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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Foreign exchange rates, oils price, domestic economic policies, supply chain disruption and inflation rate in Nigeria: Evidence from ensemble learning algorithms
This study investigates the drivers of inflation in Nigeria, focusing on the influence of foreign currencies, oil prices, domestic economic policies and supply chain disruption. With Nigeria experiencing an unprecedented inflation rate, the study employs five ensemble learning algorithms; Decision Tree, Random Forest, AdaBoost, Bagging, and Gradient Boosting regressions to uncover the relationships between inflation rates and its potential drivers. These drivers considered are exchange rates of foreign currencies, oil prices, domestic economic policies (the removal of oil subsidies and the floating of the Naira), and supply chain disruptions linked to the ongoing Russian-Ukrainian War. To better capture the effect of oil subsidy removal, floating of the Naira and Russia–Ukraine war, the continuous time-based variables which created a time counters that track the number of periods since these events occurred was used. The Exchange Rate Pass-Through (ERPT) Theory and Cost-Push Inflation Theory provided the theoretical framework for this study. Data from the Central Bank of Nigeria, covering January 2012 to September 2024, were used and the time series cross validation was used to ensure robustness against temporal dependencies while multicollinearity analysis was carried out using the Variance Inflation Factor (VIF) followed by PCA for dimensionality reduction. To estimate the possible effect of outliers the result was presented with and without robust scaler. Preprocessing was carried out, hyperparameters tuning were performed using Grid Search algorithm, and model performance was evaluated using metrics such as Mean Absolute Error, R-squared, Root Mean Square Error, Huber loss and adjusted R-squared using five-fold cross-validation. The results reveal a significant positive relationship between foreign currency exchange rates and inflation \( p < .05 \) with evidence of multicollinearity among features and hence the Principal Component Analysis (PCA) was used in dimensionality reduction. The use of robust scaler was found to improve the performance of the Machine Learning algorithms and the Gradient Boost algorithms outperformed other machine learning algorithms with the least RMSE (1.2990), MAE (0.8132) and Huber loss (0.5036) and the highest values of \( R^2 \) (0.9671) and adjusted \( R^2 \)(0.9634). Additional analysis using feature importance, Shapley Additive exPlanations (SHAP) and Partial Dependence (PP) plots showed that domestic policies variables particularly the removal of fuel subsidy and the floating of the Naira policy had the most significant positive impact on inflation in Nigeria while exchange rate of CFA and other global currencies such as USD and Euro were found to have moderate impact on inflation. These findings underscore an urgent need to reinvest savings from the subsidy removal into developmental projects such as building of refineries while also providing incentives and financial support to cushion the effect of subsidy removal. The need to establish currency swap agreements with key trade partners particularly China will help stabilize the Naira and its exposure to foreign currency volatility. These strategies could help in improving external balances, support industrial growth, and promote long –term resilience of Nigerian economy.
Copyright (c) 2026 Timothy Kayode SAMSON, Suraj Arya https://creativecommons.org/licenses/by/4.0
Enhanced Personalized Clinical Treatment: Regression Models’ Applications to Breast Cancer Patient Management
Incorporation of statistical predictive models into clinical practice enhances personalized treatment planning and patient management. This study compares the performance of six parametric regression models, namely Weibull, Exponential, Log-logistic, Lognormal, Gamma, and Gompertz, in identifying the prognostic factors affecting the survival of breast cancer patients. Survival models were estimated for a dataset of 686 breast cancer patients from the German Breast Cancer Study (GBCS). The following comparison criteria were used to compare the survival models: Akaike Information Criterion, Bayesian Information Criterion, Log-Likelihood, and Deviance. While all the models yielded similar outcomes, the model selection criteria indicated that the Lognormal model had the best fit for the data. All models identified the same set of significant predictors, suggesting that tumor size, tumor grade, number of lymph nodes involved, and progesterone receptor count significantly influenced survival. The findings from this study show the lognormal model to be the best fit for survival and the importance of early detection and testing to improve the prognosis of breast cancer patients.
Copyright (c) 2026 Isaac T. ADEDOSU, Dorcas M. OKEWOLE, John O. OLAOMI, Ayobami F. AKINTOLA https://creativecommons.org/licenses/by/4.0
Next-Generation Driver Monitoring Systems: A Review of Multimodal Detection of Distraction, Drowsiness, and Sudden Medical Conditions
Driver impairment caused by distraction, drowsiness, and sudden medical conditions remains a major contributor to road traffic accidents worldwide. This paper presents a comprehensive review of recent advances in real-time detection systems targeting these three domains, with the goal of informing the development of effective and deployable driver monitoring solutions. Literature on distraction detection reveals a transition from traditional gaze-based thresholds to efficient deep learning architectures deployable on embedded platforms, including privacy-preserving approaches using vehicle dynamics alone. Drowsiness detection re- search has progressed toward multimodal and personalized frameworks that integrate physiological, behavioral, and contextual indicators, enhancing detection accuracy and reducing false alarms. Research on sudden medical condition detection, although less extensive, demonstrates promising results through wearable biosensors, in-cabin sensing technologies, and automation fallback strategies capable of executing controlled vehicle stops during acute health events. Across these domains, the review identifies persistent challenges: limited availability of large-scale, diverse, and naturalistic datasets; reliance on controlled laboratory or simulator environments; and the need for adaptive, context- aware, and computationally efficient algorithms suitable for real-world deployment. The integration of multimodal sensing, personalized detection thresholds, and uncertainty estimation is highlighted as a promising direction for improving robustness and user acceptance. By synthesizing findings across diverse methodologies and application contexts, this review provides a consolidated understanding of the state of the art, identifies critical research gaps, and outlines pathways toward next-generation driver monitoring systems capable of reducing crash risk and enhancing road safety.
Copyright (c) 2026 Statistics, Optimization & Information Computing https://creativecommons.org/licenses/by/4.0
A new two-parameter flexible extension of Lindley distribution with variable shapes for hazard rate function
Statistical distributions are important to describe the phenomena in real world. A new two parameter extension of Lindley distribution which called new Lindley (NLi) is intro- duced and studied in details. This model is right skew and left skew in PDF. It isa unimodal PDF. The hazard rate function of this model is very flexible. Statistically important properties, including the quantile function, asymptotics for the CDF, PDF, and HRF, extreme values, and moments of the new model, are obtained. Parameter estimates process are conducted by the well-known methods of maximum likelihood, weighted least square method Cramér- von Mises method and Anderson Darling method. Tre tables of simulation show that the Anderson Darling method and Cramér von Mises methods are better for estimating the pa- rameters of NLi model. We fit our new model to five real data sets and comapre it with some Lindley extensions and some well-known two-parameter distributions like Gamma, Weibull, Generalized Exponential distribution. The results of tables 12-16 verified that this model is more consistently than other competitive models for real data sets.
Impact of Universal Social Coverage on Life Expectancy: An Econometric Analysis Using an ARDL Model
The objective of this study was to examine the impact of Universal Health Coverage (UHC) on life expectancy, along with other determinants of the health status of the population in Morocco, using indicators representing health status measured by indicators such as education rates, urbanization rates, or indicators specific to access to healthcare and other variables divided into socioeconomic variables and healthcare system variables. As a method, we estimated an ARDL (AutoRegressive Distributed Lag) model on annual data representing the health status of the population in Morocco covering the period from 1990 to 2023. To do this, the ARDL (AutoRegressive Distributed Lag) approach was used to estimate the short-term and long-term dynamic relationship between life expectancy and its determinants using EViews software. As the results confirm the hypothesis of a long-term cointegration relationship and equilibrium between life expectancy and universal health coverage. They reveal that universal health coverage has a positive and statistically significant effect on life expectancy with a long-term coefficient of +1.249 (p<0.001). This estimate empirically validates the hypothesis that universal health coverage is a key determinant of improved population health.
Copyright (c) 2026 MANAL DOUGADIR, AMINE RADAH, Mohamed ELHIA https://creativecommons.org/licenses/by/4.0
Unified Estimation Framework for Multilevel Logistic Regression: Integrating Maximum Likelihood, Entropy-Based Regularization, and Bayesian MAP via Newton-Raphson Optimization
Multilevel logistic regression is foundational for the analysis of nested binary data but practitioners face a trade-off: maximum-likelihood estimators are computationally efficient yet vulnerable to separation and rare-event instability, whereas fully Bayesian sampling confers stability at substantial computational cost. To reconcile these competing demands, we introduce a unified estimation framework that reframes Bayesian MAP estimation with an entropy-based prior as a regularized optimization problem, thereby integrating data fidelity (maximum likelihood), entropy-based regularization that penalizes overly certain or implausible parameter configurations, and a probabilistic MAP interpretation within a single objective. We derive the joint penalized marginal log-likelihood under a Laplace approximation for random effects, provide explicit expressions for gradient and Hessian contributions from both likelihood and entropy terms, and implement a Newton–Raphson optimizer that exploits the block-diagonal Hessian structure typical of hierarchical models and incorporates numerical safeguards (ridge augmentation, line-search) for stability. Theoretical analysis establishes consistency and asymptotic normality of the estimator under regularity conditions and clarifies the dual frequentist–Bayesian interpretation of the entropy penalty. Extensive simulation studies, designed to probe separation, rare events, unbalanced cluster sizes, and a broad range of random-effect variances, demonstrate that the unified estimator substantially reduces non-convergence and extreme coefficient estimates relative to unregularized ML, attains point-estimation accuracy and interval calibration comparable to MCMC-based Bayesian methods in representative settings, and requires far less computational effort than full posterior sampling—making empirical-Bayes selection of the regularization weight practically feasible. The proposed approach thus provides a computationally efficient and theoretically principled alternative for estimating multilevel logistic models, bridging the gap between frequentist and Bayesian paradigms while preserving interpretability and reproducibility.
A New Flexible Marshall–Olkin Extended Odd Weibull–G Family of Distributions: Theory, Properties and Applications
In this paper, we introduce the Marshall–Olkin Extended Odd Weibull–G (MO–ExOW–G) family of distributions (FoD), as a flexible framework for modeling lifetime data characterized by skewness, heavy tails, and complex hazard rate structures. The proposed family integrates the Marshall–Olkin (MO) generator with the Extended Odd Weibull–G (ExOW–G) family to enhance shape adaptability and tail behavior. This combination enables the new MO-ExOW-G to capture a wide spectrum of hazard rate forms, including increasing, decreasing, unimodal, bimodal, bathtub, and inverse bathtub patterns. We derive some statistical properties of the MO–ExOW–G family. Parameter estimation is addressed through various techniques, and the performance of the estimators is assessed via Monte Carlo simulations. Special cases of the MO-ExOW-G are applied to real-world datasets from clinical studies and engineering contexts, where the proposed models exhibit superior goodness-of-fit compared to several established alternatives. Overall, the MO–ExOW–G FoD provides a powerful and unified approach for analyzing survival and reliability data, offering substantial improvements in flexibility and interpretability.
A Novel Compound G-Family: Statistical Characteristics and Applications to Aircraft Windshield Service Durations and Survival Time Data
The aim of this scientific research is to present a thorough comprehension of the innovative G families of Statistical models incorporating different copulas, elucidating their statistical characteristics, and illustrating their practical utility across various real lifetime data scenarios. Researchers, statisticians, and practitioners stand to gain valuable insights from the findings of this study, as it equips them with the ability to model intricate relationships and employ multivariate data analysis for informed decision-making. The versatility of compound classes of continuous Statistical models makes them instrumental in simulating diverse phenomena. They can be effectively utilized to represent the frequency of accidents within a defined time frame, the duration until system failure, or the financial losses associated with a transaction. The adaptability of these compound models is a significant advantage, enabling them to address a wide array of situations, including those challenging for other models to adequately capture. Furthermore, these models frequently exhibit ease of application and understanding. A significant advantage of utilizing compound classes of continuous Statistical models lies in their capacity to represent the interdependence of two or more continuous random variables. This research introduces and investigates a novel compound G class of Statistical models, derived from the truncated-Poisson distribution following a zero, which subsequently generates Weibull G classes, thereby establishing the foundation for this new compound G family. The statistical properties of this family are meticulously scrutinized, thereby enhancing comprehension of its mathematical foundations.
Adaptive Liu estimator modified for estimating regression coefficients in the presence of Multicollinearity
Multicollinearity among predictor variables remains a major challenge in regression analysis. This issue arises when predictors are highly correlated, leading to inflated variances of ordinary least squares (OLS) estimators and unstable coefficient estimates. Several remedial methods have been proposed to mitigate Multicollinearity, including ridge regression, the Liu estimator, and principal component regression. A critical factor determining the performance of shrinkage estimators such as the Liu estimator is the selection of an appropriate shrinkage parameter, denoted by $d$. This study proposes a novel method for estimating the optimal value of $d$. The performance of the proposed estimator was evaluated through Monte Carlo simulations under varying levels of {{Multicollinearity}} severity and sample size. The method was also applied to a real-world dataset. Results demonstrate that the proposed estimator achieves a substantially lower mean squared error ($MSE$) and mean absolute error ($MAE$) compared to existing estimators, indicating superior estimation accuracy and stability.
Impact of Remittances, Foreign Direct Investment, and Official Development Assistance on Economic Growth in Morocco: An ARDL approach
This article examines the impact of foreign direct investment (FDI), migrant remittances, and official development assistance (ODA), as well as control variables such as gross fixed capital formation and trade openness, on economic growth in Morocco between 1980 and 2022. The analysis uses the ARDL (Autoregressive Distributed Lag) model to capture short-and long-term dynamics. The results of the cointegration test confirm the existence of a long-term relationship between the variables. Moreover, the empirical results show that, in the long term, FDI and remittances contribute positively to growth, while ODA has a negative effect. In contrast, in the short term, none of these financial flows have a significant impact on growth. The article proposes recommendations aimed at maximizing the effect of financial flows, such as encouraging productive investment, improving financial inclusion, and promoting institutional reforms. Finally, it proposes directions for future research, including the use of nonlinear models and sectoral analysis of financial flows.
Penalized Spline Semiparametric Logistic Regression for Modelling Coronary Heart Disease Risk Based on Demographic and Lifestyle Factors
This study presents a novel application of Penalized Splines Semiparametric Binary Logistic Regression (PS-SBLR) to evaluate Coronary Heart Disease (CHD) risk. By combining parametric and nonparametric components, the established PS-SBLR method extends classical logistic regression to effectively model both linear and non-linear relationships simultaneously. To estimate the nonparametric component, a penalized spline estimator is used to produce smooth adaptive curves. At the same time, Generalized Approximate Cross Validation (GACV) is employed for smoothing parameter selection to bypass the nonconvergence issues often found in standard CV or GCV methods. While the theoretical foundation of PS-SBLR has shown strong potential in medical research, it has not yet been applied within an integrated framework for both CHD prediction and prevention. To address this gap, we developed a specific PS-SBLR predictive framework using real-world data to enhance the accuracy and efficiency of CHD risk prediction. This applied approach provides valuable, practical insights for the management and mitigation of CHD risk. The resulting predictive model achieved 84.38% accuracy on the training data with an AUC of 0.90, and 87.5% accuracy on the test data with an AUC of 0.98, demonstrating its excellent performance in distinguishing CHD risk profiles. The analysis revealed that, while age and sugar consumption show a linear positive correlation with CHD, continuous variables such as body weight and stress levels exhibit significant non-linear relationships.
Simulating the Interdependence Between Electricity Production and Water Release, Based on a Methodology that Combines Statistical Models and Artificial Intelligence
The research presents a hybrid innovative model that improves the accuracy of the prediction of water release and energy production through the combination of artificial neural networks with econometric modeling techniques. The research assumed a combined approach, taking advantage of the strength of neural networks to discover complex and nonlinear relationships in data and the benefits of the vector regression models in long-term equilibrium relationships. Two integrated hybrid models that address each variable independently while maintaining their interrelationships were created. With notable gains in a number of evaluation criteria on both training and future data, the suggested model outperformed conventional models in terms of prediction accuracy. Utilizing the combination of cutting-edge statistical approaches and artificial intelligence technologies, the created methodology is distinguished by its capacity to handle the intricacies of time series.
What Drives Tourists to Use Conversational AI? Evidence from an Extended UTAUT2 Model
This study examines the factors shaping tourists’ adoption and use of artificial intelligence (AI) tools in tourism by drawing on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Focusing on tourists visiting Fes, Morocco, AI is operationalised as a conversational assistant similar to ChatGPT that helps travellers search for information, plan itineraries, and make decisions during their trip. The research investigates how performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit influence behavioural intention and usage behaviour, and whether trust in AI technology moderates the intention–behaviour link. Data were collected through an online survey of N=183 tourists in Fes, Morocco. Respondents completed a screening question on prior use of a ChatGPT-like assistant for travel tasks. Only “Yes” respondents proceeded. Usage behavior reflects general self-reported use of AI for travel purposes, not verified frequency of use during the Fes stay. The collected data was then analyzed using a consistent “partial least squares” (PLS) SEM technique. The findings show that performance expectancy, hedonic motivation, price value, and habit significantly predict behavioural intention. Facilitating conditions and habit significantly predict usage behaviour. Effort expectancy and social influence are non-significant predictors of intention. Trust in AI technology does not significantly moderate the relationship between intention and usage behaviour. The study contributes to tourism technology literature by refining the application of UTAUT2 in an AI-intensive context and highlighting the role of habit and perceived value in tourists’ continued reliance on AI assistants. Practical implications are discussed for tourism managers and destination stakeholders seeking to design and communicate trustworthy AI-based services.
Research on Mortality Models Incorporating Cohort Effects from a Compositional Data Perspective
Against the backdrop of accelerating global population aging, traditional mortality models are often sensitive to outliers and struggle to capture tail characteristics, leading to an underestimation of future trends. It is worth noting that mortality models with cohort effects, by introducing cohort effects on top of the age-period only framework, can capture mortality patterns more comprehensively. To enhance the robustness and accuracy of predictions, this study embeds the Renshaw–Haberman (RH) model with cohort effects into the framework of Compositional Data Analysis (CoDa) to construct a CoDa-RH model. This study uses data from six countries in the Human Mortality Database (HMD), including four developed countries (Australia, Spain, the United States, and the United Kingdom) and two developing countries (Chile and Bulgaria). The centered log-ratio (clr) transformation is applied to the age distribution structure of death counts, and the bootstrap method is used to construct prediction intervals for life expectancy. Empirical results show that, compared with the traditional Lee-Carter (LC) model and the RH model, in terms of indicators based on log mortality rates, the CoDa-based models exhibit lower Aitchison Distance (AD) and Mean Absolute Error (MAE) on the test set. In terms of life expectancy prediction, this study randomly selects three of the six countries (Spain, UK and Chile) and provides their life expectancy forecasts up to 2035.
A Hybrid Support Vector Machine–Genetic Algorithm Framework Approach for Parameters Estimation for the [0,1] Truncated Nadarajah Haghighi Inverse Rayleigh Process in nonhomogenous Poisson Process
The estimation of three unknown parameters of [0,1] Truncated Nadarajah Haghighi Inverse Rayleigh Process of nonhomogeneous Poisson Process (TNHIRP). It discusses nine of those estimation methods, which are the maximum likelihood, maximum product spacing, Anderson Darling, Right Anderson-Darling, ordinary least squares, weighted least squares, Cramer-von Mises, Hybrid Support Vector Machine–Genetic Algorithm, and percentiles to estimate points. These estimators were compared in terms of bias and mean squared errors using detailed analysis and a lot of simulation experiments. According to the simulation outcomes, all the estimators work well under these considerations and show similar values in case of large sample size. There are also some statistical properties of the new distribution derived. The estimators were also applied to two real life datasets and the values of the Kolmogorov-Smirnoff statistics reported. Lastly, the [0,1] Truncated Nadarajah Haghighi Inverse Rayleigh process was tested on two real world datasets, and the performance was compared with other well-known extensions of Inverse Rayleigh process using four selection criteria.
A New Untied Prakaamy Distribution: Properties and Application
This study introduces the Unit Prakaamy Distribution (UPD), a bounded version of the Prakaamy distribution, and examines its statistical and reliability properties. The theoretical properties of the UPD include mean, variance, skewness, kurtosis, non-central moments, Bonferroni and Lorenz curves, as well as the survival and hazard functions. Measures of uncertainty, such as Shannon and R´enyi entropy, are also considered. Parameter estimation is performed using Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE),Weighted Least Squares Estimation (WLS), and the Cram´er–von Mises (CvM) method, with Monte Carlo simulations showing that MLE consistently provides the most accurate estimates in terms of bias and mean squared error. The applicability of the UPD is further demonstrated through real-world datasets, where it outperforms competing models, highlighting its flexibility and robustness for modeling data constrained to the unit interval.
The Double-log-exponential Family for Risk Analysis: Properties, Characterizations and Application under the U.K. Motor Non-Comprehensive Claims Triangle Data
This paper introduces the Double-Log-Exponential-Exponential (DLEE) distribution, a new special case of the double-log-exponential G (DLEG) family, designed for flexible modeling of insurance claim sizes. The DLEE exhibits remarkable adaptability in density shapes including left-skewed, bimodal, and light-tailed configurations via a single shape parameter θ, whose sign governs skewness direction. We derive some expressions for its density, and provide rigorous characterizations based on truncated moments and reverse-hazard identities. A comprehensive simulation study evaluates six estimation methods namely, maximum likelihood estimation (MLE), ordinary least squares (OLS), Cramer–von Misesestimation (CVME), Anderson–Darling estimation (ADE), right-tail Anderson–Darling estimation (RTADE), and left-tail Anderson–Darling estimation (LTADE)) across multiple parameter scenarios and sample sizes. Finally, the estimated Key Risk Indicators (KRIs), namely Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), Tail Variance (TV), Tail Mean–Variance (TMV), and Expected Loss (EL), under the six estimation methods applied to the real U.K. motor non-comprehensive claims triangle.
Regression Model for gamma Lindley distribution with Application
The present study introduces a novel regression model where the response variable follows the gamma Lindley distribution. The model's unknown parameters are estimated using the maximum likelihood method. To assess the effectiveness of these estimates, a simulation study is carried out. Furthermore, a residual analysis is conducted to examine the proposed model's adequacy. The gamma Lindley model is compared with other regression models, such as Weibull and gamma regression, based on various statistical criteria. The findings indicate that the proposed model provides a superior fit to the data compared to the alternatives. The model is expected to be applicable in diverse fields, including economics, biological research, mortality and recovery analysis, health studies, hazard assessment, measurement sciences, medicine, and engineering.
An Alternative Pearson Residual-Based Method for Outlier Detection in a Gamma Regression Model
This study develops six novel outlier-detection methods for Gamma regression (TSPR, TJPR, TAPR, GASPR, GAJPR, and GAAPR) that combine Pearson residuals scaling with Tukey’s boxplot rules and grouped absolute residuals, where thresholds are adapted from the residual behavior within successive groups of observations. These methods address outlier detection when the response is nonnegative and right-skewed, a common situation in applied settings. We compare the new procedures to six existing methods (SPRs, JPRs, APRs, Z, Z*, and G) using three performance metrics: pout (probability of detecting all true outliers), pmask (probability of masking true outliers as inliers), and pswamp (probability of misclassifying inliers as outliers). Performance is evaluated via simulation (uncontaminated data and contamination at 5% and 10%) and on a real dataset. Results show that GAJPR and GAAPR achieve the best detection power in simulations, while GASPR and GAJPR perform best on the real data; overall, GAJPR is the most effective method. The grouped absolute residuals approach prioritizes sensitivity and reduces masking but tends to increase false positives, so we recommend grouped-absolute-residuals screening followed by casewise validation or conservative re-testing before excluding observations.
Improved Inference for the XLindley Distribution: A Comparative Analysis of Confidence Intervals and Applications
The XLindley distribution is a one-parameter lifetime distribution obtained as a mixture of the exponential and Lindley distributions and is suitable for modeling positively skewed reliability and survival data. Despite its wide applicability, confidence interval (CI) estimation for its parameter has not been adequately studied. This paper proposes four CI estimation methods for the XLindley distribution parameter: the likelihood-based CI, Wald-type CI, bootstrap-t CI, and bias-corrected and accelerated (BCa) bootstrap CI. Their performance is examined through Monte Carlo simulation studies under various sample sizes and parameter values. Empirical coverage probability (ECP) and average width (AW) are used as evaluation criteria. The results indicate that the likelihood-based and Wald-type CIs provide stable and reliable coverage, particularly for small to moderate samples, whereas the bootstrap-t and BCa methods yield narrower intervals but may exhibit undercoverage in small samples. The proposed methods are further illustrated using two real datasets on bladder cancer remission times and light bulb failure times. Model comparison results based on information criteria, goodness-of-fit tests, and graphical analysis confirm the suitability of the XLindley distribution. Overall, the proposed CI methods provide practical tools for inference in lifetime data analysis.
Benchmarking Metaheuristics for Neural Network Optimization: A Comprehensive Study
Deep learning is a machine learning method that has been successfully applied in various applications. However, these methods have structures and parameters that vary according to the specific application. Training a neural network is a highly complex task, but it is crucial to the process of learning the network. This step is generally carried out using gradient descent methods. Alternatively, metaheuristics are approximate optimization algorithms grounded in specific theories and objective functions that have shown better results in various domains. The idea of this work is therefore to use metaheuristics to train a deep neural network and compare the obtained results. To evaluate the different metaheuristics, we tested them on six different machine learning datasets. We have found that the PSO algorithm gives the best results in the training data. While its accuracy deteriorates on the testing data, we can conclude that the PSO is prone to overfitting. On the other hand, simulated annealing, and genetic algorithms generalize better to the test data. Comparing the execution times of the algorithms, we have found that simulated annealing is the fastest. This makes it the algorithm with the best trade-off between execution time and accuracy.
Optimality of internal time moments in 1-bullet silent duels with generalized exponentially-convex rewards
The finite 1-bullet silent duel is a 0-value timing game, in which the duel time span is equidistantly quantized, and each of the two duelists has a generalized exponentially-convex reward function. The duel is a symmetric matrix game, and each of the duelists has the same set of optimal strategies. Such a set can consist of either optimal time moments or of probabilistic mixtures over the finite set of successive time moments of possible acting. A particular interest is in optimality of internal time moments, wherein no duel starting and duel final moments are considered. However, the starting moment is never optimal in duels with generalized exponentially-convex rewards, whichever the factor of reward steepness is. Next, there is no optimal internal moment in $3 \times 3$ duels. In $4 \times 4$ duels, the second time moment is never optimal, and the third time moment is optimal only if the reward steepness factor does not exceed a unique positive root of an algebraic equation with a sum of two exponential functions. The conditions of when an internal time moment is optimal in bigger duels are specified as well, where another algebraic equation with a sum of four exponential functions is included. In general, if the number of all time moments is even, then no optimal time moments exist in the first half of the duel span. This is also true if the number of all time moments is odd, where the time moment being right in the duel middle is non-optimal as well.
Applying Goal Programming Model and Data Envelopment Analysis to Enhance Sustainable Agricultural Efficiency
Optimising resource use for efficiency enhancement can be achieved by balancing multiple objectives/goals (i.e., economic, environmental, and social) that serve sustainable agriculture. Integrating a hybrid model of Goal Programming (GP) and Data Envelopment Analysis (DEA) proposed in this paper improves decision-making in agriculture. This model has addressed the complexities of multiple objective goals in sustainable agriculture. As a case study, the empirical results of such a hybrid model have enhanced the efficiency through optimising resource use by balancing the competing goals. The flexibility of this model makes it adaptable to various scenarios in the agriculture sector.
A Lexicographic Bi-Objective Chance-Constrained Model for Asset-Liability Management with Endogenous Reliability: Theory and Application to Banque Misr
Banks in emerging markets face persistent commercial and regulatory challenges. Profits must be maximized while ensuring solvency against economic volatility. Traditional Asset-Liability Management (ALM) models typically address this problem by treating reliability targets as constant, static parameters. A generalized Lexicographic Bi-Objective Chance-Constrained Programming model is introduced to endogenize these regulatory reliability levels as decision variables. The framework uses a hierarchical optimization system that prioritizes Capital Adequacy Surplus over Loan Returns. Historical financial data from Banque Misr (2011–2021) supplied the empirical baseline. Dynamic calibration demonstrated superior performance compared to standard static compliance. Setting the strategic floor to 80\% yields a maximized Capital Surplus of EGP 247.3 million and drives Loan Returns to a peak of EGP 28.1 million. Because the solver strictly anchors both reliability objectives at this exact boundary, the maximum possible Capital Surplus is secured alongside peak Loan Returns. The practical value of this endogenous framework becomes evident when measured against a rigid 90% static benchmark. Escaping that static rule releases EGP 2.43 million in Capital Surplus and generates an additional EGP 6.44 million in yield, preventing a 22.96% contraction in Loan Returns.
Enhancing Distribution Network Performance Through Optimal PV and D-STATCOM Placement and Sizing Using the Newton-Metaheuristic Optimizer
This paper presents the application of the Newton-metaheuristic algorithm (NMA) for the integrated optimal placement and sizing of photovoltaic (PV) systems and distribution static compensators (D-STATCOMs) in medium-voltage distribution networks. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model aimed at minimizing total annualized costs---including energy procurement, investment, and operation and maintenance---while satisfying power flow constraints, voltage limits, and device capacity bounds. To address the computational complexity, a master--slave hierarchical framework is proposed: the upper level employs the NMA to explore the discrete--continuous search space of installation locations and capacities, while the lower level employs a successive-approximation power flow solver to ensure technical feasibility. The performance of the proposed methodology is validated on standard 33-bus and 69-bus radial test systems and compared against established metaheuristics such as the Vortex Search Algorithm (VSA), Sine--Cosine Algorithm (SCA), and Atan-Sinc Optimization Algorithm (ASOA). Results demonstrate that the NMA achieves the lowest total costs: USD 2,290,259.24 and USD 2,395,022.55, respectively, corresponding to a reduction of approximately 35.55\% compared to the benchmark case without distributed resources. Moreover, the NMA exhibits superior computational efficiency, with execution times 4.8--17.1% faster than those of the benchmark algorithms, underscoring its scalability and practical applicability for real-world distribution system planning. The proposed approach thus offers a robust, efficient, and economically effective tool for integrating renewable-based active power sources and reactive compensators in modern distribution grids.
Modifying spectral conjugate gradient method for solving Iteration Problems
One diffuse and simple technique for minimizing the models is to use spectral conjugate gradients. In this work, we construct a unique Spectrum using the gradient approach to expansion a novel search Path, we have proven that the novel spectral approach Possesses the descent property and that the spectrum methodology is globally convergent. The experimental results indicate that for the test problems, the suggested methodology may be computed in conjunction with alternative conjugate gradient techniques.
Improving pelican optimization algorithm for solving integer and mixed integer optimization problems
Mixed-integer linear programming (MILP) problems, prevalent in logistics, scheduling, and resource allocation, pose significant computational challenges due to their NP-hard nature, prompting the need for efficient metaheuristic approaches. This study proposes an enhanced pelican optimization algorithm (POA) that adapts the original population-based method inspired by pelican hunting strategies of prey approach (exploration) and surface winging (exploitation) for discrete domains via novel tent-shaped transfer functions. These functions enable seamless discretization of continuous solutions into binary and integer variables, offering computational simplicity and gradual curvature superior to traditional S- and V-shaped transfers. Tent-shaped functions outperformed others, yielding lowest standard deviation values and means closest to optima, demonstrating superior stability and precision for practical optimization.
Copyright (c) 2026 Lamyaa Mohammed, Ghalya Basheer, Zakariya Algamal https://creativecommons.org/licenses/by/4.0
Improving Solutions for a Fuzzy Random Multi-Objective Linear Fractional Program
Mathematical techniques that combine fuzziness and stochastics are powerful tools for modelling and managing probabilistic, vague, and imprecise uncertainties. They are valuable because they can efficiently solve concrete problems in which data are affected by imprecision and randomness. From this perspective, this article proposes a new technique for converting multi-objective linear fractional programs whose coefficients are random and fuzzy. Using weights to represent decision-maker preferences, independent of $\alpha$-cuts representing satisfaction levels related to fuzzy quantities, transforms the random fuzzy program into an equivalent random fuzzy linear programming problem. Subsequently, the optimal solution of the random fuzzy linear programming problem is obtained within a class of optimal solutions of the weighted relative pseudo-random linear programming problem. The proposed method generates a multitude of solutions that correspond to the decision-maker's desired level of satisfaction. The solutions found in the didactic examples validate the theory. Finally, applying the method to a specific inventory management issue involving fuzzy and random trapezoidal parameters demonstrates its effectiveness and practical relevance.
AI-Enhanced ECG Diagnosis System for Myocardial Infarction with RBBB: Constant-Q Transform and ResNet50 Integration
Myocardial infarction, more commonly referred to as heart attack, is a significant cause of mortality related to cardiovascular diseases. Conduction disorders are commonly associated with this condition, the most prominent being RBBB. RBBB refers to an anomaly in electrical conduction of the heart, which can cause distortions to standard ECG patterns. Both of these can mask and mimic the classical ECG signs of MI and therefore result in misdiagnosis or delayed diagnosis. Moreover, early and accurate diagnosis is very important in saving patients from MI with a good prognosis; hence, it becomes a prime concern for the clinician. In this work, a novel approach to the accurate classification of ECG signals has been proposed with a Q-transform deep learning model of horizontal data concatenation. This study focuses essentially on the differentiation of myocardial infarction from RBBB-associated myocardial infarction. Further, the proposed model uses collective information from various ECG leads, drastically improving its capability to capture intricate cardiac patterns. In addition, the proposed model harnesses the unique electrical signatures of MI and MI with RBBB, which may manifest differently between leads. By merging multi-lead data and spectral-temporal features, the proposed model gains a comprehensive understanding of these conditions and leads thus to a substantial improvement in diagnostic accuracy. However, publicly available digital PTB-XL datasets are also used for the evaluation of the suggested architecture, where the ECGs are categorized into two classes: MI and MI associated with RBBB. In this regard, this system demonstrates exceptional performance, achieving an impressive 97. 82% precision and an exceptionally low 0.0032% training loss after 100 trained epochs. Stringent 10-fold cross-validation reinforces and strengthens these results. This groundbreaking approach simplifies diagnostic complexities by consolidating 12-lead ECG data and using CQT for precise analysis in the time-frequency domain.
Copyright (c) 2025 Statistics, Optimization & Information Computing https://creativecommons.org/licenses/by/4.0
Development of method for using a neural network for voice identification taking into account specific accents
The study aimed to investigate neural networks for voice identification with accented speech. The main models of Bidirectional Encoder Representations from Transformers (BERT) for Russian, English, Spanish, and Kazakh and their indicators were studied using the comparative and contrastive method. The main stages of creating a model for accent recognition include audio data preprocessing (noise removal, volume normalisation, fragmentation), extraction of low-frequency cepstral coefficients for the audio format suitable for analysis, Mel filtering, transformation of low-frequency cepstral coefficients for the model, training and evaluation. BERT models show different performances depending on the language. Language features such as morphology and syntax require unique customisation. For instance, BERT for Russian and Spanish incorporates declensions, and for Chinese – ambiguity and characters. The BERT for English reaches 90-96% accuracy, as the model was initially trained on English-language data. Multilingual BERT processes several languages, but the accuracy (70-85%) and F1-measure (70-80%) are lower than those of models configured for specific languages. The kazakhBERTmulti model demonstrates high accuracy (F1-measure – 0.68), outperforming Multilingual BERT Russian, and is better adapted to the Kazakh language with its agglutinative structure. The transformation of the low-frequency cepstral coefficients and using BERT achieved an accent recognition accuracy of 92%. More Kazakh data could have improved the model's accuracy, as accents are reflected in pronunciation, not spelling, so eliminating the spelling check focuses the model on accent.
EKT-XAI: Efficient Kolmogorov-Arnold Network Transformer for Lightweight Smishing Detection with Explainable AI
SMS phishing (smishing) attacks represent an escalating cybersecurity threat, with traditional detection approaches suffering from limited adaptability, computational inefficiency, and lack of interpretability critical for security applications. This paper introduces EKT-XAI (Efficient Kolmogorov-Arnold Network Transformer with Explainable AI), a novel framework that integrates learnable activation functions within transformer architectures to enhance SMS phishing detection while providing comprehensive model interpretability. The approach replaces traditional fixed activation functions with Kolmogorov-Arnold Network (KAN) layers that implement adaptive B-spline basis functions, enabling task-specific nonlinear transformations learned during training. The framework incorporates four complementary explainability mechanisms: attention weight visualization, LIME feature attribution, KAN activation pattern analysis, and decision path tracing, operating simultaneously with prediction to provide real-time model interpretation without computational overhead. Comprehensive evaluation on the SMS Spam Collection Dataset (5,574 messages) and SMS Phishing Dataset (5,971 messages) demonstrates exceptional performance, achieving 99.99% and 99.89% accuracy respectively, outperforming existing state-of-the-art approaches including CNN-LSTM ensembles (99.74%), BERT-based models (99.28%), and traditional ensemble methods (99.58%). Ablation studies validate the critical contribution of KAN layers (1.33% accuracy improvement) and attention mechanisms (2.77% improvement) while maintaining computational efficiency suitable for mobile deployment. The integrated explainability framework enables security analysts to understand classification decisions, validate model reasoning, and identify potential attack vectors through interpretable visualizations. The framework's computational efficiency (256-dimensional embeddings, 4 attention heads, 3 transformer layers) enables real-time inference on mobile devices while preserving privacy through on-device processing. This work establishes the first successful integration of learnable activation functions within transformer architectures for cybersecurity applications, demonstrating that adaptive neural networks combined with built-in interpretability can significantly advance mobile security capabilities while addressing practical deployment requirements for next-generation SMS protection systems.
New Insights into Fixed Points Results in Neutrosophic Metric Like-Spaces and Their Applications
This paper introduces and develops the concept of neutrosophic metric-like spaces, extending both intuitionistic fuzzy metric spaces and traditional metric-like spaces. These spaces offer a more comprehensive framework for exploring mathematical structures and their properties. Key topics such as sequence convergence, G-Cauchy sequences, contractive mappings, and fixed-point theorems are examined within this context. To demonstrate the practical significance and utility of the proposed framework, illustrative examples and a discussion of potential applications are provided.
The Implementation of Artificial Neural Networks and Resolving Efficient Dominating Set for Time Series Forecasting on Vertical Farming Soil Moisture
Vertical farming is the practice of growing crops, where the cultivated plants are grown vertically rather than horizontally using soil media. Vertical farming is developed specifically in certain indoor by using Controlled Environment Agriculture (CEA) technology. Since we can cultivate some plants combination in the vertical farming, thus the use of CEA technology is inevitable. The control mechanisms of the vertical farming is more complex, especially related to the control of air temperature, air humidity, light intensity, and soil moisture. In recent times artificial neural networks (ANN) has become popular and helpful model for forecasting, classification, clustering, and forecasting especially in a precession agriculture. ANN is one type of model for machine learning (ML) and has become relatively competitive to conventional regression and statistical models regarding usefulness. In this paper, we will analyze the effectiveness of two artificial neural network architectures in time series forecasting on soil moisture in the vertical farming and analyze it in term of iteration number, mean square error, regression, and their learning rate. The results shows that the best architecture for phase 1 is obtained by Feedforwardnet ANN-567 model, with MSE of 0.3810. The best architecture for phase 2 is obtained by Paternet ANN- 567 model with an MSE of 1,1374x10^(-9). The best architecture for phase 3 is obtained by Cascadeforwardnet AA-567 model with an MSE of 1,1232x10^(-10). Finally, the best architecture for phase 4 is obtained by model Cascadeforwardnet ANN-567 with an MSE of 1,0780x10^(-17).
Restricted additive Schwarz algorithm for a θ finite difference method to solve a two-dimensional 4th-order partial differential equation with variable coefficient
This paper deals with a θ finite difference scheme to solve two-dimensional 4th-order partial differential equation with variable coefficient, which governs the transverse vibrations of a thin plate. First we establish some a priori estimates for the weak solution of our problem. Then, we introduce a new variable, allowing us to convert the plate equation into a system of two second-order differential equations. Stability and convergence analysis are carried out by employing the energy estimate method. We present a class of domain decomposition methods (DDM) called restricted additive Schwarz (RAS). This method is used as a preconditioner for the GMRES algorithm to solve systems of linear equations arising from the discretization of the plate equation by the present scheme. Numerical experiments are provided, confirming the effectiveness of the algorithms.
Representation of solutions of partial differential equations by contour integrals in two-dimensional complex space
The study aimed was to develop an efficient method for obtaining solutions to partial differential equations (PDE), in particular, equations with polynomial coefficients and Helmholtz-type equations. The methodology included the analysis of the properties of differential forms, which made it possible to represent solutions of the PDEs in the form of series, terms being contour integrals in a two-dimensional complex space. For some cases of equations, contour integrals were reduced to special functions of two variables. This approach significantly simplified the analysis and application of such solutions. The main results of the study show that the proposed scheme for constructing solutions was effective and can be applied to a wide class of PDEs, including equations with polynomial coefficients and Helmholtz-type equations. Specifically, the study determined that solutions of Helmholtz-type equations can be represented in the form of special functions, particularly Bessel functions, which ensures the accuracy and universality of the results obtained. It was also proved that the use of contour integrals to construct solutions in the form of series provided stable and convergent results, which was critical for practical applications. The obtained results also indicate the possibility of applying this methodology to the analysis of more complex systems described by the PDE. The conclusions of the study emphasise the importance of the obtained results for the development of the PDE theory. The proposed approach is based on the use of the method of contour integrals, which allows us to construct explicit solutions of boundary value problems in the half-plane, strip and circular domains. The obtained formulas, which use variable substitution and contour integrals, ensure the accuracy and efficiency of solutions, and also allow the use of methods of the theory of analytic functions for the construction of boundary conditions.
LLM-Based Optimized Adaptive Threat Monitoring Framework for Malicious Domain and Adversarial URL Detection Process
The malicious domains and adversarially crafted URLs in cyber threats evolve at a very high speed. The detection frameworks need to be robust, adaptive, and scalable in such scenarios. Traditional detection mechanisms are static feature-based approaches that cannot perform well against unseen threats, adversarial manipulations, and long-term attack evolutions. Existing systems lack granular threat attribution, cross-organization intelligence sharing, and adversarial robustness, making them unsuitable for modern cyber defenses. To address these limitations, we introduce an LLM-Based Frequent Monitoring Framework that combines five advanced techniques: Meta-Learned Self-Supervised Domain Generalization (ML-SSDG), Reinforcement Learning-Augmented Adversarial Training (RL-AdvTrain), Hierarchical Multi-Task Threat Classification (HMT-TC), Temporal Memory-Augmented Transformer for Sequential Threat Detection (TMAT-STD), and Federated Privacy-Preserving Threat Intelligence Learning (FPPTIL). ML-SSDG can achieve zero-shot detection for novel attack domains with a reduction of false negatives by 20% and enhancement of zero-shot accuracy by up to 30%. RL-AdvTrain strengthens the model against masked malicious URLs, detecting 40% more adversarial threats. HMT-TC improves threat attribution and increases classification accuracy for attacks by 50%. TMAT-STD allows for identifying emerging domain threats in real-time, while such detection reduces the response time to domain-based malware campaigns by 30%. The last one is FPPTIL, which allows shared cross-organization threat intelligence without sharing the source of private data. The process of global threat detection improves by 30%. Our framework achieves a holistic, real-time, and privacy-preserving cyber defense solution that adequately outperforms traditional approaches in adversarial resilience, threat attribution, and zero-shot detections. Taken together, these improve the cybersecurity posture, reduce false positives, and support proactive mitigation of emerging cyber threats at scale in process.
Advanced Partitioned Neutrosophic Offsets, Oversets, and Undersets: Modeling “Neither Agree nor Disagree” and Beyond
Modern uncertainty-modeling frameworks-fuzzy sets, intuitionistic fuzzy sets, hyperfuzzy sets, neutrosophic sets, and plithogenic sets-provide powerful tools for capturing vagueness and imprecision. In particular, neutrosophic sets characterize each element by three independent degrees: truth, indeterminacy, and falsity. Classical neutrosophic sets have been refined by partitioning the membership degrees into additional components. Recently, the Hexapartitioned Neutrosophic Set, Octapartitioned Neutrosophic Set, Nonapartitioned Neutrosophic Set, and Decapartitioned Neutrosophic Set have been defined. In this paper, we introduce four novel partitioned models-hexapartitioned, octapartitioned, nonapartitioned, and decapartitioned neutrosophic offsets/oversets/undersets-and show how each can be seamlesslyembed ded into the plithogenic-set framework.
Fast and Secure Color Image Cryptosystem Based on 2D Henon Chaotic Map and Josephus-ZigZag Permutation
In this study, we propose a color image encryption scheme that integrates multiple techniques, including chaotic maps and DNA encoding, to ensure high security and robustness. The encryption process begins with circular shift and rotation operations to permute pixel positions and disrupt spatial correlations. Subsequently, two chaotic maps, including a one-dimensional (1D) chaotic system, are employed to generate key sequences. These sequences are combined with the original image through XOR operations among the three derived matrices to introduce strong confusion. To further enhance security, an additional layer of DNA-based encryption is applied, reinforcing resistance against statistical and differential attacks. The robustness of the proposed scheme has been thoroughly evaluated using several standard test images and assessed through common performance metrics such as histogram analysis, correlation coefficient, entropy, PSNR, and MSE. Moreover, NPCR and UACI analyses confirm the algorithm’s high sensitivity to slight variations in the encryption key or the plain image. Comparative experiments with several well-established image encryption methods demonstrate that the proposed approach achieves superior security performance and efficiency.
On a New Subclass of Bi-Univalent Functions Associated with Gregory Numbers
The purpose of this article is to provide the inclusive subclass YΓ(⅁, ς, σ, φ,m) of the class of bi-univalent functions that make use of Gregory numbers. At each of these subclasses of analytic functions, we investigate the Fekete-Szego functional, in addition to the estimations of the Taylor-Maclaurin coefficients, which are denoted by the symbols |ϱ2|and |ϱ3|. It is possible that such a subclass will be the focus of research in the future due to the unprecedented nature of their characterisations and the proofs.
New Types of Integral Contractions in Supra Metric Space
In the present article, we shall define the new notions of generalized (S − ψ) contractions of integral type A and B and prove the related fixed point theorems in the setting of supra metric space. Then, we shall deduce some new results from the proved results in the form of consequences. An example will also be given to show the real existence of a proved result. Finally, as an application a Fredholm integral equation is solved.
A Smart System for Optimal Harvest Crop Time Prediction Using Historical Climate and Market Price Data
The most important function for the farmer cultivating more crops is to correctly predict the time and price for crop harvests. However, the existing methods' functions cannot predict the crop harvest time and prices accurately due to the fewer features they use. Therefore, in this existing research work, an innovative method known as the Hyena Zfnet Encoder Framework (HZEF), acting as the forecasting system for accurately estimating crop harvest prices and crop harvest times, has been proposed. The proposed method started by considering crop information, past climatic information, crop harvest, and crop harvest prices. Filtered images were employed for removing image noise obtained from the learned image representation. For selecting features, techniques were applied.Consequently, through the matching of the harvesting data and the climate change data, the harvesting season was projected, and the historical data on the prices was applied in deriving the probable prices. The voice assistant functionality proceeded to analyze the harvesting times and the prices associated with each tested agricultural produce. Finally, the new model attained the R-square value of 99.7%, the Root Mean Square Error value at 0.007%, and the computation time of 0.0003s compared to the conventional models. Keywords: Best harvesting time, Price estimation, Voice assistant, Feature analysis, Deep learning, Climate change
Unpacking ORB-SLAM for UAV Navigation and Evaluating Its Efficiency Across Diverse Environments: A Systematic Review
Vision-based Simultaneous Localization and Mapping (VSLAM) has become increasingly important for autonomous navigation of Unmanned Aerial Vehicles (UAVs), as it enables simultaneous localization and mapping of unknown environments using visual sensing alone. Among existing VSLAM approaches, ORB-SLAM is distinguished by its robustness under diverse operating conditions, real-time capability, and computational efficiency. This study provides a systematic and comprehensive review of the ORB-SLAM framework, with emphasis on its application in UAV navigation and mapping. We present a detailed analysis of the algorithm’s core components, namely tracking, local mapping, and loop closure. In addition, the challenges associated with deploying ORB-SLAM on aerial platforms are examined, including high dynamic motion, environmental variability, and limited onboard computational resources. This work further offers a comparative evaluation of ORB-SLAM with other SLAM methodologies, highlighting its advantages and limitations across its successive variants. Finally, potential future research directions are discussed to address existing challenges, incorporate deep learning techniques, and evaluate real-world deployment feasibility. This review aims to clarify the role of ORB-SLAM in UAV applications and outline prospective development pathways.
A CNN2D-LSTM Framework for Rule-Based Pedestrian-Vehicle Risk Scenario Detection
In this work, we developed an approach for rule-based pedestrian-vehicle risk scenario detection from video sequences. The contributions lie in the classification of "risky" and "non-risky" situations automatically derived from behavioral and physical cues, which could improve accident prevention and intelligent driver-assistance systems. A two-dimensional convolutional neural network (CNN2D) is employed over the frames of the videos for visual features extraction, while the LSTM recurrent network models the temporal dynamics of the sequences. The data used in these experiments are sequences of video frames extracted from the JAAD dataset. Behavioral and physical variables include pedestrian crossing, gaze direction, vehicle action, proximity, which are used only for generating the risk labels. They are not provided as explicit input to the model. While these labels are heuristically generated and may not capture all possible risky scenarios, they provide a practical framework for model training and evaluation. The CNN2D extracts the spatial visual features from the frames, while the LSTM captures temporal dependencies, which permits the model to learn both in the spatial and temporal axes for the prediction of risk. Tests on the JAAD dataset composed of varied traffic conditions and images of pedestrian crossings report overall accuracy of 97% and class-wise precision 99.5% for the "No Risk" class and 92.2\% for the "Risk" class. These results confirm the effectiveness of the suggested model and demonstrate the usefulness of fusing visual and temporal information collectively for automatic risk detection in difficult traffic environments.
Integrating Tuned Ensemble Learning and Explainable AI for Reliable Stroke Prediction
A stroke occurs due to a sudden interruption of blood flow to a specific region of the brain. Although extensive research has already been conducted using this dataset, previous studies have not effectively addressed the issue of data leakage during testing. The primary contribution of this study is to mitigate both the data leakage and class imbalance problems byapplying three different balancing techniques. We employed nine classification algorithms in this study: Random Forest (RF), AdaBoost (AB), Logistic Regression (LR), Gradient Boosting (GB), K-Nearest Neighbors (KN), Decision Tree (DT), Naive Bayes (NB), Voting (VT), and Stacking (ST). A key aspect of our approach involved using hyperparameter tuning to determine the optimal configuration for each model. Additionally, we proposed a novel ensemble method, named Voting, which combines hyperparameter-optimized LR, DT, GB, and RF classifiers. The performance of this method was compared with other models using various evaluation metrics, including accuracy, precision, ROC curve, PR-AUC curve, and more. To enhance the interpretability of important features within the dataset, we also applied two explainable AI (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP).
A two-step fixed-point iterative scheme for solving Generalized Absolute Value Matrix Equations
This study introduces a numerical approach for solving the Generalized Absolute Value Matrix Equation. The motivation of this work lies in the fact that such equations arise in various applied mathematical and engineering problems, where the presence of the absolute value term makes the system strongly nonlinear and difficult to solve using standard linear algebra techniques. The elementwise absolute value introduces a nonlinearity, which makes standard linear solvers unsuitable. A tailored two-step fixed-point iteration is developed and tested on problems of varying sizes. Numerical experiments demonstrate that, with a suitable relaxation parameter, the method achieves reliable convergence, maintaining robustness and accuracy even for moderately sized problems.
Copyright (c) 2026 Nesrine Tabchouche https://creativecommons.org/licenses/by/4.0
Predicting Sunspot Numbers Based on Neutrosophic Time Series
Neutrosophic Time Series applies Neutrosophic principles for resolving forecasting problems. It can be applied to analyze ambiguous or imprecise data that are difficult to process using conventional methods. 1D Convolutional Long Short-Term Memory Network (Conv1D-LSTM) is a hybrid of two approaches that are frequently employed to manage time series data and produce an accurate predictive model. The monthly Sunspot number data were used from the WDC-SILSO, Royal Observatory of Belgium, from January 1900 to December 2024. The dataset consists of 125 years, including 1500 months. Data normalization (Min-Max Scaling) was used to uniformly scale sunspot values to enhance model performance and stabilize training for predictive modeling. The data was divided from January 1900 to June 1987 as a training set (70%) and from July 1987 to December 2024 as a test set (30%). A hybrid method was proposed to improve the accuracy of predictions by integrating the outputs of a neutrosophic model as an input for the Conv1D-LSTM model. We compared the proposed method with neutrosophic, and Conv1D-LSTM. The evaluation metrics highlight that the proposed model outperforms other models across all performance metrics, indicating its superior forecasting ability. It is sufficiently flexible and can be readily extended to other phenomena exhibiting comparable characteristics. The neutrosophic model closely follows the Conv1D-LSTM model, indicating highly competitive results. The highest number of sunspots is predicted for June, July, September, and October 2026
Cascading Instruction Influence Indirect Prompt Injection in Hierarchical Multi-Agent Systems
Hierarchical delegation in multi-agent LLM systems, architectures now common across AutoGen, LangGraph, and CrewAI deployments, creates attack surfaces that single-agent security models simply do not address. This paper examines indirect prompt injection propagation through three-tier command chains. The effect is substantial. Comparing hierarchical to centralized topologies yields Cohen’s d = 2.34 (95% CI: 1.82–2.86). This effect size indicates that the mean compromise rate in hierarchical configurations exceeds that of centralized systems by approximately 2.34 standard deviations. The 95% confidence interval ranging from 1.82 to 2.86 suggests with 95% certainty that the true population effect lies within this bound, ruling out null or trivial effects. Conventionally, d > 0.8 represents a large effect; the observed magnitude exceeds this threshold substantially, indicating not merely statistical significance but practical importance. To emphasis: hierarchy depth nearly triples the standardized risk exposure. We formalize this propagation via the Cascading Instruction Influence (CII) model, validated against behavioral data from ten production LLMs. Depth increases compromise probability nonlinearly; blast radius plateaus only when context windows saturate. Two mechanisms drive this. Context window pollution accumulates across tiers. Privilege boundaries erode through delegation chains. Sandboxed mitigations reduced attack success to 23.4%, significant, yet clearly insufficient. The assumption that hierarchy enhances security is contradicted by these results. We mapped vulnerabilities to EU Cyber Resilience Act requirements. Architectural redesign, not incremental patching, appears necessary.
Relation type-aware knowledge graph embeddings for biomedical data: a semantically adaptive framework
Knowledge graphs (KGs) are increasingly used in biomedicine to integrate and reason over heterogeneous data such as genes, proteins, drugs, and diseases. However, existing knowledge graph embedding (KGE) methods typically rely on a single, fixed scoring function for all relation types, which limits their ability to model the semantic diversity of biomedical interactions. In this work, we propose a relation type-aware KGE framework that dynamically adapts scoring functions to the structural nature of relations hierarchical, symmetric, or asymmetric thereby improving the semantic fidelity of embeddings. We improve training with a constraint-aware negative sampling approach that creates realistic false examples. This forces the model to focus on learning true biomedical relationships instead of dismissing easy, nonsensical ones. We built our knowledge graph by grabbing biomedical papers from PubMed. Then, we ran them through some natural language processing tools. After that, we used the SemRep server to make subject-relation-object triples, which gave us the graph structure for studies on colorectal cancer. Our KG includes entities and relations at the molecular, clinical, and pharmacology levels. We set up the learning objective as a minimization problem with regularization. This combines margin-based ranking loss with relation-specific changes. We checked our work using MR, MRR, Hits@1, and Hits@10, which let us do a good comparison with baselines like TransE, ComplEx, RESCAL, and ConvE. The results showed our system improved MRR by 9-14% and Hits@10 by as much as 12% compared to other good systems. It was especially good at understanding things like subtype of and interacts with. In short, this research gives a way that can be scaled, is based on math and biology for filling in the blanks in biomedical knowledge graphs. By putting together relation-aware modeling, constraint-guided learning, and several ways of measuring, our method moves forward knowledge graph-based in cancer research and gives a method that can be copied for use in other bio fields.
Comparative Analysis of LSTM and GBR for BLDC Motor Speed Estimation Under Noisy Conditions
Estimation of BLDC motor speed plays a critical role in enhancing the efficiency and reliability of electric motors, particularly with the complexities of real-world operating conditions. In this work, we applied the techniques Long Short-Term Memory (LSTM) networks and Gradient Boosting Regressors (GBR) for estimating motor speed in environments characterized by noise and unpredictable changes. The dataset was generated from a numerical simulation of the BLDC motor dynamic model (Linix 45ZWN24-40), implemented in MATLAB/Simulink. This simulation-based approach was adopted to allow controlled introduction of Gaussian noise and abrupt torque transitions, enabling systematic evaluation of model robustness before future physical implementation. The input features consist of voltage, load torque, and motor parameters (B, Ls) varied across scenarios to represent real-world parameter uncertainty. The simulations conducted with the LSTM model resulted in a mean squared error (MSE) of 2580.12 and an R-squared value of 0.95. In contrast, the Gradient Boosting Regressor (GBR) achieved an MSE of 3150.87 and an R-squared value of 0.93. While GBR requires less time for training, LSTM consistently provided higher accuracy, particularly during the rapid variations in torque. This systematic comparison of two machine learning models offers practical insights for engineers tasked with developing motor control systems in unpredictable and dynamic environments.
Copyright (c) 2026 Hamza Aggoun, Brahim Rouibah, Ahlam Labdaoui, Abderraouf Boumassata, Güler İnan https://creativecommons.org/licenses/by/4.0
Enhancing Surface Water Potability Assessment through a v-SVM Hybrid Statistical Learning Model
The overall aim of the study is to improve the classification of water as either drinkable (potable) or non drinkable. Traditional laboratory monitoring is time consuming, expensive and inefficient in monitoring quickly or in large scale. Alternatives to machine learning may be faster, although their performance is identifiable by the hyperparameter tuning procedure, which entails the choice of model settings. This paper proposes a new hybrid algorithm, that combines v-Support Vector Regression (v-SVR) with the sparrow search algorithm (SSA) in order to achieve the task of hyperparameter tuning automatically. The hybrid model referred to as VSRM-SSA is tested on a dataset of the water quality in the form of 3,276 samples and 10 water quality items. The results depict that the VSRM-SSA model is much better than the others in terms of high accuracy in classification. In the case of the data to be trained on, the accuracy was as high as 97.1 percent with G-mean and MCC being equal to 0.966 and 0.961 respectively. The model has already demonstrated good generalization capability using the test data with the accuracy of 91.7% and G-mean of 0.912 and the MCC of 0.907. Obviously, these values are greater than those that have been obtained with random search, Bayesian optimization, cross-validation, or grid search. Also, VSRM-SSA is the fastest method to compute (109 seconds) of all the methods tested. All in all, the suggested VSRM-SSA model offers rapid, precise, and consistent water potability classification. Its sensitivity is high and its overall performance is equal making it promising in cases where the real-time water-quality and public-health is required.
Copyright (c) 2026 Ahmed Naziyah Alkhateeb, Ahmed Mutlag Algboory, Zeina Ameer Hadied, Oday Esam Al-Saqal, Zakariya Yahya Algamal https://creativecommons.org/licenses/by/4.0