Journal of Al-Qadisiyah for Computer Science and Mathematics
How to publish in Al-Qadisiyah Journal of Computer Science and Mathematics The Journal of Al-Qadisiyah for Computer Science and Mathematics (JQCM) is a scientific journal published by the College of Computer Science and Information Technology at the University of Al-Qadisiyah. It has been published since 2009, initially two issues a year, and since 2019, four issues a year. To date, the journal has published 15 issues. The articles published in Volume 5 and above have the submission and acceptance dates indicated, whereas the articles published in Volume 10 and higher have the online availability date indicated. ISSN 2521-3504 (Online), ISSN 2074-0204 (Print)
Next-Generation Biometric Authentication: Overcoming the Twin Identification Challenge with Advanced Facial Recognition and Multi-Modal Analysis Techniques
Conventional facial recognition struggles with individuals sharing near-identical facial features, particularly monozygotic twins. This research introduces a robust, real-time methodology to overcome this by integrating geometric, textural, and dynamic facial characteristics. The framework employs Multi-Task Cascaded Convolutional Networks (MTCNN) for face detection and alignment, followed by FaceNet for 128-dimensional facial embedding generation. MediaPipe's 468-point facial landmark extraction quantifies subtle structural variations via transformation matrix analysis and blend-shape evaluation, capturing static geometric discrepancies and dynamic micro-expressions. Validated on 7,200-image dataset (70% training, 30% testing), the system achieved 97.73% accuracy, operating efficiently on consumer-grade GPUs. This approach significantly enhances biometric technology, offering improved identity verification for genetically similar individuals in critical security applications like border control and secure access management, thereby addressing a key limitation in current facial recognition systems.
Copyright (c) 2025 Azhar Hasan Nsaif , Rawsam Abduladheem Hasan http://creativecommons.org/licenses/by-nc-nd/4.0
Robust and Accurate Phishing Detection Using Enhanced DistilBERT: A Transformer-Based Approach
Phishing scams are still a big problem in email, constantly changing and finding ways to sneak past regular filters by using tricky language and structures. To tackle this, we developed an improved system for spotting these fake emails. Our approach leverages an enhanced version of DistilBERT, fine-tuned and optimized specifically for phishing detection. The proposed “Enhanced DistilBERT” was trained on a comprehensive Email Phishing Dataset—a carefully curated collection of around 82,500 emails drawn from multiple trusted sources, including Enron, Ling, CEAS, Nazario, Nigerian Fraud, and Spam Assassin. This dataset provides a balanced mix of 42,891 phishing attempts and 39,595 legitimate messages, with a strong focus on both the subject lines and email body text. Such diversity enables robust text analysis and helps the model effectively learn the subtle distinctions between phishing and genuine emails. When evaluated, Enhanced DistilBERT outperformed other leading models, including RoBERTa and the original DistilBERT, by achieving perfect scores in accuracy, precision, recall, and F1-score, alongside a near-perfect ROC AUC of 99.76%. The model not only excelled in standard evaluations but also demonstrated resilience against noisy and adversarial data, maintaining high accuracy and low false-positive rates under challenging conditions. These results confirm the superiority and practicality of our approach, establishing Enhanced DistilBERT as a reliable and ready-to-deploy tool for combating phishing threats in today’s dynamic digital landscape.
Copyright (c) 2025 Hamad Abed Farhan http://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning-Based Heart Disease Detection with ANOVA Feature Selection
Heart disease(HD) has emerged as one of the most critical health issues that significantly impact human existence. It has become one of the primary causes of mortality worldwide over the past decade. The World Health Organization announced in 2022 that heart disease was the cause of death for nearly one million people, equivalent to 33% of global mortality. In the current century, there is an increase in the use of non-surgical medical technologies, including artificial intelligence methods in the medical field. Machine learning employs many widely utilized algorithms and techniques that are essential in the rapid and efficient diagnosis of heart issues. However, diagnosing heart disease is a difficult task. The vast and expanding scale of medical datasets has hindered professionals' ability to comprehend the intricate correlations among variables and generate precise predictions. Accordingly, the proposed research aims to examine the role of feature selection techniques in supporting machine learning algorithms and improving model accuracy. A medical database of heart diseases with different features was relied upon. In the first stage, data analysis was conducted to understand the nature of the data and ensure its balance before the classification. This encompassed displaying statistical distributions of the data, identifying missing values, and analyzing the relationships between the variables that are independent and the target variable. This step was followed by implementing feature selection techniques, specifically using the ANOVA algorithm to identify the most pertinent features for heart disease detection. Finally, the machine learning algorithms were used on both the complete and reduced datasets to perform the classification. Accuracy, precision, recall, and F1-score were used to evaluate the trained classifiers. The results also show that when the number of features is reduced, the accuracy of classification models improves slightly compared to models trained on the entire set of features
Copyright (c) 2025 Fatima Shaker, Rana Raad Shaker Alnaily, Saja Naeem Turky, Elham Kareem Wanas, Saja Sadiq Sadon http://creativecommons.org/licenses/by-nc-nd/4.0
Toward a Trustworthy and Interoperable Metaverse: A Systematic Review of Blockchain-Based Enhancements
The integration of blockchain technology with the metaverse has the potential to revolutionize online interaction by enabling secure, trustworthy, and interoperable virtual environments. This systematic literature review (SLR) explores how blockchain technologies can address fundamental challenges within the metaverse, particularly concerning security, trust, and interoperability. Eighteen research articles published between 2020 and 2025 were examined from top academic databases and grouped into three main topics: security methods using blockchain, systems for decentralized trust, and protocols for working across different platforms. The results show that blockchain's decentralization, immutability, and cryptographic foundations enable robust authentication, secure data exchange, and trustless identity systems. Additionally, blockchain facilitates seamless integration between diverse metaverse platforms through smart contracts, cross-chain protocols, and tokenized assets. Despite significant promise, challenges such as computational overhead, real-world deployment limitations, and lack of standardization persist. This review offers a consolidated foundation for ongoing research and proposes directions for integrating blockchain into scalable and reliable metaverse ecosystems.
Copyright (c) 2025 Muhammed Abdulkhaliq Naseef, Ahmed Chalak Shakir http://creativecommons.org/licenses/by-nc-nd/4.0
Enhanced Phishing URL Identification through Recurrent Neural Networks: A Comparative Study of LSTM and BiLSTM
Phishing attacks continue to pose a serious threat to cybersecurity, underscoring the need for effective and scalable detection methods. This study evaluates the performance of Recurrent Neural Network (RNN) architectures—specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM)—for detecting phishing websites based on the sequential patterns in URL structures and webpage content. The LSTM model achieved an Area Under the Curve (AUC) of 0.92, with an overall accuracy of 98.4%, precision of 98.9%, and recall of 97.1%. These results indicate a strong ability to identify phishing URLs with a low false positive rate, although performance declined when detecting sophisticated or zero-day phishing attempts. The BiLSTM model, which incorporates bidirectional context, achieved a higher AUC of 0.95 and improved precision of 91% at a recall of 89%. However, it exhibited a slightly lower overall accuracy of 97.9% and a higher false negative rate. Both models effectively differentiated phishing from legitimate URLs, with BiLSTM offering improved context awareness but at the cost of reduced recall. The results suggest that while BiLSTM enhances contextual understanding, the LSTM model offers better generalization and computational efficiency for real-time deployment. This work highlights the potential of RNN-based models in phishing detection and the importance of balancing sensitivity and specificity in cybersecurity applications.
Copyright (c) 2025 Wadhah Sata Kathum Ajjam, Abdullahi Abdu Ibrahim http://creativecommons.org/licenses/by-nc-nd/4.0
Real-Time Fall Detection for the Elderly in Home Settings Using a Vision-Based YOLOv8-Pose Model
Fall constitute a significant source of death and morbidity among the elderly, particularly in domestic settings when quick assistance is unavailable. Accurate and timely detection of falls is crucial to prevent associated health repercussions. The study proposes a deep learning-based fall detection model YOLOv8-pose that features simultaneous fall detection and pose estimation. A new dataset was created, including 186 high-resolution videos (1080p, 30 fps) captured by a smartphone camera in the home-like environment simulating falling and everyday activity. The video frames were marked and organized according to the COCO format to attain effective training. We used transfer learning to train the state-of-the-art YOLOv8-Pose model in order to detect falls. The system also analyses human skeletal keypoints in real time to accurately differentiate between fall events and non-fall activities, resulting in strong detection performance and a low false positive rate. An automatic alarm system was implemented to deliver immediate fall alerts to providers using the Telegram messaging bot. The proposed approach is an economical, non-invasive, and highly effective alternative for continuous monitoring of the elderly in a household environment, potentially enhancing safety and reaction time during emergencies. The experimental findings revealed that all of the models did a good job of detecting falls and estimating poses. Larger models, such YOLOv8m-pose, got a 99% accurate mAP@50 and the confusion matrix showed that the model was able to classify falls well, with a very low error rate in classifying other cases, reflecting its high effectiveness in distinguishing between falls, normal falls, and bending. This study found that YOLOv8-Pose is a good model for finding falls in older people since it is fast and accurate. In the future, we want to make the model better at imaging under difficult situations and broadening the model to incorporate more activities.
Copyright (c) 2025 Qaseem Riyadh Khawam, Muhaned Al-Hashimi, Salwa Khalid Abdulateef http://creativecommons.org/licenses/by-nc-nd/4.0
Hybrid Ant Colony-Particle Swarm Optimization for Dynamic Resource Allocation in Cloud Data Centers
Effective use of computational resources is a very challenging issue in cloud data centres as demands from users are very high. However, classical optimization methods are often not able to cope with changing workloads, which means they can yield to inefficient decisions. A Hybrid Optimization Algorithm based on PSO Ant Colony algorithm hybrid PSO–ACO is presented in this paper for the purpose of optimizing resource allocation efficiency in cloud environment. In this hybrid model, the heuristic search ability of ACO and exploitative nature of PSO is synergized to deliver the best heuristics to meet the demands of dynamic resource provisioning with minimum energy consumption, reduced SLA violation and improved load balancing. The results supported that the hybrid PSO–ACO algorithm achieves the highest resource efficiency with reduces execution time and SLA violations, balances load effectively and reaches optimal solutions quickly and stably and this means that the hybrid ACO-PSO approach clearly outperforms both ACO and PSO individually in all performance indicators, making it the best choice for dynamic cloud computing systems.
Copyright (c) 2025 Hiba Abdulrazzak Ahmed http://creativecommons.org/licenses/by-nc-nd/4.0
Integrating Harris Corner Detector and SIFT Descriptor for High-Accuracy Feature Matching in Image Processing
Feature matching is one of the primary operations in image processing and computer vision. In this work, the limitations of the Harris corner detector—sensitivity to scale and noise—are highlighted and its performance enhanced through the integration of the SIFT descriptor that is invariant to rotation and scale. The aim is to make feature matching more accurate and robust under varied conditions. The approach that was suggested was implemented on the Ishtar Gate, the Egyptian Pyramids, and the Save Iraqi Culture Monument photos. The process involved keypoint detection with Harris, description with SIFT, and feature matching using the nearest neighbor distance ratio. Precise and accurate results were obtained, particularly with structured images like the Ishtar Gate (100% precision, 99% accuracy). The joint approach is strong in overcoming conventional detector limitations and improving reliability in image matching. There is potential for further research in extending the model to medical imaging and real-time applications.
Copyright (c) 2025 Ali Salam Al-jaberi, Sura Fadhil Rahman, Ihsan Faisal Raheem http://creativecommons.org/licenses/by-nc-nd/4.0
Innovations, Challenges, and Applications of IoT in the Healthcare Industry
The Internet of Things (IoT) is transforming the healthcare industry by enhancing patient care, optimizing workflows, and improving the efficiency of medical systems IoT adoption is projected to reach 75% by 2024. This paper provides a comprehensive analysis of the innovations, challenges, and applications of IoT in healthcare Industry. It discusses advanced IoT-enabled technologies, explores security and privacy concerns, and evaluates real-world applications. Additionally, it identifies future trends and potential solutions to existing barriers. Through this analysis, the paper emphasizes the profound impact of IoT on healthcare delivery and outlines strategies for overcoming associated challenges.
Copyright (c) 2025 Dhafer Sabah Yaseen http://creativecommons.org/licenses/by-nc-nd/4.0
Federated Learning for Credit Card Fraud Detection: A Privacy-Preserving Approach with Smote Optimization
Credit card fraud poses significant global challenges, costing financial institutions billions annually while evolving in sophistication. Traditional machine learning approaches for fraud detection face limitations in data privacy and scalability when dealing with distributed transaction data across multiple institutions. This paper presents a novel federated learning framework for credit card fraud detection that addresses these challenges while maintaining detection accuracy. Our approach leverages distributed machine learning across multiple client institutions (ranging from 5 to 260 clients) without requiring direct data sharing, thus preserving privacy. Through extensive experimentation, we demonstrated that our federated model achieved consistent accuracy (99% with 5–50 clients; 95–98% with 100+ clients) while effectively handling class imbalance through SMOTE integration (AUC = 1.00). The system showed particular effectiveness at mid-range client participation (10–50 clients), establishing an optimal balance between detection performance and computational efficiency. Compared to traditional centralized approaches and alternative data balancing methods (undersampling AUC = 0.97, oversampling AUC = 0.99), our federated solution provides superior privacy preservation without compromising fraud detection capability. The results indicate that this framework offers financial institutions a practical, scalable solution for collaborative fraud detection while maintaining strict data confidentiality requirements.
Copyright (c) 2025 Hassan W. Hilou, Mohsin A. Ahmed , Shaymaa A. Dheeb, Ahmed A. Radhi, Zainab M. Khadim, Mohammed N. Majeed, Ali Ismail Jadaan http://creativecommons.org/licenses/by-nc-nd/4.0
Design and evaluation of two proposed techniques using DCT and LBP for Arabic "Kana Group” classification and recognition in digital images
The article presents a detailed examination of two different algorithms designed to recognize the Arabic "Kana Group" of words in digital images, employing different feature extraction techniques: Local Binary Patterns and Principal Component Analysis (LBP-PCA) and Discrete Cosine Transform (DCT). The results obtained after testing all models trained using the proposed methods showcased the same classification quality metrics. Our findings demonstrate that the two proposed models yielded high classification quality metrics exceeding 98% for all criteria, including precision, TPR, accuracy, F1-score, thereby confirming the models' reliability for accurate and rapid recognition. While DCT outperformed LBP in most categories, all evaluation indicators for the word recognition accuracy were 100%, except for the term “اضحى “, which was erroneously classified as “امسى “at 8%. This lowered DCT's performance relative to LBP in this case, accounting for the overall diminished accuracy of the metric values for this technique.
Copyright (c) 2025 Anwar Hassan Al-Saleh http://creativecommons.org/licenses/by-nc-nd/4.0
Improving YOLO Efficient for Knee Osteoarthritis Detection Using Minkowski Distance
This study presents an enhanced YOLO-based deep learning model for automatic detection of knee osteoarthritis (KOA) from X-ray images. The integration of Minkowski distance into the loss function improves the model’s sensitivity to spatial variations and noise. Extensive preprocessing and a lightweight YOLO architecture ensure real-time performance with high accuracy. Experimental results demonstrate superior detection rates compared to traditional methods, especially in identifying early-stage KOA.
Copyright (c) 2025 Marwah Fadhil Najim, Atheer Alrammahi, Ahmed Mohsen Mahdi http://creativecommons.org/licenses/by-nc-nd/4.0
A Deep Dive into Deep Learning and Machine Learning: A Review Study
The current comprehensive review paper focuses on the analysis of the differences and similarities between deep learning and traditional machine learning. This paper analyses the architectural differences between DL and ML systems and include a comparison of the suitability of the two for tasks such as computer vision, NLP, speech recognition and Reinforcement Learning. The differences of data demand, training procedures, and computational power necessary for each method are discussed. The paper also focuses on the difficulties of hyperparameter tuning / optimization and research methodologies in both paradigms: ML and DL. Additionally, it introduces aspects of deployment and a range of size and speed values for models, inference, and resources. Problems as to how interconnections between ML and DL can provide the greatest performance are analyzed along with the viability of the mentioned integrated models. Moving to the future advancements, we discuss about the trends and the future work in both the fields which gives some idea about the upcoming building of artificial intelligence. In this extensive analysis, we hope that the target audience of researchers, practitioners, and decision-makers will be equipped with the knowledge regarding when and how to appropriately use ML and DL approaches while considering the complexity of a given task, availability of data, and computing resources. This work in turn, adds its voice to the ever-increasing discussion in the AI world with regards to the relative advantages and suitable uses of these strong methods. This review quantitatively compares performance (e.g., 92% DL vs. 85% ML accuracy in image tasks) and resource requirements across domains.
Copyright (c) 2025 Hind Khalid http://creativecommons.org/licenses/by-nc-nd/4.0
Forest Fire Image Classification via Hybrid Deep Learning and Stacking Ensemble Technique
In recent years, forest fires have increased significantly and in an intimidating manner worldwide, thereby affecting both the environment and human life. When they are overlooked and not detected at an early stage and in a short period of time, they will spread rapidly. Accurate and early fire detection and classification are crucial for disaster control and well-timed emergencies. To address this problem, a hybrid model of pre-trained embedding and diverse classifiers to extract features, detect, and classify fire images with optimized performance and stacking with different meta-learners to improve the reliability, is applied in this paper. Three types of pre-trained models, including InceptionV3, SqueezeNet, and DeepLoc, were used for feature extraction with different classifiers: Neural Network (NN), Decision Tree (DT), and XGBoost. The proposed model, which involves stacking multiple classifiers, provides an accurate, optimal, and efficient response for fire classification. A total of 1520 images were used in this study. The best performance was achieved by integrating InceptionV3 with NN, XGBoost, and Logistic Regression as a meta-learner, yielding 98.9% accuracy, 99.9% AUC, and 98.9% for each of (F1-score, Precision, and Recall) with 97.8% MCC, with 10 False Negatives and 7 False Positives in error types. This combination of deep and machine learning with stacking shows consistent progress across all experiments, and these results can enhance and improve the quality, leading to more dependable detection and classification in safety-critical applications.
Copyright (c) 2025 Israa Ali Alshabeeb http://creativecommons.org/licenses/by-nc-nd/4.0
Developing Self-Optimising 5G Communication Frameworks Using a Deep Reinforcement Learning Approach
This study introduces a self-optimising fifth-generation (5G) communication architecture that utilises Deep Reinforcement Learning (DRL) to meet the increasing demands of ultra-reliable low-latency communication (URLLC) and extensive device connectivity. A DRL agent built on the PPO algorithm is designed to autonomously orchestrate resource allocation within a virtual environment comprising 30,000 nodes, with the dual objectives of minimising end-to-end latency and enhancing overall network effectiveness. The agent learns to adjust resource assignment in response to varying traffic fluctuations and interference patterns, rendering traditional, static, heuristic strategies obsolete. Simulation experiments reveal a 51% decline in average latency, decreasing from 88.7 ms to 43.6 ms, thus assuring compliance with URLLC strictures. The architecture concurrently produces a 39.9% uplift in throughput and a 49.3% rise in the Quality of Service (QoS) satisfaction rate. A comparative evaluation validates the framework’s dominance over conventional benchmarks, underscoring its viability for expansive, intelligent, and self-segregating 5G and forthcoming 6G networks.
Copyright (c) 2025 Shaymaa Shaalan Jawad http://creativecommons.org/licenses/by-nc-nd/4.0
IoT Intrusion Detection Using Transformer-Based Anomaly Learning
We present a Transformer-based intrusion detection system (IDS) for IoT network flows. Raw traffic is converted into windowed flow sequences (47 features; 30-s window; 10-s stride; sequence length 64) and fed to a compact Transformer encoder (4 layers, 8 heads, hidden size 256) with dual heads for binary (anomaly) and multiclass (attack type) inference. Evaluated on UNSW-NB15, BoT-IoT, and ToN_IoT against CNN, LSTM, Random Forest, and SVM baselines, the model achieves state-of-the-art discrimination with lower false-alarm behavior: UNSW-NB15: F1 = 95.1%, FAR = 2.1%, ROC-AUC = 0.984; BoT-IoT: F1 = 97.2%, FAR = 1.4%, ROC-AUC = 0.992; ToN_IoT: F1 = 92.9%, FAR = 2.6%, ROC-AUC = 0.973. Precision–Recall analysis confirms higher PR-AUC and better precision at matched recall than all baselines, which aligns with fewer benign flows escalated as alerts. Attention maps and SHAP attributions surface feature-time drivers (e.g., SYN bursts, DNS probing, TLS exfiltration cues) and are distilled into short reason codes attached to each alert. A deployment-oriented alert policy (default threshold with abstain band, 2-of-3 window aggregation, session de-duplication, and rate limiting) turns scores into compact, auditable outputs suitable for operations
Copyright (c) 2025 Hayder Salah Abdulameer http://creativecommons.org/licenses/by-nc-nd/4.0
Employing Machine Learning with Discriminative Function for Predict Heart Diseases
Heart disease is also one of the leading causes of death and therefore there is need to have a readily accurate way of diagnosing it. Based on this study, a strong basis of machine learning based on predicting heart disease has been proposed with the combination of ensemble learning algorithms (XGBoost, Random Forest, Gradient Boosting, and Extra Trees) and classic discriminant analysis based (Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)). The models were tested with two test benchmark datasets following rigorous pre-processing and feature engineering. The experimental findings show that XGBoost performed best in the first dataset with the accuracy of 98.54, perfect precision of 100 and the F1 score of 0.985. The best results by Random Forest on the second dataset, compared to the rest were 94.96 and the F1 score of 0.955. Comparatively, LDA achieved 82.44 and 87.39, accuracy rates out of the first and the second dataset respectively, whereas, QDA did not do as well in 54.15 and 44.96 respective accuracy levels. The results provided clearly indicate that compared to alternative discriminant models, machine learning models considerably outperformed the discriminant ones, which also shows that the XGBoost model would be the most suitable option in classifying the data. The outcomes confirm the usefulness of machine learning as a reliable and accurate diagnostic aid in detecting preliminary heart diseases in the clinical setting.
Copyright (c) 2025 Firas Abdulrahman Yousif, Rasha Raad Al-Mola; Muthanna Subhi Sulaiman http://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning for Alzheimer’s Disease Diagnosis from Brain MRI: Review
Alzheimer's disease (AD) is a progressive neurodegenerative condition that strongly impacts cognition and quality of life. Accurate and early diagnosis is essential for effective management and treatment planning. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have been successful methods for automatic AD detection from brain Magnetic Resonance Imaging (MRI). This review synthesizes advances in deep learning using MRI for AD, focusing on preprocessing steps (rescaling, grayscale, normalization) and data augmentation techniques that ensure generalization and account for dataset imbalance. Explainable AI is also promoted for its ability to enhance transparency and clinical confidence. By description of strengths and limitations of existing approaches, this paper aims to guide researchers toward the design of accurate, interpretable, and clinically relevant AI systems for diagnosing Alzheimer's disease.
Copyright (c) 2025 Zainab Abdalhussain Kareem, Ahmad Shaker Abdalrada http://creativecommons.org/licenses/by-nc-nd/4.0
Task Scheduling in Cloud Computing: A Novel Approach Using Artificial Intelligence Algorithms
Task scheduling is an important problem that is encountered in distributed systems, project management, and cloud computing. Task dependencies used to model the so-called directed acyclic graphs (DAGs) and for the scheduling optimization have been widely used. However, such methods do not work well in dynamic environments and complex constraints. Task scheduling using DAGs is proposed in this paper, along with the application of Artificial Intelligence (AI) algorithms exploring Artificial Neural Networks (ANN), Artificial Intelligence algorithms, which include Genetic Algorithms (GA) and a specific type (Reinforcement Learning (RL)) as novelty addition to the scheduling problem. The proposed method is developed to enhance inventory efficiency, bound make span, and intend to dynamically cope with changes in resource availability and task priorities. The unusual effect that paper accounts for in that race was the application of artificial intelligence to enhance the process of scheduling. The workflowSim simulator is used to evaluate the proposed method on both synthetic and real-world workflows. Experimental results highlight the method’s superior effectiveness when compared with alternative algorithms.
Copyright (c) 2025 Zainab Kashlan Yaser http://creativecommons.org/licenses/by-nc-nd/4.0
k-Fractional Extension of h-Hadamard Integrals and Derivatives
In this paper, we present a generalization types of the h-Hadamard integrals and derivatives parameterized by k. The h-Hadamard integrals and derivatives are themselves generalizations of the Hadamard integrals and derivatives, defined in relation to a continuous function h. We have also developed the concepts of k-gamma and k-beta, which correlate with the definitions of k-fractional h-Hadamard integrals and derivatives. The paper includes various theorems, propositions, properties, and illustrative examples
Copyright (c) 2025 Sajad Talib Khathem, Methaq Hamza Geem http://creativecommons.org/licenses/by-nc-nd/4.0
Study a New Subclasses of Analytic Bi-Univalent Functions Defined by New Integral Operator
This paper aims to introduce and examine specific subclass and of analytic and bi-univalent functions defined in the open unit disk , linked to a new integral operator . We examine the upper bounds for the initial Taylor-Maclaurin coefficients also for functions these subclasses.
Copyright (c) 2025 Muhammed Salih Muhammed, Athra Fadel Abbas, Maryam Razzaq Kadhim http://creativecommons.org/licenses/by-nc-nd/4.0
Persistence and Extinction in Stochastic Model of an Oncolytic Virotherapy by Tumor-Virus Interaction with Statistical Analysis
In this paper, we consider the study of the persistence and extinction of cancer cells in the stochastic system that describes the relationship between cancer cells and the viral effect on the growth of cancer cells. The sufficient conditions for the extinction of cancer cells and for the survival of normal cells are proposed. In addition, we provide a statistical analysis of the stochastic model by studying the expected value, conditional expected value, variance, and conditional variance to solve the system. Finally, a numerical simulation of the system is introduced in order to illustrate the results
Copyright (c) 2025 Khansa Kadhim Hashim, Ihsan Jabbar Kadhim http://creativecommons.org/licenses/by-nc-nd/4.0
Modification Fuzzy Intelligent System For Solving Fuzzy Singular Perturbed Mixed Volterra - Fredholm Integro- Partial Differential Equations
Through this effort, we have devised a new method using a neuro-fuzzy system. The neuro-fuzzy system method (MNFS) is the name given to this revised technique. We created a numerical technique for using BC to solve partial differential equations (FSPPs). WA suggests an updated approach that uses neural networks to solve singular perturbation problems. We changed each element in the training set to a second-degree polynomial. For the purpose of solving FSPMV-FIDEs, the modified neuro-fuzzy system (MNFS) is the name given to this altered methodology The fuzzy neural network's parameters and hidden units' sigmoid function,were determined using the activation function for hyperbolic tangents, which has the formula Q(x) =Q(x) The proposed approach was compared with classical training algorithms and analysis methods. According to our research, the suggested method is notable for its high accuracy, low error rate, and significantly faster speed compared to traditional methods. The recommended approach is illustrated with several instances
Copyright (c) 2025 Zainab Raad Al-shamary, Khalid Mindeel Mohammed Al-Abrahemee http://creativecommons.org/licenses/by-nc-nd/4.0
In this work, we introduce generalizations of fuzzy -quasinormal operators defined on fuzzy Hilbert spaces over fuzzy vector spaces. We study several fundamental properties of these operators and explore specific operations associated with them
Copyright (c) 2025 Ahmed Talib Kareem http://creativecommons.org/licenses/by-nc-nd/4.0
Approximation in composition Real Functions Spaces
An approximation in space of continuous functions, as we well known had studied in many books and researches, but approximation in composition of real continuous functions space had not studied previously. In these papers we studied in detail an approximation in this space, where we studied this approximation in terms of existence, uniqueness, and its degree of an element (function) of best approximation of real function that consisting from composition of two real continuous functions. We also noticed that the extent of the impact that make submission two real functions to laws of approximation in this space on the approximation of the composite function from them and degree of that approximation. After that, we studied the relationship between the module of smoothness for the original functions and the resulting function from their composition in different forms of this study and deducing their important relations for them, in the end we studied the possibility of the compact set to providing an element of best approximates of the composite function of two real functions depending on its provision these elements for the original functions which belong in it, then we concluded that this set provides a best approximation element of the function in question also vice versa for original functions.
Copyright (c) 2025 Jawad K. Judy http://creativecommons.org/licenses/by-nc-nd/4.0
On the Relationship Between Smoothness and Best Approximation in Weighted Function Space
This work investigates the relationship between the smoothness measure and the function norm in the space , where modified symmetric difference based on the Ditzian-Totik function are employed to assess the behavior of functions within centered subintervals. The focus is on the weighted smoothness measure of order used to derive both local and global estimates of the function. The weighted smoothness measure can be bound in terms of a series involving , which represents the optimal approximation of the function by polynomials. The following estimate incorporates the effects of the weight, partial smoothness, and derivative behavior into a precise quantitative expression linking approximation properties with smoothness analysis. The results contribute to a deeper understanding of the interplay between function smoothness and behavior in approximation function spaces, and they open pathways to accurate numerical applications.
Copyright (c) 2025 Zahraa Abd ALameer Mohameed, Nada Zuhir Abd AL-Sada http://creativecommons.org/licenses/by-nc-nd/4.0
Applications on Geometric Function Theory using Symmetric Geometric Properties for a New Subclass of k-valent Functions
This study introduces a new class of k-valent analytic functions constructed through the Hadamard product within the open unit disk The paper explores several geometric characteristics of this class, including coefficient bounds, distortion and growth behavior, closure properties, radii related to convexity and starlikeness, as well as the determination of extreme points and weighted mean with some applications.
Copyright (c) 2025 Waggas Galib Atshan, Saad Raheem Bakheet, Qasim Ali Shakir, Muhammed Salih Muhammed http://creativecommons.org/licenses/by-nc-nd/4.0
In this paper ‚we introduce the definitions of some types of H-functions namely ‚H-continuous ‚H-irresolute ‚H-closed function ‚H-open function‚ H-homomorphism function and investigate some properties of composition and study relation between functions.
Copyright (c) 2025 Zahra Ali Sdaam, Ahmed Talip Hussein http://creativecommons.org/licenses/by-nc-nd/4.0
Bi-Conjugate Gradient Method for Solving Positive Triangular Fully Fuzzy Linear Systems
Fuzzy linear systems play an important and efficient role across various domains such as mathematics, engineering, physics, chemistry, economics, statistics, and so on. Dealing with these kinds of systems in the real world is still very hard. This paper suggests a new way to solve fully fuzzy linear systems quickly using the Bi-conjugate gradient method . The method builds a one-block rate matrix and lets it skip fuzzy arithmetic operations. The focus is on systems in which both the coefficients and the variables are fuzzy, aiming to produce positive results even under highly uncertain conditions. The algorithm is efficient in solving FFLS, requiring only a few iterations and converting the system into a crisp linear system first. To test the validity of the proposed method, we ran three numerical experiments, which confirmed its effectiveness and robustness. Unlike Jacobi or Gauss-Seidel, this proposed method has more efficient and quicker convergence. It is most applicable for FFLS when there are stringent demands on precision and system parameters are uncertain.
Copyright (c) 2025 Sirwan Sherko Rasheed, Ivan Subhi Latif http://creativecommons.org/licenses/by-nc-nd/4.0
New Certain Continuous Mappings via Neutrosophic Topological Spaces
In this article, we present the new class of neutrosophic continuous mappings called; -continuous map (WN -CM) strongly neutrosophic -continuous map (SN -CM), neutrosophic -weakly continuous map (N W-CM) and neutrosophic -weakly continuous map (N S-CM) includes an examination of the most notable traits and attributes associated with it. Moreover, we introduced the notions of -irresolute map (WN -CIM) strongly neutrosophic - irresolute map (SN -CIM), neutrosophic -weakly irresolute map (N W-CIM) and neutrosophic -weakly irresolute map (N S-CIM) in neutrosophic topological spaces (NTS) were investigated, coupled with a few characteristics that are connected to them. Finally, we studied the relationship among continuous and irresolute mappings for these kinds that have been presented
Copyright (c) 2025 Ghanim Aad Abod , Samer Raad Yassen http://creativecommons.org/licenses/by-nc-nd/4.0
Large Small compressible and Large Small retractable Modules
We consider R is any ring with identity and is a non-zero unitary left R-module. In this article we introduce the notion of large small Compressible modules and large small Retractable module. Also, we will discuss and study many basic properties about these concepts with its generalization to the ring. Where a submodule of is said to be large small of denoted by if for such that then is essential in .
Copyright (c) 2025 Rawnaq Khaleel Ibrahim, Sahira M. Yassen http://creativecommons.org/licenses/by-nc-nd/4.0
Numerical Approximation of Laplace's Equation Using the Finite Element Method
This article addresses the finding of an approximate solution to Laplace's equation, a basic elliptic partial differential equation, using the Finite Element Method (FEM). Laplace's equation, ∇²u = 0, plays a key role in the description of equilibrium of processes in physics and engineering, e.g., in steady state heat conduction, and in electrostatics. Whereas this type of solution is not always possible because of the restrictions it imposes on the geometry, FEM is a more general and flexible approach where complex geometries are to be treated We give the mathematical framework, weak format computation for FEM, the theory from existence and uniqueness to the regularity of its solution based on functional analysis (Lax–Milgram theorem). The FEM procedure (discretization employing basis functions and mesh generation) is explained with special focus on its influence on precision and efficiency. An important part of this work is the error and convergence analysis with computation of error estimates, confirming theoretical a priori error estimates (for instance, first order in (H¹ norm) and second order in (L² norm) for P1 elements) in numerical experiments. Visualization methods and error tables are employed to demonstrate features of solutions and measure accuracy. The tendency emphasizes the importance of mesh refinement techniques, in particular, adaptive methods based on a posteriori error estimation. This work validates the reliability and performance of the proposed FEM to obtain the solution of Laplace's equation, highlighting its significance in computational mathematics and engineering problems.
Copyright (c) 2025 Ahmed Muttaleb Hasan http://creativecommons.org/licenses/by-nc-nd/4.0
On ideal supra Z_open set in ideal supra topological space
This study introduces the ideal supra open set in ideal supra topological space, where its basic properties are defined and examined through illustrative examples and related theorems and the relations between some separation axioms ( , i=0,1,2 ) are generalized and studied over the spaces under study.
Copyright (c) 2025 Saja S. Faiad, Rana B. Yaseenb http://creativecommons.org/licenses/by-nc-nd/4.0
Novel Third-Order Differential Subordination and Superordination Results for Meromorphic p-valent Functions Involving a New Hadamard Product Operator
The purpose of this paper is to derive many third-order differential superordination and subordination results
Copyright (c) 2025 Bassim Kareem Mihsin, Waggas Galib Atshan, Shatha S. Alhily http://creativecommons.org/licenses/by-nc-nd/4.0
Distributional Solutions to Boundary Value Problems Using Fourier Series
This article explores the application of Fourier collection strategies in fixing complicated boundary charge issues (BVPs) involving fractional derivatives, distributional coefficients, and vector-valued distributions. By leveraging cutting-edge improvements in fractional calculus, tempered distributions, and multidimensional Fourier techniques, a whole framework is developed to address the stressful situations posed by using irregularities, singularities, and non-neighborhood operators in BVPs. The proposed method transforms differential operators into algebraic expressions inside the frequency place, allowing inexperienced and correct answers for issues which may be computationally high priced for conventional numerical strategies. Key consequences display the efficacy of Fourier collection in handling fractional operators, taking pictures the outcomes of distributional coefficients, and solving actual-international problems such as fractional warmness conduction and wave propagation with singular assets. While the technique exhibits speedy convergence for clean forcing phrases, challenges which includes Gibbs phenomena for non-smooth inputs and computational complexity in multidimensional domain names are mentioned. This have a test highlights the flexibility and computational overall performance of Fourier collection strategies, offering a basis for destiny studies in mathematical physics, engineering, and accomplished mathematics
Copyright (c) 2025 Nada Abdul-Hassan Atiyah http://creativecommons.org/licenses/by-nc-nd/4.0
A Novel Category of Analytic Univalent Functions that is characterized by an Integral Operator
This paper presents a novel class of analytic univalent functions, which are defined by an integral operator in the open unit disk . Various geometric properties of these functions, including coefficient inequality, growth and distortion bounds, and convolution properties, are investigated.
Copyright (c) 2025 Mustafa A. Sabri http://creativecommons.org/licenses/by-nc-nd/4.0
A Robust Statistical Framework for Outlier Detection and Its Influence on Predictive Modeling Accuracy
Outliers, defined as observations that deviate substantially from the majority of data, pose a serious challenge to predictive modeling by distorting estimation, increasing variance, and reducing model reliability. Although numerous statistical and machine learning approaches for outlier detection have been proposed, their direct influence on prediction accuracy across real-world domains has received limited attention. This study develops a robust statistical framework that integrates univariate, multivariate, and machine learning–based detection methods with confirmatory regression diagnostics and a bootstrap-driven model selection strategy. Candidate anomalies are first identified through histogram- and IQR-based screening, kNN and LOF density–proximity measures, and isolation forest and one-class SVM classifiers. They are then statistically validated using standardized residuals and Cook’s distance, while robustness is reinforced through MM-estimation and bounded loss functions. Evaluation is conducted using both synthetic contamination experiments and real datasets from finance, healthcare, and marketing, comparing models trained with and without detected outliers across classifiers such as SVM, logistic regression, KNN, random forest, and AdaBoost. The results demonstrate that excluding or down-weighting outliers consistently enhances predictive accuracy and stability, particularly in settings with heavy-tailed errors and heterogeneous distributions. The proposed framework provides a practical and statistically principled approach for improving model fidelity, offering broad applicability across diverse domains where reliable prediction is essential.
Copyright (c) 2025 Hadeel Kamil Habeeb, Faten Hatem Hassan http://creativecommons.org/licenses/by-nc-nd/4.0