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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Dynamic Swing Weights Method for Supplier Selection: A Hybrid Approach Integrating Expert Judgement and Machine Learning
Selecting the right suppliers is a crucial part of supply chain management. Choosing a supplier is a difficult strategic choice that involves weighing a number of potentially conflicting factors, and it may alter over time as providers' real performance improves. Fixed weights derived mostly from expert judgment are used in many classic multi-criteria decision-making techniques, which might add bias and reduce the outcomes' responsiveness to changes in operational conditions.The Dynamic Swing Weights Method (DSWM), a hybrid approach, is presented in this article. After obtaining initial expert opinions using the Swing Weights method, the decision weights are updated over time using SHAP-based explainable machine learning model. The model allows the weights to fluctuate as conditions change by combining operational data with expert judgment.This case study demonstrates that DSWM performs better than static techniques in terms of classification accuracy and change adaptation speed using simulated industrial data. By serving as a transparent and scalable decision support system, the proposed structure satisfies the needs of modern supply chains.
Copyright (c) 2026 Marouane EL ABBASSI, Meryem Baghdadi , Karim RHOFIR https://creativecommons.org/licenses/by/4.0
Using Stein’s loss function on Bayesian Estimation of Kumaraswamy-Weibull Distribution Shape Parameters with Application
The subject of this research is the estimate of parameters for the Kumaraswamy–Weibull distribution by employing Bayesian methods while taking Stein’s loss function into consideration. For assuring impartiality in inference, the Bayesian technique is established by adopting Jeffreys prior as a noninformative posterior distribution. We compare the Bayesian estimators with traditional methods, specifically the method of moments estimation (ME) and the maximum likelihood estimation (MLE), to evaluate the effectiveness of the previous method. The evaluation is carried out by means of simulation studies and applications involving real data, with the mean squared error (MSE) serving as the primary criterion of comparison. The Bayesian estimators under Stein’s loss function routinely outperform the conventional techniques, resulting in lower MSE values and displaying improved stability. This is demonstrated by the fact that the results consistently appear in a positive manner. These findings give further evidence that the Bayesian framework offers a tool that is both more reliable and more efficient for parameter estimation in the context of lifespan modeling and reliability applications.
Copyright (c) 2026 Ahmed Mahdi Salih, Azhar Hussein Alwan, Jinan Abdullah Anber, Wadhah S. Ibrahim https://creativecommons.org/licenses/by/4.0
A Robust Flexible Hazard Rate Mixture Anchored in a Novel Over-dispersed Count Distribution
This research presents an innovative three-parameter discrete mixed distribution for the modeling of count data in various scientific domains that often display intricate characteristics, including overdispersion, heavy tails, and unimodality. The proposed distribution is created by combining an extended geometric distribution with the discrete Burr-Hatke distribution using a weighted mixing framework. This formulation establishes a unified and adaptable modeling framework wherein the component distributions manifest as distinct limiting cases as the mixing parameter approaches its boundary values. A thorough investigation of the parameter space's identifiability, the distribution's log-convexity, and its asymptotic properties is conducted. Closed forms are used to get various statistical and reliability properties. The hazard rate function is very flexible, allowing for patterns that go up and down, are unimodal, or are shaped like a bathtub. This makes it possible for the model to capture different ways that things can fail in reliability engineering and survival analysis. The maximum likelihood method is used to estimate parameters, and the log-likelihood function and score equations are derived in detail. The performance of the estimators on finite samples is carefully tested through a lot of Monte Carlo simulations with different sample sizes and parameter settings. These simulations show that the estimators are consistent, $\sqrt{n}$-consistent, and asymptotically normal. Evidence-based guidelines for suitable sample sizes are offered to aid practitioners in implementation. Three real-world datasets show that the proposed distribution works well in practice and outperforms other distributions. The proposed model consistently surpasses various established discrete distributions across all applications, as validated by standard information criteria and goodness-of-fit metrics. These results show that it is a strong and useful tool for modeling complex over-dispersed count data that have outliers and extreme values.
Copyright (c) 2026 Hend S. Shahen, Mohamed S. Eliwa, M. El-Morshedy https://creativecommons.org/licenses/by/4.0
HyperInterval-valued and SuperHyperInterval-valued Fuzzy/Neutrosophic Set
We study uncertainty models built from interval families over a finite universe. An interval set collects all subsets bounded between a designated lower and upper set. A HyperInterval set assigns to each base interval a nonempty family of admissible refinements, while a SuperHyperInterval set of order ? maps elements of the ?-fold iterated powerset to (?−1)-nested families, enabling hierarchical evidence organization. On the numeric side, an interval-valued fuzzy set attaches to each element an interval of admissible memberships, and an interval-valued neutrosophic set assigns independent intervals for truth, indeterminacy, and falsity. Building on these primitives, we introduce HyperInterval- and SuperHyperInterval-valued fuzzy/neutrosophic sets, define conjunctive “core” (intersection) and disjunctive “hull” semantics, and prove embedding theorems showing that classical interval, fuzzy, and neutrosophic models appear as singleton or degenerate cases. Realistic examples from commute planning, delivery scheduling, and clinical assessment illustrate the methodology. The framework unifies multi-source and hierarchical evidence, offering transparent bounds for conservative and exploratory decision policies
Copyright (c) 2026 Takaaki Fujita, Ahmed Heilat, Ajoy kanti Das https://creativecommons.org/licenses/by/4.0
The Factor-Augmented Regression Model (FARM) for Solving the Problem of High-Dimensional Data
High-dimensional data analysis is a sub-topic of modern statistical applications that has become more significant when the number of explanatory variables grows bigger than the sample size. Such scenarios tend to make classic regression methods ineffective in the form of multicollinearity, high variance, and lack of the ability to estimate the parameters. This paper explores the effectiveness of the Factor-Augmented Regression Model (FARM) as an effective way of handling the issues of high-dimensional data. The given model splits the explanatory factors into latent variables and idiosyncratic elements that allow reducing the dimension but do not lose the necessary structural data. To maximize the prediction accuracy and the selection of the variables, the penalized estimation methods are used. The methodology is analyzed and approximated under the extensive simulation experiments in a high-dimensional data model, with varying sample sizes to assess the predictive performance. In addition, the model is used on real-world health-related data on body composition in order to determine its usefulness on enhancing the predictability of the model and to determine important predictors. The findings prove that Factor-Augmented Regression Model (FARM) is stable in its estimates and better predicting models than the conventional regression techniques in high dimensional situations. These results verify the appropriateness of the model in scientific applications that are based on data that are complex and large in scale.
Copyright (c) 2026 Zainab Asaad Jasim, Emad Hazim Aboudi https://creativecommons.org/licenses/by/4.0
A Super-Efficient and Compact CNN for Tomato Leaf Disease Detection on Resource-Constrained Devices
The diseases that affect tomato leaves have a major impact on the agricultural industry. Thus, it is crucial to develop an automated detection system that is both accurate and lightweight. In this context, the implementation of lightweight CNN architecture using the concept of depth-wise separable convolution and squeeze-excitation has been used for the purpose of accurate classification of diseases that affect the tomato leaves. The results that are obtained using the proposed CNN architecture indicate that the classification of diseases that affect the tomato leaves is done in a more accurate manner. In this context, it is observed that the accuracy for the training set is 100%, the loss is 0.0217, the accuracy for the validation set is 99.83%, the loss is 0.0263, and the accuracy for the test set is 99.75%. Thus, it is observed that a high AUC score is obtained, which is 0.999996. The proposed CNN architecture has a total of 219,651 trainable parameters. In this context, it is observed that the total parameters are of a size of 858.01 KB.
Copyright (c) 2026 Maha Rahrouh , Rana Ali salim , Sara Salman Qasim, Marwa Hussien Mohamed , Nader Mahmoud https://creativecommons.org/licenses/by/4.0
Rolling-Calibrated Conformal Decision Support for Seasonal Airport Taxi-Time Planning under Distribution Shift
Seasonal airport taxi-time planning is considered to be an asymmetric-risk decision problem, under-planning in operations can propagate delay and disrupt airline schedules, whereas excessive padding can reduce aircraft utilization and contribute to schedule creep which explains the importance of smart balance. This paper formulates seasonal taxi-time planning as a one-sided risk-control problem and studies a rolling-calibrated conformal decision-support framework for airline scheduling under non-stationary operating conditions. The proposed framework combines a pooled seasonal point forecaster with rolling one-sided conformal calibration to convert point predictions into planning recommendations with an explicit marginal under-planning target. This paper operationalizes existing one-sided conformal calibration for a real aviation planning problem, evaluates its empirical behavior over 17 IATA seasons and 722 airports in the EUROCONTROL CODA dataset, and provides deployment-oriented diagnostics for airport-level monitoring, cohort instability, sparse-history airports, and wake-turbulence-category heterogeneity.Results show that traditional persistence heuristics can fail during the disruption/recovery period, whereas rolling conformal calibration substantially reduces under-planning toward the nominal planning target, at the cost of additional padding. We explicitly distinguish formal conformal guarantees under exchangeability from empirical rolling adaptation under distribution shift. The framework is therefore best interpreted as an auditable, human-supervised decision-support layer for seasonal planning, not as an autonomous scheduling rule or a guarantee-restoration mechanism under arbitrary drift.
Copyright (c) 2026 Soufiane Momtaz https://creativecommons.org/licenses/by/4.0
Adaptive Multi-Variant Enhancement and OCR Selection for Robust License Plate Recognition in Hazy Conditions
Automatic License Plate Recognition (ALPR) systems often experience performance degradation under adverse visual conditions such as haze, low contrast, and limited image resolution. This study proposes an adaptive ALPR framework that integrates Dark Channel Prior (DCP)-based dehazing, YOLOv11-based license plate detection, lightweight multi-variant image enhancement, and plate-aware OCR selection to achieve robust recognition under hazy conditions. After dehazing and plate detection, each cropped license plate image is processed with three enhancement variants: RAW, CLAHE-light, and SR2x + CLAHE-light. Each variant is recognized with PaddleOCR to generate multiple OCR candidates, which are then evaluated with a plate-aware scoring mechanism that combines OCR confidence, structural validity, and ambiguity penalties. Experimental evaluation on 945 hazy license plate images achieved a readability rate of 100.00%, an exact plate accuracy of 93.12%, and a character-level accuracy of 98.63%. Ablation analysis showed that the proposed framework improved exact plate accuracy by 3.70 percentage points over the strongest single-variant baseline, and statistical significance testing confirmed that the improvement was not due to random variation. Parameter sensitivity analysis further demonstrated that the proposed scoring mechanism remained stable across different weighting configurations. Although the complete pipeline requires an average processing time of 471.42 ms per image (2.12 FPS), the results indicate that adaptive multi-variant enhancement combined with structure-aware OCR selection provides a robust and accurate solution for ALPR under degraded hazy conditions without requiring retraining of either the detector or the OCR model.
Copyright (c) 2026 Arafat, Pulung Nurtantio Andono , Abdul Syukur , Affandy https://creativecommons.org/licenses/by/4.0
Predicting the inflation rate in Iraq using multi-time series models ARIMAX with Daubechies Wavelet
This research utilizes a hybrid model combining the Daubechies Wavelet Transform with the ARIMAX model to eliminate noise and improve the characteristics of the two time series for forecasting the inflation rate in Iraq. Inflation is a major negative economic phenomenon affecting most economies worldwide due to its impact on various economic, social, and other sectors. This study compares the performance of the ARIMAX model before and after applying the wavelet transform to the inflation rate (an internal variable) and oil prices (an external variable) during the period from December 2010 to January 2024. The RMSE and MAE criteria were used to assess the forecast accuracy. The results showed the model’s superiority after applying the Daubechies Wavelet, as it exhibited the lowest values for the forecast accuracy criteria. This hybrid approach is preferred for forecasting time series with high volatility.
Copyright (c) 2026 Adhab Nawaf Mahmood Al-Hussein, Najlaa Saad Ibrahim https://creativecommons.org/licenses/by/4.0
Customer Satisfaction with Digital Insurance Services Quality: Evidence from Saudi Arabia
The study aimed to investigate the impact of digital insurance services quality dimensions on policyholders' satisfaction among Saudi insurance companies, utilizing Expectancy Confirmation Theory (ECT) and Equity Theory. The study incorporated nine independent variables, namely user interface design, information quality, security, ease of use, reliability, personalization, responsiveness, speed of transaction completion, and competitiveness of insurance premiums, while the dependent variable was policyholder satisfaction. Data collected via an electronic questionnaire targeting a purposive sample of 436 insurance policyholders in the Saudi market. Data were analyzed and hypotheses tested using the PLS-SEM method via SmartPLS 4 software. The study's findings revealed a positive impact of user interface design, competitive premium pricing, and transaction speed on customer satisfaction with the quality of digital insurance services. In contrast, however, the study found no statistically relevant impact of the remaining dimensions (information quality, security, reliability, ease of use, customization, and responsiveness) on satisfaction levels. This finding suggests that customers within the Saudi digital insurance sector prioritize the financial value, procedural efficiency, and visual appeal of digital platforms. In light of these findings, it is imperative that Saudi insurers enhance the quality of their digital services by prioritizing the design of such services to align with customer preferences, optimizing user interfaces, ensuring the integrity of information, fortifying security protocols, expediting insurance procedures, and extending their reach. These results are valuable for professionals, academics, and the management of Saudi insurance companies, as they offer valuable suggestions to help Saudi insurance companies modernize their operations through digital transformation. By improving the quality of their digital services, these companies can enhance policyholder satisfaction and expand their market reach.
Copyright (c) 2026 Nourelhuda Abdelrhaman https://creativecommons.org/licenses/by/4.0
Predicting Public Budget Surplus and Deficit Using a Hybrid 1D-CNN–LSTM Model
The fiscal position of governments in rentier economies depends heavily on oil revenues. The relationship between oil prices and the budget surplus or deficit is often nonlinear and characterized by complex temporal dependencies, which may limit the predictive capability of conventional econometric models. Accordingly, this study aims to forecast the Iraqi budget surplus and deficit and compare the predictive performance of the ARDL, NARDL, LSTM, 1D-CNN, and hybrid 1D-CNN-LSTM models using oil prices as the primary predictive variable. The hybrid model integrates the feature-extraction capability of One-Dimensional Convolutional Neural Networks (1D-CNN) with the ability of Long Short-Term Memory (LSTM) networks to capture long-term temporal dependencies. The analysis is based on monthly Iraqi data covering the period 2008-2025 (216 observations), with the final year reserved for out-of-sample testing. Model performance was evaluated using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Directional Accuracy (DA), and the Diebold-Mariano test. The results confirm the existence of a long-run equilibrium relationship between oil prices and the fiscal surplus/deficit under both the ARDL and NARDL models. The NARDL model further reveals asymmetric effects of positive and negative oil price shocks. In terms of predictive performance, the hybrid 1D-CNN–LSTM model outperformed all competing models, achieving the lowest out-of-sample RMSE$ (4.008)$ and the highest DA $(0.636)$. The Diebold-Mariano test also indicates statistically significant superiority of the hybrid model over the NARDL and 1D-CNN models. These findings suggest that the hybrid 1D-CNN-LSTM model provides a more effective framework for modeling the nonlinear and dynamic relationship between oil prices and the fiscal surplus/deficit, making it a promising tool for fiscal forecasting and policy support in oil-dependent rentier economies such as Iraq.
Copyright (c) 2026 Sulaiman Hussien Jawad, Munaf Yousif Hmood, Zahraa Yousif Matrood https://creativecommons.org/licenses/by/4.0
New Fixed point Results for Interpolative Gamma-k Contractions
The main aim of this paper is to introduce several new and innovative fixed point results. We use the concept of gamma-k distance mappings together with interpolative contractions and the H-simulation function to develop important new types of contractions. In addition, to highlight the practical relevance of our work, we present several numerical examples to demonstrate the application of our findings.
Copyright (c) 2026 Tariq Qawasmeh https://creativecommons.org/licenses/by/4.0
Linearized Ridge-Type Estimator for the Poisson Modification of the Quasi-Lindley Regression Model
This paper develops a linearized ridge-type estimator for the Poisson-modification of the quasi-Lindley regression model (PMQL-RM), which is designed for overdispersed count responses frequently encountered in epidemiology, insurance, and related fields. The PMQL-RM extends classical Poisson regression by incorporating a flexible mixed Poisson distribution with two dispersion parameters, thereby accommodating substantial overdispersion while retaining a generalized linear model structure and maximum likelihood estimation via iteratively weighted least squares. However, in many practical applications, the explanatory variables are highly correlated, leading to multicollinearity that inflates the variance of the PMQL maximum likelihood estimator (PMQL-MLE) and yields unstable inference. Existing shrinkage estimators for this model, including the PMQL ridge estimator (PMQL-RRE), the PMQL Liu estimator (PMQL-LE), and the PMQL Liu-type estimator (PMQL-LTE), partially address this issue by introducing biasing parameters, but there remains room for further reduction in mean squared error (MSE). Motivated by the superior MSE properties of the linearized ridge regression estimator in linear and generalized linear models, we propose its extension to the PMQL-RM, termed the PMQL linearized ridge estimator (PMQL-LRE). We derive the bias, variance–covariance matrix, and matrix MSE of the PMQL-LRE and establish theoretical conditions under which it dominates PMQL-MLE, PMQL-RRE, PMQL-LE, and PMQL-LTE in both matrix and scalar MSE senses. A Monte Carlo simulation study, conducted under varying levels of multicollinearity, sample sizes, numbers of predictors, and dispersion parameter settings, demonstrates that the PMQL-LRE generally achieves the smallest MSE, especially under severe multicollinearity. An empirical application to real overdispersed count data further illustrates the practical advantages of the proposed estimator in terms of improved estimation accuracy and more stable coefficient estimates.
Copyright (c) 2026 Noora Suhail Hawa, Zeina Ameer Hadied, Oday Esam Al-Saqal, Zakariya Yahya Algamal https://creativecommons.org/licenses/by/4.0
Hybrid RSM–GA Hyperparameter Tuning of Artificial Neural Networks for Academic Performance Prediction
The development of artificial intelligence has encouraged the use of Artificial Neural Networks (ANNs) for academic performance prediction in competency-based education. However, in automotive vocational education, ANN hyperparameters are commonly determined through trial-and-error procedures, which may produce unstable and suboptimal models. This study proposes an integrated Central Composite Design (CCD), Response Surface Methodology (RSM), and Genetic Algorithm (GA) framework to optimize ANN hyperparameters for predicting the academic performance of Automotive Vocational Education (AVE) students at Vocational High School (VHS) NU 1 Karanggeneng, Lamongan. The quadratic RSM model explained 82.09% of the response variation and identified the learning algorithm as the most influential optimization factor. At the original optimization-response scale, the RSM–GA procedure produced an optimal ANN configuration with three hidden layers, 20 neurons, a tansig–purelin transfer-function combination, the trainlm learning algorithm, a learning rate of 0.005, and 200 epochs, achieving an MSE of 0.07 and an S/N Ratio of 22.94. In the normalized response-level benchmark, the Opt RSM–GA ANN obtained the most favorable normalized repeated MSE response, with a Mean MSE of 0.012, SD MSE of 0.0019, and S/N Ratio of 38.65. These findings indicate that the CCD–RSM–GA workflow provides a structured and reproducible approach for ANN hyperparameter optimization. Broader validation using larger, multi-school, and multi-cohort datasets is still required before practical implementation.
Copyright (c) 2026 Sudirman Rizki Ariyanto, Bambang Suprianto, Warju, Ata Syifa Nugraha https://creativecommons.org/licenses/by/4.0
Dynamic Persistence and Nonlinear Resilience: A Hybrid Econometric–Machine Learning Framework for Life Insurance Market Forecasting
This paper develops and validates a hybrid forecasting framework for life insurance market dynamics that integrates panel econometrics with machine learning. Using a panel dataset of OECD countries spanning 2005-2022, we construct dynamic features including lag structures, volatility proxies, trend components, and a stability index. Our empirical strategy employs fixed-effects panel models, gradient boosting (XGBoost), and ensemble aggregation. The results reveal three fundamental insights: (1) life insurance markets exhibit near-perfect autoregressive persistence, with the first lag (y_{i,t-1}) explaining approximately 75\% of predictive variance; (2) risk-related variables (volatility, stability) show weak linear effects but contribute meaningfully through nonlinear machine learning interactions; (3) the 2008–2009 financial crisis has negligible direct predictive importance, as its effects are fully mediated through the dynamic lag structure. The ensemble model achieves outstanding out-of-sample performance (RMSE = 4.23, MAE = 2.81, MAPE = 8.30%, R² = 0.971), significantly outperforming benchmark linear models. We conclude that life insurance markets are fundamentally autoregressive systems where the primary forecasting challenge is not identifying external shocks but accurately modeling temporal persistence. Our framework offers both theoretical clarity and practical utility for actuaries, regulators, and industry forecasters. We note that these results are specific to the five countries studied (Poland, Switzerland, Turkey, Australia, Denmark) and require validation on broader samples.
Copyright (c) 2026 Mohamed Lotfy, Hebatalh hassan, Fatma A. Atia, Sayed Youssef, Noura Roushdy, Ahmed Amar https://creativecommons.org/licenses/by/4.0
Determinants of life insurance market share: a comprehensive panel econometric and machine learning analysis of seven industrialized countries (2005-2022)
Herein, this research analyzes the determinants affecting life insurance market share in seven prominent industrialized countries: France, Germany, Italy, Japan, Spain, Sweden, and the United Kingdom from 2005 to 2022. We use a large panel dataset with 126 country-year observations and a wide range of advanced methods, such as two-way fixed effects, system GMM dynamic panel, panel cointegration, Granger causality, threshold regression, and ensemble machine learning (XGBoost). Our results show six important things. First, retention ratios have a strong positive and statistically significant effect on life insurance market share (coefficient = 1.498, p < 0.01), and system GMM backs this up. Second, there is a strong negative correlation between reinsurance acceptance and reinsurance acceptance (coefficient = -7.892, p < 0.01). Third, our new Reinsurance Dependency Index shows nonlinear interaction effects (coefficient = 598.23, p < 0.05). Fourth, life insurance markets show
Copyright (c) 2026 Mohamed Ibraheem, Hebatalh hassan, Sayed Youssef, Mousa Selmey, Noura Saad https://creativecommons.org/licenses/by/4.0
Modeling and Optimal Control Analysis of Lassa Fever with Lockdown and Treatment Interventions
Lassa fever is an acute zoonotic viral disease caused by the Lassa virus and remains a major public health concern in endemic regions. In this study, a mathematical model is proposed to investigate the transmission dynamics of Lassa fever incorporating hospital quarantine and control interventions in the form of lockdown measures and treatment strategies. The model considers both human to human and rodent to human transmission pathways. The model admits two equilibrium points, namely the disease free equilibrium and the endemic equilibrium. The existence and local stability of these equilibria depend on the basic reproduction number, $R_0$. The disease free equilibrium is locally asymptotically stable when $R_0<1$, whereas the endemic equilibrium exists and becomes locally asymptotically stable when $R_0>1$. Sensitivity analysis is performed to identify the parameters that most strongly influence disease transmission. The results indicate that the human to human transmission rate, the recovery rate of infectious individuals, and the treatment uptake rate are among the most influential factors affecting disease spread. An optimal control problem is formulated and analyzed using the Pontryagin Maximum Principle to determine effective intervention strategies that minimize both the infected population and implementation costs. Numerical simulations demonstrate that treatment interventions significantly reduce the number of infected individuals, while the combined implementation of lockdown and treatment controls provides additional benefits in reducing disease transmission. These findings provide qualitative insight into the role of quarantine and intervention strategies in controlling Lassa fever transmission.
Copyright (c) 2026 Cicik Alfiniyah, Fauziah Putri Batubara, Ahmadin Ahmadin, Reuben Gweryina https://creativecommons.org/licenses/by/4.0
Reassessing Statistical and Hybrid Forecasting Models for Flexible Packaging Demand under Post-COVID Structural Changes
Accurate demand forecasting is essential for production planning, inventory control, and supply chain management in the flexible packaging industry, particularly under structural changes following the COVID-19 pandemic. This study evaluates statistical, machine learning, and hybrid forecasting models for monthly demand prediction in the Indonesian flexible packaging market using data from January 2012 to December 2024. The evaluated models include ARIMA, SARIMA, SARIMAX with selected exogenous variables and a COVID-19 intervention term, standalone Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM), and hybrid SARIMAX–MLP and SARIMAX–SVM models. To ensure a fair comparison, all models are assessed using RMSE, MAE, MAPE, and R² under a unified rolling-origin framework over the January 2023–December 2024 out-of-sample period, resulting in 24 one-step-ahead forecasts for each model. The empirical results show that ARIMA(1,1,3) achieves the lowest RMSE and highest R², indicating better performance in reducing larger forecast deviations, while SARIMA(0,1,1)(0,1,0$)_{12}$ achieves the lowest MAE and MAPE, indicating superior average absolute and percentage forecasting accuracy. In contrast, SARIMAX, standalone machine learning models, and hybrid models do not provide consistent improvements over simpler statistical benchmarks. The inclusion of exogenous variables and residual-based machine learning components does not improve forecasting performance, suggesting that the remaining external and nonlinear error structures are not sufficiently stable or learnable within the available monthly sample. The Harvey–Leybourne–Newbold corrected Diebold–Mariano test further indicates that the observed differences in forecast accuracy are not statistically significant at conventional levels. These findings show that increased model complexity does not necessarily lead to better forecasting performance. For practical industrial forecasting, simpler and more interpretable statistical models may be preferable when data are limited and short-term one-step-ahead forecasting accuracy is the main objective.
Copyright (c) 2026 Bagas Anindito, Basuki Widodo, Erwin Widodo https://creativecommons.org/licenses/by/4.0
A Deep Learning Approach for Classifying Generated Geometric Pattern Datasets
Deep learning is now widely recognized as a powerful method for solving complex problems, such as image classification and object detection. This study focuses on applying deep learning to classify a custom-generated dataset of geometric patterns. We have implemented a binary classification system to distinguish between acceptable and non- acceptable geometric patterns based on criteria established by artisans working with wood. Due to the absence of a standard dataset, we created our own dataset, adhering to the construction rules for these patterns. In this study, two pre-trained CNN- driven models (Inception-V3 and ResNetV2) have been suggested in this paper for the classification. Numerous criteria, including precision, accuracy, recall, and F1 score, are used to illustrate this superiority. The experimental system focuses on evaluations based on loss, precision, and confusion matrices and uses Jupyter Notebook, Python, TensorFlow, and Keras. The pre-trained Inception-V3 model achieves the best classification performance, with an accuracy of 97.80%, according to the experimental results.
Copyright (c) 2026 yassine AIT LAHCEN, Essaid ELBACHARI, Slimane ENNAJAR, Abdelilah ET-TALEBY https://creativecommons.org/licenses/by/4.0
An Intelligent Computational Framework for Multi-Criteria Decision Analysis under Uncertainty
Uncertainty, vagueness, and inadequate knowledge sometimes make it hard to make decisions in complicated real-world situations. This research provides an intelligent computational paradigm for multi-criteria decision analysis under uncertainty to address these problems. The suggested approach combines rough set theory with advanced methods for modelling uncertainty to accurately show decision information that is unclear or contradictory. A structured decision-support method is created to assess and prioritize many choices according to a range of criteria that may be conflicting. A sustainable system selection problem, which takes into account technical, environmental, and economic factors all at once, shows how the proposed framework can be used. The experimental findings demonstrate that the suggested methodology improves decision robustness, interpretability, and reliability in comparison to current decision-making models. The suggested intelligent computational framework can be adapted for various decision-support and artificial intelligence applications functioning in uncertain environments, due to its flexibility and universality.
Copyright (c) 2026 Ahmed El-Douh, Mousa Janat, Myvizhi M, Hanadi Simmak https://creativecommons.org/licenses/by/4.0
Investor Sentiment and Stock Market Volatility in the Egyptian Exchange: A GARCH and EGARCH Analysis with Persistence and Asymmetry Checks (2009-2025)
This study empirically investigates the impact of investor sentiment on stock market volatility in the Egyptian Stock Exchange (EGX) over the period 2009–2025, using daily data from 22 non-financial firms listed on the EGX 30. A composite sentiment index (SMI) is constructed using Principal Component Analysis (PCA) as the primary aggregation method, with a simple average approach used for robustness, based on four market-based proxies: stock turnover ratio (STURN), money flow index (MFI), advancing-to-declining ratio (ADR), and relative strength index (RSI). We employ a GARCH(1,1) model with SMI in the conditional variance equation to capture volatility clustering and persistence, and we extend the analysis to an EGARCH(1,1) specification to test for asymmetric effects of positive versus negative sentiment. The transmuted normal distribution is implemented in the main empirical analysis to model the error term distribution more flexibly, alongside quasi-maximum likelihood estimation with robust standard errors. We also incorporate macroeconomic controls (inflation, interest rates, GDP growth, political instability) and conduct Granger causality tests to address endogeneity concerns. Diagnostic tests confirm the absence of multicollinearity and remaining heteroscedasticity. The results show that investor sentiment has a positive and statistically significant impact on volatility (coefficient =0.278, p < 0.001). The GARCH(1,1) results reveal high volatility persistence (α + β = 0.97), supporting the hypothesis that sentiment shocks have long-lasting effects. The EGARCH model indicates asymmetry: negative sentiment shocks increase volatility more than positive shocks of the same magnitude (asymmetry coefficient γ = −0.073, p < 0.05). Granger causality tests provide evidence of bidirectional causality between sentiment and volatility. Out-of-sample forecasting demonstrates that the SMI has modest but significant predictive power for volatility, though the results should be interpreted as suggestive rather than definitive. These findings contribute to behavioral finance literature by providing evidence from an under-researched frontier market (Egypt) and offer potential tools for regulators and investors to monitor sentiment as an early-warning indicator of excessive volatility.
Copyright (c) 2026 Heba M. Srour, Sayed Youssef, Nesma M. Darwish, Mohamed F. Abouelenein, Ola Mohamed Shawky Eissa https://creativecommons.org/licenses/by/4.0
Uncertainty-Aware AI: Conformal Prediction versus Reinforcement Learning for Optimal Trade Execution
Optimal trade execution is the problem of working a large parent order through a trading session at the lowest possible cost relative to a benchmark. It is sequential, and it is taken under uncertainty, so in principle it looks like an ideal candidate for learning-based control. In practice, a lot of the gains people report do not survive a second look by someone else. We go back to the volume-weighted average price (VWAP) execution problem and study it inside a controlled simulator that we can reproduce in full, one with stochastic volatility and AR(1) return momentum. Everything sits inside a single Markov decision process. Within that, we compare the usual schedules (TWAP, Almgren–Chriss, VWAP-tracking) with a proximal-policy-optimisation (PPO) agent and with forecast-driven policies that rest on a normalised split-conformal predictor of next-interval returns. The predictor is well calibrated. It reaches 90.2% empirical coverage at a 90% nominal level. Acting on its point forecast lowers mean slippage below VWAP-tracking, but it adds cost variance in the process. Gating the same bets by the half-width of the conformal interval behaves very differently: it produces a tunable, monotone reduction in cost variability, from 19.1 bps down to 10.0, at almost no cost in the mean, and it sweeps out an explicit cost–risk frontier set by one threshold. The PPO agent, trained over three seeds, is high-variance. It does not beat the simple schedules in any reliable way. So, at least in this setting, a distribution-free conformal gate is a more reproducible and more interpretable path to uncertainty-aware execution than an off-the-shelf reinforcementlearning agent. We release the code and the simulator. On real intraday data (thirty US large-caps, 5-minute bars) the predictor’s coverage holds up almost exactly, at 90.7% empirical against the 90% level, and the variance reduction from gating is still there. The forecast edge, though, is thin, because real high-frequency returns are barely predictable to begin with.
Copyright (c) 2026 Asadullah Irshad, Shaon Biswas https://creativecommons.org/licenses/by/4.0
Coupled dynamics of Dust storms and vegetation: A mathematical approach to restoration
Dust storms significantly threaten ecosystems, biodiversity, and human health. Understanding its interactions with plant biomass and revegetation initiatives is crucial for developing effective mitigation strategies. In this study, we present a dynamic Dust Storms-Plant Biomass-Revegetation (SPR) model based on a system of differential equations to explore the relationships among dust storms, vegetation growth, and revegetation activities. The model incorporates key ecological and environmental factors, such as wind effects, logistic growth, Allee thresholds, and saturation dynamics, to reflect real-world complexities. Dust accumulation adversely affects plant health by obstructing photosynthetic processes, whereas vegetation helps reduce dust storms by acting as a natural filter. Revegetation initiatives further enhance the ecosystem's ability to stabilize dust levels and promote plant growth. This study investigates the thresholds and tipping points within the ecosystem that determine its trajectory toward recovery or collapse. Through analytical and numerical methods, we assess the impact of environmental parameters and management strategies on system dynamics. The findings reveal the critical role of vegetation and revegetation in mitigating dust storms and underscore the importance of considering wind, saturation effects, and natural depletion rates in environmental planning. This research provides actionable insights into balancing ecological restoration efforts with dust storm management, contributing to sustainable development goals.
Copyright (c) 2026 Mohammed Tahseen Hussein, Shireen Jawad, Shubhadeep Ghosh, Bapin Mondal, Anirban Tarafdar https://creativecommons.org/licenses/by/4.0
Dual Reciprocity Boundary Element Method-based analysis of infiltration in heterogeneous soils under different irrigation channel types
The research discussed analysis of infiltration in heterogeneous soils under different irrigation channel types. The heterogenous soils consist of layer 1 is silt loam (SL) and layer 2 is pima clay loam (PCL). Irrigation channel types that discussed consist of trapezoidal, rectangular, semi-circular, triangular, impermeable based of trapezoidal , and impermeable based of rectangular channel. The problem is governed as Richards' equation converted into a modified Helmholtz equation to serve as a mathematical model. This model is then solved numerically using the Dual Reciprocity Boundary Element Method (DRBEM). The resulting numerical solution is the suction potential in each type of irrigation channels. The area below of the channels with the highest suction potential value are the irrigation channels without impermeable. In irrigation channels with impermeable based, the area below of the channels has a low suction potential value. The solution also indicate that there are jumps of suction potential value from layer 1 to layer 2. The order of highest to lowest suction potential values obtained from the solution are triangular, rectangular, semi-circular, impermeable based of trapezoidal, and impermeable based of rectangular channel
Copyright (c) 2026 Millatuz Zahroh, Maq Rifatur Rosiyanti , Moh. Hasan https://creativecommons.org/licenses/by/4.0
Biomimetic Design of a Brown Bear Claw-Inspired Subsoiling Shank: Design, DEM Simulation, and Field Validation
Subsoiling is essential for alleviating soil compaction, improving water infltration, and enhancing soil structure; however, conventional tools often require high draft force and produce ineffcient soil disturbance. Inspired by the penetration mechanism of brown bear claws, this study proposes a biomimetic subsoiling shank to improve soil-cutting effciency and disturbance quality. A discrete element method (DEM) model was developed using a linear cohesion-integrated hysteretic spring contact model to simulate soil–tool interaction. Model parameters for loam soil from the Yellow River Delta (China) were calibrated using angle of repose tests, direct shear tests, and bulk density matching. Validation against experimental data showed good agreement, with a draft force error of ∼10%. While the DEM model demonstrated good accuracy for draft force prediction, the simulation of soil disturbance geometry was moderately accurate, with relative errors ranging from 16.6% to 32.8%. Soil disturbance profles were quantifed using gradient-based image processing in MATLAB and geometric reconstruction in AutoCAD. Field experiments were conducted to validate the DEM model and measure the absolute performance of the bioinspired shank; owing to unavoidable variation in operating conditions between tools, the controlled DEM model was used for the formal comparative analysis. Comparative DEM simulation results indicated that the biomimetic shank reduced draft force by 6.1%, increased furrow cross-sectional area by 8.6%, and decreased ridge height by 25.6% compared to a conventional design. Parametric analysis confrmed that rake angle and tillage depth signifcantly affect performance. The improved effciency is attributed to optimized stress distribution and soil ﬂow induced by the clawinspired geometry. The fndings demonstrate the potential of biomimetic design and the effectiveness of the validated DEM model for optimizing soil-engaging tools.
Copyright (c) 2026 Ahmed M. Hassan, Abouelnadar El Salem, Reham M. Kamel, Xiaoshuai Zheng, Fatma A. Atia, Sayed Youssef, Fatma Shaaban https://creativecommons.org/licenses/by/4.0
Intelligent Investment Predictor: An Explainable Machine Learning Framework for Investment Sector Preference Prediction
With the changing environment and the occurrence of events around the world such as recession, shifting societal needs and technology among other factors, it has become difficult for investors to determine the right investment sectors. The global economy is developing rapidly and influenced by environmental changes, economic recessions, development of social needs, and new life technologies. Due to these factors, the identification of optimal investment sectors becomes challenging for investors. Traditional models of forecasting often fail to accommodate this complexity because of limited granularity and explainability, which requires more intelligent, explanatory, and robust methods. This research proposes the Intelligent Investment Advisor, a system leveraging Machine Learning (ML), Deep Learning (DL), and Explainable AI (XAI) to predict promising investment sectors. The framework combines supervised classification algorithms such as Logistic Regression, Random Forest, Support Vector Machine (SVM), Decision Tree, Naive Bayes, K-Nearest Neighbour (KNN), and Gradient Boosting with ensemble approaches like voting and stacking classifiers. In addition, an optimized RF-SVM hybrid is implemented to boost predictive capability. The workflow includes data preprocessing, model training, hyperparameter optimization, and ensemble integration. To ensure transparent decision-making, the framework incorporates LIME (Local Interpretable Model-agnostic Explanations), enabling stakeholders to understand the reasoning behind sector predictions. Empirical analysis shows that ensemble classifiers perform better than single models, with the stacked classifier achieving Accuracy: 92.8%, Precision: 91.5%, Recall: 93.2%, and F1-score: 92.3%. Compared to conventional models averaging 85% accuracy, the proposed system improves performance by 7 to 10 percent. These findings show significant computational improvements in predictive accuracy and statistical robustness, providing a reliable decision-support tool for investors, policymakers, and financial institutions.
Copyright (c) 2026 Mst Deloara Khushi, T. M. Mehrab Hasan, Rakib Hossen, Anichur Rahman, Md Masud Rana, Jerin Akter https://creativecommons.org/licenses/by/4.0
An Adaptive Support Method for Training SVM Problems
In recent years, extensive research has been conducted on support vector machines (SVMs) and their applications across various scientific fields. Among the various algorithms used for classification and regression in different domains, SVMs are distinguished by their remarkable power and stability. In this context, this study introduces the design and implementation of the Adaptive Support Method-Support Vector Machine (ASM-SVM), a fast training algorithm for SVM classification, a fundamental task in machine learning. The proposed approach is iterative and determines in a finite number of iterations an optimal or suboptimal solution for the QP (Quadratic Programming) problem. At each iteration of our algorithm, a descent feasible direction and a step size are calculated in order to decrease the value of the objective function and the suboptimality estimate. The major advantage of our algorithm resides in its ability to modify several non-basic variables simultaneously, unlike active-set methods, which only permit changing one variable per iteration. This feature permit reducing the number of iterations and the computational cost that is particularly important for many applications. Furthermore, our solution eliminates the need to compute the null-space matrix, making it possible to propose a simple and efficient method suitable for machine implementation. Numerical results obtained on several datasets prove its effectiveness in training both linear and nonlinear SVM classification problems. The ASM-SVM method is designed for easy integration into machine learning software systems.
Copyright (c) 2026 Belkhiri-Brahmi Louiza, Belkacem Brahmi, Mohand Ouamer Bib https://creativecommons.org/licenses/by/4.0
Algorithmic optimization of cattle breed portfolios: application of the Markowitz-Freund model to breeding in Cˆote d’Ivoire
Optimizing the cattle portfolio is a major challenge for the sustainability and competitiveness of livestock production in Côte d’Ivoire. This paper proposes a modelling approach inspired by the Markowitz-Freund model (Hardaker, Huirne, Anderson, and Lien 2004); applied to the optimal selection of cattle breeds. The aim is to determine the combination of breeds that maximizes overall yield while minimizing economic and biological risk, particularly in terms of susceptibility to tsetse fly. Using real data on five local breeds (N’Dama, Baoulé, Z´ebu, Métis, and Lagunaire), quadratic programming was implemented in IBM CPLEX to solve the risk-return trade-off problem, with constraints on the total proportion, diversification, and tsetse fly rate. The results show that a balanced allocation, dominated by the Baoulé and Lagunaire breeds, makes it possible to reconcile economic performance and biological robustness. This approach, at the crossroads of bioeconomic and computational intelligence, paves the way for optimized decision-making that can be adapted to African agro-economic realities.
Copyright (c) 2026 Yaya Kone, Moustapha Diaby, Ouagnigna Hili https://creativecommons.org/licenses/by/4.0
Mathematical Modeling of Investments in the Development of an Information Security System
This paper develops a methodological optimal-control model for investments in the development of an information security system (ISS). Rather than treating the problem as a purely generic financial portfolio, the study explicitly interprets security controls as investable assets. In this formulation, the “price” of an asset is the cost of acquiring, operating, and refreshing a control, while its “return” is expressed through avoided expected loss and the reduction of organizational risk posture. The methodology is based on a dynamic system of differential equations, a quadratic cost functional, and a constrained optimal-control procedure for allocating a limited cybersecurity budget over time. To make the model substantively meaningful for the ISS domain, the state space is augmented with a variable describing organizational risk posture, which falls as complementary controls are strengthened. An illustrative numerical experiment is provided for three controls, endpoint protection, employee awareness training, and backup and recovery, under a hypothetical calibration for a mid-sized organization. Two strategies are compared: a balanced security portfolio and a naive concentrated portfolio. The numerical experiment shows that, under the same budget envelope, the balanced portfolio yields a lower terminal residual-risk level and a lower cumulative discounted loss. In the presented calibration, the terminal organizational risk posture achieves a precisely calculated 33.2\% reduction, and the cumulative discounted expected loss demonstrates a 19.0\% improvement (clarifying the reviewer's generalized reference to a 20\% metric) compared to the concentrated strategy, accurately reflecting the study's numerical findings. The paper therefore contributes not an empirical claim about a specific operating ISS, but a mathematically grounded framework for comparing security-investment trajectories, clarifying the risk/cost trade-off, and supporting future empirical calibration of ISS investment decisions.
Copyright (c) 2026 Arailym Yessenbayeva, Talgat Mazakov, Aiman Mailybayeva, Sholpan Jomartova, Aigerim Mazakova https://creativecommons.org/licenses/by/4.0
Algorithmic approaches to bitstream optimization in real-time video services
The article examines a deterministic content-dependent approach to optimising the bitstream in real-time video services, aimed at improving coding efficiency under conditions of limited computing resources and variable network bandwidth. The purpose of the research is to create a universal model that ensures a stable bit rate and bit load reduction without degrading perceptual quality. The proposed methods are based on the integration of the significant coordinate luminance (SCL) and non-uniform linear scaling (NLS) components into the filled code structures of fixed length , which allows to eliminate local redundancy and balance the bit distribution in accordance with the spatial and temporal coherence of scenes. The developed model adaptively adjusts the quantisation parameters, which ensures robustness to dynamic changes in frame complexity and object motion. Experimental results on the HEVC Class B, UVG and Xiph sets confirm a 14-15% reduction in BD-Rate compared to HEVC, maintaining perceptual quality at VMAF (Video Multi-Method Assessment Fusion) ≥ 93 and reducing the average delivery delay by more than half compared to JPEG2000. An analytical evaluation showed a 7-12-fold reduction in computational complexity, allowing this method to be used in mobile, embedded, and low-frequency real-time systems.
Copyright (c) 2026 Anton Sorokun, Tatiana Meleshko, Yurii Zadontsev, Viacheslav Treitiak, Dmytro Chyrva https://creativecommons.org/licenses/by/4.0
Contextual Detection of Cross-Site Scripting Attacks Using RoBERTa-Based Deep Learning Model
As internet usage grows, web applications remain vulnerable to Cross-Site Scripting attacks, in which malicious actors construct malicious scripts via user input, posing major threats to data security and program integrity. This paper presents a contextual detection method of cross-site scripting attacks using a RoBERTa based deep learning model, which uses advanced natural language understanding to observe payload patterns. The model's ability to understand contextual relationships reduces the need for manual feature engineering, resulting in a reliable solution for detecting both basic and sophisticated obfuscated assaults. To improve the model's contextual learning capabilities, the proposed methodology included carefully dataset selection and preprocessing procedures such as decoding, tokenization, and the removal of redundant patterns. The model underwent specialized fine-tuning using a balanced dataset of benign and malicious payloads. The results of the research show outstanding performance, with the model obtaining 99.38% accuracy and a false positive rate of 0.71%. These results indicate the model's ability to accurately detect different Cross-Site Scripting payloads while minimizing false detections. The findings make major contributions to web security by providing a highly accurate detection method for one of the most popular cybersecurity threats, laying the groundwork for future advances in automated threat detection.
Copyright (c) 2026 Thamer Almansour, Marwah M. Almasri, Shimaa A. Nagro https://creativecommons.org/licenses/by/4.0
Supply Chain Optimization Encompasses Integrated Forward and Reverse Logistics using Memetic Algorithm
Distribution allocation is one of the main problems in the logistics network models, particularly during the attempt to enable quick and efficient decision making strategically in the industrial context. Once the examination has been done in inspection centre, the recycled materials are assumed to be of the same quality as the raw materials in general. As not every recycled material will always be of high quality so we have created a recycle center node with a novelty to determine the quality of the products that were collected. Another point that we discussed about was how materials move with relevant assessment in the inspection system, which resulted in informed decisions on recycling, reusable/remanufacturing, or disposal in the reverse logistic system. This not only enables a clear picture of the resources that have been collected, but it also guarantees that the needs of the raw materials remain balanced; that the demand is met. Our approach proposes a model of integrated forward and reverse logistics network to determine the actual transportation cost in two different cases, with or without the new node open so that the cost of inventory transportation is optimized. The model is implemented by use of optimization software and the results of the simulation are compared by use of a numerical example.
Copyright (c) 2026 N. Sharmila, M. Balamurugan, Bharathi Ramesh Kumar https://creativecommons.org/licenses/by/4.0
The new integral transform, DPR Transform and its Application
In this paper, a new integral DPR transform is developed and introduced. It is a superior transform, exhibiting dualities with more than eighty other integral transforms. Many researchers have been introduced different integral transform with various kernels. This transform is generalization of all such more than eighty integral transforms. Instead of studying eighty different transforms this single transform can solve the various problem solved by these different integral transform. A connection is established between this recent transformation and the previously recognized advantageous transforms, thereby extending the theoretical framework.We further discuss fundamental properties of the DPR transform which include linearity property, scaling property, domain shifting property, and its operation on derivatives are rigorously demonstrated. The transform is applied to a variety of equations,which include ordinary differential equations, partial differential equations, integro-differential equations, fractional differential equations, difference equations, and differential–difference equations. In addition, the practical utility of the DPR integral transform is illustrated through applications in forensic science, where it provides outstanding results without requiring extensive computational effort.
Copyright (c) 2026 Rohini Deshmukh, Vishwajeet S Goswami, Dinkar P. Patil https://creativecommons.org/licenses/by/4.0
Decision Making Using The Score Function And New Distance Measure: A Comparative Study
In this paper, we define a new metric (distance function) between the dual fuzzy set of hesitation and also between the weighted dual fuzzy set of hesitation. This new measure is made by adding the relative coefficient of variation to the previous measure [53], a comparison is obtained between the previous measure and the present measure. In addition, a comparison is presented between the score function and the distance measure to see which is more efficient and easier to use in decision-making, in the medical diagnosis application. We have proved the triangle inequality of the new normalized Hamming metric, the new Euclidean metric, and the new generalized metric between two dual hesitant fuzzy elements. We have used a Python program to write the code of distance measure calculations. The score function is very important for ordering the dual hesitant fuzzy elements, so it is very important in simplifying the calculations for decision-making.
Copyright (c) 2026 Kamal Abdelraouf Eldahshan, Salah Boulaaras, Ghada Mostafa, Zeinab Mohammed, Ibrahim Mekawy https://creativecommons.org/licenses/by/4.0
Mycobacterium Tuberculosis Hotspots: A Spatial Analysis of Tuberculosis Cases in West Java, Indonesia
Globally, tuberculosis ranks as the second most prevalent cause of mortality resulting from a single infectious disease. In 2023, Indonesia experienced a 24-year peak in tuberculosis cases, with West Java Province exhibiting the highest occurrence rate, reflecting a 24.2% increase compared to the previous year. This study aims to investigate the determinants of tuberculosis cases in West Java by employing a spatial approach to account for regional heterogeneity. The Geographically Weighted Negative Binomial Regression (GWNBR) method is applied due to its suitability for handling count data with overdispersion while accommodating spatially varying relationships. Parameter estimation is conducted using the Maximum Likelihood Estimation (MLE) method enhanced with the Nelder--Mead optimization algorithm. Four kernel function types are incorporated in the modeling process, namely Fixed Gaussian, Adaptive Gaussian, Fixed Bi-square, and Adaptive Bi-square. Based on the Akaike Information Criterion (AIC), the Adaptive Gaussian kernel demonstrates the best model performance, achieving an AIC value of 1,000.11. Population density emerges as the most influential determinant, showing statistical significance across all regions. These findings emphasize the importance of developing localized tuberculosis control strategies that are tailored to specific regional characteristics.
Copyright (c) 2026 Arip Ramadan, Dwi Rantini, Della Lukita Wardhani, Septia Devi Prihastuti Yasmirullah, Najma Attaqiya Alya, Alhassan Sesay, Bela Sonia https://creativecommons.org/licenses/by/4.0
Estimation in Generalized Lindley distribution with randomly censored data
In this article, we obtained the point and interval estimations for a generalized Lindley distribution (GLD) based on randomly censored data. The maximum likelihood (ML) and Bayes estimation method are used to estimate the unknown parameters of the GLD. Furthermore, approximate confidence intervals (ACIs) for the unknown parameters were constructed. Markov chain Monte Carlo (MCMC) method applied to find the Bayes estimation. Also, highest posterior density (HPD) credible intervals (CRIs) were obtained for the parameters. Gibbs within Metropolis-Hasting samplers used to generate samples from the posterior density functions. A real data set is discussed to illustrate the proposed methods. we performed a Monte Carlo simulation study.
Copyright (c) 2026 Shahdie Marganpoor, Vahid Ranjbar https://creativecommons.org/licenses/by/4.0
Estimation of the Dirichlet–Multinomial distribution parameter using Schur Complement based Newton-Raphson method for modeling a road safety measure
In this article, we propose to use the Dirichlet–Multinomial distribution to generalize the conditional multinomial model used by N’Guessan et al. (2008, 2015) in the context of modeling road accident data on an experimental site. This distribution allows for an explicit formulation of the data likelihood without relying on a conditional representation. Parameter estimation is performed using the Newton–Raphson algorithm, with the invertibility of the Hessian matrix ensured through a Schur complement-based approach. This approach allows explicit determination of the inverse of the Hessian matrix. The choice of the initial parameter vector a0 which is crucial for the algorithm’s convergence, is related to the overdispersion parameter rho, whose impact is systematically investigated. The results show that for values of rho close to zero, the algorithm is more stable and converges more rapidly.
Copyright (c) 2026 Aboubacari Abdou Amadou, Assi N'Guessan, Ibrahim Sidi Zakari, Bisso Saley https://creativecommons.org/licenses/by/4.0
Convolutional Neural Networks (CNNs) are widely used for tasks such as image segmentation, object recognition, and classification, with pooling operations playing a key role in reducing computational complexity and model parameters. While traditional and fuzzy-based pooling methods have been extensively studied, recent approaches have introduced intuitionistic fuzzy pooling to address local imprecision in feature maps. However, this method relies on the assumption that the indeterminacy is simply the complement of membership and non-membership degrees—an assumption that can be inaccurate in complex environments. To overcome this limitation, we propose a novel pooling operation based on Neutrosophic Fuzzy Sets (NFSs), which explicitly incorporates a degree of indeterminacy. Our method, called Neutrosophic Pooling, operates in four stages: bi-fuzzification using membership, non-membership, and indeterminacy maps; a first aggregation converting Neutrosophic Fuzzy Set NFS into a classical fuzzy set; a second aggregation using a sum operator; and finally, defuzzification through a max operation. This new pooling layer can be seamlessly integrated into CNN architectures, replacing standard, fuzzy, or intuitionistic pooling layers. Experimental evaluations across several benchmark datasets demonstrate that the proposed NFS-based pooling significantly enhances classification performance, especially in uncertain or noisy environments, outperforming state-of-the-art pooling methods.
Copyright (c) 2026 Chaymae Rajafillah, Karim El Moutaouakil, Mouhcine Kahlaoui, Abderazzak Mouiha https://creativecommons.org/licenses/by/4.0
Flood Forecasting Using Non-linear Autoregressive Exogenous Neural Networks with Radial Basis Function
River stage and discharge monitoring plays a significant role in making sound forecasts of floods, early warning, and preventing risk posed by disasters, especially when rain falls heavily in the areas of river catchment. These variations in the water level are difficult to model and the time series models which are commonly used to recapitulate such a behavior are not able to reflect the complexity. The paper is a discussion on the importance of enhancing the accuracy of flood forecasting by combining two forecasting models, that is, Radial Basis Function (RBF) and Nonlinear Autoregressive Exogenous (NARX) with simple averaging and weighted averaging techniques, and comparing the performance of the two in terms of their respective impact. With the help of a genetic algorithm, the most suitable combination of weights is sought, and the contribution of each of the individual models is adjusted. Therefore, models used singly and intelligent committee machine learning model (NARX, RBF, ENS-AVG, and ENS-GA) are used to generate river level forecasts of the Selangor River at the lead times of 1, 3, and 6 hours. Accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R-squared). These results suggest that the weighted averaging (ENS-GA) offers better results in most of the assessment measurements in comparison to the simple averaging. The magnitude of difference between the values of RMSE between the 1-hour forecast as a result of substituting simple averaging with weighted averaging by the use of GA is approximately -98.39% and the 3-hour forecast is approximately 78.57% and 6-hour forecast is approximately -92.86%. The proposed strategy implies the establishment of a consistent and correct time-series forecasting model by integrating a set of forecasting tools and streamlining their work, which will then have a beneficial effect on the quality of flood forecasts.
Copyright (c) 2026 Sumia Hussin Mohamed Abdualkarim, Muhammed Fadhil, Mohsen Ayyash, Caiyan Long https://creativecommons.org/licenses/by/4.0
Machine Learning for Financial Market Prediction : A Systematic Literature Review
Artificial intelligence has recently become a powerful tool for market stock prediction. Financial markets are complex and volatile; each market behaves differently. The machine learning field hasand inputs known extensive study over the last few years. Researchers have experimented with a range of inputs for basic price prediction, employing basic market data from historical data and technical indicators to market indexes and alternative data sources such as sentiment indicators and news. However, as the goal is to generate forecasts of prices and trends, it is crucial to assess models through adequate metrics. Yet the field suffers from methodological inconsistency and a persistent disconnect between predictive accuracy and economic viability. This systematic literature review employs a structured PRISMA protocol to collect 63 studies and to examine forecasting models, evaluation metrics, alternative data integration, and real-world deployment challenges. Our analysis reveals that Long Short-Term Memory networks appear in 53% of reviewed papers and dominate the literature, and hybrid architectures account for 61% of the total. To bring conceptual clarity, we propose a four-tier taxonomy encompassing data-level, model-level, parallel-ensemble, and architectural hybrid models. The literature reveals critical gaps: 97% of papers use technical metrics while only 9% report the use of profitability, and only 4 studies apply backtesting with realistic transaction costs. Furthermore, survivorship bias is acknowledged in only three papers, while geographical coverage is heavily concentrated in the United States and Chinese markets, leaving African and Latin American markets understudied. Notably, memorizing narrow market regimes rather than generalizing exhibits overfitting risk. We conclude that the assumption of accuracy equaling profitability is unsupported by current evidence, as experiments reporting profitability and backtesting performance are largely absent, which undermines claims about the model's robustness. We advocate for standardized benchmarks, rigorous ablation studies, profitability evaluation protocols, and controlled experiments studying the effect of preprocessing and architectural synergy separately.
Copyright (c) 2026 Imane Bahammou, Soumia Ziti, Karim El Bouchti https://creativecommons.org/licenses/by/4.0
Numerical Study of Compressible Newtonian Fluid Flow with Low Viscosity through a Lid-Driven Cavity
This paper focuses on numerical analysis of Newtonian compressible fluid flow in a square cavity driven by a lid, where the motion of the fluid is generated by moving the upper wall with prescribed velocity. The numerical solution of the governing equations is performed within a Taylor--Galerkin/Pressure--Correction finite element framework (TGPC--FEM). The pattern of the flow is governed by Navier--Stokes equations, consisting from continuity equation describing mass conservation and unsteady momentum equations written in Cartesian coordinates. The objective of this work is to investigate the role of low dynamic viscosity (µ) on some significant flow properties. The influence of the viscosity change on the velocity field, shear stress, pressure and density distributions is investigated in details. Numerical solutions show that, when increased (µ), the trajectory of velocity becomes smaller in magnitude, whereas the other hydrodynamic profiles become larger. In general, the expected tendencies are in qualitative agreement with basic physical principles of viscous compressible flows.
Copyright (c) 2026 Hassan K. Ismail, Hayder A. Janabi, Anas Al-Haboobi https://creativecommons.org/licenses/by/4.0
Dual support M-method for solving linear programs with non-negative variables
This paper presents a version of the dual support method specifically designed to handle linear programming (LP) problems with non-negative variables. Since an initial support feasible solution is generally unavailable in advance, we introduce a dual support M-method to solve LP problems without requiring a prior starting point. Additionally, we develop efficient updating formulas for the inverse matrix and the pseudo-feasible solution utilized within the algorithm. To evaluate the performance of the proposed method against the dual simplex and interior-point methods, we implement the proposed algorithm in MATLAB. Computational experiments evaluating CPU time and the number of iterations across both randomly generated test problems and standard NETLIB benchmarks demonstrate the efficiency of the proposed approach, especially on solving dense problems.
Copyright (c) 2026 Abdelhak Hebbache, Mohand Bentobache, Mohand Ouamer Bibi https://creativecommons.org/licenses/by/4.0
Optimization and Heuristic Approaches for k-Domination Connectivity
Let $G$ be a connected graph. Let $\kappa^{\gamma(k)}(G)$ denote the $k$-domination connectivity. This is defined as the minimum number of vertices whose removal disconnects $G$ so that every resulting component has domination number exactly $k$. This parameter combines the structural robustness of vertex connectivity with domination-based coverage constraints. In this paper, we study structural properties and computational aspects of $\kappa^{\gamma(k)}(G)$. General bounds for the parameter are established. Its behavior is analyzed for several structured graph classes, including trees, grid graphs, corona graphs, and Cartesian product graphs. We also discuss relationships between $\kappa^{\gamma(k)}(G)$ and classical graph parameters such as vertex connectivity and domination number. From the computational perspective, we show that the decision version of the $k$-domination connectivity problem is DP-hard for fixed $k \ge 2$. We formulate the computation of $\kappa^{\gamma(k)}(G)$ as an integer optimization problem. We propose both an exact enumeration algorithm and a heuristic optimization strategy to determine the parameter in general graphs. Our results provide a framework for analyzing domination-based connectivity and help understand how networks can maintain connectivity and monitoring capability under vertex failures.
Copyright (c) 2026 Meghna K M, Minirani S https://creativecommons.org/licenses/by/4.0
Medical Data and Services in Efficient Personal Care Delivery: A Novel Dynamic Semantic Microservice CQRS Framework
Healthcrises often overwhelm people, causing significant stress for physics not only during the active operational period but also throughout the entire patient care life cycle.In these kinds of scenarios, the pressure on medical professionals can lead to service inconsistencies and increased complexity in delivering ongoing and coordinated healthcare services. Previous research studies have been mostly focused on wireless biomedical sensors and big data analytics, often neglecting patient context under several moving locations where service tends to break as a result of unexpected events while running. To address this gap, we explore Command Query Responsibility Segregation (CQRS) pattern and ConteXt-aware Agent Oriented Semantic MicroServices (CxASMS), to ensure service continuity with less computational complexity. As the case study, we employed pregnant women, which is often used in the health field. Results of our study prove that context-aware semantic models based on CQRS can deal with service volatility induced by context changes while improving accuracy throughout the semantic service life cycle. The proposed platform reuses previous responses and views within the patient and service contexts to optimize the response time. This is the first dynamic semantic CQRS framework in the field of smart health for service continuity using context-aware sensing, which is then properly evaluated using a real-world study
Copyright (c) 2026 NASSIM BOUKEZOULA, Abdelhamid Malki , Mimoun Malki, Adel Alti https://creativecommons.org/licenses/by/4.0
Modified Numerical and Exact Solutions of Fuzzy Wave Equation under Neutrosophic Environment
In this paper, a comprehensive study of the fuzzification and defuzzification of wave equation in neutrosophic fuzzy environment is presented for first time in literature including Truth (T), Indeterminacy (I), and Falsehood (F) fuzzy component. Moreover, a numerical and analytical methods that are explicit finite difference method and D'Alembert's method are reformulate, developed, and applied for solving the neutrosophic fuzzy wave equation. The neutrosophic triangular number concept was utilized for both neutrosophic numerical and exact solutions. The error bound between the neutrosophic numerical and exact solution evaluated at (?,?,?)-cut level set is illustrated and evaluated. Finally, a numerical example is carried out and solved using both analytical and numerical techniques and the results indicate the effectiveness and feasibility of the proposed developed methods.
Copyright (c) 2026 Hamzeh Zureigat, Ayah Salhieh https://creativecommons.org/licenses/by/4.0
Linearized ridge estimator in Poisson-inverse Gaussian regression model
This paper suggests a new linearized ridge estimation of the Poisson-Inverse Gaussian Regression Model (PIGRM) which is expected to resolve the problem of multicollinearity and overdispersion in the analyses based on the count data modeling. Using asymptotics of the maximum likelihood estimator (MLE), an advantage of the suggested estimator will be harnessing the possibility of adding a parameter associated with shrinkage in order to stabilize coefficient estimates as well as decrease their variance. Wide-ranging Monte-Carlo analyses were induced to assess selection of the linearized ridge estimator in comparison to conventional MLE and various types of biased estimator in an assortment of circumstances of precinct size, engagingness of predictors and variability. It is showed consistently that linearized ridge estimator exhibits lower values of mean squared error (MSE) which implies its better accuracy in estimation and robustness. In addition, the practical utility of the approach is confirmed by the application to real-world data that demonstrate better model fit and predictive performance. These results indicate that the linearized ridge estimator should be of great use to the reliable inference about PIGRM, particularly with multicollinearity and overdispersion.
Copyright (c) 2025 Statistics, Optimization & Information Computing https://creativecommons.org/licenses/by/4.0
A Comparative Statistical Analysis of Decision Tree and AdaBoost Ensemble for Employee Performance Classification in the Hospitality Sector
This study aims to classify the factors affecting employee job satisfaction in Hotel X using the Decision Tree (DT) and Adaptive Boosting (AdaBoost) methods. The hospitality industry relies heavily on human capital to deliver high-quality services, and employee satisfaction is directly linked to service excellence, loyalty, and organizational performance. Data were collected from Hotel X’s internal Employee Satisfaction Index (ESI), comprising 70 records and 9 response indicators across multiple departments. Exploratory Data Analysis (EDA), correlation analysis, and label encoding were performed to prepare the dataset. The Decision Tree was first utilized to model the classification of employee satisfaction levels, followed by optimization using the AdaBoost ensemble method to enhance predictive accuracy. Three simulations were conducted using training-to-testing ratios of 70:30, 75:25, and 80:20, respectively. The results show that AdaBoost consistently improved the classification performance, achieving the highest accuracy of 93% in the third simulation. These findings underscore the significance of ensemble learning techniques in enhancing model reliability for human resource analytics in the hotel industry. This research demonstrates the practical value of combining DT and AdaBoost for workforce data analysis in service-based organizations. The model can be adapted to various industries that prioritize employee satisfaction as a key performance driver.
Copyright (c) 2026 Statistics, Optimization & Information Computing https://creativecommons.org/licenses/by/4.0
Cocoa Beans Roasting Classification using Hybrid Multi-Objective Golden Eagle Optimiser and Graph Convolutional Networks
The roasting process greatly influences cocoa beans’ quality and antioxidant potential, transforming phenolic compounds and flavour precursors. However, the conventional method of evaluating roasting levels relies on subjective human perception, which can lead to inconsistencies in quality control. This study proposes an intelligent and objective classification system for cocoa bean roasting levels (light, medium and dark) based on RGB images, using a Hybrid Multi- Objective Golden Eagle Optimiser approach integrated with Graph Convolutional Networks (HMOGEO–GCN). The study uses 1000 images for each roasting class. The ResNet18 model is a feature extractor optimised through multi-objective GEO to balance accuracy, computational cost and convergence speed. GCN strengthens population topology learning for a more effective global search. Using a 10-fold cross-validation, experimental results show that HMOGEO–GCN achieves superior performance, with an average accuracy of 90.5% ± 0.8, an F1-score of 0.913 and an AUC of 0.904. This surpasses the performance of standard CNNs and single optimisation approaches. Furthermore, the classification results demonstrate a strong correlation with chemically reported antioxidant activity and total polyphenol content, showing the potential of this model for predictive quality control in the cocoa roasting process. This approach contributes to developing deep learning for food analysis and supports implementing intelligent, precision cocoa roasting systems in industry.
Stability Analysis of Influenza Virus Spread Model in Bird Population with Reaction-Diffusion
The avian influenza virus is a pathogen that predominantly infects wild birds but can also affect domestic poultry. This study established an epidemiological model, $SEIZ$, which incorporates infection factors from contaminated environments and transmission resulting from contact among avian species. The model was mathematically analyzed to identify two equilibrium points: a disease-free equilibrium point that is locally asymptotically stable when $\mathcal{R}_0<1$, indicating potential extinction of the bird, and an endemic equilibrium point that is locally asymptotically stable when $\mathcal{R}_0>1$, leading to disease persistence within the population. The model was subsequently transformed into a reaction-diffusion model featuring Gaussian initial conditions and Neumann boundary conditions. The developed reaction-diffusion model was subsequently solved numerically employing the finite volume method, utilizing parameters derived from empirical data. Simulation results indicated that an increase in the basic reproduction number can expedite viral infection. Moreover, augmenting the diffusion coefficient accelerates the propagation of infection into regions with the highest density of susceptible individuals and broadens the geographical extent. Virus concentrations in the environment exhibit longer persistence than those in infected populations, thereby increasing the probability of serving as a source of future infections. This study's findings suggest that control strategies for avian influenza virus should account for both direct and indirect infections, as well as the spatial movement of avian hosts, since such movement substantially affects virus spread and the efficacy of control measures.
Inference based on Dual Generalized Order Statistics from Unit Teissier Distribution
In this study, we utilize dual generalized order statistics (dgos) to explore the moment characteristics of the unit Teissier (UT) distribution. Using this method, we obtain exact and explicit expressions for single moments and develop recurrence relations for both single and product moments. Furthermore, we provide a characterization of the UT distribution, along with additional results related to the moments of record values and reversed order statistics (os). We estimate the unknownparameter of the UTdistribution using the maximum likelihood estimation (MLE) based on dgos. The effectiveness of the derived maximum likelihood estimates (MLEs) is assessed through extensive simulation studies, which emphasize various moments and their corresponding relative mean squared errors (MSEs). This research enhances the understanding of the UT distribution’s properties and offers valuable insights into its parameter estimation using dgos.