221 research outputs found
Developing and Building Ontologies in Cyber Security
Cyber Security is one of the most arising disciplines in our modern society.
We work on Cybersecurity domain and in this the topic we chose is Cyber
Security Ontologies. In this we gather all latest and previous ontologies and
compare them on the basis of different analyzing factors to get best of them.
Reason to select this topic is to assemble different ontologies from different
era of time. Because, researches that included in this SLR is mostly studied
single ontology. If any researcher wants to study ontologies, he has to study
every single ontology and select which one is best for his research. So, we
assemble different types of ontology and compare them against each other to get
best of them. A total 24 papers between years 2010-2020 are carefully selected
through systematic process and classified accordingly. Lastly, this SLR have
been presented to provide the researchers promising future directions in the
domain of cybersecurity ontologies.Comment: 8 pages, 2 figure
Heart Diseases Prediction Using Block-chain and Machine Learning
Most people around the globe are dying due to heart disease. The main reason
behind the rapid increase in the death rate due to heart disease is that there
is no infrastructure developed for the healthcare department that can provide a
secure way of data storage and transmission. Due to redundancy in the patient
data, it is difficult for cardiac Professionals to predict the disease early
on. This rapid increase in the death rate due to heart disease can be
controlled by monitoring and eliminating some of the key attributes in the
early stages such as blood pressure, cholesterol level, body weight, and
addiction to smoking. Patient data can be monitored by cardiac Professionals
(Cp) by using the advanced framework in the healthcare departments. Blockchain
is the world's most reliable provider. The use of advanced systems in the
healthcare departments providing new ways of dealing with diseases has been
developed as well. In this article Machine Learning (ML) algorithm known as a
sine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the
Hearth disease with the maximum accuracy among the existing approaches.
Blockchain technology has been used in the research to secure the data
throughout the session and can give more accurate results using this
technology. The performance of the system can be improved by using this
algorithm and the dataset proposed has been improved by using different
resources as well.Comment: page 23, figurse 1
Prediction of Citrus Diseases Using Machine Learning And Deep Learning: Classifier, Models SLR
Citrus diseases have been major issues for citrus growing worldwide for many
years they can lead significantly reduce fruit quality. the most harmful citrus
diseases are citrus canker, citrus greening, citrus black spot, citrus leaf
miner which can have significant economic losses of citrus industry in
worldwide prevention and management strategies like chemical treatments. Citrus
diseases existing in all over the world where citrus is growing its effects the
citrus tree root, citrus tree leaf, citrus tree orange etc. Existing of citrus
diseases is highly impact on economic factor that can also produce low quality
fruits and increased the rate for diseases management. Sanitation and routine
monitoring can be effective in managing certain citrus diseases, but others may
require more intensive treatments like chemical or biological control methods.Comment: 13 pages, 9 figure
Harnessing the Potential of Blockchain in DevOps: A Framework for Distributed Integration and Development
As the use of DevOps practices continues to grow, organizations are seeking
ways to improve collaboration, speed up development cycles, and increase
security, transparency, and traceability. Blockchain technology has the
potential to support these goals by providing a secure, decentralized platform
for distributed integration and development. In this paper, we propose a
framework for distributed DevOps that utilizes the benefits of blockchain
technology that can eliminate the shortcomings of DevOps. We demonstrate the
feasibility and potential benefits of the proposed framework that involves
developing and deploying applications in a distributed environment. We present
a benchmark result demonstrating the effectiveness of our framework in a
real-world scenario, highlighting its ability to improve collaboration, reduce
costs, and enhance the security of the DevOps pipeline. Conclusively, our
research contributes to the growing body of literature on the intersection of
blockchain and DevOps, providing a practical framework for organizations
looking to leverage blockchain technology to improve their development
processes.Comment: pages 10, figures
A Blockchain-Based Framework for Distributed Agile Software Testing Life Cycle
A blockchain-based framework for distributed agile software testing life
cycle is an innovative approach that uses blockchain technology to optimize the
software testing process. Previously, various methods were employed to address
communication and collaboration challenges in software testing, but they were
deficient in aspects such as trust, traceability, and security. Additionally, a
significant cause of project failure was the non-completion of unit testing by
developers, leading to delayed testing. This paper integration of blockchain
technology in software testing resolves critical concerns related to
transparency, trust, coordination, and communication. We have proposed a
blockchain based framework named as TestingPlus. TestingPlus framework utilizes
blockchain technology to provide a secure and transparent platform for
acceptance testing and payment verification. By leveraging smart contracts on a
private Ethereum blockchain, TestingPlus can help to ensure that both the
testing team and the development team are working towards a common goal and are
compensated fairly for their contributions.Comment: 4 figures, 12 page
Urdu Poetry Generated by Using Deep Learning Techniques
This study provides Urdu poetry generated using different deep-learning
techniques and algorithms. The data was collected through the Rekhta website,
containing 1341 text files with several couplets. The data on poetry was not
from any specific genre or poet. Instead, it was a collection of mixed Urdu
poems and Ghazals. Different deep learning techniques, such as the model
applied Long Short-term Memory Networks (LSTM) and Gated Recurrent Unit (GRU),
have been used. Natural Language Processing (NLP) may be used in machine
learning to understand, analyze, and generate a language humans may use and
understand. Much work has been done on generating poetry for different
languages using different techniques. The collection and use of data were also
different for different researchers. The primary purpose of this project is to
provide a model that generates Urdu poems by using data completely, not by
sampling data. Also, this may generate poems in pure Urdu, not Roman Urdu, as
in the base paper. The results have shown good accuracy in the poems generated
by the model.Comment: 11 pages, 2 figure
Predicting environment effects on breast cancer by implementing machine learning
The biggest Breast cancer is increasingly a major factor in female
fatalities, overtaking heart disease. While genetic factors are important in
the growth of breast cancer, new research indicates that environmental factors
also play a substantial role in its occurrence and progression. The literature
on the various environmental factors that may affect breast cancer risk,
incidence, and outcomes is thoroughly reviewed in this study report. The study
starts by looking at how lifestyle decisions, such as eating habits, exercise
routines, and alcohol consumption, may affect hormonal imbalances and
inflammation, two important factors driving the development of breast cancer.
Additionally, it explores the part played by environmental contaminants such
pesticides, endocrine-disrupting chemicals (EDCs), and industrial emissions,
all of which have been linked to a higher risk of developing breast cancer due
to their interference with hormone signaling and DNA damage. Algorithms for
machine learning are used to express predictions. Logistic Regression, Random
Forest, KNN Algorithm, SVC and extra tree classifier. Metrics including the
confusion matrix correlation coefficient, F1-score, Precision, Recall, and ROC
curve were used to evaluate the models. The best accuracy among all the
classifiers is Random Forest with 0.91% accuracy and ROC curve 0.901% of
Logistic Regression. The accuracy of the multiple algorithms for machine
learning utilized in this research was good, which is important and indicates
that these techniques could serve as replacement forecasting techniques in
breast cancer survival analysis, notably in the Asia region.Comment: 8 pages, 7 figures, 2 table
Traffic Road Congestion System using by the internet of vehicles (IoV)
Traffic problems have increased in modern life due to a huge number of
vehicles, big cities, and ignoring the traffic rules. Vehicular ad hoc network
(VANET) has improved the traffic system in previous some and plays a vital role
in the best traffic control system in big cities. But due to some limitations,
it is not enough to control some problems in specific conditions. Now a day
invention of new technologies of the Internet of Things (IoT) is used for
collaboratively and efficiently performing tasks. This technology was also
introduced in the transportation system which makes it an intelligent
transportation system (ITS), this is called the Internet of vehicles (IOV). We
will elaborate on traffic problems in the traditional system and elaborate on
the benefits, enhancements, and reasons to better IOV by Systematic Literature
Review (SLR). This technique will be implemented by targeting needed papers
through many search phrases. A systematic literature review is used for 121
articles between 2014 and 2023. The IoV technologies and tools are required to
create the IoV and resolve some traffic rules through SUMO (simulation of urban
mobility) which is used for the design and simulation the road traffic. We have
tried to contribute to the best model of the traffic control system. This paper
will analysis two vehicular congestion control models in term of select the
optimized and efficient model and elaborate on the reasons for efficiency by
searching the solution SLR based questions. Due to some efficient features, we
have suggested the IOV based on vehicular clouds. These efficient features make
this model the best and most effective than the traditional model which is a
great reason to enhance the network system.Comment: pages 16, figures
Comparative Analysis of Widely use Object-Oriented Languages
Programming is an integral part of computer science discipline. Every day the
programming environment is not only rapidly growing but also changing and
languages are constantly evolving. Learning of object-oriented paradigm is
compulsory in every computer science major so the choice of language to teach
object-oriented principles is very important. Due to large pool of
object-oriented languages, it is difficult to choose which should be the first
programming language in order to teach object-oriented principles. Many studies
shown which should be the first language to tech object-oriented concepts but
there is no method to compare and evaluate these languages. In this article we
proposed a comprehensive framework to evaluate the widely used object-oriented
languages. The languages are evaluated basis of their technical and
environmental features.Comment: 30 pages, figures
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