devices of
Internet of Things can be divided into two broad categories. These are
Wearable and embedded controllers. One of the important examples of this
is digital watches. The following are the five features of wearable devices.
1. Easy to use: These devices are successful innovative products for reality
of services and easy to use. Wearable devices are most waterproof products.
2. Better battery life: These devices can be able to work with less power and
contains greater life of battery.
3. Ecosystems: This app contains proper set of instructions to get the real
life examples.
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4. Data security: Physically assist the devices from others and assist as
information related things like secure mail applications, secure browsers
and secure clouds.
5. Managing hands free: These things manage in reality of applications
through phone.
Figure 1: Basic architecture of IoT`
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4. SOME PLATFORMS OF IOT
These platforms can be divided into two ways that are wearable and
embedded. Programmers develop their apps for wearable like Samsung
gear, peeble and make own platform for embedded solutions and build apps
for that platforms [6].
4.1 Wearable Platforms
Most powerful platform for mobile and wearable devices is Tizen. It makes
to build some solutions for this platform. Digital or smart watches are
familiar in daily life. These types of Android applications can be building
and tested in Eclipse environment. Another wearable platform is salesforce
through this the users can develop good applications. These can be
upgraded from peeble to googleglass.
4.2 Embedded Platforms
The best platform for embedded IoT is Arduino. In this platform users want
to set Android through Ethernet shield. These platforms work with serial
communications and develop smart applications with this.
4.2.1Raspberry Pi is perhaps the greatest thing to ensue in DIY Internet of
Things. Broad series of data drive applications such as domicile compact
disk servers, automations of home server; file server can be built with this
Pi. Pi has commonly used these IO pins like Arduino. But working of these
sensors is little much dreary in Pi.
4.2.2 Intel Edison is another IoT platform incorporated Wi-Fi and BLE. It
covers huge place of industries with seventy pin interface. It supports some
other platforms like node.js and Arduino.
4.2.3Intel Galileo is an extra platform by Intel which chains the Arduino
Uno. It is the first Arduino compatible device of Intel devices.
4.3 Cloud Platform for IoT
In the predictable vending machine the user wants to compress a button or
locate a coin to generate the procedure of fluid flow, which stops once
particular magnitude. At present how to integrate Google or Paypal hard
cash through the retailing contraption? How a client discovers the retailing
contraption as Website in conjunction through this place and then pays
online pro a glass of beverage. Formerly after successful payment client get
an access of token [13]. Client knows how to surpass the indication to the
contraption through NFC and bingo punter receives his/ her slurp. This is
for the most part proficient and vital logic to understand the Internet of
Things and various services such as online payment gateway. Different
hardware platforms reminiscent of implanted embark of the retailing
contraption and elegant things or objects, data such as GPS, Near Field
Communication towards this environment [12]. At present the user can be
able to mix payment through online into potion retailing contraption, and
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not deliberate for a district Washing Machine, payment services of toll gate
and data of ECG for medical diagnosis into cloud and various doctors may
view it and give their valuable suggestions about the state of patients.
All the above things are possible in cloud environment and to understand
the web and designing the applications towards the cloud. Various modules
like web of machines in M2M, M2O, any communication modules are
common and demand the data which is available for sharing and API ‘s of
Cloud also help for this. The user can make a discovery of devices in web
and to allocate permanent IP address, manage routers and for various skills
of networking. The user can not have any knowledge to make the network
for Internet of Things.
Yaler is an enormous exemplar of what Services and Cloud is able to
convey to table. This provides alliance when a Service is to facilitate the
gadget is simply ascertain and commune on the network exclusive of a lot
hassle and takes much care on beneath security. Some of the following are
examples of these platforms [13]. Axeda provide Infrastructure for Machine
to Machine architecture. OpenIoT is free open source platform to provide
other services and different Sensing as a Service (Se aa S).Google also
previously combines the location services during the cloud. It knows how to
be extracted some devices and keep updates of status in Face book and
Twitter for other searching purposes. Hence, CloudAPI has a enormous
talent in IoT of all architecture levels from firmware to hardware to most
top level architectures.
5. CONCLUSION
In this paper explained about significance of embedded systems and Internet
of Things. Embedded systems can be able to develop single applications
like ovens, washing machines etc. By using this Internet of Things the users
can be able to develop smart applications like automation of homes, health
care data etc. To develop these types of applications we know about
embedded boards which are used in embedded systems and Internet of
Things. And which platforms are related to this.
6. REFERENCES
[1] J. A. Stankovic, "Research directions for the Internet of Things", IEEE Internet Things J., vol. 1,
no. 1, pp. 3-9, Feb. 2014.
[2] Laurence T. Yang, Hong Liu, Huansheng Ning, "Cyberentity Security in the Internet of Things",
Computer, vol. 46, no. , pp. 46-53, April 2013, doi:10.1109/MC.2013.74
[3] Steven Lanzisera, Andrew R. Weber, Anna Liao, Dominic Pajak, Alan K. Meier
“Communicating power supplies: Bringing the internet to the ubiquitous energy gateways of
electronic devices,” IEEE Internet Things J., vol. 1, no. 2, pp. 153–160, Apr. 2014.
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[4] Yan Chen, Feng Han, Yu-Han Yang, Hang Ma, Yi Han, Chunxiao Jiang, Hung-Quoc Lai, David
Claffey, Zoltan Safar, and K. J. Ray “Time-reversal wireless paradigm for green Internet of
Things: An overview,” IEEE Internet Things J., vol. 1, no. 1, pp. 81–98, Feb. 2014.
[5] J. Yang and Z. Fei, “Broadcasting with prediction and selective forwarding in vehicular
networks,” Int. J. Distrib. Sensor Netw., vol. 2013, pp. 1–9, 2013.
[6] Tao Zhang, Helder Antunes, Siddhartha Aggarwal “Defending connected vehicles against
malware: Challenges and a solution framework,” IEEE Internet Things J., vol. 1, no. 1, pp. 10–
21, Feb. 2014.
[7] Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, Michele Zorzi “Internet of
Things for smart cities,” IEEE Internet Things J., vol. 1, no. 1, pp. 22–32, Feb. 2014.
[8] Panagiotis Vlacheas, Raffaele Giaffreda,, Vera Stavroulaki, Andrey Somov and Abdur Rahim
Biswas, Klaus Moessner “Enabling smart cities through a cognitive management framework for
the Internet of Things,” IEEE Commun. Mag., vol. 51, no. 6, pp. 102–111, Jun. 2013.
[9] Antonio M. Ortiz, Dina Hussein, Soochang Park, Son N. Han, Noel Crespi “The cluster between
Internet of Things and social networks: Review and research challenges,” IEEE Internet Things
J., vol. 1, no. 3, pp. 206–215, Jun. 2014
[10]C. E. A. Mulligan and M. Olsson, “Architectural implications of smart city business models: An
evolutionary perspective,” IEEE Commun. Mag., vol. 51, no. 6, pp. 80–85, Jun. 2013.
[11]] N. Walravens and P. Ballon, “Platform business models for smart cities: From control and
value to governance and public value,” IEEE Commun. Mag., vol. 51, no. 6, pp. 72–79, Jun.
2013.
[12]Stefan Foell, Gerd Kortuem, Reza Rawassizadeh, Marcus Handte, Umer Iqbal, Pedro Marrón
“Micro-navigation for urban bus passengers: Using the Internet of Things to improve the public
transport experience,” in Proc. 1st Int. Conf. IoT Urban Space (Urb-IoT), Rome, Italy, 2014, pp.
1–6.
[13]] Marcus Handte, Eva Maria Muñoz, Sara Izquierdo “Crowd density estimation for public
transport vehicles,” in Proc. Workshop Min. Urban Data (MUD) Joint Conf. EDBT/ICDT,
Athens, Greece, Mar. 2014, pp. 315–322.
[14]Henrich C Pöhls, Vangelis Angelakis, Santiago Suppan, Kai Fischer, George Oikonomou, Elias
Z Tragos, Rodrigo Diaz Rodriguez, Theodoros Mouroutis “RERUM: Building a reliable IoT
upon privacy- and security- enabled smart objects,” in Proc. IEEE Wireless Commun. Netw.
Conf. Workshops (WCNCW), Apr. 2014, pp. 122–127.
[15]L. Tan, Z. Zhu, F. Ge, and N. Xiong, “Utility maximization resource allocation in wireless
networks: Methods and algorithms,” IEEE Trans. Syst. Man Cybern. Syst., vol. 45, no. 7, pp.
1018–1034, Jul. 2015
[16]V. Angelakis, I. Avgouleas, N. Pappas, and D. Yuan, “Flexible allocation of heterogeneous
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(INFOCOM), Apr. 2015, pp. 99– 100.
Lakshmi, P. R. S. M., Narayanamma, P. L. and Sri, P. L., 2016.
Significance of Embedded Systems to IoT. International Journal of
Computer Science and Business Informatics, Vol. 16, No. 2, pp. 15-23.
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Cognitive Abilities, Information Literacy
Knowledge and Retrieval Skills of
Undergraduates: A Comparison of Public
and Private Universities in Nigeria
Janet O. Adekannbi, Testimony Morenike Oluwayinka
Africa Regional Centre for Information Science
University of Ibadan, Nigeria
ABSTRACT
This study investigated the differences in the cognitive abilities, information literacy knowledge
and skills as well as information retrieval skills between students in public and private
universities in Nigeria. Multistage sampling technique was adopted. Two colleges, Natural
Sciences and Management Sciences were purposively selected from the Federal University of
Agriculture, Abeokuta (FUNAAB) while three schools, Basic and Applied Sciences,
Computing and Engineering and Babcock Business School were selected from Babcock
University. The colleges/schools were later stratified into six related departments/courses for
ease of comparison. Convenience sampling was used to select the total of 235 respondents that
participated in the study. Four hypotheses were tested. Result of the test of hypotheses showed
no significant difference in the cognitive abilities, information literacy knowledge and skills of
students in public and private universities. However, a significant difference was observed in
information retrieval skills of the students. Students of Babcock University, a private institution
had higher level of information retrieval skills than FUNAAB students, a public university. The
study recommends library and information professionals in public universities should be
encouraged by administrative heads of such institutions to periodically organize practical
workshops on information retrieval skills for students.
Keywords
Cognitive Abilities, Information Literacy, Information Retrieval, University, Nigeria.
1. INTRODUCTION
Information has obviously been an important part of human lives. Humans have
long been processors and users of information to help in their decision making
[1], a trend that has also been widely observed among students. However, the
various trends and level of information explosion, and the emergence of new
technologies [2] in the information society have made students to rely on
electronic resources found in the University libraries, technology centers and
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computer laboratories to meet their information needs. [3]. Hence, in order to
satisfy their information needs, students engage in information retrieval
activities.
Information retrieval basically involves retrieving documents that a user
perceives to be relevant his information need as expressed by his request [4].
According to Omuinu [5], such request which must have been defined might
not be a perfect expression of the user’s information need. In spite of this, the
user is still the only person who can tell the relevance of the document retrieved
to his information need [4]. The implication of this is that the relevance of a
document might be different for two users with identical requests. According to
Royal College of Nursing [6], steps to ensuring effective information retrieval
skills include: identifying where relevant information can be found; checking
the suitability of information sources to meet the information need; consults
with colleagues and information specialists to help identify other tools, such as
indexes, for accessing information; using appropriate information services to
retrieve information, among others.
For students to efficiently retrieve information, information literacy is
important [4,7]. Information literacy (IL) is the set of skills and knowledge that
enables us to find, evaluate and use the information we need, and at the same
time filter out the information we do not need [1]. With adequate Information
literacy skills, potential information users can successfully explore the
landscape of information with less stress especially in the era of information
explosion and society. According to Eisenberg [1], there are many information
literacy standards, some of these are the Big6 model [8], AASL/AECT IL
Standards [9] and ACRL IL Competency Standards for Higher Education [10].
In most cases, the relationship between some independent variables and
information retrieval skills are being interfered with user’s characteristics such
as cognitive abilities [11]. Some cognitive processes involved in information
retrieval include learning, comprehension and speed in spotting information.
These factors have a role to play in the effectiveness of users during a search
process [11].
Previous studies have investigated students’ information literacy skills [12,13],
cognitive skills [14,15] and information retrieval skills [2,16-20]. However,
none of these studies have considered another variable which has received
some attention in other study areas, namely whether these skills will be
different between students in public and private universities.
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Public universities are generally funded by governments while private
universities rely heavily on tuition fees paid by students. Mazumber [21]
investigated the satisfaction of students with the quality of higher education in
public and private universities in Bangladesh. The study showed students from
private universities are more satisfied than those of public universities. A later
study comparing Bangladesh and USA however showed that while there is a
larger gap in student satisfaction in public universities compared to private
universities in Bangladesh, smaller gaps were observed between private and
public universities in USA. This indicates that there is no significant difference
in student satisfaction with public and private universities in USA [22]. An
earlier study carried out in Nigeria revealed that while there is no significant
difference in student entry requirement between public and private universities
in Nigeria, there is significant difference in resource availability, resource
utilization, governance, and students’ academic performance [23]. Moreover,
Fordjour et al [4] and Ilogho and Nkiko [24] linked students’ search difficulties
and poor academic performance in school to ignorance of information literacy
and retrieval skills. Hence, this study investigated and compared the following
in public and private universities:
i. information literacy knowledge possessed by students
ii. information literacy skills.
iii. cognitive abilities.
iv. information retrieval skills
Based on these objectives, the following hypotheses were tested:
H1: There is no significant difference in the information knowledge of students
in public and private universities.
H2: There is no significant difference in the information literacy skills of
students in public and private universities.
H3: There is no significant difference in the cognitive abilities of students in
public and private universities.
H4: There is no significant difference in the information retrieval skills of
students in public and private universities.
2. RESEARCH METHODOLOGY
A survey research design was adopted for this study. The location of this study
is Ogun State, Nigeria. Ogun State was purposively selected for this study
because it has the highest number of tertiary institutions in Nigeria as shown on
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the list of universities in Nigeria on the website of National Universities
Commission (nuc.edu.ng). The study was carried out in two selected
universities. These are Federal University of Agriculture, Abeokuta (Public
university) and Babcock University (Private university). These universities
were purposively selected for two reasons, namely, accessibility to the
students’ population statistics and the fact that the two universities have some
departments in common as shown in Table 1.
Table 1a: Sampling Distribution - Federal University of Agriculture, Abeokuta
Colleges Departments Population Respondents
Per
Department
Natural Sciences Biochemistry
Computer Science
Microbiology
93
88
91
19
18
18
Science Accounting
Business Enterprise (Business
Administration)
Economics
22
200
103
4
40
21
Total 597 120
Source: Administrative Unit, Federal University of Agriculture, Abeokuta,
2014
Table 1b: Sampling Distribution - Babcock University
Schools Courses Population Respondents
Per Course
Basic and Applied Sciences Microbiology
Biochemistry
45
55
9
11
Computing and Engineering Computer Science 140 28
Babcock Business School Accounting
Business
Administration
Economics
157
92
164
31
18
33
Total 653 130
Source: Data Services and Archival Unit, Babcock University, 2015
The target population of this study comprised the undergraduate students in
both universities. The study purposively selected 300 level students from both
universities because it is assumed that students at this level are already familiar
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with their academic activities. However, 400 level students were excluded from
this study because of their preparation for the examinations.
A multi-stage sampling technique was used to select respondents for the study.
Two colleges, Natural Sciences and Management Sciences were purposively
selected from the Federal University of Agriculture, Abeokuta while three
schools, Basic and Applied Sciences, Computing and Engineering and Babcock
Business School were selected from Babcock University. The colleges/schools
were later stratified into six related departments/courses which were
purposively selected from both institutions. This was done to ensure uniformity
across the departments.
The total population for the six selected departments/courses in both
universities is 1,250. Nwana [25] proposed that if a population is a few
hundreds, we need a sample of 20%. Hence, using a sample size of 20%, 120
and 130 respondents were selected from 300level undergraduates in Federal
University of Agriculture, Abeokuta and Babcock University, Ilishan-Remo,
Ogun State respectively (Table 1). Convenience sampling was however used to
select the 300 level students in the selected departments/courses that
participated in the study based on their accessibility, availability and readiness
to participate in the study.
2.1 Data collection and analysis
Data was collected using a structured questionnaire. The questionnaire was
carefully designed to ensure that information and data obtained are relevant to
the objective of the study. The study adopted the questionnaire used by Ekenna
and Mabawonku (2013). The questionnaire was divided into four major
sections namely:
Section A: This section consisted of demographic characteristics of the
respondents which included gender, age group, faculty/college, course of study
etc.
Section B: This section assessed the information literacy knowledge through an
achievement test and information literacy level of the respondent.
Section C: This section assessed the cognitive abilities of the respondents.
Section D: This section assessed the students’ information retrieval skills
The items in Section B of Information Literacy Knowledge Test were
structured on four (4) options of multiple choice questions with only one
correct answer. Each respondent was scored and the pass mark was set at 40%.
Information Literacy skill Questions were structured on four (4) point rating
scale of Highly Skilled (HS), Moderately Skilled (MS), Weakly Skilled (WS)
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and Not Skilled (NS). The items in section C of Cognitive Abilities were
structured on 4 point rating scale of Strongly Agree (SA), Agree (A), Disagree
(D) and Strongly Disagree (SD) and the items in section D of Information
Retrieval Skills was structured on 5 point rating scale of Very Good (VG),
Good (G), Average (A) Poor (P) and Very Poor (VP). Respondents were
instructed on how to respond to the questionnaire.
Of the 250 questionnaire administered, 235 were returned, showing a return
rate of 94%. Frequency and percentage distributions as well as independent
sample t-test were used to analyse data collected with the questionnaire.
3. RESULTS
3.1 Demographic characteristics of respondents
Table 2 shows the socio-demographic characteristics of respondents.
Table 2: Demographic Data of the Respondents
VARIABLE MEASUREMENT FREQUENCY PERCENT (%)
GENDER MALE 123 52.3
FEMALE 112 47.7
AGE GROUP 16 – 20 122 51.9
21 – 25 102 43.4
26 – 30 7 3.0
31 – 35 2 0.9
36 – 40 1 0.4
41 – 45 1 0.4
RELIGION Christianity 202 86.0
Islam 33 14.0
MARITAL STATUS Married 8 3.4
Single 227 96.6
COLLEGES / SCHOOLS Basic and Applied Sciences 20 8.5
Computing and Engineering 28 11.9
Babcock Business School 78 33.2
College of Natural Sciences 55 23.4
College of Management Science 54 23.0
DEPARTMENTS/
COURSES
Biochemistry 30 12.8
Microbiology 27 11.5
Computer Science 46 19.6
Business Administration 47 20.0
Accounting 31 13.2
Economics 54 23.0
RESPONDENT SCHOOL Babcock 126 53.6
FUNAAB 109 46.4
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Majority of the respondents were males (52.3%) while 47.7% were females.
More than 95% of the respondents were between 16-25 years. Almost all the
respondents were single (96.6%). Larger proportion (86.0%) of the respondents
practice Christianity, while 14% practice Islam religion. The distribution of
respondents by their faculty include 4.7% from College of Medicine, 3.8%
Basic and Applied Sciences, 11.9% Computer Science and Mathematics, 33.2%
Babcock Business School, 23.4% College of Natural Sciences and 23.0% from
College of Management Science.
3.2 Information literacy knowledge test
The information literacy knowledge possessed by undergraduate students in
Babcock and FUNAAB was measured based on their understanding of the need
for information, how to locate, evaluate and use information. Table 3 shows the
performance of students in each school.
Table 3: Information Literacy Knowledge Performance of Students
BABCOCK FUNAAB
INFORMATION Failed Passed Total Failed Passed Total
How does one know when he needs
information?
Frequency 24 102 126 27 82 109
Percent 19.0 81.0 100.0 24.8 75.2 100.0
When one is given an assignment, the
first thing he should do is to
Frequency 69 57 126 63 46 109
Percent 54.8 45.2 100.0 57.8 42.2 100.0
To define one’s specific information
need, the first thing one should do is
to
Frequency 75 51 126 53 56 109
Percent 59.5 40.5 100.0 48.6 51.4 100.0
To determine whether the needed
information exists or not, the first
thing to do is to
Frequency 89 37 126 61 48 109
Percent 70.6 29.4 100.0 56.0 44.0 100.0
The easiest way to locate information
is
Frequency 50 76 126 39 70 109
Percent 39.7 60.3 100.0 35.8 64.2 100.0
To become familiar with a subject
about which one knows very little,
one should first consult
Frequency 90 36 126 68 41 109
Percent 71.4 28.6 100.0 62.4 37.6 100.0
For most current information about a
topic, one needs to consult
Frequency 74 52 126 83 26 109
Percent 58.7 41.3 100.0 76.1 23.9 100.0
If I want to find journal articles
about “The popularity of video
games”, I will search in
Frequency 77 49 126 66 43 109
Percent 61.1 38.9 100.0 60.6 39.4 100.0
Using a search engine such as Google
or Yahoo, one will not find
Frequency 55 71 126 51 58 109
Percent 43.7 56.3 100.0 46.8 53.2 100.0
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Using a search engine such as Google
to search for documents on “The
depletion of the ozone layer and the
impact on health”, one should best
use the words
Frequency 72 54 126 56 53 109
Percent 57.1 42.9 100.0 51.4 48.6 100.0
To diversify a search statement using
Boolean operators, which of the
following can be used
Frequency 63 63 126 47 62 109
Percent 50.0 50.0 100.0 43.1 56.9 100.0
In order to find more documents on
my topic I can include synonyms in
my search statement. To connect
those synonyms in my statement, I
use
Frequency 85 41 126 65 44 109
Percent 67.5 32.5 100.0 59.6 40.4 100.0
When one has found a book that is
right on his topic. Which section of
the book will he consult to find other
documents on the topic
Frequency 81 45 126 81 28 109
Percent 64.3 35.7 100.0 74.3 25.7 100.0
To find all documents about
Professor Wole Soyinka in the library
catalogue, one would do a search
Frequency 47 79 126 36 73 109
Percent 37.3 62.7 100.0 33.0 67.0 100.0
Some of the criteria used to evaluate
the quality of internet site are that
Frequency 54 72 126 48 61 109
Percent 42.9 57.1 100.0 44.0 56.0 100.0
Some of the criteria used to evaluate
the quality of print sources are
Frequency 73 53 126 72 37 109
Percent 57.9 42.1 100.0 66.1 33.9 100.0
Which of these is not a criterion used
to evaluate the quality of online
sources
Frequency 73 53 126 69 40 109
Percent 57.9 42.1 100.0 63.3 36.7 100.0
Which of the following best describes
articles published in a scholarly
journal
Frequency 75 51 126 57 52 109
Percent 59.5 40.5 100.0 52.3 47.7 100.0
Plagiarism is presenting the work of
others as though it were your own.
Which of the following is an example
of plagiarism
Frequency 56 70 126 62 47 109
Percent 44.4 55.6 100.0 56.9 43.1 100.0
When one reads a work which one
wants to use for an assignment. He
may not cite the author of the work
when
Frequency 101 25 126 84 25 109
Percent 80.2 19.8 100.0 77.1 22.9 100.0
For understanding the need for information, a higher percentage of the students
in both Babcock (81.0%) and FUNAAB (75.2%) know when they need
information. Also, as high as 70.6% of Babcock students did not know the step
to take ‘to determine whether the needed information exists or not’ compared to
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only 56.0% of FUNAAB students that failed the same question. On the other
hand, 76.1% of FUNAAB students did not know where to consult ‘for most
current information about a topic’ compared to 58.7% of Babcock students who
failed the same question. Majority of the students in both Babcock (80.2%) and
FUNAAB (77.1%) failed the question ‘when one reads a work which one wants
to use for an assignment, he may not cite the author of the work'.
Table 4 shows the overall performance of students in both schools.
Table 4: Overall performance in Information literacy knowledge test
BABCOCK Frequency Percent FUNAAB Frequency Percent
Failed 44 34.9 Failed 31 28.4
Passed 82 65.1 Passed 78 71.6
Total 126 100.0 Total 109 100.0
Table 4 shows that 34.9% got below the pass mark of 40%, while a greater
number of the respondents (65.1%) got above 40% out of the 126 students in
Babcock University. Also, 28.4% got below 40% in Federal University of
Agriculture, Abeokuta, while 71.6% got above 40% out of the 109 students.
3.3 Information literacy skills
Table 5 shows the findings on the information literacy skills of students in both
universities.
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Table 5: Descriptive of Information Literacy Skills of Students
Babcock FUNAAB
INFORMATION HS MS WS NS Mean S.D HS MS WS NS Mean S.D
Formulating question
base on my specific
information need
Frequency 46 72 7 1 3.31 0.574 51 46 10 2 3.34 0.723
Percent 36.5 57.1 5.6 0.8 46.8 42.2 9.2 1.8
Using several sources
to increase familiarity
with my topic
Frequency 45 62 15 3
3.19 0.737
39 53 15 2
3.18 0.735
Percent 36.0 49.6 12.0 2.4 35.8 48.6 13.8 1.8
Using the bibliography
or reference list on the
book to find other
documents on the topic
3.06 0.770
38 34 25 12
2.90 1.009
Percent 29.6 50.4 16.8 3.2 34.9 31.2 22.9 11.0
Using encyclopedia to
understand a
background
information to a
particular topic
Frequency 48 43 29 5 3.07 0.881 46 31 18 14 3.00 1.054
Percent 38.4 34.4 23.2 4.0 42.2 28.4 16.5 12.9
Finding all the
documents about a
particular author in the
library catalogue, by
doing access points
search either by
author, title, subject or
keywords
2.96 0.800
39 38 19 13
2.94 1.008
Percent 25.8 48.4 21.8 4.0 35.8 34.9 17.4 11.9
Using Google scholar Frequency 51 57 14 3 3.25 0.748 43 33 25 8 3.02 0.962
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as Google features to
find a research article
online Percent 40.8 45.6 11.2 2.4 39.4 30.3 22.9 7.4
Finding more
documents on my
topics online, by
combining synonyms in
my search by using the
Boolean operator
“OR”
2.98 0.942
29 42 25 13
2.80 0.970
Percent 36.0 33.6 23.2 7.2 26.7 38.5 22.9 11.9
Narrowing my search
on a particular topic,
by using the Boolean
operator “AND”
2.76 0.954
32 36 29 12
2.81 0.986
Percent 24.0 40.0 24.0 12.0 29.4 33.0 26.6 11.0
Removing unwanted
documents from my
search, by using the
Boolean operator
“NOT”
2.69 1.066
27 37 30 15
2.70 0.995
Percent 27.2 32.8 21.6 18.4 24.8 33.9 27.5 13.8
Formulating right
keywords in searching
for information online
3.15 0.783
39 37 28 5
3.01 0.897
Percent 35.2 48.8 12.0 4.0 35.8 33.9 25.7 4.6
Competently
evaluating information
no matter the source
2.92 0.771
30 47 22 10
2.89 0.916
Percent 21.8 52.4 21.8 4.0 27.5 43.1 20.2 9.2
Evaluating print
sources based on its
criterion
3.06 0.780
26 47 23 13
2.79 0.944
Percent 31.2 46.4 20.0 2.4 23.9 43.1 21.1 11.9
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Evaluating online
sources based on its
criterion
3.10 0.728
35 41 21 12
2.91 0.977
Percent 30.4 51.2 16.8 1.6 32.1 37.6 19.3 11.0
Selecting materials and
summarizing them in
my own words for
personal use
3.21 0.786
52 35 12 10
3.18 0.964
Percent 40.0 44.0 12.8 3.2 47.7 32.1 11.0 9.2
Preserving and storing
information for future
use
3.12 0.812
46 42 14 7
3.17 0.887
Percent 34.7 47.6 12.9 4.8 42.2 38.5 12.8 6.5
Using acquired
information as a lead to
produce an article or
thesis
3.14 0.836
35 38 25 11
2.89 0.975
Percent 37.6 43.2 14.4 4.8 32.1 34.9 22.9 10.1
Communicating and
presenting information
to others in
appropriate and usable
format
3.07 0.825
44 35 25 5
3.08 0.904
Percent 32.8 46.4 16.0 4.8 40.4 32.1 22.9 4.6
Competently citing and
acknowledging other
people’s work that I
used
3.14 0.807
31 47 20 11
2.90 0.932
Percent 36.8 43.2 16.8 3.2 28.4 43.1 18.4 10.1
Key: - HS – Highly Skilled; MS – Moderately Skilled; WS – Weakly Skilled; NS – Not Skilled
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Results in Table 5 show that generally Babcock students were only moderately
skilled in all the items used in measuring information literacy skills. However,
an exception was observed with ‘Using encyclopedia to understand a
background information to a particular topic’ and ‘Finding more documents on
my topics online, by combining synonyms in my search by using the Boolean
operator “OR”’, where more students were highly skilled than moderately
skilled. In contrast, FUNAAB students were highly skilled in 9 of the 18 items
used to measure information literacy skills. The information literacy skills of
students in both schools can be considered satisfactory in view of the fact that
the lowest mean observed was 2.69 from Babcock University.
3.4 Cognitive abilities
The cognitive abilities of students in FUNAAB and Babcock University was
measured by their responses to the questions asked in this section which was
classified into strongly agree, agree, disagree and strongly disagree. The result
is presented in Table 6.
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Table 6: Descriptive of Cognitive Abilities of Students
INFORMATION Babcock university FUNAAB
SA A D SD Mean S.D SA A D SD Mean S.D
I remember things very
easily
Frequency 55 67 3 1
3.40 0.581
48 51 9 1
3.34 0.670
Percent 43.7 53.2 2.4 0.7 44.0 46.8 8.3 0.9
My memory can contain a lot
of things and not forget them Frequency 29 68 26 3
2.98 0.732
31 53 22 3
3.03 0.775
Percent 23.0 54.0 20.6 2.4 28.4 48.6 20.2 2.8
I find it easy to express my
ideas to people orally
3.06 0.787
39 42 22 6
3.05
0.886
Percent 30.2 50.0 15.8 4.0 35.8 38.5 20.2 5.5
I find it easy to express my
ideas to people in writing
3.15 0.760
46 33 15 15
3.01
1.058Percent 34.1 50.0 12.7 3.2 42.2 30.3 13.8 13.7
I concentrate very well and
with minimal distraction
during retrieval processes
Frequency 35 64 23 4 3.03 0.769 28 53 19 9 2.92 0.873
Percent 27.8 50.8 18.3 3.1 25.7 48.6 17.4 8.3
I have the ability to
automatically and fluently
perform simple mental
processes
3.17 0.716
24 50 27 8
2.83 0.859
Percent 33.3 53.2 11.1 2.4 22.0 45.9 24.8 7.3
I have the ability to
deliberately control mental
processes to solve problems
Frequency 37 63 18 8 3.04 0.817 32 48 18 11 2.93 0.930
Percent 29.4 50.0 14.3 6.3 29.4 44.0 16.5 10.1
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In both schools, most of the students only agreed to the items under cognitive
abilities. An exception was however observed in FUNAAB where many of the
respondents (42.2%) strongly agreed to the item, ‘I find it easy to express my
ideas to people in writing’ (Table 6). It was surprising to observe that over 20%
of students in each school disagreed with the item ‘My memory can contain a
lot of things and not forget them’. Similarly, over 20% of FUNAAB students
disagreed with the items ‘I find it easy to express my ideas to people orally’
(20.2%) and ‘I have the ability to automatically and fluently perform simple
mental processes’ (24.8%).
3.5 Information retrieval skills
The results from the information retrieval skills of Babcock and FUNAAB
students are presented in Table 7.
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Table 7: Descriptive of Information Retrieval Skills of Students
Babcock FUNAAB
INFORMATION VG G A P VP
Mean S.D
VG G A P VP
Mean S.D
Definition of your needs
for research
Frequency 53 53 13 1 1 4.29 0.758 49 36 11 11 2 4.09 1.059
Percent 42.1 42.1 10.2 0.8 0.8 45.0 33.0 10.1 10.1 1.8
Locating information in
e-resources.
Frequency 35 63 21 2 4.08 0.726 33 43 14 16 3
3.80 1.112
Percent 27.8 50.0 16.6 1.6 30.3 39.4 12.8 14.7 2.8
Selecting
articles/journals/books
with ease.
Frequency 35 56 26 3 1
4.00 0.827
33 33 21 13 9
3.62 1.260
Percent 27.8 44.4 20.6 2.4 0.8 30.3 30.3 19.3 11.9 8.2
Summarizing materials
in your own words.
Frequency 39 52 21 7 2 3.98 0.940 43 32 25 8 1 3.99 1.005
Percent 30.9 41.3 16.7 5.5 1.6 39.4 29.4 22.9 7.3 0.9
Understanding
terminologies used in
databases.
3.93 0.868
28 32 23 12 14
3.44 1.329
Percent 24.6 46.0 20.6 3.2 1.6 25.7 29.4 21.1 11.0 12.8
Use of reference sources
to increase familiarity of
topics
Frequency 26 54 24 13 4 3.70 1.030 30 31 19 16 13
3.45 1.350
Percent 20.6 42.9 19.0 10.3 3.2 27.5 28.4 17.4 14.7 11.9
Use of mouse and
keyboard.
4.22 0.962
59 17 21 11 1
4.12 1.103
Percent 47.6 29.4 14.3 2.4 2.4 54.1 15.6 19.3 10.1 0.9
Copying information
into your storage device
such as flash drive¸ CD
ROM
Frequency 63 36 13 7 1 4.28 0.935 42 31 15 19 2
3.84 1.172
Percent 50.0 28.6 10.3 5.6 .8 38.5 28.4 13.8 17.4 1.8
Retrieving information
from internet
4.31 0.866
43 36 12 14 4
3.92 1.164
Percent 50.8 28.6 13.5 2.4 .8 39.4 33.0 11.0 12.8 3.7
Retrieving information Frequency 57 38 16 7 3 4.15 1.022 41 36 15 14 3 3.90 1.130
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Key: - VG – Very Good; G – Good; A – Average; P – Poor; VP – Very Poor
from flash drive Percent 45.2 30.2 12.7 5.6 2.4 37.6 33.0 13.8 12.8 2.8
from CD ROM
Frequency 55 39 20 5 2 4.16 0.957 37 35 22 12 3
3.83 1.101
Percent 43.7 31.0 15.9 4.0 1.6 33.9 32.1 20.2 11.0 2.8
from articles/journals
Frequency 51 37 23 6 4 4.03 1.056 36 25 22 18 8
3.58 1.300
Percent 40.5 29.4 18.3 4.8 3.2 33.0 22.9 20.2 16.5 7.3
Scanning images.
Frequency 39 49 18 11 4 3.89 1.063 26 25 27 17 14
3.29 1.335
Percent 31.0 38.9 14.3 8.7 3.2 23.9 22.9 24.8 15.6 12.8
Access of on-line
databases.
Frequency 60 44 15 1 2 4.30 0.842 45 31 16 8 9
3.87 1.263
Percent 47.6 34.9 11.9 .8 1.6 41.3 28.4 14.7 7.3 8.3
Download files from
online databases.
Frequency 50 52 14 4 2 4.18 0.882 34 33 20 16 6
3.67 1.218
Percent 39.7 41.3 11.1 3.2 1.6 31.2 30.3 18.3 14.7 5.5
Use of Boolean operators
(OR, AND, NOT).
Frequency 24 41 40 11 6 3.54 1.061 22 25 29 20 13
3.21 1.292
Percent 19.0 32.5 31.7 8.7 4.8 20.2 22.9 26.6 18.3 11.9
Combining two terms to
retrieve information.
Frequency 25 52 27 14 4 3.66 1.035 25 31 17 24 12
3.30 1.337
Percent 19.8 41.3 21.4 11.1 3.2 22.9 28.4 15.6 22.0 11.0
Use of truncation search
techniques ($, *, +) to
retrieve information.
Frequency 26 32 36 17 11 3.37 1.221 25 17 34 14 19
3.14 1.378
Percent 20.6 25.4 28.6 13.5 8.7 22.9 15.6 31.2 12.8 17.4
Use of search engines
such as Yahoo, Google,
Alta Visa and Google
scholar etc.
Frequency 64 36 11 7 4 4.22 1.049 56 19 11 21 2
3.97 1.251
Percent 50.8 28.6 8.7 5.6 3.2 51.4 17.4 10.1 19.3 1.8
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Table 7 shows that generally students in both universities have very good
information retrieval skills. On most of the items, more than 60% of students of
Babcock reported being good and very good. The exceptions were on the items,
‘Use of Boolean operators (OR, AND, NOT)’ and ‘Use of truncation search
techniques ($, *, +) to retrieve information’, which had 51.5 and 46%
respondents respectively. In contrast to the result from Babcock University,
FUNAAB had less than 60% respondents reportedly good and very good on the
following items, namely, Understanding terminologies used in databases
(55.1%)., Use of reference sources to increase familiarity of topics’ (55.9(%),
Retrieving information from articles/journals (55.9%), Scanning
images.(46.8%), Use of Boolean operators (OR, AND, NOT) (43.1%),
Combining two terms to retrieve information (51.3%) and Use of truncation
search techniques ($, *, +) to retrieve information (38.5%).
Test of Hypotheses
This section contains the results of the independent sample T-test used to test
the 4 hypotheses. The level of significance was set to 5%. Thus, if the p-value
is less than 0.05, the null hypothesis is rejected; but if p is greater than 0.05, the
null hypothesis is not rejected. The results for Hypotheses 1-4 are presented in
Table 8.
Hypothesis One
H0: There is no significant difference in the information knowledge of students
in Federal University of Agriculture, Abeokuta and Babcock University.
H1: There is a significant difference in the information knowledge of students
in Federal University of Agriculture, Abeokuta and Babcock University.
The result from Table 8 shows that at a significant level of 0.05, p=0.851 is not
statistically significant. Hence the null hypothesis is not rejected. This implies
that significant differences do not exist between students from Babcock and
FUNAAB universities in terms of information literacy knowledge.
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Table 8: Independent Samples Test result for Hypotheses 1-4
Levene's Test for
Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
Information literacy
knowledge
Equal variances assumed 3.084 0.080 -0.188 233 0.851 -0.386 2.051 -4.426 3.655
Equal variances not assumed -0.189 232.645 0.850 -0.386 2.035 -4.395 3.624
Information Literacy skill
Equal variances assumed 1.839 0.176 1.119 230 0.264 0.53010 0.47381 -0.40347 1.46366
Equal variances not assumed 1.108 213.397 0.269 0.53010 0.47841 -0.41291 1.47310
Cognitive Abilities
Equal variances assumed 1.052 0.306 1.522 232 0.129 0.69556 0.45709 -0.20501 1.59613
Equal variances not assumed 1.516 223.676 0.131 0.69556 0.45884 -0.20863 1.59975
Information Retrieval
Equal variances assumed 33.638 0.000 3.086 232 0.002 5.46613 1.77119 1.97645 8.95581
Equal variances not assumed 3.016 190.944 0.003 5.46613 1.81233 1.89137 9.04089
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Hypothesis Two
H0: There is no significant difference in the information literacy skills of
students in Federal University of Agriculture, Abeokuta and Babcock
University.
H1: There is a significant difference in the information literacy skills of
University.
From Table 8, at a significant level of 0.05, p=0.264 is not statistically
significant. Hence the null hypothesis is not rejected. This implies that
significant differences do not exist between students from Babcock and
FUNAAB universities in terms of information literacy skills.
Hypothesis Three
H0: There is no significant difference in the cognitive abilities of students in
Federal University of Agriculture, Abeokuta and Babcock University.
H1: There is a significant difference in the cognitive abilities of students in
Federal University of Agriculture, Abeokuta and Babcock University.
As seen from Table 8, p=0.129 is not statistically significant. Hence the null
hypothesis is not rejected. This implies that significant differences do not
exist between students from Babcock and FUNAAB universities in terms of
cognitive abilities.
Hypothesis Four
H0: There is no significant difference in the information retrieval skills of
University.
H1: There is a significant difference in the information retrieval skills of
University.
The result from Table 8 shows that at a significant level of 0.05, p=0.02 is
statistically significant. Looking at the Group Statistics in Table 9, we can
see that Babcock students had higher level of information retrieval skills
than FUNAAB students.
Table 9: Group Statistics
Respondent
school
N Mean Std.
Deviation
Std. Error
Mean
Information
Retrieval
Babcock 125 75.5120 11.15696 .99791
FUNAAB 109 70.0459 15.79462 1.51285
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Hence, this study found that Babcock students had statistically significantly
higher information retrieval skills (75.51±11.16) compared to FUNAAB
students (70.05±15.79) and the null hypothesis was rejected.
4. DISCUSSION OF FINDINGS
Babcock and FUNAAB undergraduate students possessed information
literacy knowledge to a reasonable extent. They understand when and why
they need information and are generally able to locate and evaluate such
information. However, there is no significant difference in the information
literacy knowledge of Babcock and FUNAAB students. On the surface,
information literacy skills level seemed higher among FUNAAB students
based on the number of items the students reported being highly skilled as
compared to Babcock where many of the students were only moderately
skilled on the items. However, this seeming difference was not statistically
significant. According to Newton [26], information literacy knowledge
deals with knowing: when you have a need for information; the resources
available to you; how to find information and the need to evaluate results.
In addition, Ojedokun [27] also noted that information literacy skills in all
disciplines requires an individual to be able to define a problem; initiate a
plan to find information; locate and access resources; use the information;
synthesize information; and carry out some form of evaluation. Students of
both institutions could be considered generally not below average in their
level of information literacy skills. This can also be said about the cognitive
abilities of the students and the study revealed that cognitive abilities were
not statistically different between students of both schools.
Findings however revealed that there is a significant difference in
the information retrieval skills of students in both universities, as students of
Babcock University had higher level of information retrieval skills
compared to FUNAAB students. It is not a surprise that Babcock students
had higher information retrieval skills than FUNAAB students. A similar
finding was previously reported by Quadri, Adetimirin and Idowu [28]. The
authors investigated the availability and utilization of electronic resources
by students of Babcock and Redeemers universities, Ogun State, Nigeria.
Among other findings, the authors noted that lack of ICT skills as a barrier
to the use of electronic resources was more peculiar to respondents in
Redeemers University than Babcock. The fact that students of Babcock
University, a private university had higher information retrieval skills than
FUNAAB, a public university also agrees with a similar finding by Ojo and
Akande [29]. The study examined students’ access, usage and awareness of
electronic information resources at the University College Hospital (UCH),
Ibadan, Nigeria, and reported low level of usage of electronic resources by
students in this public university due to lack of information retrieval skills.
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Low level of information retrieval skills among students of FUNAAB might
not be unconnected with the generally known scarce resources in most
public universities due to under-funding by governments. It is a known fact
that resources for public universities have continuously been inadequate
despite growing desire for university education by students. Many facilities
in these public universities are dilapidated. On the other hand, some private
universities have state-of-the-art facilities such as well-furnished
classrooms, standard laboratories, well-equipped libraries, technologies
used in teaching and learning which are incomparable to those in most
public universities.
5. CONCLUSION
This study has shown that although significant differences do not exist in
the information literacy skills and cognitive abilities of students in Babcock
and FUNAAB, there is a significant difference in their information retrieval
skills. Babcock students had a higher level of information retrieval than
FUNAAB students. This study and other related studies are quick to
attribute this finding and others to the poor level of funding of public
universities by government. The reality however, is that this situation might
not change in the very near future. Hence, rather than endlessly waiting for
a time when government would be able to adequately fund public
universities, library and information professionals in public universities can
in their own little ways help out. Administrative heads of public universities
should encourage library and information professionals to periodically
organize practical workshops on information retrieval with the aim of
improving students’ information retrieval skills so that students can obtain
the needed information to solve their information need. A limitation of this
study is that only two universities in Ogun State were used due to
availability of students’ population statistics and the fact that they have
related courses. This may however affect the generalization of the findings.
Hence, further studies can be geographically expanded to include
universities located in other states in Nigeria.
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ISSN: 1694-2108 | Vol. 16, No. 2 48
Risk Assessment in Constructing
Horseshoe Vault Tunnels using
Fuzzy Technique
Erfan Shafaghat
Instructor of Mehr university, Arak, Iran
Mostafa yousefi rad
Associated professor of arak technology university, Arak, Iran
ABSTRACT
Risk assessment is one of the projects’ important issues and by applying fuzzy method,
subsidence possibility can be investigated. In this paper, subsidence has been examined
using fuzzy method and investigating 81 and then 6000 soil samples in order to obtain
subsidence possibility of soils those which have these features. Results of fuzzy rules in this
dissertation have been analyzed about tunnel vault height, the height of the soil, modulus as
well as density which can be used in applicable matters. According to the results obtained
from this study deformation modulus has the greatest impact on subsidence so that when it
is less than 100, soil is not usable more. The higher the slag soil is, the more subsidence is
so that from a height of 6 m, the retaining structures must be used. The higher the tunnel
vault is, the more the subsidence is and the more the soil density is increasing, subsidence is
also increasing. When deformation modulus becomes 100, fuzzy digit is reducing and it
stands between 3.8 and 4.2. When slag soil height is increasing, subsidence is more likely
to go up. When tunnel vault is from 1 to 4, fuzzy digit stands from 0 to 2.5 and when slag
soil density stands between 2200 and 2400, fuzzy digit is between 5 and 6. Risk assessment
carried out in fuzzy logic indicates that whenever slag soil height, slag soil density and
tunnel vault is increasing, subsidence rate is also going up and whenever deformation
modulus is decreasing, subsidence rate is increasing.
Keywords
Tunnel, Horseshoe, Fuzzy logic, Subsidence.
1. INTRODUCTION
Tunnel projects are always along with high percentage of risks due to
uncertainties related to them. Accurate and proper management of these
risks and predicting them is reducing their possibility or negative
consequences. In a global scrolling about tunnel projects, 30 to 50 percent
increase in time and costs has been reported due too partial and fault
management. Risk assessment has been one of the fundamental bases of risk
management and anticipating risk will offer a great aid to the experts to
cover risk better. The main aim of risk assessment is to measure risk based

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