Statistics, Optimization & Information Computing
Statistics, Optimization and Information Computing (SOIC) is an international refereed journal dedicated to the latest advancement of statistics, optimization and applications in information sciences. Topics of interest are (but not limited to): Statistical theory and applications Statistical computing, Simulation and Monte Carlo methods, Bootstrap, Resampling methods, Spatial Statistics, Survival Analysis, Nonparametric and semiparametric methods, Asymptotics, Bayesian inference and Bayesian optimization Stochastic processes, Probability, Statistics and applications Statistical methods and modeling in life sciences including biomedical sciences, environmental sciences and agriculture Decision Theory, Time series analysis, High-dimensional multivariate integrals, statistical analysis in market, business, finance, insurance, economic and social science, etc Optimization methods and applications Linear and nonlinear optimization Stochastic optimization, Statistical optimization and Markov-chain etc. Game theory, Network optimization and combinatorial optimization Variational analysis, Convex optimization and nonsmooth optimization Global optimization and semidefinite programming Complementarity problems and variational inequalities Optimal control: theory and applications Operations research, Optimization and applications in management science and engineering Information computing and machine intelligence Machine learning, Statistical learning, Deep learning Artificial intelligence, Intelligence computation, Intelligent control and optimization Data mining, Data analysis, Cluster computing, Classification Pattern recognition, Computer vision Compressive sensing and sparse reconstruction Signal and image processing, Medical imaging and analysis, Inverse problem and imaging sciences Genetic algorithm, Natural language processing, Expert systems, Robotics, Information retrieval and computing Numerical analysis and algorithms with applications in computer science and engineering
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An improved hybrid nonlinear conjugate gradient method and application to image restoration problems
Optimization methods are widely used to obtain the numerical solution of the optimal control problems arising in scientific and engineering computation, especially for solving large-scale problems. In this paper, based on some modern and computationally efficient methods, a new conjugate gradient method ( named ICG method) is proposed for unconstrained optimization. Under the strong Wolfe line search (SWLS), the presented method is proven to be sufficient descent at each iteration. Moreover, we proved that the proposed method is globally convergent for arbitrary functions and the line search satisfies the strong Wolfe conditions. Numerical tests demonstrate the effectiveness of the ICG method when compared to certain existing methods in view of the Dolan and Mor´e performance profile. In particular, the practical application of this method in image restoration problems is explored.
Copyright (c) 2026 Mehamdia Abd Elhamid, Issam A. R. Moghrabi, Basim A. Hassan, Nivine Guler https://creativecommons.org/licenses/by/4.0
Joint Irrigation and Fertilization Scheduling in Multi-Crop Agricultural Systems Using the Gliding Snake Optimizer
Efficient irrigation and fertilization scheduling for multi-crop farmlands represents a critical challenge in modern precision agriculture, particularly under increasing water scarcity and food security pressures. This paper proposes the application of the Gliding Snake Optimizer (GSO), a recently introduced bio-inspired meta-heuristic algorithm, to solve the combined crop irrigation and fertilization scheduling problem. The proposed model encodes water volumes, nutrient dosages (N, P, K), and event timing into a unified decision vector and optimizes a composite objective function that simultaneously maximizes normalized crop yield while minimizing water cost, fertilizer expenditure, and phonological timing penalties. Seven agronomic and resource constraints are enforced via an adaptive penalty mechanism. Extensive experiments on a benchmark scenario comprising four representative crops (wheat, corn, tomato, olive) demonstrate that GSO achieves statistically superior performance compared to four established algorithms -- Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) -- across all evaluation metrics. GSO reduces water consumption by up to 23.4% and improves simulated yield by 18.7% relative to a conventional scheduling baseline. Wilcoxon signed-rank tests confirm that performance differences are statistically significant (p < 0.05) in 30 independent runs. The results demonstrate the strong suitability of GSO for constrained agricultural scheduling optimization problems.
Copyright (c) 2026 OLA Aljaafreh, Orowah Mahmoud Abd Al-Slaibi, Abbas A. Metawea, Islam S. Fathi https://creativecommons.org/licenses/by/4.0
A Hybrid COA-CNN Framework for Credit Card Fraud Detection Using Hyperparameter Optimization and Metaheuristic Feature Selection
Credit card fraud identification remains a difficult problem due to the enormous class imbalance and the evolving nature of fraudulent behavior. The traditional machine learning methods, including Support Vector Machines (SVMs), and even a basic deep learning model, are often sensitive to the parameters of the employed algorithms, are constrained by scaling issues, and highly dependent on data resampling. To address these drawbacks and apply the optimization-based learning, the present paper will introduce a new hybrid framework which will compare the Cuckoo Optimization Algorithm (COA) with the Convolutional Neural Network (CNN). The COA is an optimizer that is two-way, and may be deployed either as automated hyperparameter optimization or as metaheuristic feature selection such that the model may be very high discriminative without necessarily aggressive oversampling or intricate network structural forms. The provided strategy was trained strictly on the Kaggle credit card fraud detection data set and Precision, Recall, F1-score, and ROC-AUC metrics were used to evaluate the performance. As the experiment results indicate, the state-of-the-art ROC-AUC of the COA- CNN is 0.990 and the model is more balanced in regards to the detection and computational performance. This information shows that nature-inspired metaheuristic optimization is efficient in enhancing the resilience and scalability of financial security systems based on deep learning.
Copyright (c) 2026 Sarah Hassan Awad Al-taee, Ban Hamed Al-miyahi, Melad Jameel Hamoud, Waleed Amin Mahmoud Al-Jawher, Hayder Najm, Mohammed Salih Mahdi https://creativecommons.org/licenses/by/4.0
Discrete Odd Weibull-Lindley Distribution Featuring Adaptable Hazard Rate and Dispersion Index
This paper introduces the discrete odd Weibull--Lindley (DOWL) distribution, a flexible three-parameter model for analyzing complex count data frequently encountered in sustainable development research. The distribution is constructed by discretizing the continuous Lindley distribution within the discrete odd Weibull-G transformation framework. Its fundamental statistical and reliability properties are derived, including the identifiability of the mode and parameter effects, quantile function, hazard and reversed hazard rate functions, moments, information measures, residual and past lifetime functions, and equilibrium distribution. The DOWL distribution exhibits substantial flexibility, accommodating symmetric and asymmetric shapes, various kurtosis levels, and different dispersion patterns (over-, under-, and equi-dispersion). Its hazard rate function supports multiple aging behaviors, such as increasing, decreasing, bathtub, and unimodal shapes, making it suitable for diverse reliability and survival contexts. Parameter estimation is conducted via maximum likelihood, and extensive Monte Carlo simulations demonstrate satisfactory finite-sample performance of the estimators. The practical utility of the model is illustrated through three real data applications related to the United Nations Sustainable Development Goals: kidney cyst counts (SDG~3), European red mite infestation counts (SDGs~2 and~15), and daily COVID-19 mortality counts in Greece (SDG~3). Comparative analyses show that the DOWL distribution provides improved fit over competing discrete models, particularly for over-dispersed and highly skewed count data.
Copyright (c) 2026 Hend Shahen, Mahmoud El-Morshedy, Mohamed S. Eliwa https://creativecommons.org/licenses/by/4.0
A Fuzzy ARDL Model for Estimating the Dynamic Relationship between Government Revenues and Fiscal Balance
The relationship between government revenues and the fiscal balance represents a central pillar in the analysis of fiscal sustainability. However, its modeling faces a fundamental challenge in the form of structural uncertainty, which is not captured by point estimates in traditional models such as ARDL, as these models assume structural stability that is inconsistent with the nature of rentier economies. The current study aims to develop a fuzzy framework by constructing a Fuzzy Autoregressive Distributed Lag (FARDL) model. This is achieved through integrating the Autoregressive Distributed Lag (ARDL) approach with fuzzy logic theory, thereby enabling the incorporation of uncertainty into the inherent structure of the economic relationship rather than confining it to the stochastic error term. The fuzzy parameters are estimated using a Quadratic Programming (QP) algorithm, and the proposed model is empirically applied to quarterly data for the Iraqi economy over the period (2013-2025). The results provide evidence of a long-run equilibrium relationship between government revenues and the fiscal balance, alongside a persistent structural tendency toward fiscal deficits. Government revenues are shown to have a positive impact on the fiscal balance, however, this impact is uncertain due to revenue uncertainty and resource efficiency. The results of stability tests reveal the dynamic stability of the model, suggesting the existence of a self-equilibrating mechanism. Additionally, the difference between the ARDL and FARDL models shows a descriptive superiority of the fuzzy model in terms of forecasting performance (in-sample and out-sample) with statistically equivalent predictive efficiency. At the same time, the FARDL model offers a more flexible representation with the possibility of interval estimates and multiple scenarios, thus improving the analysis of fiscal sustainability under structural uncertainty.
Copyright (c) 2026 Sulaiman Hussien Jawad, Munaf Yousif Hmood https://creativecommons.org/licenses/by/4.0
A Robust Cubic Spline Approach for Hierarchical Regression Models with Outliers
This study is based on parameter estimation for hierarchical regression models in the presence of outliers. The robust cubic spline and the maximum likelihood estimation are compared. In order to test the efficiency of the two methods, Monte Carlo simulation experiments were conducted on sample sizes 15, 25, 40, and 60 at three different percentages of outliers 1%, 3%, and 5%. The assessment of performance was done by the absolute deviation error (ADE) and the coefficient of determination (R2). The outcome of this study showed that the robust cubic spline performed better than maximum likelihood estimation.
Copyright (c) 2026 Hayder Talib, Sarah Sabah Akram, Noor Abdul-Kareem Fayadh https://creativecommons.org/licenses/by/4.0
Trade-offs between Accuracy and Efficiency in Fake News Detection
The fast spread of fake news on digital platforms needs effective and scalable fake news identification technologies. While deep learning models are very accurate, their computational cost restricts their use in limited-resource scenarios. Using FakeNewsNet dataset, this study provides an evaluation of a lightweight TF-IDF and logistic regression framework against SVM, LSTM, and BERT baseline models across headline-only and full-text scenarios. Experimental results show that the lightweight TF-IDF+LR model achieves competitive accuracy (78.8% on GossipCop and 83.7% on PolitiFact) with dramatic efficiency gains, with 20,000 times faster training than BERT, 0.55 ms inference time, and much smaller memory usage than BERT. An error analysis demonstrates that lexical representations struggle to capture semantic and contextual nuances. It also highlights TF-IDF paired with logistic regression as an acceptable baseline for detecting fake news while outlining its performance limitations. Also, a practical decision matrix is provided for model selection based on the environment's primary constraints.
Copyright (c) 2026 Ahmed Abdelhafeez, Tareef S. Alkellezli, Moshira Ebrahim https://creativecommons.org/licenses/by/4.0
Robust tests for the Behrens-Fisher problem when the underlying distribution is short-tailed symmetric: An application to dopamine and schizophrenia data
In this study, the robust versions of the well-known Welch (W) and generalized p-value (GP) tests, i.e., robust Welch (RW) and robust generalized p-value (RGP) tests, are proposed for testing the equality of two means when the underlying distribution is short-tailed symmetric (STS) and variances are unknown and arbitrary. They are based on modified maximum likelihood (MML) estimators which have closed forms and are approximately equivalent to the maximum likelihood (ML) estimators. Under various scenarios, the proposed and existing tests are compared in terms of Type I error rates and powers via a Monte Carlo simulation study in R software program. Also, robustness of the proposed tests is investigated. Simulation results indicate that proposed tests perform as well as or better than the W and GP tests, while they satisfactorily control the Type I error rates. Moreover, RW and RGP tests are generally more robust to departures from the assumed model than their normal-theory counterparts. Finally, a real data set taken from psychology literature is used for illustrative purposes.
Copyright (c) 2026 Gamze Güven, Birdal Şenoğlu https://creativecommons.org/licenses/by/4.0
Employing A Wavelet To Predict Gold Prices Using Generalized Self-Regression Models Conditioned On Heterogeneity Of Variance
The importance of using self-regression models is conditional after smoothing the variance with the fluctuations of the daily closing price of gold globally for the period 1/1/2023 until 26/12/2024, including the GARCH-M(p,q) and TEGARCH(p, q)models, and diagnosing the models with the problem of heterogeneity of variation, estimating the parameters of the models used in the greatest possible way, examining the models using tests and statistical criteria to obtain the best models that represent real data, and then processing them using the Daubechies Wavelet and the Symlets Wavelet, and examining the suitability of forecasting models, it turned out that processing data with a wave gives better results than in real data, since a model with fewer parameters was obtained, which is the TGARCH model(1,1).
Copyright (c) 2026 Saif Ramzi Ahmed, Hutheyfa H. Taha , Heyam Hayawi https://creativecommons.org/licenses/by/4.0
On the Inclusive Local Irregularity Vertex Coloring of Centripetal Graph Families
A graph $G$ is an ordered pair of sets denoted by $G=(V,E)$ where $V(G)$ is the vertex set and $E(G)$ is the edge set. Graph coloring requires that all vertices be colored using as few colors as possible such that no two adjacent vertices share the same color. One extension of graph coloring is the inclusive local irregular vertex coloring, which is a coloring that also takes into account the label of each vertex itself. The number of distinct colors obtained is called the inclusive local irregular chromatic number, denoted by $\chi_{lis}^i(G)$. This research presents five new theorems related to inclusive local irregular vertex coloring of centripetal graph families, namely the octopus graph $(O_{n})$, sandat graph $(St_{n})$, butterfly graph $(BF_{m,n})$, tunjung graph $(Tj_{n})$, and the sunflower graph $(Sf_{n})$.
Copyright (c) 2026 Arika Indah Kristiana, Ridho Alfarisi, Hutkemri, Susanto, Rafiantika Megahnia Prihandini, Marcha Dwi Prihandini https://creativecommons.org/licenses/by/4.0
Quantifying the impact and recovery of South Africa’s manufacturing sales from the COVID-19 pandemic using a time series intervention analysis
The South African manufacturing sector, a cornerstone of the nation’s economy, was significantly affected by the COVID-19 pandemic. Significant disruptions in production networks and supply chains compelled manufacturing firms to adapt their operations. Although there is increasing interest in exploring the effects of the COVID-19 pandemic, quantitative analyses of its impact and recovery across various economic sectors remain limited. This study aims to quantify the pandemic's impact on South Africa’s manufacturing sector using time series intervention analysis. Intervention analysis plays a crucial role in understanding the effects of significant events on time series data, providing essential insights for policy decisions and strategic actions. An intervention function was incorporated into a SARIMA model. A SARIMA (0, 1, 1)(0, 1, 1)12 plus the COVID-19 pandemic intervention and innovative outlier (IO) fitted well to the monthly South African manufacturing total sales. The results indicated that the SARIMA intervention model effectively captured the seasonal patterns in South African manufacturing sales. The sector witnessed a significant decline of 63.99% in total sales in April 2020, immediately following the implementation of the economic lockdown due to the COVID-19 pandemic. The intervention’s impact was abrupt yet temporary, with the sector bouncing back within approximately fourteen months. This analysis highlights the importance of quantitative models, particularly SARIMA models with intervention functions, in comprehending and forecasting the effects of sudden disruptions on all sectors of the economy, including manufacturing. Policymakers and industry leaders may utilise these advanced approaches to foresee and alleviate the impacts of future economic shocks, facilitating informed decision-making and prompt responses.
Copyright (c) 2026 Tendai Makoni, Delson Chikobvu https://creativecommons.org/licenses/by/4.0
Boundary Control of Systems Governed by Semilinear Elliptic Equation for Infinite Order Operator with Finite Dimension
This paper studies a boundary optimal control problem governed by a semilinear elliptic equation involving an elliptic operator of infinite-order with a finite dimension. The state equation is defined on a bounded domain with a nonlinear boundary condition where the control variable acts on the boundary. The analysis is carried out within the framework of Sobolev spaces of infinite order. We first establish the existence and uniqueness of the solution to the state equation and define the associated control-to-state mapping. Under suitable assumptions on nonlinear boundary term, we give the differentiability properties of this mapping. Furthermore, we derive the first-order necessary optimality conditions for the optimal control problem through the associated adjoint system and a variational inequality. Finally, we analyze the second order derivatives of the reduced cost functional.
Copyright (c) 2026 Samira El-Tamimy, Basima Abdelhakim https://creativecommons.org/licenses/by/4.0
Tail Risk Modelling in Foreign Exchange Markets: Evidence from Botswana Pula, Bitcoin, and South African Rand
The volatility of an event often indicates the probability of it occurring within a designated timeframe. The aim of this paper was to assess the efficacy of the Generalised Extreme Value (GEV) and Generalised Pareto Distribution (GPD) models in accurately representing extreme quantiles of return risks for the BWP/USD, BTC/USD, and ZAR/USD exchange rates. The weekly maxima and minima from the return series were used to analyse tail behaviour. Thresholds for the GPD were established between the 90th and 99th percentiles through the analysis of mean residual life and parameter stability plots of potential thresholds. Parameters were estimated using maximum likelihood, and $95\%$ confidence intervals were utilised to evaluate significance. Model performance was assessed through information criteria (AIC and BIC) and goodness-of-fit tests, specifically the Kolmogorov–Smirnov, Cramér–von Mises, and Anderson–Darling tests. The findings demonstrate that BTC/USD shows relatively constrained extremes under the Generalised Pareto Distribution, while ZAR/USD and BWP/USD exhibit heavy-tailed characteristics, especially in the upper tails, indicating a heightened vulnerability to significant positive shocks. The GPD demonstrated a strong fit in all tests, establishing it as the preferred model for both minima and maxima of BWP/USD returns. The GPD effectively captures extreme quantiles for all three currencies, except BTC/USD maxima. The findings illustrate the importance of suitable model selection in risk management, especially concerning Value-at-Risk (VaR) and Expected Shortfall (ES). Future research may expand this analysis to include multivariate extremes and integrate regime-switching frameworks to improve the comprehension of dynamic exchange rate risks.
Copyright (c) 2026 Edwin Moyo, Dr. Katleho Makatjane, Prof. Ramasaymy Sivasamy https://creativecommons.org/licenses/by/4.0
The new heavy-tailed Weibull exponentiated half logistic-G family of distributions: Properties, actuarial measures and inference
Accurate statistical modeling of complex real-world data, characterized by heavy tails, skewness, and non-monotonic hazard rates, presents a significant challenge that often exceeds the capabilities of traditional distributions. To address this, we introduce the Heavy-Tailed Weibull Exponentiated Half Logistic-G (HT-W-EHL-G) family of distributions, a novel flexible framework that synthesizes extreme-value robustness with versatile hazard rate shapes. This paper derives the fundamental statistical properties of the proposed family and establishes six estimation methods, whose efficiency is verified via Monte Carlo simulation. The model's practical utility is demonstrated by its robustness to censored data, a critical requirement in survival and reliability analysis, and its direct applicability for computing key actuarial risk measures, including Value at Risk (VaR) and Tail Value at Risk (TVaR). Extensive empirical analyses across diverse domains confirm the model's efficacy and statistically significant superiority in goodness-of-fit over established benchmarks.
Copyright (c) 2026 Wilbert Nkomo, Broderick Oluyede, Thayaone Moakofi, Fastel Chipepa, Welington Fredrick Charumbira https://creativecommons.org/licenses/by/4.0
Modeling Pulmonary Tuberculosis Case Based on HIV and AIDS Cases in Indonesia Using Negative Binomial Regression Least Square Spline
The incidence of active pulmonary tuberculosis (TB) is on the rise, particularly among individuals co-infected with HIV and AIDS. This heightened susceptibility stems from the compromised immune systems in these populations, which increases the likelihood of TB bacteria activation. In Indonesia, TB remains a significant opportunistic infection among HIV/AIDS patients, complicating treatment efforts and frequently resulting in severe health outcomes, including mortality. Statistical analyses of TB incidence often reveal overdispersion, a phenomenon that can introduce bias into estimation results. To address this, a nonparametric negative binomial regression model utilizing the least square spline (NNBR-LSS) estimator was employed to model pulmonary tuberculosis incidence, considering the influence of HIV and AIDS case numbers. Data for this study were sourced from the 2023 Indonesian Health Profile, published by the Ministry of Health (Kemenkes). Model estimation results indicate a positive correlation: an increase in HIV and AIDS cases is associated with a corresponding increase in pulmonary tuberculosis cases. This relationship is characterized by a pseudo R-square value of 64.8%, suggesting a strong epidemiological link between the two diseases. The deviance test statistic for the NNBR-LSS model was calculated to be 19.525, which is lower than the deviance statistic obtained using a standard parametric regression approach (40.92087). This suggests that the NNBR-LSS model offers improved predictive accuracy compared to traditional parametric methods. These findings are valuable for informing targeted epidemiological interventions, strategic planning, and aligning public health initiatives.
Copyright (c) 2026 Statistics, Optimization & Information Computing https://creativecommons.org/licenses/by/4.0
Hybrid QLoRA-RAG Architecture for Saudi End-of-Service Benefits Calculation: Synthetic Data Generation and Uncertainty Quantification for Legal Reasoning
Deploying large language models for high-stakes domain-specific reasoning requires addressing challenges absent from standard benchmarks: handling incomplete information, quantifying uncertainty, and performing multi-step numerical calculations with authoritative source attribution. We present a hybrid architecture combining parameter-efficient fine-tuning via Quantized Low-Rank Adaptation (QLoRA) with Retrieval-Augmented Generation (RAG), evaluated on Saudi Arabia’s End-of-Service Benefits calculation—a legally binding financial computation involving 16 interacting legal provisions across 35 termination scenarios. Our contributions include: a comprehensive synthetic dataset of 10,000 samples systematically modeling real-world legal consultation complexities—incomplete information (15%), conflicting evidence (10%), legal interpretation ambiguities (5%), and adversarial examples (5%)—grounded in empirical distributions from 47,382 actual cases, 3,847 labor court disputes, and expert interviews (n=23); a hybrid architectural approach demonstrating that combining QLoRA fine-tuning (0.42% trainable parameters, 93.5% memory reduction) with retrieval-augmented generation yields complementary benefits, outperforming isolated components by 5.8–8.7 percentage points; and integrated uncertainty quantification mechanisms combining epistemic (MC Dropout), aleatoric (retrieval confidence, linguistic hedging), and calibration (temperature scaling) methods achieving Expected Calibration Error of 0.043 and 89.4% precision in detecting ambiguous cases requiring human review. Evaluation on 1,000 held-out synthetic test cases—stratified across six complexity tiers—shows 94.2% accuracy (±5% tolerance), 91.5% legal citation correctness, and graceful degradation across complexity tiers (98.7% standard cases → 82.0% adversarial examples). We note that all quantitative evaluation is conducted on synthetic data; real-world deployment validation remains an important next step. Human evaluation by five Saudi legal experts (inter-rater κ = 0.73) yields 4.4/5 overall rating with unanimous recommendation for pilot deployment. While our primary evaluation relies on synthetic data and focuses on a single legal calculation domain, the methodological framework—synthetic modeling of domain ambiguity, architectural patterns for parametric-retrieval integration, and uncertainty-aware human-AI collaboration—provides a transferable template for specialized reasoning tasks requiring numerical precision, source attribution, and confidence calibration. We discuss threats to external validity and outline concrete steps toward real-world validation.
Parameter Selection for a Neural Network Model for Forecasting Plant Pest Population Growth
The aim of the study was to optimise the parameters of a neural network model for forecasting plant pest population dynamics in agrocenoses with variable environmental characteristics. The methodology included weekly field data collection over five months at 18 sites in northern Kazakhstan, taking into account six pest species, environmental parameters, crop characteristics, and pest control schemes. Data cleaning, linear interpolation, normalisation, and one-hot encoding were applied. A multilayer perceptron (MLP) architecture with two hidden layers (32 and 16 neurons) and Rectified Linear Unit (ReLU) activation, trained using various configurations, was used as the baseline. The results showed that the highest values of the determination coefficient (R²>0.82) were achieved when modelling Helicoverpa armigera and Frankliniella occidentalis, while the poorest performance was observed for Loxostege sticticalis (R²≈0.64). In plots with tomatoes and sunflowers, forecast accuracy was higher, which was associated with stable irrigation conditions and protective measures. The average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values were 4.36 and 3.52, respectively. The optimal configuration was identified as a two-layer model (64 and 32 neurons) with ReLU activation and the Adaptive Moment Estimation (ADAM) optimiser, yielding MAE=2.9 and R²=0.83. Deeper models led to overfitting, whereas simplified ones resulted in insufficient accuracy. Statistical tests confirmed the significance of differences between configurations (p<0.001). Cross-validation and sliding window analysis demonstrated the model’s high robustness to spatiotemporal variability, with minimal accuracy degradation when transferred across regions and seasons. The results demonstrate the high practical value of the developed approach for integration into pest monitoring and forecasting systems in agricultural practice.
Tree Decompositions in Fuzzy Graphs: Foundations and Structural Insights
A Fuzzy Graph extends classical graph theory by incorporating uncertainty, assigning a membership degree to each edge. Tree-width is a basic metric that assesses how much a graph resembles a tree [31, 32], making it an essential tool in algorithm design and combinatorial optimization. Path-width quantifies how much a graph resembles a path, using smallest bag size minus one in path-decomposition of the graph. The main aim of this work is to present the Fuzzy Tree-Decomposition and Fuzzy Path-Decomposition, extending the classical notions of tree- and path-decompositions to the realm of fuzzy graphs. We anticipate that these novel ideas will expand the possible uses of fuzzy graphs and encourage more investigation into the mathematical structure of graph-width parameters.
A Hybrid Support Vector Machine–Genetic Algorithm Framework for Estimating the DUS-Transformed Generalized Polynomial Quadratic Failure Rate Distribution
Probability distributions are essential statistical instruments, which make it possible to model and analyze some probability events in various spheres, including engineering, medicine, finance, and environmental science. Classical distributions, however, have been observed to have constraints in describing the complexity of real-life data. This paper will discuss these shortcomings by proposing the DUS-transformed Generalized Polynomial Quadratic Failure Rate (DUS-GPQF) distribution, which is a new extension of the generalized linear failure rate (GLF) distribution via DUS transformation method. The DUS-GPQF distribution increases the flexibility in the GLF model which provides it with greater ability to support a larger spectrum of data behaviors. The DUS-GPQF distribution has important statistical properties which include the hazard rate functional, moments, incomplete moments, entropy and the extropy. The DUS-GPQF distribution has seventeen estimation methods, which guarantee that it is practical to apply. The tests on the DUS-GPQF distribution are performed on two real-world data sets, and the results show that the model is better than other competitive models in goodness of fit and predictive accuracy. The study offers a powerful statistical model to a complex data so as to improve theoretical as well as practical statistical analysis.
Integration of Smart Technologies into Architecture of Residential Buildings: Challenges and Prospects
Abstract. The rapid advancement of digital technologies and the growing demand for energy efficiency in the residential construction sector create the need to develop a theoretical framework for the integration of smart technologies into building solutions. The study aims to establish an optimal balance between technological capabilities, architectural solutions and user needs, which will provide a methodological basis for the application of smart technologies in residential construction. The study is based on a systematic analysis of scientific literature in the field of construction for the period 2020-2024 using the methods of comparative and morphological analysis. A structured model of interaction between building and technical systems is developed, considering multi-level control architectures and ensuring a balance between automation and manual control. A comprehensive methodology for assessing the effectiveness of integration is proposed, which covers technical, architectural and social aspects, including criteria for assessing the technical readiness of buildings. The synergistic effects of scaling up solutions at the level of public housing through optimisation of energy flows between buildings are identified. A system of criteria has been created to assess the effectiveness of integration at different stages of the building life cycle, considering both technical capabilities and user needs. Recommendations for the design of technical infrastructure and control systems have been developed to ensure the flexibility and adaptability of smart systems. The results of the study are of practical importance for architects and engineers in the design and modernisation of both new and existing residential buildings, as they provide a systematic approach to the implementation of smart technologies. The theoretical provisions are the basis for the further development of adaptive design methods and standardisation of the process of integrating smart technologies into housing construction.
Solution of Implicit Fractional Differential Equation in the Setting of New Perturbed Spaces
Distance error between two points, which arises from various causes directly impact measurement accuracy. Although these errors may seem small individually, their cumulative effect can lead to significant discrepancies. Considering this, we introduce the concept of perturbed $S$-metric spaces and present a noteworthy generalization of fixed points theorems in this context. Also, we prove existence and uniqueness of a solution for a deformable fractional order implicit differential equation.
A Tokenized Sovereign Debt Conversion Mechanism for Dynamic Public Debt Reduction
In this paper, we present the Tokenized Sovereign Debt Conversion Mechanism (TSDCM), a smart-contracted instrument that, upon meeting both debt-to-GDP and GDP-growth thresholds, automates the retirement of sovereign debt. TSDCM initiates the conversion of a portion of outstanding bonds into performance-linked tokens by integrating a two-state regime-switching jump-diffusion framework into decentralized protocols. We prove finite-time activation and expected debt reduction through new propositions, establish the existence and uniqueness of the underlying stochastic processes, and introduce a main theorem that ensures a strict decline in expected debt levels. With significant tail-risk mitigation, calibration using IMF data and MATLAB Monte Carlo simulations shows a 20–25\% decrease in expected debt-to-GDP ratios over a ten-year period. A transparent and incentive-aligned route to sustainable sovereign debt management is provided by TSDCM.
A certain algebraic structure of bipolar neutrosophic subgroups
The notion of a bipolar neutrosophic set (BNS) was created as an expansion of a neutrosophic set when every single-value neutrosophic membership function has two poles (a positive pole and a negative pole). The BNS environment is an innovative tool for addressing ambiguous situations in several decision-making problems, especially in situations where the human mind perceives two patterns of thinking: positive and negative. In this study, we apply this idea in an algebraic environment when we initiate the novel concept of bipolar neutrosophic subgroups and prove that every BN subgroup generates two BN subgroups. We explain several ideas based on this environment, such as the level set, support, kernel for BNS, BN characteristic function, and BN-point. Then, we illuminate the BN-subgroup, BN-normal subgroup, BSVNconjugate, normalizer for BN-subgroup, BN-abelian subgroup, and BN -factor group. Furthermore, we present the linked theorems and examples and prove these theorems. Moreover, we discussed the image and pre-image of BN-subgroups under homomorphism and proved the related theorems.Continuing our application of algebraic concepts to BNS, we will present a new mathematical framework for the concept of BN-matrecs and some related mathematical properties. In addition, we will provide a practical application of these tools in dealing with a decision-making (DM) problem. Finally, in the conclusion section, we recommended that these results be a qualitative addition to the fuzzy algebraic environment and that they open up research doors for other future research work that takes advantage of the bipolar environment.
Explainable Decision Support System for Lung Cancer Diagnosis from CT Images Using Hybrid AI Models
Lung cancer is one of the main causes of death around the world, so early detection is crucial. Diagnosing small lung nodules can take a lot of time and require skilled radiologists. This research presents an AI-based system for classifying lung cancer using CT images, focusing on its performance and ease of understanding. The study combines carefully crafted features—including texture, shape, intensity, and wavelet descriptors—with deep features from various deep learning models, such as CNNs, CoAtNet, and EfficientNet. It highlights the most important attributes through feature selection and fusion strategies. Different machine learning and hybrid deep learning models were tested. In the end, the best-performing model with explainable AI was combined to give radiologists insights into how the model makes decisions. Experiments were conducted on 30,020 CT images from the BOWL2017 dataset, comprising 790 patients. Up to 98% accuracy was noted, with 98.4% precision and 98.0% recall. Radiologists confirmed that the model's attention maps match clinically relevant patterns, which improve efficiency. This framework demonstrates how interpretable AI can improve CT-based lung image classification and support radiologists in their decision-making.
Intelligent and Bayesian Models for Estimating the Transition Probability Matrix of Markov chains: Applications to the Analysis of Sustainable Development Indicators
In discrete-time Markov chains, the correct estimation of the transition probability matrix is important in defining the dynamics of the system. But classical estimators may become erratic when the data that is available is sparse or irregular. The study will discuss four ways of estimating the transition matrix, including the Maximum Likelihood Estimator (MLE), Bayesian-Dirichlet estimator, the shrinkage estimator, and an intelligent optimization-based estimator built on the Artificial Bee Colony (ABC) algorithm. The developed methodology is implemented to the real-life data associated with the Sustainable Development Goal 4 (Quality Education) i.e. the annual literacy level of adults (percentage of the population aged 15 and above) in the course of a long-term. Three measures were used to ensure a good comparison between the estimators: the total variance of the transition matrix, the spectral gap and the mixing time. As suggested by the experimental results MLE is most fluctuating and slowest to converge especially when the transition structures are weak. The Bayesian-Dirichlet estimator has significant benefits in the form of probabilistic smoothing alone, whilst the shrinkage estimator has additional benefits in the form of a reduction in the variance and a faster convergence rate at the expense of empirical information and structured targets. Among the estimators we have experimented with, the ABC-based estimator was the best in terms of the smallest total variance, largest (lazy) spectral-gap proxy and shortest mixing time. These findings imply that smart optimization is a good alternative on estimating the well-conditioned transition matrices in irregular and complicated socio-educational time series.
Strategies for Predicting and Managing Life Credit Insurance Claims Using Machine Learning Models
The rising severity of credit life insurance claims makes it necessary to develop new methods for managing claims effectively. Machine Learning (ML) offers a powerful response to this challenge. Since improving customer service is a central objective for insurers, companies have increasingly turned to ML techniques to better understand and assess their data. This paper makes a scientific contribution to life credit insurance pricing by modelling the total claim amount using ML algorithms such as Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP). A comparative analysis is carried out using statistical performance measures (e.g., Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), etc.) on both training and testing datasets. The results indicate that the Multi-Layer perceptron model delivers excellent predictive accuracy and outperforms the other models according to the Taylor diagram. However, its visual distribution of predictions is less satisfactory than that of the XGBoost and MLP models. Overall, the main value of this study lies in the in-depth analysis of the dataset, which provides insurers with meaningful insights to support more effective loss management.
The Earthquake Prediction Using Machine Learning Algorithms
Earthquakes are among the most destructive natural phenomena, occurring suddenly and often causing severe human and infrastructural losses. Accurate and timely prediction of significant seismic events remains a major challenge in seismology. This study presents a systematic machine-learning-based framework for earthquake prediction using historical seismic data. A three-phase methodology is adopted: (i) data acquisition and preprocessing of seismic records obtained from the United States Geological Survey (USGS), (ii) class balancing and feature preparation to address extreme class imbalance inherent in earthquake datasets, and (iii) model development and evaluation using multiple supervised learning algorithms. Binary classification was first employed to distinguish significant from non-significant earthquakes, followed by multi-class classification to categorize events into Minor, Light, Moderate, and Strong classes. Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Neural Network models were evaluated. After balancing, the binary dataset achieved a 50–50 class distribution, while the multi-class dataset was uniformly balanced across all four classes. Experimental results demonstrate that the Random Forest model achieved the highest binary classification accuracy of 98.15%, with an Area Under the Curve (AUC) of 0.683, indicating strong discriminative capability. The Neural Network model achieved the highest recall (0.611), making it suitable for early warning scenarios where missed detections are critical. For multi-class classification, Random Forest achieved the highest overall accuracy of 87.78%, outperforming other models in robustness and stability. These results confirm the effectiveness of ensemble-based learning for seismic event prediction and highlight the trade-off between accuracy and sensitivity across different algorithms. The proposed framework provides a reliable foundation for earthquake early-warning systems in high-risk regions.
Addressing Question Repetition in Academic Assessments: An Empirical Study and Generative AI-Based Solution
Frequent reuse of exam questions harms the integrity of assessments and pushes students towards learning by automatism rather than understanding. It is a fundamental problem, yet the scientific literature has paid little attention to it so far. Our research addresses precisely this lack. Initially, we measured the concrete impact of this phenomenon through a survey among 194 actors from the academic community. Faced with this observation, we have developed an automatic question generation system based on the fine-tuned Transformers, in order to ensure a continuous renewal of the proposed topics. The results of the survey confirm the relevance of this approach: 80.4% of respondents state that they regularly encounter questions already seen, a situation that 63.4% of them consider penalising. In addition, 72.7% of participants advocate for an accelerated renewal of evaluation content. To meet this identified need, we designed an automatic question generation (AQG) architecture based on transformers, by fine-tuning the T5-small, T5-base and BART models on the SQuAD dataset. The comparative evaluation, supported by the BLEU, ROUGE and METEOR metrics as well as a multi-domain qualitative semantic analysis, established the constant superiority of the T5-based model over the other approaches (BLEU = 0.1765; ROUGE-1 = 0.5317; METEOR = 0.4085). These findings empirically validate the urgency of renewing assessments and demonstrate the effectiveness of transformer-based systems in ensuring diversity of tests, while easing teachers’ workload. This study thus establishes the pedagogical necessity, as well as the technical feasibility, of an AI-assisted generation of questions in the service of educational equity.
Performance of Bayesian Kriging under Different Levels of Spatial Autocorrelation: A Simulation Study of Rainfall Interpolation
This study investigates the influence of spatial autocorrelation on the performance of Bayesian kriging for interpolating daily rainfall in East Java, Indonesia, using both empirical observation data and controlled simulation experiments. Daily rainfall records from multiple monitoring stations were analyzed to assess spatial dependence via Global Moran’s I, revealing that the onset phase of the rainy season exhibits moderate to strong positive spatial autocorrelation, whereas the peak phase shows weak or negligible spatial structure dominated by local-scale variability. Cross-validation results indicate that stronger spatial autocorrelation significantly improves Bayesian kriging performance, as evidenced by lower RMSE and MAE. Simulation results further corroborate that Bayesian kriging yields substantial predictive gains when Moran’s I exceeds approximately 0.30–0.40, while predictive improvement is marginal when Moran’s I falls below 0.15–0.20. Under weak spatial dependence, the model effectively reduces to a nugget-driven process with limited spatial continuity. These findings underscore that the effectiveness of Bayesian kriging for daily rainfall interpolation is fundamentally contingent upon the magnitude of spatial autocorrelation, highlighting the necessity of preliminary spatial diagnostics to guide appropriate methodological choices in geostatistical applications.
Rismania Hartanti Putri Yulianing Damayanti
Stability Study of Exponential Smooth Transition Double Autoregressive Model with Application
In this research, we propose a novel non-linear time series model referred to as the Exponential Smooth Transition Double Autoregressive Model of order p denoted as EXPSTDAR (p). This model is predicated on the double autoregressive framework and incorporates an exponential function. Through the application of a local linearization technique, we derive a stability condition for a non-zero singular point of the model. And applying these conditions to the acquired models by modeling real data that represents a weekly average closing price of Iron ore powder in dollars for the period from October 2010 to May 2025 for several suggested orders of the model.
Statistical Inference on Accelerated Odd Fréchet Half-Logistic Distribution under Progressive Type-II Adaptive Hybrid Censoring with Application to Dielectric Circuits Using Binomial Removals
This research presents a statistical work, where the sample is under a progressive stress-accelerated life test (PSALT) generated from the odd Fréchet half-logistic distribution (OFHLD) under adaptive progressive type-II hybrid censored (AP-II-HC) samples. The cumulative exposure model is applied to generate incremental stress samples. Both classical and Bayesian methodologies are used to estimate the unknown parameters of the distribution. Furthermore, the reliability function of the OFHLD is also calculated. The Metropolis-Hastings( MH) algorithm is applied to generate samples from the distribution. Moreover, the asymptotic and bootstrap confidence intervals (CIs) are constructed. A real data set is analyzed to illustrate the methodologies suggested in this study. Finally, some intriguing conclusions are noted and associated with future work suggestions.
Copyright (c) 2026 Ehab M. Almetwally, Samirah Alzubaidi, Taher Sobh, Eslam Hossam https://creativecommons.org/licenses/by/4.0
Understanding Factors Influencing Customers’ Intention to Use Digital insurance platforms: evidence from Saudi Arabia
Abstract This study explores the key influences on Saudi insurance company customers' behavioural intent to use digital insurance platforms. The present study employs a theoretical model integrating both the (UTAUT2) theory and the adapted D&M(ISS) model. To achieve the study objectives, an electronic questionnaire was implemented and disseminated to a purposively selected cohort of customers belonging to Saudi insurance companies. A final total of 353 valid questionnaires were analysed using SmartPLS (v.4.1.1.5) software, which uses a structural equation model (SEM) to analyse large amounts of data.The findings indicated a statistically significant positive impact on behavioural intention for all structural pathways, with the exception of those relating to information and service quality, where an insignificant effect on usage intent was observed. Furthermore, the explanatory variables (PE, EE, IQ, SI, TRU, SEQ, and SQ) accounted for 59% of this variation, thereby demonstrating their capacity to influence behavioural intention with regard to platform utilisation, and consequently, the utilisation of actual services. The study's findings indicate that Saudi insurance companies should address the shortcomings in their digital platforms and services. These shortcomings primarily concern the responsiveness, reliability, and personalisation of these services. In order to address these issues, the insurance companies should develop data security protocols. The implementation of such protocols would enhance trust and transparency. The present study makes a significant academic contribution by providing companies with a practical approach for improving digital platform efficiency and fostering positive customer intent to use digital insurance services.
Copyright (c) 2026 Nourelhuda Abdelrhaman https://creativecommons.org/licenses/by/4.0
Adaptive Generative Bootstrap: A Robust Inference Method for Small and Multimodal Samples
The standard nonparametric bootstrap performs reliably across many settings, but small samples n<30 and multimodal data tend expose its limitations under-coverage and discreetness artifacts are the most commonly reported problems. We developed an Adaptive Generative Bootstrap (AGB) method to directly address these gaps. Rather than resampling from the observed data, generative sampling is drawn from a variable-bandwidth kernel density estimate (KDE), with bandwidths adapted locally through Abramson's square root scaling. That adaptation carries a concrete theoretical payoff: under the regularity conditions set out in Section 2 and Appendix A, the leading pointwise bias of the adaptive KDE is reduced from \(O\left(h^2\right)\) to \(O\left(h^4\right)\). It should be noted, however, that this result pertains to the density estimator itself, not to bootstrap coverage properties directly. For that reason, the finite-sample inferential behavior of AGB is assessed empirically through Monte Carlo simulations and benchmark examples, with coverage probability, interval length, bias, and RMSE serving as the primary performance criteria.
Copyright (c) 2026 Saifldeen Alrefaee https://creativecommons.org/licenses/by/4.0
Developing an Adaptive Learning System for Agricultural Extension Using Painting Training-Based Optimization
Agricultural extension services face significant challenges delivering effective training to heterogeneous farming populations with diverse knowledge levels and resource constraints. Traditional uniform training approaches result in inefficiencies where experienced farmers encounter redundant content while novice farmers struggle with excessive complexity. This research develops an adaptive learning system for agricultural extension using Painting Training-Based Optimization (PTBO), a human-inspired metaheuristic algorithm. A multi-objective optimization framework was formulated incorporating knowledge gain maximization, time efficiency, sequence validity, difficulty appropriateness, and knowledge coverage, subject to time, budget, and prerequisite and essential knowledge constraints. A quasi-experimental study with 75 wheat farmers in Irbid Governorate, Jordan (2024-2025), randomly assigned participants to PTBO-personalized (n=25), GA-personalized (n=25), and traditional-uniform (n=25) groups. PTBO demonstrated superior performance: 15.3% improvement in knowledge gain over GA (32.4 vs. 28.1 points), 29.9% faster convergence (87.3 vs. 124.6 iterations), 96.2% knowledge retention at four-week follow-up, and 80.0% practical adoption versus 69.7% for traditional methods. Novice farmers achieved normalized learning gains of 0.76 compared to 0.68 (GA) and 0.66 (traditional). The research provides a deployable framework demonstrating that metaheuristic optimization effectively addresses agricultural knowledge dissemination challenges while maintaining computational efficiency for resource-constrained contexts.
Hybrid SAR-Optical Remote Sensing for Flood Inundation Mapping: Feature Contribution Analysis in a Wetland Environment
Flooding is a recurrent problem in Banjar Regency due to its low-lying wetland topography, high rainfall, and river sedimentation. Nonetheless, precise flood mapping continues to be a challenge due to the interference of cloud cover on optical imaging and spectral ambiguity in diverse wetland ecosystems. To address these limitations, this study proposes a hybrid SAR–optical method that combines Sentinel-1 backscatter with Sentinel-2 water indices (FWEI and AWEI) using pixel-level feature stacking and feature contribution analysis. Four machine learning classifiers—Random Forest, Logistic Regression, Support Vector Machine (SVM), and XGBoost—were evaluated using a spatially independent validation scheme to ensure robust generalization. The results indicate that AWEI surpasses individual features in water discrimination, whereas the combination of SAR and AWEI consistently enhances classification stability and spatial coherence. Among the assessed models, SVM and RF exhibit similar performance, with SVM attaining the optimal classification balance (OA = 0.98, Kappa = 0.97, F1 = 0.98) for water detection, while the difference lacks statistical significance. Flood inundation maps were derived through change detection between independently classified pre- and post-flood water maps. The results indicate that multisensor integration enhances the delineation of flood-affected areas, particularly in complex wetland environments. However, the study also highlights that the reliability of flood inundation mapping is inherently dependent on the accuracy of pre- and post-flood water classification as well as the temporal consistency of the input imagery. Furthermore, feature contribution analysis combined with spatial validation reveals that AWEI provided the strongest standalone classification performance, while SAR contributed complementary structural information that improved classification robustness. These findings demonstrate that spatial validation effectively mitigates autocorrelation bias, leading to more reliable and generalizable water classification performance for flood inundation analysis.
Copyright (c) 2026 Desy Ika Puspitasari, Edi Noersasongko, Purwanto, Moch Arief Soeleman, Catur Supriyanto https://creativecommons.org/licenses/by/4.0
Variable-Order Fractional Differential Equations: Existence, Stability, and Application to 3D Noise Evolution
This paper studies a boundary value (BV) problem characterized by variable order(VO) Caputo fractional derivatives to study the existence, uniqueness, and stability of solutions under well-defined boundary conditions. Fixed point theory is used, where the Banach contraction principle ensures the uniqueness of solutions, while the Krasnoselskii theorem confirms their existence. Furthermore, the notion of Ulam-Hyers stability is used to investigate the response of the solutions to small perturbations. Numerical examples are presented to illustrate the theoretical results and to validate the approach under practical conditions. Additionally, an application concerning the evolution of features in three - dimensional noise fields is included. The highlights its engineering relevance, particularly in image processing and signal analysis, where modeling noise behavior and memory effects is important for tasks such as denoising and feature extraction
Copyright (c) 2026 Nidal Anakira, Jaya Priya Dhanasekar, Tharmalingam Gunasekar, Osama Ogilat, mani Sheimat https://creativecommons.org/licenses/by/4.0
Novel Quantile Regression Model Using Extended Exponential-Geometric Distribution with Real Data Application
In this research, we propose a quantile regression framework that accounts for the nature of the dependent variables that are restricted to the interval between 0 and 1, specifically through the utilization of the Unit Extended Exponential, Geometric (UEEG) distribution. The method is capable of capturing the effects of covariates at different quantiles of the distribution, thus enabling a more comprehensive representation of the changes in behavior across the entire range. We analyze the distribution characteristics of the model and recommend maximum likelihood parameter estimation. A simulation experiment is performed to test the properties of the proposed estimators at finite samples with respect to bias and MSE. The methodology is demonstrated through the application to a real data set in order to showcase its practical utility. The new framework is compared against the analogous ones based on Unit Exponential, Unit Extended Exponential, and Unit Lindley distributions using the Akaike information criterion (AIC), Bayesian information criterion (BIC), and HannanQuinn information criterion (HQIC). The findings suggest that not only does the new model fit the data better, but it is also less biased and has a lower MSE, therefore, providing more flexibility for data restricted to a unit interval.
Copyright (c) 2026 Amal Sadeq Hamoodi, Ahmed Mahdi Salih https://creativecommons.org/licenses/by/4.0
Q-Learning Driven Artificial Bee Colony Algorithm for Solving the Weapon-Target Assignment Problem
The Weapon-Target Assignment (WTA) problem is a critical, NP-complete combinatorial optimization problem in military operations research. It aims to allocate defense weapons to incoming targets to minimize the total expected damage to protected assets. This study proposes a Q-learning-driven artificial bee colony (QABC) algorithm that incorporates an adaptive operator selection mechanism based on Q-learning into the standard artificial bee colony (ABC) algorithm. Five permutation-based neighborhood operators are employed to balance exploration and exploitation during the search. We evaluated the proposed algorithm on twelve benchmark WTA instances with problem sizes ranging from five to 200 weapons and targets across 30 independent runs. The experimental results demonstrate that the QABC algorithm consistently outperforms the state-of-the-art method on nine of the twelve instances, achieving substantially lower cost values and standard deviations, especially for large-scale problems.
Copyright (c) 2026 Kadir Yıldız, Emrullah Sonuç https://creativecommons.org/licenses/by/4.0
Restricted Bayesian Inference for the Misspecified Random Repeated Measurements Model
This article presents a new technique for Bayesian inference in the random repeated measurements model. The fundamental idea underlying this work is the application of Bayesian inference to estimate the model parameters of interest. We then focus on theoretical results that allow the incorporation of linear constraints into the Bayesian estimator under model misspecification. It is essential to investigate the mathematical properties of the parameters; accordingly, this article also examines the asymptotic properties of the restricted Bayesian estimator for the misspecified repeated measurements model. It is shown that the Bayesian estimator of the second component of the parameter vector, under an underfitted model, is weakly consistent provided that certain conditions are satisfied. Moreover, in the presence of linear constraints, overfitting reduces the asymptotic efficiency of the Bayesian estimator of the second component of the parameter vector under certain conditions.
Copyright (c) 2026 Ameera J. Mohaisen, Abdul-Hussein S. AL-Mouel, Ali Hasan Ali https://creativecommons.org/licenses/by/4.0
Cluster Analysis of Traffic Accident Patterns in Iraq: An Exploratory Statistical Study
Traffic accidents are considered one of the serious societal phenomena that are no less impactful than security threats such as terrorism, due to the significant losses they cause in lives and property, as well as the depletion of economic and human resources. This phenomenon has become a real challenge to sustainable development efforts, necessitating its analysis using advanced statistical methods to understand its dimensions, identify its patterns, and determine the factors influencing it. Based on this importance, this research examines recorded traffic accidents in Iraqi provinces, according to the Central Statistical Organization's data for 2024, in coordination with the Ministry of Interior. Cluster analysis was employed using the statistical software (SPSS Version 23) to explore the structural homogeneity among the provinces based on a set of relevant qualitative and quantitative variables. The researcher used three methods in the practical aspect: (1- Single linkage method, 2- Complete linkage method, 3- Ward's method). These methods were chosen because they yield less stringent clusters, especially Ward's method, which reduces variance when merging groups. The analysis of accidents was based on the nature of the accident (collision, overturning, run-over) as it is more accurate and available comprehensively for all provinces, whereas choosing fatalities or injuries might be linked to other reasons outside the current analysis scope or the type of road, which lacks temporal standardization or spatial accuracy in official records. The analysis was conducted with the aim of classifying the provinces into homogeneous clusters that reveal statistical variations in the pattern of accidents between different regions, thereby enabling the possibility of providing realistic recommendations based on scientific foundations to reduce these accidents or mitigate their impacts. The results of the analysis demonstrate the potential of exploratory statistics in supporting decision-makers and providing quantitative insights that help understand the dynamics of traffic accidents and guide preventive policies based on accurate and reliable data.
Copyright (c) 2026 Hayder Majeed Hammadi https://creativecommons.org/licenses/by/4.0
Multivariate Hourly Air Quality Forecasting with MES-LSTM as the Core Framework
Accurate air quality forecasting plays an important role in supporting environmental management, protecting public health, and enabling timely early-warning interventions. However, many existing forecasting approaches rely on univariate modeling and may not adequately capture interactions among multiple pollutants. This study therefore explores multivariate hourly air quality forecasting using Multivariate Exponential Smoothing--Long Short-Term Memory (MES-LSTM) as the primary forecasting framework. Experiments were conducted using the Beijing Air Quality dataset to predict carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO$_2$), ozone (O$_3$), and sulfur dioxide (SO$_2$). A standardized evaluation protocol was adopted for all models, including identical preprocessing procedures, chronological data partitioning, a 24-hour lookback window, and a one-hour forecasting horizon. The proposed MES-LSTM model was evaluated against Long Short-Term Memory (LSTM), Temporal Fusion Transformer (TFT), and a hybrid MES-LSTM--TFT architecture. The results show that MES-LSTM achieved the lowest forecasting errors for several pollutants, particularly CO, NO$_2$, and O$_3$, while remaining competitive for NO and SO$_2$. In contrast, the hybrid MES-LSTM--TFT model did not improve forecasting performance and generally produced larger prediction errors than the corresponding single-stage models. Additional robustness analyses, including five-seed experiments, persistence baselines, and multivariate coupling ablations, indicate that the effectiveness of pollutant interaction modeling depends on the target pollutant being predicted. Overall, the findings suggest that combining exponential smoothing with residual learning provides an effective and robust solution for short-term multivariate air quality forecasting. The study further demonstrates that MES-LSTM provides a strong and competitive forecasting framework under a unified evaluation protocol, while increased architectural complexity does not necessarily lead to improved predictive performance. Additional robustness analyses, including five-seed experiments, persistence baselines, and coupling ablations, were conducted to assess stability and reproducibility. Computational cost was not formally benchmarked and remains an avenue for future investigation.
Copyright (c) 2026 Lilis Anggraini, Edi Noersasongko, Aris Marjuni, Purwanto https://creativecommons.org/licenses/by/4.0
Graph Optimization and Programming through Interval Reduction: Case for Secure Control Flow Graph Analysis
Control Flow Graphs (CFGs) provide a central representation for reasoning about program structure, but their increasing size can limit both manual interpretation and automated analysis. This paper revisits interval-based reduction as a structurally grounded approach to CFG simplification. Based on classical notions of dominance, intervals, and reducibility, we reformulate the reduction process in an explicit algorithmic form and study the structural properties preserved by the resulting derived graphs. The proposed approach is implemented and evaluated on a diverse collection of CFGs, with comparison to Sequential Node Merging as a baseline reduction technique. The results indicate that interval reduction offers a principled way to reduce graph complexity while maintaining the hierarchical organization of control flow, thereby supporting its use in modern program analysis settings, including software security and reliability. Our results guarantee a formal basis for future malware-detection frameworks built upon interval-reduced control-flow graphs.
Copyright (c) 2026 Meryem HAFID, Seddik ABDELALIM, Driss KARIM, Ilias ELMOUKI https://creativecommons.org/licenses/by/4.0
Grundy Chromatic Number of Shuriken Graphs and Applications to Regulatory Network Modeling
The concept of Grundy coloring plays an important role in the study of graph coloring and its applications in sequential allocation problems. In this paper, we study the Grundy (first-fit) chromatic number of the Shuriken graph $Sh_n$ and some of its associated derived graphs obtained through standard graph operations. In particular, we determine the Grundy chromatic numbers of the line graph $L(Sh_n)$, the middle graph $M(Sh_n)$, and the total graph $T(Sh_n)$. The results are obtained by utilizing the structural properties of the Shuriken graph together with the principles of Grundy coloring and suitable upper bound techniques. In addition, we discuss the relevance of these graphs in the framework of Gene Regulatory Networks (GRNs), where the Grundy chromatic number may represent the maximum level of hierarchical activations arising in sequential regulatory interactions. Thus, the Shuriken graph and its related derived graphs provide meaningful mathematical models for studying interaction complexity and stability in biological systems.
Copyright (c) 2026 M. Kamalnath, Muthukani Vairavel T https://creativecommons.org/licenses/by/4.0
Multiset dimension of kayak paddles graph and cycles with chord
Suppose the set $W=\{s_1, s_2,\dots, s_k \}$ is a subset of the vertex set $V(G)$. The representation of a vertex $v$ of $G$ with respect to $W$ as follows \[r_m(v|W)=\{d(v,s_1), d(v,s_2),\dots, d(v,s_k)\}\] where $d(v,s_i)$ is the distance between the vertex $v$ with the vertices of set $W$ together with their multiplicities. The set $W$ is called the {\it m-resolving set} of $G$ if every vertices of $G$ have distinct representation with respect to $W$. If $G$ has an m-resolving set, then an m-resolving set having minimum cardinality is called a multiset basis and its cardinality is called the multiset dimension of $G$, denoted by $md(G)$. We say that $G$ has an infinite multiset dimension and we write $md(G)=\infty$. In this paper, we determine the multiset dimension of kayak paddles graph and cycles with chord.
Copyright (c) 2026 Ridho Alfarisi, Arika Indah Kristiana https://creativecommons.org/licenses/by/4.0
Dynamic and Persistence of the Heteroclinic orbit for the model of the cantilever beam
We study the dynamics for a nonlinear model of a cantilever beam, the dynamics of the system are governed by a quasilinear equation. We analyze the existence of a pitchfork bifurcation on the conservative case with the presence of heteroclinic orbit, then we study the persistence of heteroclinic orbits when the system is subjected to a periodic perturbation of the form $f(t) = \gamma cos(wt)$.
Copyright (c) 2026 Andres Felipe Camelo Munoz, Diego Alexander Castro Guevara, Guillermo Villa Mart https://creativecommons.org/licenses/by/4.0
Advancing Agricultural Yield Forecasting through Geomagnetic Navigation Algorithm-Optimized Machine Learning
Accurate crop yield prediction is essential for food security and resource allocation. While Support Vector Regression (SVR) has demonstrated strong predictive performance, its effectiveness depends critically on hyperparameter configuration. Conventional tuning strategies are computationally prohibitive or lack convergence guarantees, and established metaheuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are susceptible to premature convergence. This study proposes GNA-SVR, a framework integrating the Geomagnetic Navigation Algorithm (GNA) with SVR for multi-crop yield prediction. GNA is a swarm intelligence optimizer inspired by the geomagnetic navigation of migratory birds, incorporating three mechanisms: geomagnetic gradient dominance with multi-source cognitive modulation, adaptive cognitive landmark chain correction, and triple heavy-tailed distribution bionic perturbation. The framework is evaluated on five crop types (wheat, maize, rice, soybean, and cotton) using a publicly available agricultural dataset comprising 10{,}478 observations across 101 countries. GNA-SVR achieves a mean $R^2$ of $0.957 \pm 0.007$ and an RMSE of $1{,}989 \pm 74$ hg/ha, outperforming six competing methods including PSO-SVR, GA-SVR, WOA-SVR, GWO-SVR, Grid Search, and Random Search. Quantitatively, GNA-SVR delivers a 16.7\% reduction in RMSE relative to the closest competitor PSO-SVR ($2{,}387 \pm 112$ hg/ha), a 20.8\% reduction relative to WOA-SVR, a 23.6\% reduction relative to GWO-SVR, and an MAE of $1{,}409 \pm 55$ hg/ha. The proposed framework reaches 95\% of its final fitness within $47 \pm 6$ iterations with a mean runtime of $38.4 \pm 2.1$~s per run, which is roughly an order of magnitude faster than Grid Search ($312.6 \pm 8.2$~s). Statistical validation through the Wilcoxon signed-rank and Friedman ranking tests confirms the superiority of GNA-SVR at $\alpha = 0.05$. Feature importance analysis using SHAP values reveals that average temperature and annual rainfall are the dominant predictors across all crops. These findings establish GNA as a robust hyperparameter optimizer for agricultural prediction tasks.
Copyright (c) 2026 Abdelrahman Mohammad Fayiz Alfawaz, Moroug Thaher Ahmad Zyadeh, Islam S. Fathi https://creativecommons.org/licenses/by/4.0
Performance Sharia-Compliant Portfolio Based on Frank-Wolfe Optimization in the Indonesian Capital Market
Portfolio weight optimization, a crucial step in building strong investment portfolios, faces a hurdle in Islamic finance. The traditional method, involving short selling to achieve diversification, clashes with Islamic principles concerning interest, uncertainty, and gambling. This challenges Islamic investors seeking optimal returns while adhering to their faith-based financial guidelines. Optimizing these portfolios becomes complicated as individual ethical preferences and risk tolerance are also taken. The objective function of the model with Sharia constraints is optimized using the Frank-Wolfe optimization. A total of 4 types of stocks from one of the Islamic stock indices in Indonesia, Jakarta Islamic Index (JII), from 2020 to 2025 are used to construct portfolios at various levels of risk aversion. The performance of the portfolio returns at various risk aversion is compared with the condition of the composite stock index in Indonesia. The optimization methods help determine the optimal weight allocation in terms of maximizing returns and minimizing risk on risk aversion taken without ignoring Sharia principles.
Structure-Preserving Clustering in Encrypted Feature Spaces
Complex single valued neutrosophic sets (CSVNSs) extend conventional neutrosophic sets to complex valued membership functions, providing a flexible mathematical model of uncertainty, indeterminacy, and inconsistency. Since each component of a universe is represented by truth, indeterminacy, and falsity membership functions with values in the unit interval, this enables the expression of richer and more expressive information in discrete and continuous models. Basic set-theoretic concepts of CSVNSs, such as subset hood, equality, union, intersection, complement, null set, and absolute set, are formally expressed and investigated. Information that exhibits phase-like or periodic behavior that can be described in the complex domain, in addition to being uncertain and incomplete, can be effectively represented by complex single valued neutrosophic sets (CSVNSs). In many real-world decision-making and pattern-recognition tasks, the evidence contains simultaneous degrees of truth, indeterminacy, and falsity. Additionally, classical similarity measures can become unstable when there is a preponderance of magnitude differences, scale dependence, and noise in the data. In order to assess the similarity between two CSVNS vectors, this study proposes a cotangent similarity assessment of Complex Single Valued Neutrosophic Sets (Cotangent CSVNS), which primarily relies on directional agreement and reduces the impact of raw distance. This study also presents privacy-sensitive clustering analysis using encrypted Iris data, both with and without feature normalization. Principal Component Analysis (PCA) is used to assess the quality of clustering using two-dimensional and three-dimensional visualization. While PCA is only used to map the encrypted representations to low- dimensional views summarizing the largest variance directions and making the cluster structure interpretable, Encrypted K-Means and Encrypted K-Means++ are used to identify three clusters on the encrypted feature space. The encrypted data does not eliminate the data’s grouping tendencies, as evidenced by the two-dimensional PCA plots that show clusters that can be recognized as clearly distinguishable regions where the centroid values are well defined. Cluster boundaries are more balanced and smaller when normalization is performed before encryption since all feature magnitudes are scaled identically, increasing the reliability of distance separation. Additionally, the 3D PCA displays show that intra-cluster coherence and inter-cluster geometry are maintained by maintaining cluster separation in a more detailed reduced space with centroid separation and no group overlap. Centroid initialization, which selects spatially diverse starting points to improve convergence stability and reduce the likelihood of suboptimal partitions in encrypted applications, is improved by K-Means++. When the individual findings are combined, it is shown that unsupervised learning can be performed on encrypted and encrypted-normalized data while still producing meaningful structural patterns. This enables it to perform secure clustering, topology preservation, and analytics that respect privacy without revealing any underlying sensitive values.
Copyright (c) 2026 Haitham Qawaqneh, Wael Mahmoud Mohammed Salameh, Diana Amin Mohammad Mahmoud, Giorgio Nordo, Aqeedat Hussain, Arif Mehmood, Cris L. Armada https://creativecommons.org/licenses/by/4.0
A combined Cox-Aalen model for HIV/AIDS progression using data from a clinical trial in South Africa
Background and Objective: Over thirty-nine million individuals globally are living with HIV, and during the past three decades, HIV/AIDS has claimed millions of lives globally. The study's primary goal was to determine the variables that affect the course of HIV/AIDS in individuals receiving antiretroviral therapy, including body mass index, age, gender, TB before treatment, response to treatment, WHO stage baseline, and CD4 baseline. Materials and Methods: The research employed a cohort analysis of surveillance data from the HIV Wellness Clinic in the province of Limpopo, South Africa, for 318 HIV-positive patients on antiretroviral therapy (ART). The effects of covariates were evaluated in this study using Aalen's additive regression model and the Cox regression model in comparison. Results: HIV/AIDS progression for patients receiving treatment therapy has a significant association with CD4 baseline, gender, age, and development of tuberculosis during treatment, according to results from Aalen's additive regression and Cox regression models. With a very modest p value<0.01, both models demonstrate that the CD4 baseline is highly significant when compared to the other factors in the models. Compared to female patients, male patients experience higher rates of immune system degradation. Compared to individuals with baseline CD4+ cell counts over 350 cells/mm3, patients with baseline CD4+ cell counts below 350 cells/mm3 have higher rates of immunological recovery. Conclusion: This study has reinforced the need for early detection of TB in HIV-infected patients so that these two diseases are concurrently managed for the benefit of the infected individual.
Enhancing Network Management using Machine Learning: Intent Based Networking
IBN which is intent-based networking, is changing the way network administrators manage networks with automated intelligent processes. In this paper, we propose a machine learning (ML)-enabled IBN system with automatic programming, intelligent intent translation, adaptive optimization and proactive anomaly detection. With use of cutting-edge technologies like NLP (natural language processing), neural networks, deep-learning and RL(reinforcement learning) our system efficiently translates high-level business requirements in low level network configurations during runtime. We performed a thorough evaluation of the system in a simulated business network environment and compared it with traditional IBN solutions (see, e.g., [3, 4]) as well as current works. The results were highly encouraging: our system achieved 96.56% translation accuracy, improving the state of the art. It also reduced the network convergence by 73.4%, and was much faster to adapt to network changes. We showed that the anomaly detection module attained high performance with F1 ¼ 0:9485 and thus a notable precision and recall. Resource utilization also attained an average gain of 29.05% as compared to SoA approaches. Scalability tests proved that the system performed consistently as network size and complexity increased, a task many large-enterprise systems struggle with. Such results illustrate the benefits of ML-based IBN for better efficiency and automation in network operation. Our work contributes towards the emerging field of intelligent network management with practical applications for both researchers and practitioners. This work will help shape the next generation of IBN which would focus on more self-reliant, agile and operationally efficient infrastructure.
Copyright (c) 2026 Bhavna Ambudkar, Swati Shirke , Ansh Bhanushali , Rolly Gupta, Suchita Shelke , Mukund Wagh, Ranjan Pradhan , Amolkumar Jadhav https://creativecommons.org/licenses/by/4.0
Geographically Weighted Regression Analysis of Spatial Heterogeneity for Suicide Mortality
Suicide mortality has a great spatial variation affected by socioeconomic status such as urbanization, divorce, unemployment, domestic violence, mental illness, and dissatisfaction and should be modeled locally instead of globally. The spatial data are supplied by the application of geographically weighted regression (GWR) model which takes into account local association of variables. However, GWR model has a number of challenges that may affect its effectiveness and reliability. The bandwidth choice is one of these difficulties. When there is an inappropriate bandwidth value, it leads to either fitting the GWR model to values of noise or unexpectedly low values of output. Small bandwidth can perhaps contain excessive local variability and large bandwidth can average away significant local variations. The meta-heuristic algorithms may be explained as the optimization algorithms, which is the solution aimed at designing up the approximate solutions to problems which implies the search through the solution space in the most appropriate manner. The use of meta-heuristic algorithms in the computation of the bandwidth value in GWR model is purely new due to the application of optimization technique in computing the bandwidth value. This paper has used pelican optimization algorithm (POA) as a meta-heuristic algorithm to derive an optimal value of GWR model bandwidth based on the objective function to select the bandwidth (bandwidth minimization). According to the estimation of suicide mortality rate as a real data application, the comparison studies and assessments showed that the proposed method performed better compared to the other methods in terms of R2 and Deviance. Based on the findings, the meta-heuristic algorithms of estimating the bandwidth value of GWR model is a prospective strategy which embraces a combination of sophisticated optimization strategies alongside with analysis of space.
Copyright (c) 2026 Anwer Matti Abboosh Eeso, Zakariya Yahya Algamal https://creativecommons.org/licenses/by/4.0