Journal of Kufa for Mathematics and Computer
Journal of Kufa for Mathematics and Computer (JoKMC) ISSN (Print): 2076-1171 | ISSN (Online): 2518-0010 The Journal of Kufa for Mathematics and Computer (JoKMC) utilizes advanced online technology to streamline the dissemination of research, enhancing accessibility and service for its readership. Since its inception in 2010, JoKMC has focused on publishing high-quality original research articles in the fields of mathematics and computer science. Through a robust open-access policy, JoKMC ensures that all papers are freely available online, and every submission undergoes a thorough peer-review process managed by an expert Editorial Board, upholding the journal’s commitment to academic excellence.
Faculty of Computer Science and Mathematics - University of Kufa
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Forward -Backward System for Delay Doubly Stochastic Differential Equations
The research aims to study a general category of forward-backward double delay differential equations system , with a focus on solutions and their characteristics. The existence and uniqueness of solution to the front and rear equation of the system is proven according to the conditions of Lipchitz, which confirms the existence of a maximum solution under the conditions of unilateral continuity
Forward Doubly stochastic differential equations
Backward Doubly stochastic differential equations
Copyright (c) 2025 Ghufran Abdul Amir, Falah H. Sarhan https://creativecommons.org/licenses/by/4.0
Applying Modren Fuzzy Partial Transform in Solving A Heat Equations
Fuzzyi partialy integro-differentiall equationss are used extensively in the scientific and engineering domains. This work presents a new method for using the fuzzya Roi-transform to solvee fuzzy partial differential equations. We use highly expanded notions of H-differentiability to establish formulas for fuzzy partial derivatives of first and second order. Fuzzy partial Ro-transforms are introduced at the beginning of the work, and fuzzy partial differential equations are then developed using these novel findings. Lastly, an application shows the potential of the suggested approach.
Copyright (c) 2025 Roaa H. Hasan, Ameraa N. Alkiffai https://creativecommons.org/licenses/by/4.0
Maclaurin Coefficient Estimates for Bi‑Univalent Function Classes Generated by a Parameterized Integral Transformation
This paper introduces new subclasses of the bi-univalent function class , defined via a newly constructed integral operator denoted by ​, acting on analytic functions within the open unit disc . These subclasses are formulated based on multi-parameter operator structures that exhibit rich analytic behavior. We investigate the coefficient bounds associated with functions belonging to these classes, with particular focus on deriving sharp estimates for the first two Taylor–Maclaurin coefficients, namely . The findings contribute to the structural understanding of bi-univalent functions and offer new directions for application within the framework of geometric function theory.
Copyright (c) 2025 Amal Darweesh, Waggas Galib Atshan , Ali Hussein Battor https://creativecommons.org/licenses/by/4.0
Chromaticity of Sylow Graph for the Cyclic Groups
In our work, we will find the new results on the chromatic uniqueness of Sylow graphs of cyclic group , . Moreover, we will determine the results about the chromatic number and chromatic polynomial of these graphs. We will discuss all the cases of , where is the prime number. Moreover, we prove that if , , and , then is not , where and , otherwise it is .
Copyright (c) 2025 Abdullah AbdulRazzaq Obaid , Haneen Al-Janabi https://creativecommons.org/licenses/by/4.0
Applications of Novel Integral transform: 'Kuffi-Abbas-Jawad' (KAJ) Transform to Damped Mechanical and Electrical Oscillators
This study utilizes the 'Kuffi-Abbas-Jawad' (KAJ) transform to determine the responses of mechanical and electrical oscillators. The paper introduces the KAJ transform as a novel approach for analyzing simple harmonic oscillators, as well as damped mechanical and electrical oscillators. Similar to other integral transform techniques, the 'Kuffi-Abbas-Jawad' (KAJ) transform serves as a practical and effective mathematical tool for obtaining these responses.
'Kuffi-Abbas-Jawad' (KAJ) transform inverse
Electrical Oscillators, Differential Equation Responses.
Copyright (c) 2025 Dr.Prakash Chand Thakur https://creativecommons.org/licenses/by/4.0
A Tau Method for Solving Second-Order Partial Integro-Differential Equations with Weakly Singular Kernels
This paper introduces a numerical method to approximate the solutions of initial-boundary value problems for a specific class of partial integro-differential equations. The approach utilizes Volterra Fredholm Integro Differential with Weakly singular kernel for spatial derivatives and the backward Tau method for temporal derivatives. The study delves into detailed Converting BVP to Volterra Fredholm Integral Differential Equation of the Second Kind with tau methode and using the Legendre or Chebyshev polynomials, proving their convergence and stability. The proposed method is then applied to several test cases, with the numerical outcomes compared with The Finite Element Method . The results demonstrate the computational efficiency of the method, leading to the conclusion that it is effective for solving initial-boundary value problems.
Volterra-Fredholm Integro Differential Equation
Copyright (c) 2025 Amenah Hadi AlSharmani, Ahmed M. Rajab https://creativecommons.org/licenses/by/4.0
Harnessing the Iman Transform for Efficient Solutions to Heat and Wave Problems
The heat and wave equations are prominent partial differential equations with extensive applications in fundamental sciences and engineering. Integral transform methods provide effective solutions to various challenges in these fields. This chapter explores the Iman transform technique as a tool for solving heat and wave equations represented by partial differential equations.
Copyright (c) 2025 Dr.Prakash Thakur, Dinesh Thakur, Emad A. Kuffi https://creativecommons.org/licenses/by/4.0
Crypto Algorithm for Binary Numbers using Knapsack via Bezier curve
These days we are constantly sharing and storing sensitive information online. From credit card numbers to personal identity details, our data is vulnerable to cyber-attacks and breaches. With the increase in cybercrime and hacking techniques, it is becoming increasingly important to protect our data through encryption. This work aims to create a secure binary number of the system of encryption and to make cipher analysis impossible. We will combine Knapsack’s cipher with the equations of Bezier curves using the three-pass protocol. The robustness of the proposed methods has been proved and the high security level by the experimental results, where the proposed methods have been more efficient and complex. The computational complexity of this method has been implemented in detail.
Copyright (c) 2025 Adil AL-Rammahi, Baleegh Alshaeer https://creativecommons.org/licenses/by/4.0
An Approach for Emotion Detection Using a Hybrid Vision Sequence-Based Convolutional Neural Networks (Vi-CNN) Model
Emotion represents the significant reflection or indicator of human mental states, biological effects, and physiological statements, and it also plays a vital role in human interpersonal communication and decision-making. People convey their emotions naturally via everyday interactive communication due to the increasing advancement of social media platforms. Nowadays, detecting emotions from extensive textual data helps to provide expressive information for understanding the behavior of humans. Meanwhile, most prior techniques used for emotion detection are insufficient in providing promising results from long-term contextual information. Thus, it motivates introducing a hybrid deep learning model, Vision sequence-based Convolutional Neural Network (Vi-CNN), from text data to detect emotion. The Bidirectional Encoder Representation from Transformer (BERT) transforms the input texts into tokens. Then, extracting the most suitable and appropriate features from the text tokens is executed. Following this, the Vi-CNN model significantly detects emotions from the extracted features and classifies the recognized emotions. Furthermore, the results obtained by Vi-CNN are compared with those of other prevailing schemes. The experimental results highlight that the Vi-CNN attained promising results with maximum recall of 94.765%, precision of 92.988%, and F-measure of 93.867%.
Copyright (c) 2025 Vian Sabeeh, Ahmed Bahaaulddin Alwahhab https://creativecommons.org/licenses/by/4.0
A Proposed Method by Using the Adjacency Matrix to Solve the Edges Deletion Problem of Complete Graphs
Graph theory approaches can be used to investigate a network’s dependability and effectiveness, and the network’s connectivity can be used to assess its dependability. The efficiency of a network is measured by its diameter, which can be somewhat increased by removing some vertices or edges. In this work, the maximum diameter fn(tt) of the modified graph G with n (n≥4) vertices, which resulted from removing t edges from the complete graph Kn ,(n≥4) was determined using the adjacency matrix adj(Kn).
Copyright (c) 2025 Anwar Nasif Jasim https://creativecommons.org/licenses/by/4.0
On Double AL- ughair Transform Partial Technique and Solving Telegraphic Equation
In this paper, we developing one of the new transform, namely (AL- ughair transform),was introduced through the study of (Double AL- ughair transform, inverse Double AL- ughair transform), method is introduced used to solve the one dimensional partial differential equations with variable coefficients and then we discussed some examples to illustrate this concept and properties. On the other hand, (Double AL- ughair transform ) was presented for the partial derivatives with two variables and the transport equation and some other partial equations were solved, which indicates the effectiveness of this transformation in solving them in a quick and simplified manner.
Copyright (c) 2025 Rakzan Shaker Naji, Ali Hassan Mohammed https://creativecommons.org/licenses/by/4.0
New Applications of A Generalization of Tremblay Fractional Differential Operator for Defining Subclasses of Analytic
In this paper, we study basic geometric properties of analytic and univalent functions in the open unit disk. A new family of analytic and univalent functions is defined by using a generalization of Tremblay fractional differential operator associated with the convolution product of a multiplier transformation and a Ruscheweyh derivative, especially. Coefficient-related studies and extreme points, distortion properties for the functions in class are given and properties of starlikeness and the convexity of this class are also presented.
generalization of Tremblay fractional differential operator
Copyright (c) 2025 Asst. Prof. Dr. Zainab Esa Abdulnaby, Mohammed Ali https://creativecommons.org/licenses/by/4.0
Link Quality Estimation VBF Routing for Underwater Wireless Sensor Networks (UWSNs)
The Underwater Wireless Sensor Networks are necessary for many applications such as marine search and underwater monitoring like measuring water speeds, currents and temperatures. The challenges is, needing for the special underwater environment, which is, require specified routing and security protocols and the traditional protocols are suffer many problems. Therefor there is continuous motivation for search and develop to enhance its performance in viral applications. Vector Based Forwarding is an Underwater Wireless Sensor Networks protocol. This protocol recognized by its depending on defaults pipelines for routing, the packets are routed through these pipes. The disadvantages of this protocol are, the sensitivity to the pipes radius, which is effect on routing efficiency and in the case of spaces found (voids) the protocol may fail to fined communication path. This paper conduct enhancing on VBF by Link Quality Estimation to enhance packet delivery ratio, end to end delay and energy efficiency. The simulation was done and we fined regarded enhancement in these terms.
Copyright (c) 2025 Hayder Khudhair https://creativecommons.org/licenses/by/4.0
A Review of Sentiment Analysis in Social Media Perspectives
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.
Copyright (c) 2024 noralhuda alabid https://creativecommons.org/licenses/by/4.0
A Study of The Neighborhood of A New Class of Analytic Functions Containing The q-Symmetric Multiplier Transform Operator
In this paper I have defined new subclasses of analytic functions characterized by negativity of their coefficients by the q-symmetric multiplier transform operator. In fact, they are subclasses of the class of starlike and convex functions of complex order, and through them I studied the neighborhood of these subclasses and the inclusion relations between them. So I put forward the conditions through which these subclasses belong to the neighborhood or vice versa, the neighborhood belongs to them, and I explained the special cases of them.
Copyright (c) 2025 Hazha Zirar Hussain https://creativecommons.org/licenses/by/4.0
Farsi Digit Recognition Using GAN-Generated Data and Convolutional Neural Networks
Handwritten digit recognition is one of the most active study areas in computer vision because to its numerous applications such as automatically identifying the digits in bank checks and car numbers. Handwritten Latin digits have been the subject of extensive research over the last three decades, whereas Persian handwritten digits have received far less attention. For this reason, we will concentrate on the problem of recognizing Persian (Farsi) handwritten numerals. The main challenge in the recognition of Persian handwritten digits is the presence of different patterns in Persian digit writing, which complicates the feature extraction process. An appropriate approach for automated feature extraction has been the focus of most earlier investigations since handcrafted feature extraction methods are complex and have unstable performance levels. This paper studies the use of a dataset of Persian handwritten digits generated by a Generative Adversarial Network (GAN) to develop a highly accurate Convolutional Neural Network (CNN) model for digit recognition. The proposed CNN architecture achieved a test accuracy of 99.7%, demonstrating its effectiveness. This study highlights the viability of GAN-generated datasets for machine learning applications, especially in resource-constrained scenarios.
Copyright (c) 2025 Farah Jawad, Nisreen Ryadh Hamza https://creativecommons.org/licenses/by/4.0
A Proposed Approach for Crime Type Prediction using Machine Learning Techniques
Abstract— Crime and suspect prediction represent an immerging field where machine learning can be useful when applied efficiently. The available data can be used to train the proposed prediction model (building the model, classifier for example) and then this model can be tested and used to predict the crime type and suspect information such as sex, race and age category. The work in this paper presents a proposed approach for such prediction by using real world dataset available on the internet. The gathered data were preprocessed(deletion of the rows with missing and unknown attribute content, converting some attribute values into nominal or categorical data and concept hierarchy techniques) and reduced the number of attributes by choosing the most important features using different approaches and algorithms, then different Decision Tree types, Naïve Bayes Classifier and Association Rules prediction techniques were used to create the required models and to find out associations between different attributes of the given data. Testing phase shows high efficiency and effectiveness of the proposed approaches which in turn provide models that can be used as reliable predictors. An accuracy of more than 85% was achieved when using different classification techniques. WEKA software, Microsoft Excel and XLSTAT mining software were used to analyze the given data.
Copyright (c) 2025 seham Adnan, Kadhim B. S. Aljanabi , Salam Alaugby https://creativecommons.org/licenses/by/4.0
A Detecting Fake News Using Machine Learning: A Comparative Study of Techniques
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.
Copyright (c) 2024 Howaida Al Ibraheemi, Mohammed Jabardi https://creativecommons.org/licenses/by/4.0
On Some Operators in Anti-Topological Spaces
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.
Copyright (c) 2024 Qays R. Shakir https://creativecommons.org/licenses/by/4.0
Legendre Operational Differential Matrix for Solving Fuzzy Differential Equations with Trapezoidal Fuzzy Function Coefficients
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 exact-analytical 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.
Copyright (c) 2024 zainab imran, Mazin H. Suhhiem https://creativecommons.org/licenses/by/4.0
A Comparative Study Between Two Cybersecurity Attacks: Brute Force and Dictionary Attacks
In 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.
Copyright (c) 2024 Nabaa Alaa, Farah https://creativecommons.org/licenses/by/4.0
SEE Transform Technique in Control Theory
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.
Copyright (c) 2024 nour kareem, Emad Kuffi, Eman Mansour, Ahmad Jalal Issa https://creativecommons.org/licenses/by/4.0
Employee Attrition Prediction Using Machine Learning Techniques
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.
Copyright (c) 2024 Dr. Anamika Rana, Sushma Malik, madhu Chauhan https://creativecommons.org/licenses/by/4.0
Exploiting Conventional MANET Routing in UAV’s Based Environment
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-to-End 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.
Copyright (c) 2024 Marwan Al-Dabbagh https://creativecommons.org/licenses/by/4.0
Iris Identification Based on SVM-CNN Method
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.
Copyright (c) 2024 Suhad A. Ali, Asmaa Khudhair Abbass https://creativecommons.org/licenses/by/4.0
Strong Solutions of the Neuronal Responses System
We explore a connected reaction-diffusion system involving neural responses within limited convex open domains . Utilizing the Classical Faedo-Galerkin technique and employing compactness arguments, we establish the existence, uniqueness, and continuous dependence on initial data for strong solutions.
Copyright (c) 2024 Raad Abd, Ghassan A. Al-Juaifri https://creativecommons.org/licenses/by/4.0
A Comparing Robust Wilks’ statistics in Multivariate Multiple Linear Regression
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
Minimum Covariance Determinant Estimator
Copyright (c) 2024 Thamer, Abdullah A. Ameen https://creativecommons.org/licenses/by/4.0
Six Points Implicit Block Method with Extra Derivative for Solving Second Order Ordinary Differential Equations
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.
Copyright (c) 2024 Barakat Sajid Dahi, Mohammed Yousif Turki https://creativecommons.org/licenses/by/4.0
A Survey of Resource Allocation in V2V Communications
In 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.
Copyright (c) 2024 Ali Ayad Km, Fahad Ghalib Abdulkadhim https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2024 ahlama abd, Ahmed A. Omran https://creativecommons.org/licenses/by/4.0