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Assessing scour depth (Sd) near side-by-side, tandem, and eccentric bridge piers is crucial for designing resilient structures. Researchers employed soft computing techniques to enhance Sd prediction models, focusing on ensemble machine learning (ML) methods for isolated piers. However, this research is limited on such two-pier groups, which necessitates a detailed study of how pier spacing and positioning collectively affect Sd predictions. A thorough examination is needed to analyze scouring patterns and the collective two-pier impact on estimating Sd using ensemble ML models. This study employs two ensemble ML models, random forest (RF) and extreme gradient boosting (XGBoost), to collectively predict circular two-pier Sd. Input parameters such as flow characteristics, sediment properties, time, pier gaps, and flow skew angle are rigorously evaluated to assess their combined impact on Sd. Partial dependence plots (PDP) and SHapley Additive exPlanations (SHAP) are used to visualize feature importance and effects on predicting Sd after training ML models, providing insights into individual features' influence on predictions. Performance indicators [coefficient of determination (R2), mean absolute error, and root mean squared error] assess the ML models' performance. Results demonstrate that XGBoost outperformed RF in training and testing phases with random search cross validation (CV) optimization for both piers. However, RF excelled over XGBoost in training using grid search CV and random search CV for both piers. Flow intensity was identified as the most influential variable, making the phenomenon highly vulnerable during model training with SFS and SHAP. Using ensemble ML models with detailed parameter evaluations and visualizations, engineers can predict Sd more effectively, thus enhancing scouring pattern understanding to design resilient structures. Practical Applications: The practical application of this study shows where new bridges are needed next to old bridges for traffic in rapidly populating cities. Bridge piers are placed side-by-side, in tandem, or eccentrically. Scour depth can increase or decrease due to dynamic interference if these piers are not studied properly. This study examines how interference impacts scour depth in various positions and estimates it using an ensemble ML model. This ensemble model accurately predicts scour depth around such interfering piers, which outperforms the classic model. Partial dependence plots show how parameters affect scour depth, considering interference effects. The model shows how interference impacts the scour depth for designing such piers. This model can be used to analyze the impact of such two-pier configurations after integrating the field data for studying field installation effects. Experts and practitioners can utilize the model to improve bridge placements by predicting how pier interference affects scour depth, thus enhancing safety in the design process. [ABSTRACT FROM AUTHOR]

In this study, 60 cherry samples with native varieties were selected from the Hir region in Ardabil province. They were classified into four growth stages, including before the optimal harvest date, the day before the optimal harvest time, the optimal harvest time, and after the optimal harvest date, by a panel of human experts. Next, by combining the feature selection method (relief) and the spectrometry method (vis-NIR), the effective wavelengths were extracted to estimate the soluble solid content (SSC) values and firmness of the cherry product. In the continuation of the process of this method, a list of inputs was formed, and by applying the life cycle assessment method, the environmental effects of the process of estimating SSC values and cherry hardness in the presence of tests and obtained data was performed. In the final stage, with the help of the radial basis function neural network method, a relationship was established between the reflection intensity values in the effective wavelengths and the endpoint effects of the life cycle assessment to estimate the environmental effects. It was found that the radial basis function neural network could estimate the environmental effects of the experimental process with an acceptable accuracy (over 95% on average). [ABSTRACT FROM AUTHOR]

This study reviews a comprehensive mathematical framework known as neutrosophic soft sets, which combines neutrosophic theory with the soft set theory. Also, we review neutrosophic fractional order functions. For decision making, this framework effectively conveys ambiguity and uncertainty. The developments in soft set theory and neutrosophic set theory are thoroughly examined in this article. We review the advancements of both theories in general. We examine the qualities, applications, and theoretical underpinnings of both theories. We study the combination of neutrosophic soft set theory and logic. The study talks about important new developments and techniques that make neutrosophic soft suites better at solving difficult real-world problems that aren't always clear. To promote the advancement of the discipline, we also provide a comprehensive overview of the theories derived from literature methodologies, and propose potential avenues for future research. This review serves as an important resource for researchers and practitioners wishing to utilize neutrophil suites in their work. It provides a deeper understanding of the potential effects and applications. This review also addresses a discussion on fractional order neutrosophic sets (FONS). The fractional order component offers an additional degree of freedom, enhancing the adaptability of neutrosophic sets for many applications. [ABSTRACT FROM AUTHOR]

Today's educational assessment strategies require innovation and digital transformation in order to overcome biases towards data orientation in uncertain, ambiguous, and fuzzy conditions. Neutrosophic-based analysis techniques in educational assessment provide a thoughtful and highly effective approach to calibrating student learning abilities. However, there is a lack of access to resources and complete guidance on implementing soft computing methods like neutrosophic sets, resulting in a gap in knowledge and practice. Research and development have shown that neutrosophic-based analysis techniques can explain the formulation and algorithms used in multicriteria decision-making approaches. Since educational assessment also involves decision making, this paper proposes a neutrosophic-based analysis assessment framework aimed at transforming assessment strategies in education to promote soft computing. The development of this paper will begin by reviewing the literature and conducting a preliminary study, highlighting the benefits of the neutrosophic set, and then forming a framework and operational design for the assessment strategy in a systematic manner. Illustrations with numbered data will be used to explain the suitability and usability of this framework for real educational assessment. The implications of calibrating the factors that have the strongest influence on students' mathematics learning demonstrate that this assessment framework can be expanded as an innovative and flexible approach to assessment, capable of improving the efficiency of data analysis in real learning environments. This framework and initiative can be used synergistically to improve the quality of education by incorporating digital elements and providing strong support for the Sustainable Development Goal (SDG). [ABSTRACT FROM AUTHOR]

Evaluation of embankment dam displacement (D) under earthquake loading can contribute to the safe design of the dam. Due to the complexities of modeling this problem, soft computing is an appropriate solution for predicting the embankment dam displacement under earthquake loading. In this research, Artificial Neural Networks (ANN) and Swarm Optimization Algorithm (PSOA) were integrated in an attempt to present a relationship for predicting the displacement of embankment dam (D). For this purpose, data from 102 real cases was utilized. Input parameters included the height (H) and natural period of the dam (Td), minimum required yield acceleration to slide a block of the dam body (ay), magnitude (Mw), dominant frequency (Tp), and peak acceleration (amax) of the earthquake. It was figured out that PSOA-ANN outperforms PSOA in estimating earthquake-induced dam displacement. Compared to other soft-computing methods for predicting embankment dam displacement under earthquake loading, the hybrid PSOA-ANN is more powerful and suitable. [ABSTRACT FROM AUTHOR]

The class Gastropods, which includes snails and sea slugs, inhabits a wide range of environments. Members of this class move by utilizing waves generated through muscular contractions in their soft body tissues (pedal wave). This characteristic is observed in both aquatic and terrestrial species and serves as a mechanism for adapting to various environments. In this study, we developed a soft robot that generates pedal wave using soft matter. This soft robot employs sensing based on machine learning, utilizing the soft material as a physical reservoir to leverage its environmental adaptability and soft exterior. The experiments investigated in this study were the relationship between locomotion performance and environments and the identification accuracy achieved through machine learning. [ABSTRACT FROM AUTHOR]

This paper presents an approach based on eddy currents induced by suitable magnetic induction fields to test, estimate, and classify subsurface delaminations in Carbon Fibre Reinforced Polymer (CFRP) plates for biomedical devices. The two-dimensional maps obtained, characterised by high fuzziness, required the software development of a procedure based on a highly efficient fuzzy classifier that exploits fuzzy similarity computations with reduced computational load by collecting similar maps (deriving from equally similar defects) in specific defects. The hardware implementation of what is designed in software (plate-probe system) detects and evaluates the entity of the defects due to delaminations by a classification percentage comparable with the performances obtained from more sophisticated classifiers, providing a possible tool for evaluating the delaminations potentially useful to assess aircraft compliance with applicable safety standards. [ABSTRACT FROM AUTHOR]

Hardgrove grindability index (HGI) is a significant index used to determine a mill's capability and overall efficiency in the grinding of coal. There are several factors that affect HGI due to the complex nature of coal. The influence of proximate analysis, calorific value, total sulfur, and pyrite content on the HGI values of 292 coal samples from South African coalfields were examined. Predictive models of HGI are developed by using soft computing techniques such as Long Short-Term Memory (LSTM), support vector regression (SVR), and Artificial Neural Network (ANN). This study shows that ANN is the most effective predictive model for all the three coalfields, with SVR models being second and LSTM models being the least effective. The correlation between the predicted value and the input data is established by using the cosine amplitude method (CAM). It was found that HGI is most influenced by the fixed carbon content, calorific value, ash content and volatile matter content while pyrite has the least influence. [ABSTRACT FROM AUTHOR]

The permeability (K ) of tight carbonate rocks is important to maximize the efficiency of hydrocarbon production and overall reservoir management. While such property is crucial for engineering design, conducting experimental tests to determine K can be both time-consuming and expensive. As such, reliable and high-fidelity models derived with soft computing techniques become useful for estimating K . Using a data set containing samples from 130 data points published in the literature, this work developed a sensitivity-driven Evolutionary Polynomial Regression (EPR) model to predict K . The model computes the permeability, log10 K (mD), as a function of three explanatory variables: porosity, ϕ (−), formation factor, F (−), and the characteristic pore throat diameter, dPT (m). One unique feature of our approach is that it considers the physical meaning of the variables during the construction of the model. Verification of the methodology was carried out using split-sampling cross-validation. The developed model showed attributes such as parsimony (lower number of parameters and input variables), good predictive capability (accurate tracking observed log10 K ), generalization ability (preserving physical meaning), and robustness (consistent performance under cross-validation). Sensitivity analysis revealed that the model can adequately simulate the increase in K with increasing ϕ and dPT , as well as its capacity to capture the non-linear relationship between log10 K and F . Comparison of simulated K -values with results of models published in the literature, further validated the ability of our optimum EPR model structure. The proposed model shows potential as a promising method to estimate the permeability of tight carbonate rocks. [ABSTRACT FROM AUTHOR]

In the realm of business, managerial decisions have a significant impact on success in manufacturing and decision-making in manufacturing process dealing with uncertainties and uncontrollable factors resulting from globalization. When things are uncertain, especially in transportation, this paper serves as a helpful guide for manufacturer's decision, and dealing with uncertain multi-objective transportation problems (MOTPs). Uncertain parameters are transformed into manageable forms using an uncertain normal distribution, providing a foundation for subsequent calculations. This novel approach integrates fuzzy membership functions, goal programming and weighted decision-making for different confidence levels to help the manufacturing industries to address MOTPs in uncertain environments. This approach empowers company to generate compromise solutions aligned with their preferences and illustrated through a practical MOTP solved with LINGO software. This approach effectively removes uncertainty and enhances decision-making by generating compromise solutions aligned with preferences. This paper embraces the robust ranking approach, instilling confidence through its comprehensive approach to decision-making. The approach presented in this paper is superior to existing methods. Its uniqueness lies in the integration of the confidence level along with the weights; this combination sets it apart and amplifies its efficiency. [ABSTRACT FROM AUTHOR]

The construction phase of a project is a critical factor that significantly impacts its overall success. The construction environment is characterized by uncertainty and dynamism, involving nonlinear relationships among various factors that affect construction quality. This study utilized 987 construction inspection records from 1993 to 2022, obtained from the Taiwanese Public Construction Management Information System (PCMIS), to determine the relationships between construction factors and quality. First, fuzzy logic was applied to calculate the weights of 499 defects, and 25 critical construction factors were selected based on these weight values. Next, a deep neural network was used to identify the relationship between the critical construction factors (input variables) and construction quality (output variable). Finally, the prediction model's performance was evaluated to confirm the impact of these critical construction factors on project outcomes. This study employed an innovative hybrid soft computing technique, combining fuzzy logic and an artificial neural network, to effectively predict the relationship between critical construction factors and construction quality, achieving a model accuracy of 96.08%. Project managers can utilize the findings of this study to enhance project management practices and establish effective construction management strategies, thereby improving project construction quality. [ABSTRACT FROM AUTHOR]

Electrical engineering models often rely on complex circuit configurations that facilitate the dynamic flow of electrically charged particles within a closed conductive network. These circuits serve as essential tools for simulating and analyzing diverse electrical systems and components. This paper introduces a study on nonlinear fractional circuits modeling through the development of a stochastic neuro-computational artificial intelligent-based solver to address mathematical models governing the Fractional order Caputo–Fabrizio stiff electric circuit model (FO-CFSECM) by manipulating the knacks of layered recurrent neural networks (LRNNs) trained with Gradient-based local search algorithm (GLA). In fractional calculus, the Caputo–Fabrizio (CF) fractional order derivative (FOD) emerges as a powerful instrument, binding its capabilities to deliver remarkably accurate solutions for fractional stiff systems. The objective of this work is to exploit the numerical treatment comprehensively for the dynamics of fractal Resistor–Capacitor (RC) and fractal Resistor–Inductor (RL) circuit models by introducing the CF fractional operator. Through the application of artificial intelligence-based soft computing and advanced back-propagative deep neural networks, a deeper understanding of the behavior and distinctive characteristics inherent in these models is sought. The Levenberg–Marquardt optimizer serves as an efficient training GLA tool for learning of LRNNs weights of fractal RL/RC circuit models. The comparative studies on variants of FO-CFSECM demonstrate that LRNNs achieve an impressive mean square error (MSE) ranging from 10 − 9 to 10 − 1 9 and absolute error (AE) within 10 − 6 to 10 − 8 . The accuracy, reliability, and efficiency of LRNNs for solving the FO-CFSECM were further validated through MSE, AE, controlling parameters of state transitions, error histograms, and correlation measures. [ABSTRACT FROM AUTHOR]

Road surface roughness is the cause of vehicle vibration, which is considered a system disturbance. Previous studies on suspension system control often ignore the influence of disturbances while designing the controller, leading to system performance degradation under severe vibration conditions. In this work, we propose a control method to improve active suspension performance that reduces vehicle vibration by eliminating the influence of road disturbances. The proposed method is formed based on the combination of an Active Disturbance Rejection Control (ADRC) technique with control coefficients tuned by a dynamic fuzzy technique formed based on special membership functions called Active Disturbance Rejection Control Based on Fuzzy (ADRCBF). An Extended State Observer (ESO) estimates state variables and disturbances. The performance of the proposed controller is evaluated through the numerical simulation process with three different cases. According to the calculation results, the acceleration and displacement of the sprung mass are significantly reduced when the suspension system is controlled by the proposed technique, compared with the passive suspension system and the active suspension system controlled by a Proportional-Integral-Derivative (PID) technique. In addition, the suspension travel follows the road disturbance with a small error. The error estimated by the ESO does not exceed 3.5% (for sinusoidal and random excitation). In general, system adaptation is ensured under many investigated conditions based on tuning the controller parameters by the soft computing method. [ABSTRACT FROM AUTHOR]

Many researchers are working to prevent, monitor and identify drought, which is one of the most insidious and dangerous natural disasters that negatively affects life. For this purpose, various drought indices are developed and new methods are proposed. One of the most widely used of these indexes is the Standard Precipitation Index (SPI). Since it is not known when the drought will begin, taking preventive measures is a difficult and challenging task. In the last decade, machine learning techniques have been preferred to increase success in predicting droughts. In this study, SPI was used as the drought index and Support Vector Machine (SVM) and Long-Short Term Memory Network (LSTM) methods, which are increasingly reliable among the most preferred machine learning and deep learning methods in drought predictions, were used as the prediction method, and furthermore, to increase the prediction power of these methods, new powerful models have been proposed using Wavelet transform and Variational mode transform. Support Vector Machines with Wavelet decomposition (SVM-W), Long-Short Term Memory Networks with Wavelet decomposition (LSTM-W), and Long Short Term Memory Networks with Variational Mode Decomposition (LSTM-VMD) were used as prediction models for drought analysis and the performances of these models were compared. For drought analyses, 10 different models were established using SPI12 and SPI3 and as a result, the M04 model obtained with the SVM-W hybrid method was found to be the most suitable model with the results of R: 0.9971, NSE: 0.9941 and RMSE: 0.0769. [ABSTRACT FROM AUTHOR]

The current study builds a model of the link between the image-based weights of Indian Potatoes extracted using the built computer vision system using a dimensional analysis integrated with the adaptive neuro-fuzzy inference system. An atomized sorting system must be developed to enhance the exportation quality. A soft computing method is applied to nonlinear data training. The features include size (dimensions, area, and volumes), shapes, and gravimetric properties were chosen. As per standards and export criteria, potatoes should be divided into three groups (small, medium, and big) based on their estimated weight. A self-build image-based system was designed for capturing an image. The experimental outcomes were contrasted with the presented integrated predicted outcomes. A respectable level of agreement is noted with good competency and dependability by the strong correlation coefficient (0.9975). Hence, the presented approach is strongly recommended for image-based weight estimation and classification. Code: [ABSTRACT FROM AUTHOR]

The rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources, as well as for the design of hydraulic infrastructure. Furthermore, research on streamflow estimation has gained heightened importance because water is essential not only for the survival of all living organisms but also for determining the quality of life on Earth. In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network–recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. The performance of each technique was evaluated using statistical criteria such as mean square error (MSE), mean bias error (MBE), mean absolute error (MAE), and the correlation coefficient (R). The results of this study demonstrated that the GMDH algorithm produced the most accurate forecasts at the Sidi AEK Djillali station, with metrics of MSE: 0.132, MAE: 0.185, MBE: −0.008, and R: 0.636. Similarly, the CNN-RNN algorithm achieved the best performance at the Kef Mehboula station, with metrics of MSE: 0.298, MAE: 0.335, MBE: −0.018, and R: 0.597. [ABSTRACT FROM AUTHOR]

A robust, accurate and reliable approach is essential for not only examining people's acceptance of cloud-based learning management systems in developing nations but also for accurate prediction of factors affecting its implementation and progress. Therefore, in this study, three different soft computing models; Adaptive neuro-fuzzy inference system (ANFIS), Support vector regression (SVR), and Emotional artificial neural network (EANN), were employed for predictions of factors affecting cloud-based learning management systems take-up and progress using data gotten from six Nigerian colleges. The performance of the models was assessed using five arithmetic metrics; MAPE, NSE, RMSE, rRMSE, and RM. All the proposed models forecast the effects of the study inputs on LMS with higher accuracy (NSE > 0.98). However, the SVR model outshone the other models as it increased the performance of the study-reported model by 2% and 4% respectively. Based on the study results, instructors' quality, motivation, and resource availability were found to be the key factors that affect cloud-based learning technologies take-up and progress in the study area. Interestingly, unlike prior studies, this study found system ease of use and usefulness to have insignificant effects on LMS take-up. Finally, the practical implications and limitations of the study were discussed based on the study findings. [ABSTRACT FROM AUTHOR]

The present investigation studies performance modeling and analysis of an M/M/1 queue with a hybrid vacation policy and Bernoulli service interruption. When the system becomes empty, the server has the choice to switch over to a complete vacation state or a working vacation state. During working vacations, the server also serves customers through a Bernoulli service process at a slower rate. After completing the service, the server either transitions to a normal busy state or remains in the working vacation state. To analyze the performance of the system, we have developed a Markovian queueing model by formulating the Chapman–Kolmogorov governing equations for the system states. Through an iterative and difference-differential equation approach, we derived the stationary probability distribution of the queue size, along with other key queueing metrics such as average queue length, average waiting time, and throughput. To deal with a more practical scenario of imprecise information about the system descriptors, the proposed crisp queueing is transformed into a fuzzy model by retaining the features. The soft computing approach based on adaptive neuro-fuzzy inference system (ANFIS), -cut, and parametric nonlinear programming (PNLP) is employed to obtain various fuzzified indices. Moreover, to determine the optimal service rate, the differential evolution (DE) and golden section search (GSS) methods are used. By taking illustration, the proposed optimization techniques are implemented to develop a cost-effective service system and to examine the sensitivity of the key descriptors. [ABSTRACT FROM AUTHOR]

Flexible AC Transmission System (FACTS) devices are getting increasing applications in Power Systems because of their several beneficial features. The present paper proposes a model of Hybrid-Dynamic Voltage Restorer (H-DVR) which is equivalent to a compound FACTS device. But unlike other compound FACTS devices, this H-DVR consists of only one voltage source converter (VSC). Besides, this H-DVR is having no series injection transformer. This H-DVR can operate either in series compensation mode or in shunt compensation mode which one is in priority. In this paper, it is also shown that H-DVR minimizes voltage Total Harmonic Distortion (THD) and load harmonics, improves node voltages and reduces power loss. The absence of transformer in H-DVR model improves the cost savings. The effectiveness of H-DVR has been tested on IEEE-123 node test system. For having optimal position, size and number of H-DVR in test network, Cuckoo Optimization Algorithm (COA) has been adopted. The comparative study between normal operating condition and fault condition along with proposed H-DVR placement has also been illustrated in this paper in brief. The results of H-DVR installation have been compared with those of DVR installation for having acceptance of H-DVR. To establish the effectiveness of H-DVR installation, a comparative study of different soft computing techniques have been furnished. [ABSTRACT FROM AUTHOR]

Due to the global demand for dimension stones, fast and reliable evaluation tools are essential for assessing the quality of dimension stones. For this reason, this study aims to develop comprehensive tools for estimating the abrasion resistance of various dimension stones from Turkey. Non-destructive rock properties, including dry density (ρd), water absorption by weight (wa), and pulse wave velocity (Vp), were determined to build a comprehensive database for soft computing analyses. Three predictive models were established using multivariate adaptive regression spline (MARS), M5P, and artificial neural networks (ANN) methodologies. The performance of the models was assessed through scatter plots and statistical indicators, showing that the ANN-based model outperforms those based on M5P and MARS. The applicability of the models was further validated with independent data from the existing literature, confirming that all models are suitable for estimating varying Böhme abrasion values (BAVs). A MATLAB-based software tool, called Böhme abrasion calculator (v1.00), was also developed, allowing users to estimate BAV values by inputting adopted non-destructive rock properties. This tool is available upon request, supporting the dimension stone industry and fostering future research in this field. [ABSTRACT FROM AUTHOR]

Abstract Over the last two decades, the panel data model has become a focus of applied research. While there are numerous proposals for soft regression models in the literature, only a few linear regression models have been proposed based on fuzzy panel data. However, these models have serious limitations. This study is an attempt to propose a kind of two-way fuzzy panel regression model with crossed effects, fuzzy responses and crisp predictors to overcome the shortcomings of these models in real applications. The corresponding parameter estimation is provided based on a three-step procedure. For this purpose, the conventional least absolute error technique is employed. Two real data sets are analyzed to investigate the fitting and predictive capabilities of the proposed fuzzy panel regression model. These real data applications demonstrate that our proposed model has good fitting accuracy and predictive performance.

Jet scour presents a significant challenge for hydrological analysis and hydraulic design of river structures, with the temporal dynamics of scour hole dimensions posing a critical concern. This study analyzed the effectiveness of two AI-based models, extreme learning machine (ELM) and multigen genetic programming (MGGP), in predicting these fluctuations and identifying governing parameters. Both models demonstrated substantial predictive accuracy, exceeding the performance of existing empirical models. MGGP outperformed ELM in the training and testing phases, yielding four interpretable equations for practical applications. These equations enable designers to precisely predict temporal variations in scour hole dimensions based on key parameters, with nondimensional scouring time identified as the most influential factor. Surprisingly, channel width ratio and sediment standard deviation impacted model accuracy minimally. Additionally, the study emphasized the relevance of using the densiometric Froude number to capture temporal scour hole dynamics from plain wall jets. This research underscores the potential of using AI-based models to enhance scour prediction and design optimization of related structures. The proposed MGGP equations offer a practically relevant and accurate tool for managing jet scour, surpassing the limitations of previous approaches. [ABSTRACT FROM AUTHOR]

Non-destructive evaluation is advancing in examining the properties of fruits. Kiwi fruit stands out as one of the popular fruits globally. Due to the influence of various environmental factors and storage conditions, diligent checking and storage of this fruit are essential. Therefore, monitoring changes in its properties during storage in cold storage facilities is crucial. One nondestructive method utilised in recent years to investigate changes in fruit texture is the hyperspectral method. This study uses the support vector machine (SVM) method to assess hyperspectral method‘s effectiveness in examining property changes in four kiwi varieties during storage in addition to predicting the properties such as acidity and soluble solid content. The evaluation of the predictive machine learning model revealed an accuracy of 95% in predicting acidity and soluble solid content (SSC) changes in kiwi fruit during storage. Further, investigations found that the support vector machine method provided relatively lower accuracy and sensitivity in identifying product variety during storage, with an average accuracy ranging from about 91% to 94%. These findings suggest that integrating machine learning methods with outputs from techniques like hyperspectral imaging enhances the non-destructive detection capability of fruits. This integration transforms obtained results into practical outcomes, serving as an interface between software and hardware.

Abstract This study introduces three soft computing (SC) optimization algorithms aimed at enhancing the efficiency of photovoltaic water pumping systems (PVWPS). These algorithms include the Gorilla Troop Algorithm (GTO), Honey Badger Algorithm (HBA), and Snake Algorithm (SAO). The goal of the SC optimizers is to maximize the output power of the PV array (P PV ) and enhance the efficiency of the DC motor (η), thereby optimizing the water flow rate (Q) of the pumping system. The analytical modeling approach proposed in this study involves forecasting the optimal duty cycle (D op ) for a buck-boost converter, taking into account variables such as solar radiation (G) and ambient temperature (T). A comparative analysis is conducted between the suggested SC optimizers and analytical modeling. MATLAB simulation is employed to explore an adaptive neuro-fuzzy inference system (ANFIS) trained for the proposed system. The objective is to assess system performance and accuracy. Findings indicate a strong convergence between the analytical model and the simulation model utilizing SC optimizers. Moreover, the neuro-fuzzy system trained offline, coupled with the proposed SC optimizers, demonstrates superior performance compared to traditional control methods like perturb and observe (P&O) and incremental conductance (IC). This superiority is evident across various metrics including motor efficiency (η), photovoltaic (PV) output power (P PV ), water flow rate (Q), and time response.

Given the application domains of soft set theory, such as decision-making processes, image processing, machine learning, and data mining, it is natural to consider that this theory could be utilized more effectively in encryption systems. A review of the literature reveals that soft set-based encryption systems have been explored in a limited number of studies. This study seeks to develop a new approach for soft sets in encryption systems by utilizing newly introduced algebraic and topological tools. In this system, parties will be able to generate encryption keys independently using soft sets they determine themselves rather than through prior mutual agreement. Additionally, the method of key generation and the size of the key space in the resulting encryption system provides a more secure and distinct alternative compared to existing soft set-based encryption systems.