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Submit Paper / Call for Papers
Journal receives papers in continuous flow and we will consider articles
from a wide range of Information Technology disciplines encompassing the most
basic research to the most innovative technologies. Please submit your papers
electronically to our submission system at http://jatit.org/submit_paper.php in
an MSWord, Pdf or compatible format so that they may be evaluated for
publication in the upcoming issue. This journal uses a blinded review process;
please remember to include all your personal identifiable information in the
manuscript before submitting it for review, we will edit the necessary
information at our side. Submissions to JATIT should be full research / review
papers (properly indicated below main title).
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Journal of
Theoretical and Applied Information Technology
April 2020 | Vol. 98
No.08 |
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Title: |
INDOOR FIRE DETECTION SYSTEM BASED ON DATA MINING TECHNIQUES |
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Author: |
ABDELWAHAB ALSAMMAK, KHALED M. FOUAD, SHAIMA D. ALMARIE |
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Abstract: |
Fire ignition is one of the events that can cause severe humanitarian, economic,
and environmental dam-ages. Fast fire detection can significantly help in
controlling the fire and reducing the resulting losses. Effi-cient fire-fighting
in schools can save many lives and resources. In this study, a data mining-based
fire de-tection system is proposed. It can be used in detecting fire sources in
different locations of Kuwaiti schools that are listed in the ministry of
Education. The proposed system consists of four main steps: data acquisi-tion,
data preprocessing, feature analysis and selection, and classification. The data
acquisition is per-formed with the help of the General Civil Defense Department
of Kuwait. In the data preprocessing step, a set of operations is performed on
the collected data, including discretization and categorization. In the fea-ture
selection step, two feature selection techniques are used, namely Information
Gain (IG) and Principle Component Analysis (PCA). Finally, in the classification
step, five classification models are used to per-form the classification task,
including Decision Tree (DT), Linear Regression (LR), Linear Discriminant (LD),
Support Vector Machine (SVM), and Deep Belief Network (DBN). Intensive
experiments are per-formed to evaluate the proposed system, and the obtained
results are auspicious. |
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Keywords: |
Fire Detection, Data Mining, Deep Learning, Classification. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
EFFICIENT INTEGRATION METHOD FOR HUMAN FACIAL IMAGES RETRIEVAL BASED ON VISUAL
CONTENT AND SEMANTIC DESCRIPTION |
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Author: |
AHMED ABDU ALATTAB, SAMEEM ABDUL KAREEM, IBRAHEEM M.G. ALWAYLE, ANWAR ALI YAHYA,
KHALED M.A. ALALAYAH |
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Abstract: |
A semantic-content based facial image retrieval (SCBFIR) technique that
incorporates multiple visual and semantic features to improve the accuracy of
the facial image retrieval is proposed. The proposed technique based on reducing
the semantic gap between the high-level query requirement and the low-level
facial features of the human facial image. Visual features and semantic features
are extracted by different methods, moreover, some features may be considered
more important than others, so features weighting is used to distinguish the
importance of the various features. This research proposed a model that links
the high-level query requirement and the low-level features of the human facial
image. A newly proposed method based on radial basis function network is
introduced for measuring the distance between the query vectors and the database
vectors of the different features for finding, weighting, and combining the
similarities. The proposed system of SCBFIR is trained and tested on the ‘ORL
Database of Faces' from AT&T Laboratories, Cambridge, and a local database
consisting of local facial images from the University of Malaya (UM), Kuala
Lumpur. The results of the experiments show that, as compared to the current
content-based facial image retrieval technique (CBFIR), the proposed methods of
SCBFIR achieve the best performance. More precisely the CBFIR achieves 84.0% and
92.41% accuracy, while the SCBFIR achieves 97.85 % and 99.39% accuracy for the
first and second database respectively within the top 10 retrieved facial
images. |
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Keywords: |
Image Retrieval, Face Retrieval, Semantic Features, RBFN, Eigenfaces, Color |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
FEASIBILITY OF EXTERNAL RADIOTHERAPY DOSE ESTIMATION IN HOMOGENOUS PHANTOM USING
MONTE CARLO MODELING |
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Author: |
ZAKARIA AITELCADI, AHMED BANNAN, 1REDOUANE EL BAYDAOUI, MR MESRADI, ABDELLAH
HALIMI, SAAD ELMADANI |
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Abstract: |
This work relates to the study of a Varian Clinac IX 6MV photon beam from
measurement and simulation of tree fundamental functions, the Percentage Depth
Dose (PDD), the Dose Profile (DP) and the collimator scatter factor (Sc).
Simulations were performed using the recent version of the Monte Carlo
simulation code GATE (v8.1). It was used to model the geometry of the
accelerator head, optimize the configuration of the electron source and
calculate the dose for different field sizes (3x3 cm2, 4x4 cm2, 6x6 cm2, 8x8
cm2, 12x12 cm2, 15x15 cm2, and 20x20 cm2). The Geometry was modeled using the
parameters given by the Varian manufacturer under agreement. To generate the
bremsstrahlung photon beam, an electron source with a mean energy of 5.8MeV and
a half-width of 1mm was used, its parameters were determined for a relative dose
difference between 2% and 2.5% for PDD and DP respectively. After using the
simulation splitting, the variance reduction and the phase space techniques, the
computing time consumed by the simulations was reduced by a factor of 160.
Otherwise Good agreement within 1% was found for the Collimator scatter factor
(Sc). The presented results show that the GATE model of Varian Clinac IX 6MV
photon beam might be used to evaluate the systematics dosimetric errors and for
clinical applications using the recent treatment technics. |
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Keywords: |
GATE; Monte Carlo; Varian; HPC; Dosimetry; Collimator Scatter Factor; Linear
Accelerator. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
PREDICTING THE POPULARITY OF ONLINE NEWS USING CLASSIFICATION METHODS WITH
FEATURE FILTERING TECHNIQUES |
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Author: |
RUBA OBIEDAT |
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Abstract: |
Due to the expanded use of the internet and the revolution of the information
technology field, people are beginning to read news online more and more. For
that reason, online news has become the main source of information for the
majority of people, and predicting the popularity of online news has become a
hot topic as it could help writers present competitive and highly readable news.
These predictions can be done by using machine learning techniques. This paper
introduces some of the most well-known prediction classification models in data
mining like Random Forest, Bayes Net, Logistic Function, C4.5 and Simple Cart
which has been applied in order to predict the popularity of the online news.
The objective of this paper is to evaluate the performance of these different
models on real-world online news data. The experiment results revealed the
success of some of these models in predicting the popularity of online news with
relatively high accuracy. The performance of the five models is evaluated by
some of the most popular metrics such as Accuracy, Root Mean Square Error
(RMSE), Kappa Statistic, TP-Rate, FP-Rate, Precision, F-Measure and ROC Area
values. Finally, feature filtering techniques are applied in the study to
improve the model's performance and identify the most influential features
affecting news popularity. |
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Keywords: |
Online News, Classification, Feature Filtering, Popularity, Data Mining |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
METHOD FOR SPEECH INTELLIGIBILITY ASSESSMENT WITH COMBINED MASKING SIGNALS |
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Author: |
YERZHAN N. SEITKULOV, SEILKHAN N. BORANBAYEV,BANU B. YERGALIYEVA, HENADZI V.
DAVYDAU, ALEKSANDR V. PATAPOVICH |
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Abstract: |
The article is devoted to the method for speech intelligibility assessment while
protecting it from leakage through acoustic channels by masking it with combined
acoustic signals, including white noise and speech-like signals. Difficulties in
solving the tasks of voice information protection, as well as the tasks of
information protection in general, are caused by uncertainties associated with
difficulties in the mathematical formulation of protection problems on the one
hand and a large number of factors affecting on the speech information security
on the other hand. The method for speech intelligibility assessment is proposed
for estimation the speech information security when it used in voice information
protection systems according to limit states. For a correct assessment of the
speech information security by its intelligibility indicators, it is necessary
to make a number of assumptions and limitations that can be adopted on the basis
of experience in the practical implementation of the speech information
protection by known technical means and a set of organizational measures. |
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Keywords: |
Speech Intelligibility; Combined Masking Signals; Security Of Voice Information;
“White†Noise; Speech-Like Signals. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
NAVIGATION SYSTEM BASED ON BLUETOOTH BEACONS: IMPLEMENTATION AND EXPERIMENTAL
ESTIMATION |
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Author: |
ADILZHAN KUTYMOVICH KEREYEV, SABYRZHAN KUBEYSINOVICH ATANOV, KULNAR PANABEKKIZI
AMAN, ZHUMAZHAN KALDYGULOVNA KULMAGAMBETOVA, BAZARGUL TABYLGANOVNA KULZHAGAROVA |
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Abstract: |
This work describes development of positioning model as well as implementation
of algorithm of positioning based on Bluetooth technology without satellite
navigation systems.
The research is aimed at development and testing of an
internal positioning system using Bluetooth beacon with minimal error.
It is
assumed that applying the methods of filtering and data smoothing to RSSI
Bluetooth beacon data can significantly improve the positioning accuracy.
Some existing positioning technologies based on Bluetooth beacons are
overviewed, development of Bluetooth propagation model is described based on
RSSI data. Bluetooth beacons play two different roles: transmittance (beacon)
and reception (device). This enables data exchange over short distances.
Therefore, many researchers study intensively positioning methods using beacons.
However, positioning is not always accurate. Positioning system based on
Bluetooth beacons is proposed in this article. Improvement of positioning
accuracy can be improved by smoothing and filtration of data from beacons. |
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Keywords: |
Positioning, Indoor Positioning System, Bluetooth, RSSI, Bluetooth Beacon,
Location Based Services, Navigation Data Smoothing and Filtration. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
COST OPTIMIZATION OF PROCURING CLOUD COMPUTING RESOURCES USING GENETIC
ALGORITHMS |
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Author: |
RIYADH A.K. MEHDI, MIRNA NACHOUKI |
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Abstract: |
Cloud computing has given enterprises the opportunity to acquire computing
resources cost effectively and yet benefit from other key cloud features that
include scalability, instant provisioning, and virtualized resources. Cloud
service providers enable businesses to acquire resources by offering different
cloud deployment, service, and pricing models. A major challenge for cloud users
is to determine the amount of resources to be provisioned that meet their
expected needs over the planning horizon, the deployment models to opt for, and
the pricing models to adopt to minimize cost. Research in cloud economics has
focused on building analytical optimization models that require the
representation of the expected demand pattern over the planning horizon as a
probability density function amenable to mathematical analysis. In this work,
however, we have built a computational model based on simulation and genetic
programming to compute the optimal combination of own-private and public cloud
resources that satisfy a given pattern of demand as well as the optimal contract
guaranteed service level. The model incorporates into the optimization process
the different price subscription models offered by cloud providers. The
distinguishing features of our model is that it can handle any theoretical or
empirical demand probability distribution. In addition, our computational scheme
allows for any random variation in any of the parameters affecting the total
cost of cloud resources consumed as long as this variation can be described by a
theoretical or an empirical density function. The accuracy and correctness of
the model was tested against results obtained from mathematical models based on
normally and exponentially distributed demand patterns with almost identical
results. Thus, our computational model provides a valuable decision tool to help
identify the most cost-effective way of provisioning computing resources.
Results of experiments conducted in this work indicate that it is more cost
effective to use a mixed strategy rather than depend entirely on own-private
capacity or on-demand public cloud computing resources alone irrespective of the
level of variation in demand; the optimal level of own-private computing
capacity is affected by the shape of the demand curve, level of variations in
demand, guaranteed service level, and the cloud price subscription model
adopted. Future work will extend the computational model to optimize the cost of
using cloud storage and networking services. Future work will extend the model
to include the cost optimization of using cloud storage and networking services. |
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Keywords: |
Cloud Costing, Cloud Pricing, Optimal Cloud Deployment, Cloud Cost Optimization,
Genetic Programming. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
COMPARING THE CLASSIFICATION METHODS OF SENTIMENT ANALYSIS ON A PUBLIC FIGURE ON
INDONESIAN-LANGUAGE SOCIAL MEDIA |
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Author: |
DERISMA, DODON YENDRI, MEZA SILVANA |
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Abstract: |
Social media are used by people as the platform to express their opinions and
the conditions that happen around them. During the ministerial election for
2019-2024 Cabinet of Indonesia Maju, the choice of ministers of President
Indonesia Jokowi always present in the discussed topic on a social media. The
most discussed topic is the appointment of Nadiem Makarim as the Minister of
Education and Culture. In Indonesia, the ministers have to show their
performance and capability. If they failed to do so, they would be reshuffled by
the President. One of the sources of information required by President to be
able of evaluating the performance of his ministers is through the feedback from
citizens. Sentiment analysis is a computation study of opinions, attitudes, and
emotions of people toward entities, individuals, issues, events, or topics.
Sentiment Analysis The targets of sentiment analysis are to discover arguments,
identify the expressed sentiments, and then classify their polarity into
positive, negative, or neutral categories. In principle, the classification
methods on sentiment analysis can be performed through machine learning
approach. Some classification methods of machine learning such as Decision Tree,
K-NN, Naïve Bayes, and Random Forest are often used to acquire the best result.
Next, several stages were done, including the data collection by crawling
Twitter’s data via API with “Nadiem Makarim†as the keyword, pre-processing,
classification and evaluating the classification performance. Naïve Bayes
acquired the best result with 99% accuracy, 94% precision, and 99% Recall. It
can be concluded that Naïve Bayes is the best classifier to be used on the
dataset of Indonesian-Language social media because it can provide the most
accurate and appropriate prediction. |
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Keywords: |
Sentiment Analysis, Machine Learning, Text Classification, Twitter |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
FACTORS AFFECTING CONSUMER CHOICES BETWEEN B2C AND C2C MODELS IN CHINESE
ELECTRONIC COMMERCE |
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Author: |
BEIQI GU, JONGCHANG AHN |
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Abstract: |
B2C and C2C have become the most important online shopping platforms. However,
the border between B2C and C2C e-commerce platforms is increasingly blurred, and
there is a gradual trend toward unity. Previous studies have focused on shopping
motivations and consumer behavior, but comparisons of shopping motivations among
shopping platforms are sparse. In this study, B2C and C2C online shopping
platforms based on previous research were compared. Website experiences, product
experiences, and customer experiences were analyzed as main factors. The factors
encompass 12 dimensions affecting online shopping. For 324 samples, SPSS was
used to compare and analyze factors that influence consumers to choose different
online shopping platforms. Four dimensions including website design, website
content, product quality, and after-sale service influence platform selection
intentions on B2C and C2C websites. Product price and conformity dimensions
affect the customers' choices on B2C websites, but not on C2C websites. In
addition, website experiences and customer experiences among the main factors
influence the customers’ choices on B2C and C2C websites. However, product
experiences does not influence on B2C websites. Our results will enable
e-commerce platform operators to better understand consumer demands, and also to
formulate business strategies that take into account consumer intentions.
Finally, open research issues that require substantial research efforts are
summarized. |
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Keywords: |
E-Commerce, Online Shopping Platform, Platform Selection Intention, Influencing
Factor, B2C (Business To Customer), C2C (Customer To Customer) |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
AN EFFICIENT CLUSTERING TECHNIQUES FOR URBAN AREA ANALYSIS BASED ON SATELLITE
IMAGES |
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Author: |
A. TOBAL, H. FAROUK, S. MOKHTAR, H. ZIDAN |
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Abstract: |
Urban analysis provides the urban planners with the information about how to
optimally utilize the land resources and the infrastructure. It gives solutions
to many problems such as dense population, population growth, and limited
resources. The Arab Republic of Egypt is one of the most densely populated
countries in the region. In addition to the accumulation of the population in a
specific area of the land, there is limited natural resources that directly
affects the nature of life, agriculture and the population spreading. So, it is
vital to study the changes in the geography of the land in order for the urban
planners to set short- and long-term strategies to improve the quality of life
and set a sustainable development plan. The Suez region was selected for such
study. In this work, satellite images have been categorized into four segments:
Desert, agriculture, residential and water using three clustering techniques to
study the increment and decrement of urbanization, water resources, agricultural
patch and dissertation. The three clustering techniques are; Fuzzy, Kohonen
Neural Network and k-means. Each technique was applied on a low-quality
resolution Google satellite images of Suez area across 16 years from 2001 to
2016. A comparison between each technique behavior on this image style and a
ready-made program ArcGIS has been done. Astoundingly, the results show that the
Fuzzy clustering is the best technique for such kind of images. |
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Keywords: |
Clustering Techniques, Unsupervised Learning, Fuzzy Clustering, K-means
Clustering, Kohonen Neural Network. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
STRONG REDUCTION OF SUPER IMAGES BY OPTIMIZING MIXING METHODS AS VECTOR DATA |
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Author: |
OLA ALI OBEAD , AHMED AL-GHANIMI |
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Abstract: |
The ultra-spectral image set aims to extract the spectra of pure materials from
the same scene (the final member bars or orange color), as well as the average
amount per pixel of the image. Most algorithms rely on traditional end-organ
exploration techniques that often did not work in difficult scenarios. In this
work, this problem will be addressed along with the variation of the material by
considering that the final member is a direction in the surrounding space which
in this case is a one-point substitute. Under this work, we will propose a new
algorithm through which we generate a strong reference spectrum. We see the
potential of this proposed algorithm which we will apply using real spectra to a
set of synthetic data with variability within the class, and the landscape will
be the image used. |
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Keywords: |
Hyperspectral mixing, variability of end members, Extended Linear Blend Model,
Oblique Variety. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
ANALYTIC HIERARCHY PROCESS WITH FIREFLY ALGORITHM FOR SUPPLIER SELECTION IN IT
PROJECT OUTSOURCING |
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Author: |
PRASHAYA FUSIRIPONG, FAUZIAH BAHAROM, YUHANIS YUSOF |
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Abstract: |
Nowadays, most organizations have adopted IT outsourcing (ITO) into their main
business strategy as it promises several benefits such as cost reduction, staff
ability improvement and technology enhancement. Supplier selection is a key
essential process in ITO. Unfortunately, supplier selection is a complex
decision-making process as the evaluation involved with multi criteria and each
criterion carries a different weight. Usually, the weight for each criterion is
assigned by experts which might introduce uncertainty, bias, and opaqueness.
Therefore, this paper proposed a hybrid method that aimed to eliminate human
roles in determining evaluation criteria weight during supplier selection
process. The method was designed by integrating Firefly Algorithm (FA) into
Analytic Hierarchy Process (AHP) and termed as Firefly Algorithm Analytic
Hierarchy Process (FAHP). It is operationalized on three datasets which were
obtained from the referenced literature. Experimental results showed that the
obtained Consistency Ratio (CR) value (i.e. 0.001) and Sum of Bias (SB) value
(i.e. 0.351) are very close to zero. These findings show that the proposed FAHP
is feasible to identify relevant supplier even though the criteria weight was
determined without human involvement. Such an approach reduces human bias
throughout AHP synthesis process. Consequently, the obtained weights were the
optimal solution that can be adopted in the supplier selection problem. |
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Keywords: |
Analytic Hierarch Process (AHP), Firefly Algorithm (FA), IT outsourcing,
supplier selection problem |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
TOWARDS A WORD EMBEDDING BASED APPROACH FOR SEMANTIC INDEXING |
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Author: |
ILYAS GHANIMI, ELHABIB BENLAHMAR, ABDERRAHIM TRAGHA, FADOUA GHANIMI |
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Abstract: |
In this article we present a new approach to semantic indexing of documents
using word embedding relaying on representing words as numerical vectors based
on the contexts in which they appear. This approach is validated by a set of
experiments and a comparison with other approaches. We show that the proposed
approach achieves results equivalent or better. |
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Keywords: |
Information retrieval, semantic indexing, word embedding, BERT, NLP. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
IMPLEMENTATION FINITE VOLUME METHOD OF SEA WAVES IN SUNDA STRAIT |
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Author: |
NURISSAIDAH ULINNUHA, USWATUN KHASANAH |
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Abstract: |
The Sunda Strait is directly linked to the Java Sea and the Indian Ocean.
Geographically, the Sunda Strait is situated between Sumatra Island and Java
Island, which plays an important role in inter-island transport routes. Sea
transport depends very much on their own waves, if the waves of the sea are
higher than usual, they will be dangerous for the transport of the sea. Sea
waves are one of the problems that can be solved mathematically. This research
is useful for obtaining a wave model that is close to the actual situation. This
research contributes to obtain the numerical solution of a tidal wave of sea
water using finite volume method and the terms stability of shallow water wave
equation. Based on the mathematical model obtained by using the finite volume
method, that is Godunov scheme, a stable sea waves model is obtained with the
stability conditions that is ∆t/∆x √2gb≤1. |
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Keywords: |
Sunda Strait, Wave Modelling, Finite Volume Method, Godunov Scheme |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
ADJUSTMENT OF NORMALIZED CUT PARAMETERS USING NEURAL NETWORKS |
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Author: |
SAMIA ELSIMARIE, BENBELLA SAYED, AHMED OTHMAN, GHADA ELTAWEEL |
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Abstract: |
Image segmentation is an important process that used in both quantitative and
qualitative analysis of medical ultrasound images, but medical images have
features of strong noise and poor contrast and the results of image segmentation
may not be good with traditional segmentation methods. in this paper we segment
breast ultrasound medical images based on texture features and graph cut ,gray-
level spatial dependence matrix(GLSDM) used to extract texture feature
parameters ,the similarities matrix is created according to the parameters of
texture feature and gray intensity of pixels .normalized cut spectral graph
theoretic framework used to segment image depending on the similarity matrix.
This paper introduces a new approach to overcome the problems associated with
medical image segmentation such that the proposed approach (Neural Normalized
Cut) has the ability to adjust the parameters of normalized cut segmentation
technique , Neural normalized Cut has applied for breast ultrasound images , the
results show the ability of neural normalized cut to adjust multiple parameters
and enhance image segmentation ,especially for medical images. |
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Keywords: |
Medical Ultrasound Images, Texture Feature, Neural Networks, Gray- Level Spatial
Dependence Matrix (GLSDM), Graph Cut, Image Segmentation, Thresholding,
Normalized cut, Neural Normalized Cut Segmentation, Genetic Algorithms, K-Means,
SURF. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
KNOWLEDGE ACCELERATION ESTIMATOR (KAE) MODEL TO CUSTOMER BEHAVIOR USING BUSINESS
METRICS |
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Author: |
RAHMAD SYAH, M.K.M NASUTION , ERNA BUDHIARTI NABABAN, SYAHRIL EFENDI |
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Abstract: |
Business Metrics in Financial Technology 1500 clients spread across North
Sumatera Province. The effect of business advanced (business enterprise and
social business) is enormous on clients who are as of now expanding in number.
To create Knowledge Acceleration (KAE) Model utilizing Business Metrics on the
effect of Commercial Entrepreneurship and Social Entrepreneurship in their use.
Uncertainty emerging from manageable business administrators by considering
parts of Business Metrics related. MARS a direct relapse investigation strategy
nonparametric proposed for measurements with the point of encouraging
examination and displaying the connections of each of the multi factors that
emerge. |
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Keywords: |
Finance Technology, Big Data Analytic, MARS, Business Canvas Model, Business
Metrics. |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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Title: |
INVESTIGATION OF USER REQUIREMENT AND UTAUT - THEORY IN WEB BASED LEARNING FOR
GIFTED STUDENTS IN DEVELOPING COUNTRY |
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Author: |
OBAID SABAYLEH, ABDEL LATIF ALRAMAMNEH, ALI RATIB ALAWAMREH, NAMER ALI ALETAWI |
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Abstract: |
Educational software needs and establishment requirements as a part of
e-learning have become very crucial for modern societies. One of the most
important used techniques is the web-based learning. Modern societies are
divided into two types of students: i) Regular students, and ii) Gifted
students. The later has various distinguished abilities compared with regular
ones. In this work, a model to improve, establish and identify the needs and
requirements for E-learning system for gifted students in developing countries
is proposed. The proposed model is self-efficacy with UTAUT theory that is
consists of five factors which are: i) Performance expectancy, ii) Effort
expectancy iii) Social influence, and iv) Facilitating conditions, v) Behavioral
intention and vi) Use behavior. The proposed model also aims to explore the key
factors that affect gifted students’ acceptance of web-based learning. The
proposed model is applied in Jordan where web-based learning concept was
introduced in the 90s. The results gained from applying the proposed model in
Jordan shows that the proposed model is accepted. |
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Keywords: |
Web-Based Learning, Learning Management Information System, User Requirement,
UTAUT theory, Self-efficacy, Gifted and Regular Students |
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Source: |
Journal of Theoretical and Applied Information Technology
30th April 2020 -- Vol. 98. No. 08 -- 2020 |
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