This paper introduces the Adaptive Exponential Weighted Moving Average (VAEWMA) Control Chart which has a Variable Sample Size technique to improve process monitoring. Our new method involves an integer linear function that will dynamically change the sample sizes based on the AEWMA statistic value using the smoothing constant, lambda, of the EWMA chart to enhance responsiveness and efficiency. Substantial simulations are made between the VAEWMA and conventional fixed sample size EWMA, Variable Sample Size EWMA (VEWMA) and fixed sample size adaptive EWMA control charts. The proposed Dynamic Sample Size EWMA (VAEWMA) chart proposes the new combination of integer linear sampling adjustment and an adaptive exponential smoothing mechanism. The VAEWMA chart optimizes both variables in the same framework as opposed to the past research that had studied either variable sample size or adaptive weighting alone. This two-fold flexibility improves sensitivity to small and medium changes and uses the economical sampling efficiency, which is evident with obvious benefits over the current EWMA-type control charts. Findings indicate that VAEWMA has a high level of detection, reduced false alarms, and high overall performance. The usefulness and best functionality of our suggested chart is supported by a practical example based on real-life data and, therefore, has a potential to be incorporated in the contemporary quality management practice.

This study aims to analyse the trend of potato area, production, and yield in Russia and India by employing four different univariate time series models, i.e. ARIMA, Holt’s linear trend, alpha-ARIMA, and Sutte-ARIMA. Secondary data collected from the Food and Agriculture Organization (FAO) covering the period from 1992 to 2023 was utilized to forecast up to 2030. The comparative analysis represented that the α-ARIMA model was found to be the best in all three components of Russia and for area and yield in India, while the Holt linear trend model performed best for potato production in India, exhibiting maximum R2 values and minimum values of MAPE, MPE, RMSE, and MAE with a 95

This study explores the magnetohydrodynamic (MHD) flow of a Jeffrey-Hamel fluid within a convergent/divergent channel, a scenario relevant to both physical and biological sciences. The flow dynamics between nonparallel inclined walls are governed by highly nonlinear differential equations derived through conservation laws and similarity transformations. By applying similarity transformations, the governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs). The NDSolve approach is then utilized to obtain numerical solutions for these equations. A comparison with existing methods in the literature confirms the accuracy and reliability of the results. Additionally, the impact of various dimensionless physical parameters, such as the influence of the magnetic parameter, angle alpha, and the Deborah number on the velocity profile is investigated. The parameters angle alpha, Eckert number, and volume friction are examined on the temperature profile, followed by a detailed discussion of the findings.

In Odisha, potato is a key crop that experiences significant price fluctuations due to variations in production and market arrivals. This study focuses on the Banki Market in the Cuttack district, a critical trading hub, to forecast potato prices and arrivals, aiding farmers in crop planning. Data from April 2019 to May 2024 on potato arrivals and prices were analysed using machine learning models for prediction estimation. After outlier removal, artificial neural network (ANN) models, specially the time delay neural network (TDNN) model, and support vector regression (SVR) models with varying hidden layer nodes were fitted. Diagnostic test, the Box–Pierce tests were performed to validate the assumptions of error independence and normality. Models that met these criteria were evaluated using root mean square error (RMSE) and mean absolute percentage error (MAPE) to identify the best fit. TDNN(5,8) was chosen as the best model for arrival series with an improvement of 4.3

[This retracts the article DOI: 10.1016/j.heliyon.2024.e25471.].

This study investigates the yield stability and adaptability of sixteen open-pollinated tomato (Solanum lycopersicum Mill.) genotypes across multiple environments in the Kashmir Valley, employing Additive Main Effect and Multiplicative Interaction (AMMI) analysis to examine genotype-environment interactions (GEI). A Randomized Complete Block Design (RCBD) with three replications was implemented at six locations over two years, representing the region's diverse environmental conditions. Analysis of variance indicated that the effects of environment (E), genotype (G), and GEI were all highly significant (p < 0.001) in influencing yield per hectare, with the environment contributing 47.5% of the total variation, underscoring the impact of local conditions on performance. Key stability indicators, including Weighted Average of Absolute Scores (WAAS) and Multi-Trait Stability Index (MTSI), assessed genotype stability and yield, with Arka Meghali and NDF-9 emerging as top-performing varieties across locations. Arka Meghali achieved the highest yield, while NDF-9 showed remarkable adaptability. The consistent rankings provided by stability indices reinforced the reliability of WAAS and MTSI as selection tools in multi-environment trials. Based on multi-environment stability analysis, Arka Meghali and NDF-9 were identified as superior open-pollinated tomato genotypes combining high yield and stability across diverse environments. These varieties are recommended for cultivation in the Kashmir Valley to enhance tomato productivity under variable agro-climatic conditions.These findings underscore the utility of GEI analysis in identifying tomato genotypes with robust yield and stability, providing valuable insights for breeding programs and crop management in ecologically sensitive regions like the Northern Himalayas. This research establishes a foundation for future studies on environmental adaptability in crop improvement, highlighting the potential of selected genotypes for sustainable cultivation in Kashmir.

ABSTRACT Fuzzy mathematical operations play an important role in the field of decision‐making. Decision‐making tools are being used in every field of life. Fuzzy operators are the building blocks for making a decision in the realm of uncertain information. The information is often in qualitative form which needs a qualitative approach for decision‐making rather than a quantitative one. The linguistic term sets are the mathematical tools to collect the qualitative data from experts of the fields and the conversion of linguistic data in the form linguistic intuitionistic fuzzy data is the more efficient and reliable for the process of decision making. The fuzzy aggregation operators are the best tools for the aggregation of uncertain and vague data. This work addresses a real‐world decision‐making problem of choosing the best diagnostic approach for the diagnosis of cardiovascular diseases by introducing a novel decision‐making technique with fuzzy aggregation operators in the domain of linguistic intuitionistic fuzzy (LIF) sets. Two new operators are used in this method: the Dynamic Linguistic Intuitionistic Fuzzy Dombi Weighted Averaging (DLIFDWA) operator and the Dynamic Linguistic Intuitionistic Fuzzy Dombi Weighted Geometric (DLIFDWG) operator. This work aims to identify an optimal technique for diagnosing cardiovascular illness using Dombi operations in the Linguistic Intuitionistic Fuzzy environment. The Dombi Operations are highly versatile and successful in addressing vagueness and uncertainty, making them crucial in our methodology. To demonstrate the effectiveness of the offered strategies, we have implemented the recommended operators for the selection of optimized diagnostic approach for cardiovascular diseases. This showcases the significance of these strategies in facilitating decision‐making. Ultimately, we perform a thorough analysis to showcase the reliability and uniformity of the produced procedures, comparing the provided operators with various current counterparts.

Systems of partial differential equations (PDEs) serve as fundamental tools for modeling and solving complex, multidimensional problems involving interdependent processes. This paper introduces an efficient technique for solving linear and nonlinear systems of PDEs of both integer and fractional orders. The proposed method, referred to as the Modified Yang Transform Method (MYT), combines the Adomian Decomposition Method with the Yang Transform. Owing to its simplicity and versatility, this approach holds significant potential for application across diverse scientific and engineering fields. A generalized solution procedure is outlined in a step-by-step manner for both integer- and fractional-order systems. Theoretical analysis is performed to ensure the method’s convergence and stability. To demonstrate the method’s effectiveness, several illustrative examples are solved. Visual validation is provided through 2D and 3D plots that depict the behavior of the solutions, while numerical error analysis is presented in tabular form. These results reveal that the approximate solutions exhibit strong agreement with the exact ones, with improved accuracy as the number of iterations increases. Detailed discussions of the findings are included to further support the reliability and applicability of the proposed method.

This article aims to design a better creative distribution by strengthening the Frechet distribution with the family of generalized Weibull distribution for the analysis of complex data sets in a variety of domains. The generalized Weibull-Frechet distribution with four parameters is the revolutionary model. This model combines the flexibility of the generalized Weibull distribution with the heavy-tailed behavior of the Frechet distribution, resulting in a more accurate and robust distribution for modeling complex data sets. The probability density function of the distribution can take numerous shapes and graphs and can be used to efficiently describe complicated data sets. This model has the potential to improve the accuracy and efficiency of data analysis in various fields. The generalized Weibull-Frechet distribution can capture a wider range of data patterns and provide more accurate predictions. In addition, its flexibility allows it to be adapted to specific data sets and research objectives. Overall, this study contributes to the advancement of statistical modeling and provides a valuable tool for researchers and practitioners in various fields. Some distributional properties and statistical functions are given. The unknown parameters are estimated using the maximum likelihood method and Bayesian method via Monte Carlo simulations. The Bayesian approach is discussed using three different loss functions. We apply the composite distribution to four real-world applications in the medical and engineering fields. A comparative study with some competing statistical models is performed for a sample and some detection criteria. The results show that the generalized Weibull-Frechet distribution outperforms other commonly used distributions in terms of goodness of fit and prediction accuracy.

This article introduces a novel approach, the triple exponentially weighted moving average (TEWMA) control charting structure, designed to monitor the process coefficient of variation (CV) along three parametric log-normal transformations. The average run length (ARL) and standard deviation of the run length (SDRL) are considered as a performance measures, and both are computed through Monte Carlo simulations. A comparative analysis with an existing control chart enriches the study. For the practical importance of the suggested control charting structure a real-data set from the sanitary sector die-casting process is studied.

The extended exponential weighted moving average (EEWMA) statistic which is based on two smoothing constants to incorporate both past and present information. In this study, the EEWMA statistic is employed to estimate the population mean using a suitable auxiliary variable. Memory-type ratio and product estimators are developed under ranked-based sampling (RBS) schemes. The mean square error (MSE) expressions for the proposed memory-type estimators are derived. A simulation study is carried out to evaluate the performance of these estimators. Additionally, a real-life dataset is used to illustrate the practical application of the proposed estimators.

The negative binomial regression model (NBRM) is popular for modeling count data and addressing overdispersion issues. Generally, the maximum likelihood estimator (MLE) is used to estimate the NBRM coefficients. However, when the explanatory variables in the NBRM are correlated, the MLE yields inaccurate estimates. To tackle this challenge, we propose a James–Stein estimator for the NBRM. The matrix mean squared error (MSE) and the scalar MSE properties are derived and compared with other estimators, including the ridge estimator (RE), Liu estimator (LE), and the MLE. We assess the performance of the suggested estimator using two real applications and a simulation study, with MSE serving as the assessment criterion. Results from both simulations and real applications demonstrate the superior performance of the proposed estimator over the RE, LE, and MLE.

Accurately forecasting energy commodity prices and financial time series has long been a challenge for policymakers and energy market participants. Various existing decomposition techniques were introduced in this scenario, like EMD, EEMD, and CEEMDAN but these techniques in the majority of the cases perform poorly in cases when a time series data exhibits irregular fluctuations and has a trend. This study introduces a novel decomposition, the Robust Adaptive Decomposition (RAD) technique, for time-series data forecasting. As its name implies robust means not duly affected by the presence of outliers, here Adaptive means having the ability to change to suit changing conditions and in signal processing decomposition is the process of dividing a signal into its subcomponents. So, this novel technique can decompose complex time-series signals like stock prices or Brent Oil prices. The RAD technique consists of several steps (i) choosing appropriate weights for each value of the dataset (ii) finding the weighted mean of adjacent points (iii) connecting these weighted points with cubic spline (iv) performing sifting as given in methodology (v) stop sifting when stopping criteria reach. The performance of RAD-ARIMA and RAD-LSTM was compared with six hybrid models such as EMD-ARIMA, EMD-LSTM, EEMD-ARIMA, EEMD-LSTM, CEEMDAN-ARIMA, CEEMDAN-LSTM, this novel technique outperformed the other six hybrid models for the simulated scenarios and a real-world data set of daily Brent Spot prices. Also, the RAD technique copes with the problem of model fitting and the labor involved with the classical decomposition techniques.

This study examines trends in potato production across five major global producers (China, India, Russia, Ukraine and the US) using annual data from 1961 to 2022. We evaluate the T-ARMA, ARIMA-ARCH, Weibull and score-driven models to forecast production from 2023 to 2030. The results identify the T-ARMA model as optimal for China and India (validation MAE: 1,668 thousand tonnes and 3,277 thousand tonnes, respectively), while the ARIMA-ARCH and Weibull models perform best for Russia (MAE: 1,405 thousand tonnes) and the US (MAE: 547 thousand tonnes), respectively. Projections indicate a marginal decline in China (from 96,073 to 96,066 thousand tonnes; − 0.007

Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks. It is the identification of malicious activity, unauthorized access, and possible intrusions in networks and systems. Modern detection methods employ artificial intelligence and machine learning to study vast amounts of data, learn patterns, and anticipate potential threats. Real-time monitoring and anomaly detection improve the capacity to react to changing threats more rapidly. Cyber threat detection systems aim to reduce false positives and provide complete coverage against the broadest possible attacks. This research advocates for proactive measures and adaptive technologies in defending digital environments. Improvements in detection ability by organizations will assist in safeguarding assets and integrity in operations in this increasingly digital world. This paper draws on the categorization of cyber threat detection methods using hesitant bipolar fuzzy Frank operators. Categorization is a step that is necessary for systematic comparison and assessment of detection methods so that the most suitable method for particular cybersecurity requirements is chosen. Furthermore, this research manages uncertainty and vagueness that exists in decision-making by applying hesitant bipolar fuzzy logic. The importance of the work lies in how it fortifies cybersecurity architectures with a formal method of discovering optimal detection measures and improving responsiveness, resulting in holistic protection against dynamic threats.

Tornados are extremely catastrophic, and the global effect of natural calamities like tornados is enormous and needs prompt and effective management. We can tackle this problem by using measures like multi-criteria decision-making (MCDM) to identify high-risk areas of a potential tornado strike. We frequently use MCDM techniques to solve the complexities and uncertainties of modern-era problems. We present a study that builds a prediction model by combining the Dombi aggregation operator with a complex m-polar fuzzy set (CmFS) to accurately guess when a tornado will hit. Our proposed model determines an expert panel, criteria, and a set of alternatives after identifying the problem. We create summed-up decision matrices using complex m-polar fuzzy Dombi aggregation operators (CmFDAO) after experts evaluate criteria and options. The algorithm then presents the best option with the help of a final decision score matrix. Our model uses a set of eight meteorological elements and eight experts to assess four possible tornado locations and pinpoint an area with a high risk of tornado strikes. The results generated by our aggregation operator set demonstrate that our proposed method for handling complex and multi-polar data is concise and efficient when compared to other sets. This early prediction highlights the potential of significant risk reduction to the environment and human life due to catastrophic events like tornados by enhancing early warning systems and effective emergency management.

The evaluation and assessment of network security is a decision-making (DM) problem that occurs in an environment with multiple criteria, which have uncertainty, bipolarity, and extra-related information. The traditional approaches fail to address the need to acquire a wide range of information for the assessment, especially in situations where the criteria have both positive and negative aspects and contain extra fuzzy information. Therefore, in this manuscript, we aim to introduce a DM approach based on the concept of bipolar complex fuzzy (BCF) Yager aggregation operators (AOs). The related properties of these aggregation operators (AOs) are also discussed. Moreover, in this article, we diagnose the Yager operations in the setting of BCF. The basic idea of the interpreted operators and DM approach is to access the problem linked with the network security that is to evaluate and select the finest network security control and network security protocols for protecting and safeguarding the network of any organization or home (case studies). Finally, to exhibit the supremacy and success of the described theory, we examine them with the prevailing theories.

In this paper, we introduce a new, improved family of estimators aimed at improving the accuracy of finite population variance estimation. The proposed class of estimators is enhanced in terms of overall estimating performance and robustness by using the maximum and minimum values as supplementary information to improve precision and reliability. The bias and mean square error are theoretically obtained to the first order of approximation. We carried out a simulation study to evaluate the performance of the proposed estimators and validate the theoretical results. The findings show that the proposed estimators have higher percent relative efficiencies than the existing estimators across all of the simulated scenarios. Additionally, three independent symmetric and asymmetric datasets are analyzed to further confirm the better efficiency of proposed estimators compared with traditional estimators.

Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning, particularly when class overlap significantly deteriorates classification performance. Traditional oversampling methods often generate synthetic samples without considering density variations, leading to redundant or misleading instances that exacerbate class overlap in high-density regions. To address these limitations, we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE, a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap. The originality of WGAN-VDE lies in its density-aware sample refinement, ensuring that synthetic samples are positioned in underrepresented regions, thereby improving class distinctiveness. By applying structured feature representation, targeted sample generation, and density-based selection mechanisms strategies, the proposed framework ensures the generation of well-separated and diverse synthetic samples, improving class separability and reducing redundancy. The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques, achieving superior results in Fl-score, Accuracy, G-Mean, and AUC metrics. These results establish the proposed method as an effective and robust computational approach, suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling.

Artificial Intelligence (AI) based energy management systems utilize sophisticated AI algorithms to improve and control the consumption of energy in various sectors, such as power utilities, industrial systems, and smart buildings. These systems support real-time analysis of data, predictive analytics, and automatic adjustments to improve energy efficiency, reduce expenses, and lower environmental footprints. This research introduces a new method of AI-based energy management through the creation of advanced mathematical aggregation operators under the theory of hesitant bipolar complex fuzzy sets (HBCFSs). The generalized HBCFS theory is a complete decision-making model that can efficiently deal with uncertainties, hesitancy, and bipolar information in a complex setting. To solve the intrinsic difficulties of energy management decision-making, we propose a series of new HBCF Hamacher power aggregation operators. These operators improve the precision and stability of multi-attribute decision-making (MADM) processes by using the Hamacher t-norm and power aggregation rules to represent intricate interactions among decision attributes. Further, a comparative study is conducted to highlight the strength and superiority of the proposed aggregation operators that significantly contribute to AI energy management systems. The results establish that the method developed significantly improves the accuracy and reliability of decisions, warranting application in energy distribution and usage optimization.