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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
August 2023 | Vol. 101
No.15 |
Title: |
EVALUATION OF ERGONOMICS OF AN INDUSTRIAL ZONE BASED ON FUZZY COMPREHENSIVE
EVALUATION METHOD |
Author: |
EL M’HADI HAJAR , CHERKAOUI ABDELGHANI |
Abstract: |
In this paper, we will evaluate the level of ergonomics in an existing
industrial zone. This evaluation will enable the community to ensure that the
industrial zone meets the expectations of the companies and the involved actors,
to identify improvements track and to initiate actions. The aim of this approach
is to improve the quality of the area, to give it the maximum chance of
perpetuating direct and indirect jobs and to preserve environmental resources
(water, energy, landscape, air, etc.).To meet this evaluation objective, we
proposed a model based on the combination of the AHP method and the fuzzy
comprehensive technical evaluation. Considering the fuzzy characteristics of
factors influencing the level of urban ergonomics in an industrial zone, a
two-level evaluation index system was established. The degree of adherence of
factors is determined by the knowledge and experience of the experts. Then the
weight of each factor is determined by the AHP method and a final judgment
matrix is determined by the end. This method allows us a quantitative ergonomic
evaluation. |
Keywords: |
Fuzzy sets, AHP Theory, Industrial Zone, Ergonomics Evaluation. |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
FINANCIAL TECHNOLOGY ADOPTION: A CROSS COUNTRY STUDY |
Author: |
MARSHEILLA ANAK VICTOR, MOCHAMMAD DODDY ARIEFIANTO, RINDANG WIDURI |
Abstract: |
This study aims to examine how the relationship and influence of variables named
Facilitating Conditions of Financial Sector, Facilitating Conditions of non
Financial Sector, Overall Valuation on Business Climate, and Social Conditions
and GDP as a control variable towards the adoption of financial technology
across countries using some modifications of the UTAUT framework as novelty. The
data were sourced from the Global Financial Inclusion Index, the Global
Financial Development Database and the World Development Indicator on the World
Bank website in research periods of 2011, 2014, 2017 and 2021 with a total of
500 observations from 125 countries. Panel data econometric techniques employed
are pooled least square, fixed effect and random effect model. We find that
financial technology adoption is positively associated with the financial sector
and overall valuation on business climate. While social conditions have a
negative significant effect, the non financial sector has no effect on financial
technology adoption. Financial sector intervention through financial technology
has become a new instrument which triggers financial growth and helps realize
financial inclusion more quickly. Financial inclusion is one element of
financial growth and development which is generally measured by the scope and
ease of access to financial services in a country. |
Keywords: |
FinTech, Economic Growth, UTAUT, across Countries, Panel Regression |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
TASK SCHEDULING IN CLOUD COMPUTING: PRIORITY-BASED ALGORITHMS AND FUTURE
DIRECTIONS IN DATA SCIENCE |
Author: |
RASHA AL BASHAIREH |
Abstract: |
In the era of distributed computing, Cloud Computing (CC) has been considered an
emerging technology that delivers on-demand services using the worldwide online
medium. One of this technology's main challenges is scheduling tasks and
allocating resources to achieve the best performance with minimal optimized
execution time, and less resource time and usage. Thus, the importance of task
priority has been recognized by many researchers since tasks submitted to the
cloud can have various sizes, resource utilization, and execution times.
Therefore, existing task scheduling algorithms prioritize tasks, where the task
with the highest priority is allocated to the available resource. This paper
investigated the task priority problem in cloud-based Task Scheduling (TS)
algorithms via three different directions centered around the following
contributions; first, a fruitful discussion and comparison of some selected
cloud priority-based TS algorithms regarding their scheduling method and
parameters, algorithm performance, and limitations reflecting how the priority
issue is handled in the cloud environment. In the Data Science and Artificial
Intelligence age, the interaction and intervention between the Big Data sector
and CC are gradually advancing due to mutual development. Cloud environments
have become a prominent platform for big data applications, forcing the
integration between Data Science and CC techniques to evolve. On this basis,
secondly, the paper addresses the directions of potential future research of
cloud priority-based TS schemes in Data Science. Finally, as a result, a
conceptual framework incorporating a task content-type component is proposed to
support the processing and scheduling of data-related tasks submitted to the
cloud. |
Keywords: |
Cloud Computing, Task Scheduling, Priority Algorithms, Data Science, Artificial
Intelligence |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
HYBRID APPROACH FOR MOVIE RECOMMENDATION SYSTEM USING DOUBLE COLLABORATIVE
FILTERING AND SEQUENTIAL PATTERN ANALYSIS |
Author: |
HARRY YANTO, SANI MUHAMAD ISA |
Abstract: |
Recommendation systems (RecSys) are essential nowadays to handle information
overloading. RecSys algorithms usually predict whether the user will like the
content or not based on the previous consumed content by the user. User’s
preferences will be changing from time to time, but the research of RecSys
algorithm nowadays rarely involves the sequence of user preferences. We also
trying to engage two Collaborative Filtering algorithms (CF) with different
attributes. CF that use the rating as attribute (CFR), and CF that use the
user-preferred genres as attribute (CFG). We hybrid those three algorithms CFR,
CFG, and Sequential Pattern Analysis (SPA) to increase the accuracy of the
recommendation system. Then we evaluate it using the f1 score and compared it to
the CFR, CFG, and SPA alone. This research concludes that hybrid CFR, CFG, and
SPA increase the accuracy of f1 and precision score compared to the algorithm
stands alone. We also conclude that cosine is the best similarity to use in
searching similar users for RecSys |
Keywords: |
Recommendation System, Hybrid, Collaborative Filtering, Sequence Pattern
Analysis, Best Similarity |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
FAILSAFE ALGORITHM MANAGEMENT IN SMART FARMING IOT CONTROL SYSTEMS |
Author: |
MUSTAFA MAN, TOH WEI JER, AEZWANI WAN ABU BAKAR |
Abstract: |
Internet of Things (IoT) technology is an important component in the modern
smart farming system and plays a key role in improving the efficiency of various
aspects of the farm such as greenhouse troubleshooting, farm data tracking,
smart farm control and many more. Previous research has established that with
the help of IoT technologies, who will be in-charge of the data monitoring of
the farm, owners are able to perform necessary farming tasks including seeding,
fertilising, watering and so on at the perfect moment to ensure maximum
agricultural yield. Multiple sensors are used to measure the crucial environment
parameters are implemented to collect the farm data and transfers to the backend
server by the IoT module in order to perform necessary data analysis thus
completing the IoT usage in the given farm. The reliability and time accuracy of
the data collected by the sensor is crucial for the following control system. To
date, only a limited number of Smart Farming IoT system architectures have been
identified, as well as the failsafe data management issues of the IoT modules in
designated farm types is yet to be resolved. this paper is that researchers
develop a data failsafe management algorithm compatible with Arduino embedded
system platform which works together with backend server to manage and transfer
the data received from the sensors autonomously. Unlike conventional smart
farming IoT architecture, the proposed system is equipped with multiple data
storage media located in different parts of the system preventing data losses
during unwanted occasions. This review paper talk about implementing failsafe
architecture into IoT smart farming systems. Besides, the system proposed in
this research could fit with various wireless connectivity arrangements while
ensuring the smoothness of the network traffic. Expected results showed that the
proposed IoT architecture resolved the data loss issues during the internet
outage thus improved the IoT control system. Therefore, all the benefits brought
to the farm by implementing IoT technologies are well preserved. |
Keywords: |
Smart Farming, Internet of Things (IoT), ESP32, IoT Connectivity, IoT Failsafe
Algorithm, Failsafe Data Management |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING ALGORITHM |
Author: |
MOH. ALIANSYAH, FERALIANA AUDIA UTAMI, RIYANTO JAYADI |
Abstract: |
Credit card fraud is a serious issue in the financial services industry.
Billions of dollars are lost each year due to credit card fraud. Credit cards
have become one of the fastest growing financial services by banks in recent
years. However, with the increasing number of credit card users, banks face an
increasing rate of credit card defaults. Credit card fraud is related to the use
of prohibited credit card materials for acquisition. The aim of this research is
to analyze and compare the usage of three types of machine learning algorithms
consisting of Neural Network (NN), K-Nearest Neighbour (KNN), and Random Forest
(RF) to analyze and predict credit card fraud security. The credit card
transaction dataset was sourced from European cardholders and contained 284,807
transactions. The results of the analysis state that the Neural Network
Algorithm has the highest prediction capability (99.80%) and precision rate
(100%) compared to the other two machine learning algorithms. |
Keywords: |
Fraud Detection, Machine Learning, Neural Network, Random Forest, K-Nearest
Neighbor |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
INTELLIGENT COLLABORATIVE FILTERING RECOMMENDATION SYSTEM FOR MOVIE REVIEW
RATING |
Author: |
SRINIVASA RAO MANDALAPU, B. NARAYANAN, SUDHAKAR PUTHETI |
Abstract: |
Recommendation system used to analyze the data concept and discovery of data to
provide the modified approvals on the web. Various techniques have been
suggested to capture the user's interest and effectively provide accurate
recommendations. User-based collaborative filtering technique was popular and
utilized in practice extensively. Still, the system faced several vital tasks to
provide adequate scalability and qualified recommendations because of increasing
items daily and users on various webpage. So, a novel technique named Eagle Deep
Neural based Movie Recommender System (EDNbMRS) to recommend movies related to
ratings from the review. Consequently, the data was collected for the training
process. In the preprocessing phase, the noise features were eliminated from the
dataset for the recommendation process. Next, the best features were selected
based on the developed model in the feature extraction process. Then, a rating
for the movie was necessary for recommending the film to the user to perform the
similarity and prediction computation process. Finally, the recommendation
process was done using the predicted rating score for the target user.
Meanwhile, the developed mechanism was executed in the Python tool with several
performance measurements such as accuracy, Recall, f-measure and precision. This
developed model produced outstanding results compared with the previous studies
by providing accurate results for the recommendation process. |
Keywords: |
Recommendation, Target User, Feature Analysis, Eagle Optimization, Collaborative
Filtering |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
INTEGRATION OF NON-HIERARCHY CLUSTER ANALYSIS WITH SEMIPARAMETRIC TRUNCATED
SPLINE MULTIRESPONSE REGRESSION |
Author: |
EVITARI GALU ERWINDA, SOLIMUN, ADJI ACHMAD RINALDO FERNANDES, RAHMA FITRIANI,
ATIEK IRIANY |
Abstract: |
Cluster analysis is an approach used to find similarities in data and place them
in groups. Cluster analysis is divided into two methods, namely hierarchical and
non-hierarchical methods. The non-hierarchical method of several methods, namely
K-Means, K-Harmonic Means, and K-Medoids, used in this study is K-Means. Then,
the integration of cluster analysis with semiparametric truncated spline
multiresponse regression will be carried out. This study aimed to identify and
develop non-hierarchical cluster integration and semiparametric multiresponse
regression using the Truncated Spline approach in the case of Farmer
Inclusiveness data in West Nusa Tenggara. Modeling factors affecting the
inclusiveness of farmers in NTB was conducted using cluster analysis modeling
with multiresponse regression which was developed by making integration of
non-hierarchical cluster analysis with semiparametric truncated spline
multiresponse regression to help give Comprehensive information about farmer
inclusiveness in NTB. The sample used in this study was 100 farmer groups in
NTB. The results in this study produce three clusters, which have low, medium,
and high inclusiveness categories. Cluster 1 is a farmer with a low
inclusiveness category consisting of 42 farmer groups, cluster 2 is a farmer
with a medium inclusiveness category consisting of 21 farmer groups, and Cluster
3 is a farmer with a high inclusivity category consisting of 37 farmer groups.
The cluster with the highest R-squared on modeling integration of
non-hierarchical cluster analysis with semiparametric truncated spline
multiresponse regression is cluster 1 with R2 value of 0.205. Thus, modeling
using cluster integration with semiparametric truncated spline multiresponse
regression shows a change in the nature of the data that has a variable
relationship pattern and can help the government and farmers to determine the
level of inclusiveness of farmers and examine the factors that influence farmer
inclusiveness. This research’s originality is combining cluster analysis with
semiparametric truncated spline multiresponse regression with integration
between the two methods. |
Keywords: |
Integration Clusters, Multiresponse Regression Analysis Semiparametric,
Truncated Spline, Farmer Inclusivity. |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE |
Author: |
TEH NORANIS MOHD ARIS, AZURALIZA ABU BAKAR, NORMADIAH MAHIDDIN, MASLINA ZOLKEPLI |
Abstract: |
Diagnosis of diabetes is a complex decision-making process. The creation of
diabetes diagnosis models is vital in the decision-making process and requires
adequate information for fast detection and treatment. Diabetes is detected from
a set of symptoms. The symptoms data are an important reference to diagnose
diabetes which are collected and stored in datasets. Diabetes datasets are prone
to vagueness and uncertainty. In addition, insufficient information on the
diagnosis of diabetes exists and this problem is not addressed in previous
research. This research work analyzes a simulated diabetes treatments dataset
that were validated by medical expert [1]. A new fuzzy inference model based on
Mamdani method is designed to provide interpretable understanding and sufficient
information on diabetes diagnosis which is combied with the level of care to
support the vagueness, uncertainty, and insufficient information problems. |
Keywords: |
Decision-Making, Fuzzy Inference Model, Dataset, Diagnosis Of Diabetes, Level Of
Care |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
RURAL UNIVERSITY STUDENTS’ CONTINUOUS USE OF ONLINE PROCTORED EXAMINATIONS |
Author: |
ADMIRE CHIBISA, DAVID MUTAMBARA |
Abstract: |
The outbreak of the Covid-19 pandemic caused untold suffering and calamities.
Many nations introduced national lockdowns as a way of curbing the spread of the
virus. All services were halted except essential services. Unfortunately, higher
education and training was considered non-essential and hence stopped. However,
the academic project had to continue such that academic institutions had to find
other means of operating. Online learning and online assessments were introduced
and used during the pandemic. The purpose of this study was to explain rural
university students’ continuous use of online proctored examinations in the
aftermath of the pandemic. The study used the Expectation Confirmation Model to
develop the “Continuous Use of Online Proctoring Software Model” where the
online proctored software was used to shadow the use of online proctored
examinations. A cross-sectional survey was used to gather data from a sample of
335 respondents of which analysis was done using Partial Least Squares
Structural Equation Modeling, with the help of SmartPLS version 3. The developed
model explained 69,2% of the variance in continuous use of online proctored
examinations. These results showed that rural university students were in favor
of continued use of proctored examinations. The study recommended that
university authorities should consider the continued use of online proctored
examinations though this comes at an increased cost of students’ access to
relevant devices and connectivity. |
Keywords: |
Continuous Use, Online Proctored Examinations, Online Proctoring Software, Rural
University Students, Expectation Confirmation Model |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
QLAR- Q LEARNING BASED ACOUSTIC ROUTING FOR UNDERWATER SENSOR NETWORKS |
Author: |
PRATHIBA N, MALA C S |
Abstract: |
Underwater sensor networks have gained huge attention due to their significant
use in monitoring and exploring the ocean, lakes, seas and rivers. The UWSNs are
different from the ground based static sensor networks. The offshore networks or
the networks which are deployed on portion of land have been explored widely but
implementing the traditional protocols is not considered as a feasible solution
because these networks suffer from several challenges such as high- water
pressure, low bandwidth, delay and error rate etc. Therefore, we focus on
introducing a novel routing protocol for underwater sensor networks by using Q
learning based feedback mechanism. The proposed Q learning approach uses path
cost, packet delivery probability and link analysis to maximize the payoff of
the network. Moreover, the proposed approach is based on the opportunistic
routing where the relay node is selected based on the maximum payoff. Finally,
we present a simulation study to show the improved performance by using proposed
approach where average network lifetime, delay and packet delivery values are
obtained as 3522 rounds, 0.39s and 95.33, respectively. |
Keywords: |
Acoustic Networks, Q Learning, Opportunistic Routing, Underwater WSN |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
THE USE OF ONTOLOGICAL MODELING IN THE PREPARATION OF ELECTRONIC COURSES IN THE
FIELD OF INFORMATION AND COMMUNICATION TECHNOLOGIES |
Author: |
N.ZH. SABITOVA, B.SH. RAZAKHOVA, L.U. TAIMURATOVA, Y. TIKHONOV, V. LAKHNO, R.S.
SHUAKBAYEVA, R.M. BAINAZAROVA, A.A. BALEKOVA. |
Abstract: |
The methodological foundations of designing electronic courses in the field of
information and communication technologies (ICT) based on the ontological model
are outlined. This kind of modeling makes it possible to implement automated
processing of ontologies for an electronic ICT textbook. A functional and
informational model of the processes that form an ensemble of information
technologies for automated processing of ontologies of subject areas
characteristic of the field of information and communication technologies has
been implemented. |
Keywords: |
E-Courses, E-Textbooks, Ontologies, Subject Area, UML, IDEF, DFD, Composite,
Ргоху. |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
IMPACT OF ARTIFICIAL INTELLIGENCE ON THE INDIAN RETAIL INDUSTRY |
Author: |
Dr. DHADURYA NAIK M, Dr. SRINIVASA RAO DOKKU, VEERLA NAGAMALLESWARA, Dr. KONIKI
SRINIVAS, Dr VENKATA NAGA SIVA KUMAR CHALLA, Dr M SRINIVASA NARAYANA, MUNNI
VENUTHURUMILLI |
Abstract: |
Many of the previously laborious and labor-intensive duties connected with
running a successful business are being automated by AI, which is fast
revolutionising the retail industry. AI applications for retail outlets could
assist firms in pricing their products by visualising the possible effects of
various pricing strategies. To do so, systems gather information about other
items, promotional efforts, sales numbers, and other data. The objective of the
study is to know the impact of AI in Reteail industry. At Vijayawada, Andhra
Pradesh, India, 145 samples from various retail businesses were taken into
consideration for the study. Both primary and secondary data are used in the
investigation. The study was evaluated using factor analysis. The majority of
respondents are aware of the use of AI in India's retail sector, according to
data studies. It has also been noted that the majority of retail establishments
are using AI in their Business models. Particularly AI is useful in order
processing, shipping, and inventory management in the retail industry in
India.it is also identified that, most of retail owners are aware of impact of
AI on their business and also they are implementing the AI techniques in their
business models to meet the changing requirements of the industry. |
Keywords: |
Artificial Intelligence, Machine Learning, Automation, Retail, Business |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
IMPLEMENTING RESNET-50 TRANSFER LEARNING MODEL FOR DIAGNOSING OCT IMAGES FOR
DETECTING AND CLASSIFYING DME ABNORMALITIES |
Author: |
A. GEETHA DEVI, SURYA PRASADA RAO BORRA, D. HARI KRISHNA, M. RAJ KUMAR NAIK, K.
VIDYA SAGAR, LAKSHMI RAMANI BURRA |
Abstract: |
One of the major diseases that affect human eyes due to complications of
diabetes is Diabetic Retinopathy (DR). A particular type of DR affecting the
retina's central portion, called the macula, creates a vision problem. It is
called Diabetic Macular Edema (DME). The blood vessels in the eye get damaged,
and leaking fluid on the macula causes tissue thickening and swelling. The image
may be occluded for various external and environmental reasons, which can
degrade the image quality and provide a wrong diagnosis. The early diagnosis of
DME is essential to avoid vision loss, and OCT images are used for prescreening
because OCT is one of the non-invasive imaging modalities that can provide
high-resolution retina images speedily. Several earlier research works have
focused on analyzing various DR images using image processing methods, providing
less prediction accuracy. This work aims to create an automatic transfer
learning model for classifying DME using OCT images. ResNet-50 is created by
training with 80% of the training OCT images, evaluated and validated by random,
and 20% of the testing images. The reason for using the ResNet-50 model is that
it is a pre-trained model using ImageNet data. The proposed ResNet is
experimented with a benchmark dataset with Python for testing and validating its
output. The output is compared with the other earlier methods to evaluate the
performance. The comparison shows that the proposed ResNet-50 model outperforms
others by obtaining 97.56% accuracy. |
Keywords: |
Diabetic Macula Edema, OCT Images, Transfer Learning Models, ResNet-50, Diabetic
Retinopathy, Medical Image Processing. |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
REFINING THE STRUCTURE OF BAYESIAN NETWORK LEARNT WITH AGGLOMERATIVE CLUSTERING
TECHNIQUE FOR PROSTATE CANCER DISEASE |
Author: |
NAVEEN KUMAR BHIMAGAVNI, ADILAKSHMI THONDEPU |
Abstract: |
Structure learning of the Bayesian Network is a two-step process, one is
parameter learning and the other is finding the best structure among search
space using uncertain and incomplete data. Structure learning is the most
important and complex task (NP-hard problem) in estimation theory. However,
existing techniques require generating all possible graphs even for a small
number of random variables, and consume a large amount of space and time
complexity to verify each of them. Clustering-based Structure learning can be
used to learn the structure of the Bayesian network to overcome this limitation.
However, the learned structure needs to be refined as and when the new data
arrives and existing refinement techniques verify the relation of a node with
every other node; which consumes large time complexity. In this work, we
propose an algorithm that refines the structure of the Bayesian network learned
using the agglomerative clustering technique using the proposed refinement
algorithm. It considers only a subset of nodes (identified using Markov
Assumption) for comparison of a node and thereby consumes comparatively less
time complexity. Also, the Bayesian score is calculated for each candidate
structure to find the best network structure. |
Keywords: |
Refinement Algorithm, Agglomerative Clustering, Bayesian network, Bayesian
Score, Prostate Cancer, Markov Assumption |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
ENHANCED COLLABORATIVE TASK EXECUTION IN EDGE COMPUTING BASED ON BELIEF RULE GEO
CLUSTERING AND PARTICLE SWARM JOINT OPTIMIZATION |
Author: |
UDAYAKUMAR K, RAMAMOORTHY S |
Abstract: |
Multi-access Edge Computing (MEC) has recently been suggested as an addition to
cloud computing. MEC servers are placed close to the network's edge to reduce
latency and relieve demand on cloud data centres. The cloud is more resourceful
than the MEC server, which has less resource. Each MEC server cannot satisfy all
computational and large data needs from user devices when operating
independently. Collaborative edge computing (CEC) is a popular new concept in
which devices on the edge work together by sharing data and computer resources.
CEC must determine when and where each task is executed, making task offloading
an important problem to address. However, it is challenging to solve task
offloading in CEC because tasks can be offloaded to neighbouring devices,
resulting in bandwidth contention among network flows. Most existing works do
not jointly consider network flow scheduling, which can result in network
congestion and inefficient completion time performance. This paper suggests a
unique approach for planning, executing, and clustering MEC servers in a
resource allocation model. Belief rule Geo clustering method used to cluster the
MEC server based on real-time data of intensive tasks. For the simple sake,
real-time monitoring application based on video surveillance is taken. The
unsupervised cluster algorithm used to distribute the software components
optimally among mobile devices and reduce cluster traffic in relation to the
data center. The distributed MEC channel task allocation model is used to share
tasks when the MEC server is busy receiving data, and a particle swarm-based
joint optimization architecture is used to optimize clustered data. Extensive
experiments have been performed on three datasets concerning energy utilization,
delay, computational burden, throughput, and network Quality of service (QoS).
The proposed technique achieved better results than benchmark solutions, which
do not make a joint decision. |
Keywords: |
Mobile Edge Computing, Collaborative Tasks Scheduling, Resource Allocation, Geo
Clustering, Particle Swarm Optimization |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
AUTOMATIC DETECTION AND CLASSIFICATION OF LEUKEMIA FROM BLOOD SMEAR IMAGE USING
SENET CONVOLUTIONAL NEURAL NETWORK |
Author: |
SHALINI V, K. S. ANGEL VIJI |
Abstract: |
Leukaemia is a subtype of blood cancer that manifests itself by excessive
production of abnormal blood cells. Leukemia is on the rise, and it takes more
time to diagnose and treat because of the disease's high mortality rate. One
such disease is leukaemia, which affects the white blood cells. Early, reliable,
and safe detection of leukaemia dramatically improves treatment and survival
rates. Pre-processing, segmentation, extraction of features, and the leukaemia
classifier are the four components of the classification methodology. Using a
hybrid Squeeze-and-Excitation Networks (SENet)-based CNN (convolutional neural
network), this work can identify ALL (acute lymphocytic leukaemia), CML (chronic
myeloid leukaemia), CLL (chronic lymphocytic leukaemia) and AML (acute myeloid
leukaemia) in this study. The proposed SENet-CNN model detects the
classification of four leukaemia subtypes, including ALL, AML, CLL, and CML,
from blood smear images. The performance of the proposed SENet model is assessed
using metrics like accuracy (ACC), specificity (SP), Precision, F1- score, and
sensitivity (SE). The proposed model outperforms the other techniques by having
a classification accuracy of 99.98%, according to experiments on a dataset of
655 images. |
Keywords: |
Leukemia Detection, SENet-CNN, Blood Smear Images, Deep Learning, Convolutional
Neural Network, Segmentation and Classification |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
DEVELOPMENT OF NEURAL NETWORK MODELS FOR OBTAINING INFORMATION ABOUT NODULAR
NEOPLASMS OF THE THYROID GLAND BASED ON ULTRASOUND IMAGES |
Author: |
ILYA LOZHKIN, KSENIA TSYGULEVA, KONSTANTIN ZAYTSEV, MAXIM DUNAEV4, SVETLANA
ZAKHAROVA, EKATERINA TROSHINA, ALEKSANDER GARMASH |
Abstract: |
Analysis in ultrasound examinations of the thyroid gland often requires a large
amount of training data, therefore, one of the important problems of using deep
architectures in medicine is the expansion of training datasets to more
significant volumes, since in most clinics the sets of such data are not large.
The purpose of this study is to develop a set of neural network models for
solving the problems of analyzing ultrasound images of the thyroid gland to
identify nodular neoplasms, as well as to study various approaches to increasing
the amount of data for the formation of training samples. According to the
results of the conducted experiments, it was found that an increase in the
number of the same type of images in film loops did not affect the operation of
deep architectures, and therefore it was meaningless. Various approaches to the
augmentation of medical image sets were investigated, and it was observed that
the complication of the augmentation process of images containing enlarged areas
with useful information negatively affected the quality indicators of
segmentation models trained on such data. Based on the conducted research, the
authors propose neural network models to solve the problems of semantic
segmentation and classification for use in the field of ultrasound examinations
of the thyroid gland. The results obtained allow for advancing the use of
artificial intelligence methods for personalized medicine for thyroid diseases. |
Keywords: |
Machine Learning, Ultrasound Imaging, Detection, Segmentation, Classification. |
Source: |
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Title: |
THE ROLE OF TWITTER IN BUSINESS, ECONOMICS, AND FINANCE RESEARCH: A BIBLIOMETRIC
ANALYSIS |
Author: |
DIAN KURNIANINGRUM, NUGRAHA NUGRAHA, DISMAN DISMAN, BUDI SUPRIATONO PURNOMO,
MUYANI KARMAGATRI |
Abstract: |
The Twitter application was launched in 2006 and quickly became popular. In line
with the increasing popularity of Twitter, many studies have linked the role of
Twitter with business, economic and financial development. This bibliometric
research was aims to provide an overview of research developments regarding the
role of Twitter in business, management, accounting, economics, econometrics,
and finance and provide insight into what topics have not been touched on for
research. This research retrieves journal publication data from Scopus and then
visualises the data to provide an overview of the journal’s contribution and
performance using VOS viewer. This report will be divided into research
background, bibliometric method, research methods, research results (publication
structure, citation structure, and graphical analysis of publications), and
research conclusion. The author and country who publish most related articles
come from the United States. The publisher which publishes the most is an
information system and information management publisher; the other productive
publisher is the one that specialises in business, management, and marketing.
Keywords that often arise beside Twitter are social media, sentiment analysis,
Facebook, Covid-19, social networks, machine learning, big data, text mining,
and natural language processing. The research conclusion contains key funding
from bibliographic analysis and the study’s limitations. To enrich the studies,
other researchers can utilise different data sources, such as the Web of Science
and Google Scholar, which can also be used as references. Additional data
processing, such as Cite Space and SciVal, can also be added to deepen the
analyses. |
Keywords: |
Twitter, Bibliometric analysis, Business. Economics, Finance |
Source: |
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Title: |
THE INFLUENCE OF INSTAGRAM SOCIAL MEDIA ACTIVITIES AS A DIGITAL MARKETING
STRATEGY IN INCREASING PURCHASES |
Author: |
ANGGUN CYNTHIA PUTRI, ASTARI RETNOWARDHANI |
Abstract: |
The development of information technology and the advancement of business
innovation compel business owners to stay abreast of technological advances.
Businesses across various sectors are seeking innovative approaches to support
their business objectives. One such technological development is the emergence
of social media networks. With the numerous conveniences offered by this new
communication medium, social media users can swiftly disseminate or seek out
messages and information. In Indonesia alone, as of February 2022, there were
191.4 million social media users out of a total population of 277.7 million
individuals. This scenario presents an opportunity for conventional
entrepreneurs to leverage technological advancements and bolster their online
business for increased profitability. According to Indonesian Digital User Data,
the number of social media users is recorded relatively high. XYZ, operating in
the automotive sector and specializing in marketing truck and bus products, has
embraced social media as a marketing platform for its offerings. Hence, this
study aims to examine the impact of social media activities undertaken by XYZ
company on sales performance. This research employs a quantitative approach with
a target population of 521 customers familiar with Hino's Instagram social media
account. The sample consists of 150 companies classified as VIP Loyal Customers
of Hino products by the company. The study incorporates six interconnected
variables: Social Media Marketing Activities, Brand Equity, e-WOM, Purchase
Intention, Customer Satisfaction, and Customer Loyalty. The aim is to explore
the influence of Social Media Marketing Activities on Purchase Intention,
specifically the sales intensity of XYZ company. The results of hypothesis
testing conducted using SmartPLS 4.0 software yielded six significant and
accepted hypotheses. The findings indicate that Social Media Marketing
Activities positively impact brand equity, which, in turn, positively influences
e-WOM. Furthermore, e-WOM positively affects purchase intention, which, in turn,
positively impacts customer statistics. Additionally, customer satisfaction
influences customer loyalty, and social media marketing activities positively
influence customer loyalty. Consequently, the research outcomes can serve as
valuable insights for augmenting Hino product purchases at XYZ company. |
Keywords: |
Social Media, Social Media Marketing Activities, Brand Equity, ewom, Purchase
Intention, Customer Satisfaction, Customer Loyalty |
Source: |
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Title: |
DESIGN OF MUTATION OPERATORS FOR TESTING USING PARALLEL GENETIC ALGORITHM FOR
OPEN-SOURCE ENVIRONMENTS |
Author: |
SANDDEP KADAM, T. SRINIVASARAO |
Abstract: |
Specification-based testing approaches create test data without having any prior
knowledge of the program's structure. However, the quality of this test data
isn't always reliable enough to catch errors when non-functional modifications
are made to the software. We offer a novel technique that combines formal
requirements and the evolutionary algorithm to successfully produce test data.
In this technique, Parallel Genetic Algorithm (PGA) rewrites formal requirements
in order to create input values that kill as many mutants of the target
programmed as feasible. To explain how the approach works, two famous instances
are offered. The results suggest that the proposed technique may successfully
produce test cases to eliminate programmed mutants, resulting in improved
software maintenance. |
Keywords: |
Model-Based Testing; Fault Localization; Search-Based Algorithm; Automatic
Test-Case Generation; Mutation-Based Testing |
Source: |
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Title: |
LEVEL OF STUDENT SATISFACTION WITH NEW BINUSMAYA: MEASURING AND ANALYZING USING
THE END USER COMPUTING SATISFACTION (EUCS) FRAMEWORK |
Author: |
RAYHAN FADLI ROBBY , TUGA MAURITSIUS |
Abstract: |
Binus University has used Binusmaya as a learning tool since 2001. According to
the official Binus website on December 25 2019, the switch from Binusmaya to New
Binusmaya was made to make this platform better and more useful. Development is
carried out by adding the latest features and improving the appearance design.
This is done in order to make students feel comfortable in finding, exploring,
giving, and exchanging important information about knowledge and lecture
activities. Basically new binusmaya is a refinement of binusmaya. Some of the
differences between binusmaya and new binusmaya are the additional features in
the new binusmaya, which are features that function to display information about
student progress for all courses taken in a particular semester or period. This
article discusses the level of student satisfaction with the website version of
the new Binusmaya and the factors that influence student satisfaction with the
website version of the new Binusmaya. The framework used is End User Computing
Satisfaction (EUCS) which functions to determine the level of satisfaction of
application users by comparing expectations and reality from information
systems. The population to be used in this study were active students at Binus
University with a sample of undergraduate students consisting of 8 faculties
with a total of 420 students as respondents. Questionnaire distribution starts
from 26 September 2022 – 8 November 2022. The results show that the content
variables (X1) and accuracy (X2) have no significant effect on user satisfaction
(Y) while the format variables (X3), ease of use (X4) and timeliness ( X5) has a
significant effect on user satisfaction (Y). |
Keywords: |
New Binusmaya, Learning Management system, EUCS, E-learning, User Satisfaction |
Source: |
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Title: |
DETERMINING HEUTAGOGY DESIGN ELEMENTS FOR ONLINE LEARNING MODEL USING FUZZY
DELPHI METHODS |
Author: |
FAIRUZZA HAIRI, SITI NURUL MAHFUZAH MOHAMAD, SAHRUDIN SAAD, IBRAHIM AHMAD |
Abstract: |
Teaching and learning in computer application courses are being improved by
educators as technology continues to change the face of education. For the
purpose to promote student participation and self-directed learning, this study
investigates how the heutagogical online learning paradigm might be adapted to
computer application instructions. The Heutagogy Online Learning Model (HeuLEM)
is a model that this research proposes based on heutagogy principles, including
the six design elements of heutagogy. It enables learners to take control of
their educational process, make knowledgeable choices, and develop critical
skills for the digital age. Incorporating self-determined learning, reflection,
strong interpersonal skills, and collaborative online tools, instructors can
design an engaging and dynamic online learning environment that fosters
students' digital collaboration abilities. HeuLeM comprises the theoretical
underpinnings of heutagogy and offers helpful implementation tactics for the
model in computer application courses. This study is to identify whether the six
heutagogy design elements can be adapted to Heutagogy Online Learning Model to
enhance the digital collaboration skills of students. A total of 12 experts were
selected to analyze the fuzziness consensus of experts. All of the data obtained
will be analyzed using the Fuzzy Delphi method. The findings show that all the
heutagogy design elements are suitable to be adapted Heutagogy Online Learning
Model to enhance the digital collaboration skills of students. The six heutagogy
elements that explore, create, connect, share, reflect, and collaborate have
reached unity with the threshold value of d ≤ 0.2 and the percentage of the
expert group is more than 75%. Therefore, based on the consensus agreements, the
development of Heutagogy Online Learning Model for enhancing the digital
collaboration skills of students should be incorporated with the elements of
heutagogy. |
Keywords: |
Heutagogy; Collaboration Skills; Online Learning Model; Fuzzy Delphi; Heutagogy
Design |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
UTILISING A DYNAMIC APPROACH TO REDUCE TARDINESS FOR SCHEDULING ISSUES WITH
DISTINCT DUE DATES AND JOB BLOCKING TECHNIQUE TO REDUCE COST OF TARDINESS AND
EARLY ARRIVAL |
Author: |
E.JANAKI, A.MOHAMED ISMAIL, SINI RAHUMAN |
Abstract: |
The goal of this work is to reduce the overall earliness cost when scheduling
independent jobs with varied due dates on a single machine. Dynamic programming
for a single machine and Johnson's method based approach for a flow shop are the
two precise solutions suggested for the problem. In the manufacturing and
service industries, sequencing and scheduling is the critical part of
decision-making. Effective sequencing and scheduling has become a requirement
for market survival in today's competitive economy. Companies must adhere to
shipping deadlines that have been promised to clients. In this paper, we will
discuss about different types of machines for different types of jobs through
Phase –I ,Phase–IIand Phase III. The objective ofthis paper to minimize the
Tardiness by using Dynamic approach and job blocking method. |
Keywords: |
Single Machine, Flow Shop, Earliness, Tardiness, Dynamic Approach, Blocking Jobs |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
ANALYSIS OF QOS ROUTING IN MOBILE AD HOC NETWORKS USING ANTHOCNET, SHUFFLED FROG
LEAP, FIREFLY AND LION OPTIMIZATION ALGORITHMS |
Author: |
K.SUMATHI, DR.J.K.KANIMOZHI |
Abstract: |
A Mobile Ad Hoc Network (MANET) is a network of wireless nodes. Base stations,
infrastructure, and infrastructure are not required for a mobile ad hoc network.
MANET is a network that is only available temporarily. MANET technology is used
in military and search and rescue operations. Quality of Service (QoS) is a key
term for the overall optimization of network resources. Mobile Adhoc networks
are well-known for their self-organization and autonomy. QoS-based routing over
MANET necessitates an adaptive and fast path search solution. Optimization
algorithm-based techniques, such as ant colony optimization (ACO) algorithms,
shuffled frog leaping algorithm, Lion Optimization Algorithm (LOA), and Firefly
Algorithm (FF), have proven to be effective in developing routing algorithms for
mobile ad hoc networks. Then, an optimal path is computed using hybrid ACO based
routing, which is an efficient routing scheme based on foraging ant behaviour.
The proposed hybrid algorithm outperforms the Anthocnet and shuffled frog leap,
firefly, and lion optimization algorithms in simulation (LOA). The proposed
SFLAO algorithm also outperforms the standalone Anthocnet and LOA, Shuffled frog
leap, and Firefly algorithms in terms of QoS performance metrics. |
Keywords: |
Mobile Ad Hoc Network, Routing Protocol, Quality Of Service, Lion Optimization
Algorithm, Ant Colony Optimization, Shuffled Frog Leaping Algorithm |
Source: |
Journal of Theoretical and Applied Information Technology
15th August 2023 -- Vol. 101. No. 15-- 2023 |
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Title: |
FACIAL RECOGNITION MODEL AS EMPLOYEE ATTENDANCE USING MODERN DEEP LEARNING WITH
CONVOLUTIONAL NEURAL NETWORK METHOD |
Author: |
HALIM MAULANA, FERDY RIZA , AL-KHOWARIZMI |
Abstract: |
Development of technology is developing very quickly, thus providing many
benefits, especially in the field of Information Technology. Demand for services
with the use of technology is increasingly needed by various industrial fields,
especially the impact generated by Covid-19 has resulted in the application of
technology being required in various fields to minimize physical contact. The
conventional attendance process by touching the attendance device/devices can be
replaced by using face recognition technology. Face Recognition technology uses
Deep Learning discussion. Using this technology, without needing to touch
employees, they can record attendance by looking into the camera. This study
uses the Convolutional Neural Network (CNN) algorithm. The programming language
used in this program is Python. The process of making this application with the
stages of making Face Recognition, namely image acquisition, preprocessing,
extraction, classification, and identification of image data. The dataset is
separated into 3 stages of data, namely train data, validation data, and test
data. |
Keywords: |
Deep Learning, Machine Learning, TensorFlow, Python, Facial Recognition |
Source: |
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Title: |
COLLABORATIVE ANT COLONY OPTIMIZATION-ASSISTED SUPPORT VECTOR MACHINE FOR
ACCURATE COTTON LEAF DISEASE CLASSIFICATION AND YIELD PREDICTION |
Author: |
S.GOVINDASAMY, D.JAYARAJ |
Abstract: |
Cotton leaf diseases pose a significant threat to the economic viability of
cotton farming, leading to substantial yield losses. However, the lack of a
reliable classification system and accurate yield prediction based on disease
profiles hinders effective disease management and resource allocation. This
research tackles these issues by recommending a novel method for predicting
cotton production and classifying cotton leaf diseases using a combination of
Ant Colony Optimization (ACO) and Support Vector Machines (SVM). The research
objectives include developing a customized Collaborative ACO (CACO) algorithm
for feature selection, implementing an SVM model for disease classification, and
integrating the CACO-SVM framework for accurate disease identification and yield
prediction. The suggested method will be tested on a labelled dataset of cotton
leaf samples annotated with disease information. The importance of this study
rests in its ability to allocate resources to cotton growing better and improve
disease management tactics. Farmers can adopt targeted management practices,
reduce production costs, and mitigate the environmental impact of generalized
treatment approaches by accurately classifying cotton leaf diseases and
predicting crop yields. The outcomes of this study are expected to contribute to
improved agricultural practices, increased profitability for cotton farmers, and
enhanced sustainability in cotton production. The proposed CACO-SVM framework
has the potential to revolutionize disease identification and yield prediction
in cotton farming, empowering farmers to make informed decisions and achieve
higher crop management efficiency. |
Keywords: |
Classification, Ant Colony Optimization, Support Vector Machines, Yield
Prediction, And Cotton Leaf Disease |
Source: |
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Title: |
HYB-ICNN: HYBRID INCEPTION CONVOLUTIONAL NEURAL NETWORK FOR FEATURE EXTRACTION
AND HYPERSPECTRAL IMAGE CLASSIFICATION |
Author: |
EASALA RAVI KONDAL, SOUBHAGYA SANKAR BARPANDA |
Abstract: |
The remote sensing research community has devoted particular focus to the
classification of hyperspectral images (HSI). To improve the classification
accuracy of hyperspectral images (HSI), deep learning-based technology has been
proposed. However, it remains a challenging obstacle to achieve satisfactory
classification accuracy with insufficient data for training. Therefore, a more
effective neural network design needs to be devised in order to boost the
effectiveness of the HSI classification function. To address this concern, this
letter provides a novel Hybrid-Inception CNN (Hyb-ICNN) framework for
dynamically obtaining characteristics by laying inception components in the
model which can acquire better accurate properties with smaller training samples
by employing volatile spatial dimension convolutional filters and dynamic CNN
framework. The outcomes of the experiments show that the presented model can
boost classification performance by adaptively changing the network structure.
The experiments are carried out on both the new data sets and the publicly
accessible benchmark data sets to expose the efficiency and durability of the
proposed model. The proposed Hybrid-Inception CNN model has achieved accuracies
of 80.79% on the AH1 (Ahmedabad-1) dataset, 87.98% on the AH2 (Ahmedabad-2)
dataset, 99.99% on the PU (Pavia University), 99.99% on the SA (Salinas), and
99.92% on the IP (Indian Pines) dataset. Empirically, it has been demonstrated
that the presented model succeeds over the remaining state-of-the-art approaches
in terms of classification accuracy. |
Keywords: |
Hyperspectral Image, Inception Network, Convolutional Neural Network, Feature
Extraction, Classification |
Source: |
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Title: |
INDUSTRY-UNIVERSITY ENGAGEMENT, INFLUENCE, AND BENEFITS: COMPUTING JOB MARKET IN
KUWAIT |
Author: |
EIMAN TAMAH AL-SHAMMARI |
Abstract: |
Due to increasing competition among the IT sector in Kuwait it has become
difficult for fresh graduates to find a steady and inspiring positions at
private organizations. Employer’s demand updated cognitive skills which the
universities have failed to provide. Thus, the new job trend has encouraged a
strategic collaboration between industries and academic schools. This study
attempts to reconfirm the same. Through the set of questionnaires, we intend to
find the opinions of fresh graduates regarding their employment scope. Also,
whether companies’ strong associations can influence employment opportunity. To
establish reliability, Cronbach's coefficient alpha (α) was employed to analyse
the measure's internal consistency. The reliability of the questionnaires was
assessed using the partial least squares regression (PLS) approach. To assess
the data and validate the hypotheses, a structural equation model (SEM) was
used. Out of all the 4-hypothesis postulated. Our data also indicated that
the level of collaboration between Kuwaiti Universities and industries and
corporations is not so strong. In conclusion, focus needs to be on increasing
collaboration between university and corporates sector. This will have a
positive influence on Opinion and hiring strategy. |
Keywords: |
Academia–Industry, Higher Education, School-Industry Collaboration, Job Market,
Computing, IT, Employability Skills |
Source: |
Journal of Theoretical and Applied Information Technology
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Title: |
NEW APPROACH FOR RESOLUTION OF MAXWELL EQUATIONS USING GALERKIN METHOD AND
DIRICHLET CONDITIONS IN FINITE ELEMENT ANALYSIS |
Author: |
WALID EL FEZZANI, OSAMA YASEEN M. AL-RAWI, WISAM SUBHI AL-DAYYENI |
Abstract: |
The Galerkin method combined with the Dirichlet conditions provides a powerful
way of solving the Maxwell equations. This approach allows for a detailed
description of the behavior of the electric and magnetic fields in the problem
domain and can provide accurate solutions for complex engineering problems. The
method can be used in conjunction with Finite Element Analysis to solve various
types of problems, such as those involving loads, force, displacement,
vibration, and fluid flow. The use of the Dirichlet conditions to impose
boundary conditions on the system also ensures accuracy in the results obtained.
This helps engineers to better understand the physical behavior of the system,
assess any discrepancies in the data, and make more informed decisions about the
overall design. FEA combined with Dirichlet conditions offer an efficient and
accurate way of predicting design behavior and allow engineers to make changes
to the design quickly and efficiently. The proposed numerical solution for
MAXWELL equations using the GALERKIN method and DIRICHLET conditions is
presented by a Finite Element Analysis mesh distribution to calculate the flux
and the magnetic field into the air gap of the electrical motor. |
Keywords: |
Finite Element Analysis, Galerkin, Dirichlet, Maxwell Equations, Electric Motors |
Source: |
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Title: |
NEXT GENERATION SD-WAN WITH IDPS |
Author: |
JOSEPH NG POH SOON, PHAN KOO YUEN, FADHILAH NUR RANIA, CHAN JIA YI, FONG JUN
YIP, NG EE SERN, SITI ZUBAIDA BINTI ZULKIFLI |
Abstract: |
The Wide Area Network and firewall are the current networking technologies that
are widely used in most enterprises. However, due to the rapid development of
new technologies, there is a rising demand for a new efficient networking
solution that could overcome the current technologies' limitations and cater to
the end users' new requirements. This research integrated Software Defined Wide
Area Networks and Intrusion Detection and Prevention Systems. This paper aims to
conduct a systematic review where the effectiveness and benefits of the new
technologies are investigated as a proposed solution. that optimized the network
to prevent the trombone effect that degrades network performance while
recognising malicious activities and preventing attacks. It can also easily
detect and alert the network administrator. Combining both technologies is the
proposed solution that will enable network performance and security
improvements, resulting in an efficient network solution. |
Keywords: |
Firewall, WAN, SD-WAN, IDPS, Network |
Source: |
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