Journal of Kufa for Mathematics and Computer

The widespread use of the Internet and social media platforms has led to an increase in the number of
individuals who declare their feelings publicly. Therefore, sentiment analysis systems have proceeded because of their
crucial role in determining the personal opinions of users. This is can greatly influence the decision-making process
in various fields. To create a robust and reliable sentiment analysis system, it was necessary to apply techniques
capable of dealing with these scattered opinions. Natural language processing techniques are commonly used to
extract information from unstructured text data published by humans. The comments and posts in social media
platforms are often ignore the grammar rules and sentence structure. This is resulting in many ambiguities in lexical,
syntactic, and semantic aspects. As a result, researchers have developed different methods for text mining and
defining real information. This survey aims to study the different methods used in sentiment analysis filed. We
discussed two common models of classification, including the vocabulary-based model and the supervision-based
approach.

Nowadays, Unmanned Aerial Vehicles (UAVs), have indeed revolutionized various industries and opened
up new possibilities. Additionally, a Flying Ad hoc Network (FANET), a group of UAVs can perform specific
functions like delivering goods, assisting people after a disaster, monitoring the weather and environment, among
others. FANET is another kind of ad-hoc mobile network. It is capable of connecting with its environment and other
nodes to obtain critical information. It is particularly difficult to route across FANETs because of how mobility they
are and how fast they may change layouts. Therefore, routing algorithms are crucial for improving the performance
of ad-hoc networks. This article will analyse the performance of the AODV, DSDV, and TORA routing protocols in
the FANET environment. The NS-2 simulator has been used for testing parameters such as jitter, throughput, End-toEnd
delay, and
packet
delivery
ratio
(PDR). The
simulation
results
indicate
that
TORA
outperforms
AODV and
DSDV

routing
protocols
across
all
performance
metrics
except
End-to-End
Delay.

In this paper, we study Cayley graph of the semi-direct product of two finite groups where ???? is an odd
prime numbe. Specifically, we endeavor to establish a comprehensive understanding of the Cayley graph by
investigating the interrelationships among the constituent elements of the group. Leveraging this acquired knowledge, we systematically construct the Cayley graph, there by contributing to the scholarly discourse on this subject matter.

This article introduces some anti-topological operators and provides various examples and results
regarding the new operators. Specifically, We introduce the anti-frontier operator and examine essential properties of
such concept. Furthermore, we introduce anti-accumulation points and anti-exterior of sets. We also introduce
minimal and maximal anti-open sets and their dual. We show that every anti-open and anti-closed set are both
minimal and maximal.

Abstract; In this work, we used the Legendre operational differential matrix method based on Tau method to
obtain the fuzzy approximate-analytical solutions of the fuzzy differential equations in which the coefficients are
trapezoidal fuzzy function. This method allows for the fuzzy solution of the fuzzy initial ( or boundary) value problems
to be computed in the form of an infinite fuzzy series .Also, this method enables to approximate the fuzzy exactanalytical solutions with high efficiency, as these solutions can be resorted to if it is not possible to find the exact
solutions of these fuzzy problems. We introduced a comparison between the approximate solutions that we computed and the exact solutions of the chosen problem, as we found the absolute error. According to the numerical results, the series solutions that we found are accurate solutions and very close to the exact solutions.

In multivariate linear regression, the classical Wilks’ statistic is the most used method to test
hypotheses, which is extremely responsive to the effect of outliers. Many authors have examined the non-robust test
statistic established on normal theories for various cases. In this study, we constructed a robust version of Wilks’
statistic relying on a reweighed minimum covariance determinant estimator, that relies on the weight of the
observations, with the weights being determined by using the Hampel weight function and Huber weight function. A
comparison of the suggested statistics with the regular Wilks’ statistic has been discussed. Monte Carlo studies are
used to evaluate how well test statistics work with different datasets. So, this study looked at how two different test
statistics perform under a normal distribution. The first is the classical Wilks’ statistic, and the second is a proposed
new one. For both of them, the rate of type I errors and the power of the tests were close to the expected significance
levels. In situations where the distribution is contaminated, the proposed statistical method works best. If the data has
been corrupted or affected somehow, this approach seems to perform the best out of the options

Ordinary differential equations (ODEs) are important in the field of mathematics due to their spread in
various applications. Ordinary differential equations are of two types, one of which is equations with variable
coefficients and the other is equations with constant coefficients, both types are important in mathematics.
The purpose of any integral transforms is to convert functions and equations from their original form to another
simpler form, or at least another form known to us where in it, we transform the independent variable of the original
function into another variable, thus changing the scope and extent of the original function.
Many integral transformations have appeared that solve equations with variable coefficients, including the Al-
Zughair transform, the Expansion of Al-Zughair, the Extension of Al-Zughair, Al-Tememe transform, and many other
transforms that solve equations with variable coefficients, as for the integral transforms that solve equations with
constant coefficients, it was limited.

We explore a connected reaction-diffusion system involving neural responses within limited convex open
domains ???? ⊂ ℝ (???? = ????, ????, ????). Utilizing the Classical Faedo-Galerkin technique and employing compactness
arguments, we establish the existence, uniqueness, and continuous dependence on initial data for strong solutions.

A person is automatically recognized in a biometric system by analyzing their distinct traits. Most
people agree that iris recognition is the most accurate and dependable biometric identification method currently in
use. The goal is to accurately and efficiently identify a person in real time by analyzing the sporadic patterns seen in
the iris if an eye from some distance, by extracting strong features using deep learning technique. The extracting of
significant features is important step that effect the overall accuracy of iris recognition system. In order to extract the
iris characteristics, this research suggests a robust convolution neural network (CNN) structure. Then, an identity of
the person is determine based on extracted features from his iris to support vector machine (SVM). The proposed
system is examined on CASIA V. (1). Several parameters such as accuracy, precision, recall, and F-score are
computed to evaluate the performance of the proposed system. The obtained result of the accuracy is about (99%).
The proposed system results are compared with several previous methods and prove its effectiveness. A contemporary approach and the suggested method are contrasted and use deep learning for features extraction and the results depict our method outperforms other methods.

Given the prevailing circumstances surrounding the transmission of COVID-19, which has been classified
as a worldwide epidemic by the World Health Organization, a sensitivity analysis was performed on the ProtectedSusceptible-Infected-Treated-Protected- Susceptible (ℙ????????????ℙ????) epidemic model. The objective of this
analysis was to identify the specific parameters associated with the basic reproduction number that contribute
to the propagation of the COVID-19 epidemic in Najaf. The findings indicate that the sensitivity index of parameters
exhibiting a positive sign is positively correlated with the extent of the epidemic's spread in Najaf. Conversely,
parameters displaying a negative sign are inversely correlated with the spread of the epidemic.
The analysis was conducted numerically using MATLAB to confirm the results of the study.

Effective employees are regarded as the most precious assets and the foundation of any
business. Companies invest a lot of money in staff training programs because they believe they will pay
off in the long run. Therefore, it is crucial to keep a long-term, promising staff; as has been seen
throughout the years, this is one of HR's most challenging duties. This study aims to determine the key
variables influencing employee attrition and develop a machine learning model to predict employee
attrition based on the variables given. This will make it easier for management to spot workers who are
likely to quit, allowing them to take preventative measures and make wise decisions about appraisal and
recognition. The four most popular classification algorithms utilized in this study were KNN, Naive
Bayes, Random Forest and Logistic Regression. It was shown that Naive Bayes classifier outperformed
the others by 89% in terms of accuracy and produced more accurate predictions.

The construction of the six-point block methods with extra derivatives for solving ????
′′ ′ = ????(????, ????, ???? ) directly is presented in this paper. The suggested block methods concurrently approximate the problem's solution at six points and are formulated using the Hermite Interpolating Polynomial. The block methods do not reduce the equation to a first-order system of initial value problems (IVPs); instead, they directly obtain the numerical solutions.
Additionally, the order and zero-stability of the suggested techniques are examined. We present numerical results and draw comparisons with other block methods that are currently in use. The results demonstrate how effective the
suggested approaches are at solving second-order IVPs in general.

Many people get their news via the Internet and social media platforms, and given the rapid growth of
these platforms, fake news may now spread easily and quickly. False information that aims to mislead and harm
society and the individual is known as fake news. By deliberately spreading false information, the media distort public
opinion and threaten the social order by leading people to believe things that are not true. With the massive
expansion of social media networks, the spread of fake news has increased dramatically. Although interesting, it
poses some difficulties due to limited resources (such as datasets and published research). This paper presents
diverse machine-learning techniques to identify fabricated news by analyzing the textual content. Several techniques
were used, including SVM, RF, logistic regression, Naive Bayes, Gradient Boosting, AdaBoost, KNN, DT, and
XGBoost. Based on comparing the results, who got the best result with an accuracy rate of 0.9967 and the lowest loss
of 0.003 The study includes a variety of methodologies, such as natural language processing (NLP), machine
learning, and data mining, which have been found to improve the efficiency of text processing to increase accuracy
and can save time and effort by automatically identifying fake news, especially in light of the massive amount from
materials available on the Internet.

Over the years, with the help of technology, it has become much easier to analyze data in general and, more
specifically, personality. Behavioral analysis is a new trend, and discovering what people think and feel, among other
things, helps boost many things, including recommendation systems, e-commerce, fraud detection, etc. This paper
focuses on personality analysis using machine and deep learning with different datasets, focusing on computational
approaches and setting aside psychological studies.

We‏ discussed two distributions (The Lomax Distribution and The Transmuted Lomax
Distribution) and we dealt with some functions such as the PDF, CDF function, and we also discussed some
statistical properties such as moments, the moment generation function, the coefficient of variation,
flattening, torsion, and ordered statistics. The research concluded the importance of the Transmuted
Lomax Distribution on the Lomax Distribution and on all sample sizes in the practical aspect experimental.

n today's world, information drives not only business but nearly every aspect of human life. Therefore,
safeguarding valuable information from malicious activities like attacks has become essential. Brute force search and
dictionary attacks are prevalent cybersecurity threats, in which an attacker systematically attempts all possible
passwords and passphrases to gain access to a user's account. These types of attacks are common due to the
widespread reuse of simple password variations. The goal of this study is to discover as many passwords as possible
and demonstrate their predictability and susceptibility. Our focus was on comparing two cracking methods, such as
brute force search attacks and dictionary attacks, assessing their effectiveness and time requirements.

n this paper, a comprehensive analysis of resource allocation in vehicle-to-vehicle (V2V)
communication systems is presented, which is crucial for enhancing traffic control and improving road safety and
driving enjoyment. A variety of resource allocation strategies are classified and examined in this paper, including
game theory-based approaches, spectrum allocation, energy allocation, location-based allocation, and machine
learning-based procedures. Open research questions and areas that need further study in the field of V2V
communication are highlighted for future benefit. This survey helps readers make informed decisions about system
design, implementation, and improvement by providing a comprehensive overview of the latest developments in
V2V resource allocation. It also indicates areas that need further research and possible directions to enhance the
security, flexibility, and scalability of V2V communication networks.

As in the Laplace transform technique, a new integral technique called SEE (Sadiq-Emad-Eman)
transform is submitted. Showed propositions and important properties of SEE integral technique for differentiate of
functions and showed shifting property for SEE technique. Got transfer function of dynamic system in control theory
applying SEE integral technique. Solved some important problems in control theory via SEE technique. Showed that
the Laplace integral technique and every integral transform technique like the Laplace transform available in the
literature are particular cases of the SEE technique.

In this paper, a new concept of domination which is called whole even sum domination is presented,
many bounded, properties, remarks, examples, are introduced. Furthermore, some operations as join and corona
graphs of sum graphs are discussed. Finally, determined this number of the graph obtained from a graph G by the
removal of all edges between any pair of neighbours of a vertex.