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
Incidence and Non-incidence Soft Topology with Application
In this work, incidence and non-incidence relations are used to create a new type of soft topology on soft graphs. Every edge in the graph is used as a parameter, and ordered pairs are used to build soft sets. The first part of an ordered pair is the edge, and the second part is the point that is connected to the edge. The sub-base, base, and soft topologies are explained, and then properties, theorems, and a full example are given to show how they work. This method gives a combinatorial structure encoding and makes it possible to do new kinds of study in soft topological spaces.
Copyright (c) 2026 Iman Abbas Ali, Asmhan Flieh Hassan https://creativecommons.org/licenses/by/4.0
A Model-based Simulation to Evaluate the Attack's Severity in a Wirelessly Connected Mobile Vehicle Platoons
In this paper, a model-based is presented for co-simulation framework to evaluate the severity of cyber-attacks on the wirelessly connected platoons of vehicles (Vehicular network). Two representative attack scenarios are implemented (Shift & Scale) and simulated in a simplified CACC-like model. Numerical results quantify spacing deviations, acceleration anomalies and recovery behavior, illustrating the framework's utility for assessing resilience and guiding mitigation strategies.
Copyright (c) 2026 Ahmed Waleed Khalil Al-Nasir https://creativecommons.org/licenses/by/4.0
Building an Intelligent Energy Management System for Enhancing Energy Consumption in Smart Houses
This trend of increasing residential energy consumption accounts for a significant and growing fraction of total global electricity demand — it also presents a bad economic incentive while being a burden for grid stability and sustainableisation. Existing smart home energy management systems typically do not strike an adequate trade-off between the three main conflicting objectives, namely, operational cost minimization, occupant thermal comfort satisfaction, and renewable energy usage maximization. To fill this gap, this paper presents a new Intelligent Energy Management System (IEMS) based on a Mixed-Integer Linear Programming (MILP) framework. It combines all the large and complex real-world inputs — hourly solar photovoltaic (PV) generation forecasts, dynamic time-of-use (TOU) electricity pricing, user-defined appliance preference windows, lithium-ion battery dynamics, and a linearized HVAC thermal model — into one single, tractable optimization problem. Calculated for a typical winter day in Helsinki, data from Climate-Data. org and Oomi. To summarize, the proposed IEMS can reduce daily electricity expenses by 73.1% (from €1.04 to €0.2805) and 68% of grid price, while simultaneously maintaining indoor temperature strictly between comfort band of 20–24°C and without violating user scheduling preferences. Our results show that the MILP approach provides a mathematically tractable, transparent and reproducible solution which outperforms rule-based heuristics and uncoordinated operation. This work provides such a generalizable and implementable framework for sustainable and demand-centric home energy management in smart grids
Copyright (c) 2026 Waad Zaid Saleh https://creativecommons.org/licenses/by/4.0
Stock Market Forecasting Trends and Price using Different Machine Learning Techniques: Systematic Review
Financial stock markets play a significant role in supporting the economy by enhancing market liquidity, facilitating risk-sharing, minimizing the costs of financing, and contributing to the capital market. These functions offer more opportunities for economic growth. Stock market forecasting of prices or trends is a critical endeavor for investors and financial institutions to mitigate risk, optimize returns, and improve portfolio management. To address these challenges, numerous approaches have been proposed, involving time series forecasting, machine learning models, deep learning, ensemble techniques, and hybrid forecasting, which enhance the accuracy and reliability of forecasts. This study provides a systematic review of the literature on stock market forecasting, highlighting methodologies through comparative tables.
Copyright (c) 2026 mohammed jabardi, Hawraa Abdulnabi https://creativecommons.org/licenses/by/4.0
Uniformly MetriBornologies: A Unified Characterization and Applications to Fundamental Bornologies in Metric Spaces
This paper presents a study of bornological structures within metric spaces, with a central focus on the property of being a "uniform metric" bornology. The primary objectives are to characterize such bornologies and to establish necessary and sufficient conditions for this property to hold for several fundamental types. We provide a characterization theorem for uniform metric bornologies and apply this framework to analyze specific cases, including the compact, totally bounded, and Bourbaki-bornological structures. Furthermore, we demonstrate that several previously known results emerge as direct corollaries of our general theorems. The analysis employs tools from mathematical analysis and topology, utilizing the construction of characteristic functions alongside concepts of metric extensions and uniform restrictions. This work yields a deeper theoretical understanding of uniform metric bornologies and provides applicable tools for their investigation in varied contexts. The results are subsequently applied to the study of metric spaces possessing special properties, such as locally bounded and complete spaces.
Copyright (c) 2026 Hasan Khaled AL omar, Zahir Dobeas AL-Nafie https://creativecommons.org/licenses/by/4.0
Bifurcation Analysis of the Generalized Computer Virus Propagation Model
This research examines the generalized Computer Virus Propagation Model by analyzing its local bifurcation behavior, with particular attention to Hopf and transcritical bifurcations. The stability of equilibrium points and the emergence of periodic solutions are investigated through the computation of the first Lyapunov coefficient. A detailed exploration of the system’s dynamics reveals the presence of both stable and unstable periodic solutions, depending on specific parameter choices. To support the theoretical results, numerical simulations are provided, highlighting how parameter variations significantly influence the nature of the bifurcating periodic solution.
Copyright (c) 2026 Qaraman Muhammed, Rizgar H. Salih https://creativecommons.org/licenses/by/4.0
Approximate solution of integral-algebraic equations of index-2 by numerical multi-step method
This paper presents a numerical approach for solving index-2 integral algebraic equations (IAEs) based on multi-step collocation techniques. The authors first examine the solvability and smoothness of index-2 IAE solutions via the differentiation index framework. The proposed method integrates principles from multi-step methods and collocation, where the approximate solution is determined by satisfying specific matching conditions at designated collocation points. We perform a convergence analysis to prove that the algorithm’s approximations converge to the exact solution and to determine their rate of convergence. For comparison, we also solve the equation using a one-step collocation method, thereby highlighting the superior efficiency and precision of our multi-step approach.
Copyright (c) 2026 bahaa hussein Alrikabi https://creativecommons.org/licenses/by/4.0
On the graph of the Maximal subgroup of the Cyclic Group
The maximal graph is a finite graph for any two vertices that are joined if is a maximal subgroup and is denoted by .In this paper, we will present an algorithm for calculating the graph of .Where we will study several cases of the value of so there will be an algorithm for each case. To calculate the maximum number of subgroups of the cyclic group . We’ll employ the Gap program. and use this to find some of the properties of the maximal graph.
Copyright (c) 2026 Ohood A. Hadi, Hayder B. Shelash https://creativecommons.org/licenses/by/4.0
This research introduces a novel classification of s-g-coc-Proper and s-g-coc'-Proper functions within topological spaces. The study focuses on examining the fundamental properties of these functions, such as continuity, closure, and compactness, while analyzing the impact of topological operations like composition, restriction, and product on their behavior. Furthermore, the research investigates the relationships between these functions and other topological concepts, including s-g-coc-Closed and s-g-coc-Compact sets. The research includes rigorous mathematical proofs, establishing new theorems such as the stability of topological properties under composition and the analysis of compact fibers of these functions. Additionally, illustrative examples are provided to highlight specific cases where certain properties fail or results deviate.
Copyright (c) 2026 Ruaa Muslim Abd, Noor Hasan Kadhim, Saja Mahdi AL-Khafaji https://creativecommons.org/licenses/by/4.0
Generalized Bivariate Fibonacci Polynomials and a Related Class of Bi-Univalent Functions
In this study, we propose and analyze a new subclass of analytic bi-univalent functions defined on U=ζ∈C:|ζ|<1. The functions considered are characterized by subordination to a generating function constructed via generalized bivariate Fibonacci polynomials. In particular, we derive bounds for the initial coefficients in the Taylor–Maclaurin series expansion of functions belonging to this subclass. Furthermore, we obtain estimates for the Fekete–Szegö functional. The results presented here generalize and extend several known results in the theory of bi-univalent functions. The relevance of the main results and their relationship to previously defined subclasses are illustrated with examples and comments..
Copyright (c) 2026 Swamy SR, Vinutha R, Mamatha p.K https://creativecommons.org/licenses/by/4.0
Energy and Time Efficient Task Scheduling in Cloud Computing Using a Hybrid Genetic Algorithm - Approach
Efficient task scheduling in cloud computing is a challenging problem, particularly when multiple competing objectives such as execution time, power consumption, and resource utilization must be optimized simultaneously. Traditional metaheuristic algorithms like Genetic Algorithms (GA) and Multi-Objective Particle Swarm Optimization (MOPSO) have been widely applied, but they suffer from drawbacks including premature convergence, limited diversity preservation, and difficulty in maintaining a well-distributed Pareto front. This paper proposes a hybrid GA-MOPSO algorithm in which GA’s crossover and mutation operators are embedded directly into the MOPSO updating process. Unlike conventional sequential or loosely coupled hybrids, our design allows GA to dynamically inject new genetic diversity into the swarm at each iteration, thereby preventing stagnation and guiding particles towards unexplored regions of the Pareto front. This integration preserves MOPSO’s fast convergence while enhancing diversity and solution quality through GA’s exploration capabilities. Simulation results on benchmark cloud scheduling scenarios demonstrate that the proposed algorithm consistently outperforms standalone GA and MOPSO in terms of makespan, energy efficiency, and average response ratio. These results prove the effectiveness of the proposed hybrid approach in multi-objective scheduling problems of cloud environment.
Copyright (c) 2026 Zainab Kashlan Yaser, Abdulrahman D. Alhusaynat, Walaa Alajali https://creativecommons.org/licenses/by/4.0
An Approach for Prediction of Osteoporosis and Its Association with Osteoporotic Fracture Risk Using Machine Learning
Osteoporosis is a prevalent condition characterized by decreased bone mineral density and increased fracture risk, representing a significant global health concern, especially in aging populations. Early detection and risk stratification are critical for preventing fractures and reducing healthcare burdens associated with osteoporosis. A dataset of 1,958 individuals with demographic, lifestyle, and clinical features is analyzed, with the data explicitly split into 75% for training and 25% for testing. Three machine learning models—Random Forest, Decision Tree, and Multilayer Perceptron (MLP)—are developed and evaluated by using accuracy, precision, recall, F1-score, ROC-AUC, and balanced accuracy. The Decision Tree model achieved the highest accuracy (0.884) and perfect precision (1.0), while Random Forest and MLP models demonstrated competitive performance with ROC-AUC values of 0.876 and 0.875, respectively. Feature importance analysis confirmed age as the most influential predictor, with prior fractures, low BMI, glucocorticoid use, and smoking contributing to osteoporosis risk classification. On another side, to determine the degrees of fracture risks and their categories using the Fracture Risk Assessment Tool (FRAX) medical tool. So, we suggested a system using three machine learning (ML) techniques known for their success in diseases prediction, significant results were obtained compared to previous related studies according to the usual evaluation criteria for ML techniques which used to reach the best one to be relied upon in building the future model for prediction of osteoporosis, among all the algorithms, all evaluation metrics indicated that the Decision Tree (DT) was the best model (Accuracy: 0.884, Precision: 1.000, Recall: 0.767, F1-score: 0.868, and Area Under the Receiver Operating characteristic Curve (AUROC): 0.889), followed by the Random Forest (RF) and then the Feedforward Neural Network (Multi-Layer Perceptron MLP). On another side, to determine the degrees of fracture risks and their categories using the Fracture Risk Assessment Tool (FRAX) medical tool. This study is, to the best of our information, unique in linking osteoporosis with osteoporotic fracture risks by weighting important factors within using historical healthcare datasets as big data, which provides an integrated approach a solid foundation for building accurate predictive models that support researchers and healthcare professionals in enhancing diagnosis and prevention, thus saving effort, time, and cost for both the patient and the healthcare communities
Copyright (c) 2026 Muayad H. Abdul-Zahra, Salam Al-augby https://creativecommons.org/licenses/by/4.0
Properties of Asymptotic Behaviour Regarding the resolution of linear neutral difference equations of the second order with positive and negative coefficients
In this research concerns the asymptotic behavior properties for resolve second order of linear Neutral Difference equations with Positive and Negative Coefficients. Some necessary and Sufficients obtained for all solution non oscillatory tend to zero or infinity n→∞ . Example are given to illustrated the ordinary results.
Copyright (c) 2026 Wafaa Zuhar Hemead, Sattar Naser Ketab 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