Journal of Al-Qadisiyah for Computer Science and Mathematics

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.

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 whichmay 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.

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.

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.