Applied statistics has been instrumental in predicting behaviours and future market trends. In the field of financial time series analysis, the incorporation of deep learning (DL) methods and applied statistics has made a significant contribution to the prediction model. Practitioners and researchers can extract complex features and dependencies from past financial data by leveraging neural network structures like long short-term memory (LSTM) and recurrent neural networks (RNNs). These DL approaches advance the development of predictive models prone to forecasting different financial metrics, such as asset returns, stock prices, and market volatility, with outstanding accuracy. With the combination of statistical approaches with DL techniques, researchers can leverage the power of both worlds to make more informed investment decisions and improve forecasting capabilities in volatile and dynamic financial markets. This study develops a new Applied Statistics with Single Valued Neutrosophic Fuzzy Soft Expert Sets (AS-SVNFSES) technique for Financial Time Series Forecasting. The presented AS-SVNFSES technique aims to forecast the input financial time series data. The AS-SVNFSES technique primarily applies data preprocessing using a Z-score normalization approach. For the forecasting of financial data, the AS-SVNFSES technique makes use of the SVNFSES technique. Finally, the parameter tuning of the SVNFSES technique is performed using the chimp optimization algorithm's (ChOA) design. A series of experimentations have illustrated the amended performance of the AS-SVNFSES model. The experimental value inferred that the AS-SVNFSES technique gains improved performance over other models.

Brush seals represent the most effective sealing technology, offering 5 to 10 times lower leakage flow rates, resulting in an 80% to 90% increase in sealing efficiency. However, key challenges remain in optimizing brush seal performance, including managing high frictional heat, maintaining consistent leakage flow, and preventing mechanical deformation failures within the bristle pack. This study uses a fluid–mechanical coupling method to establish and refine numerical investigation procedures. Using porous media and local thermal non-equilibrium (LTNE) approaches, the effects of the pressure ratio on seal performance are analyzed. The results reveal that the difference between the maximum directional and total deformations is 0.9108 mm, with the total deformation being approximately 79,666% larger than the directional deformation. These findings highlight that the bristle pack must be designed with primary consideration of total deformation to enhance performance and efficiency. The proposed methodologies enable more robust comparative evaluations of alternative brush seal configurations, including two-stage bristle packs and inline structural models. This facilitates the identification of optimized structures that minimize leakage, enhance energy dissipation, and improve the overall seal performance, thereby advancing the porous media model from a general approximation to a design-optimized tool.

Leukemia recognition and classification contain the identification of dissimilar kinds of leukemia, a group of blood cancers that affects the bone marrow and blood. A classical model containing microscopic analysis of blood smears to classify abnormal cells analytic of leukemia. Leukemia recognition employing a united technique of neutrosophic logic and deep learning (DL) signifies a new and complete approach to handling uncertainty and difficulty in medical data. Neutrosophic logic permits the representation of unstated or imperfect data, which is general in medical analyses. DL mainly convolutional neural networks (CNN) or recurrent neural networks (RNN), which can mechanically remove difficult patterns from medicinal imageries, improving the accuracy of leukemia recognition. The neutrosophic logic module accommodates the characteristic uncertainty in medicinal data, offering a formalism to manage imperfect or inaccurate data linked with the analysis procedure. The combination of these dual techniques generates a robust structure which capable of leveraging both the control of DL in image analysis and the flexibility of neutrosophic logic in dealing with uncertainties, contributing to more trustworthy and interpretable leukemia recognition methods. This study develops a new Salp Swarm Algorithm with a Neutrosophic Logic SVM (SSA-NSVM) model for Leukemia Detection and Classification. The SSA-NSVM technique mainly exploits Neutrosophic Logic (NL) concepts with the DL model for the detection of leukemia. To attain this, the SSA-NSVM model uses bilateral filtering (BF) based image pre-processing. In addition, the SSA-NSVM approach applies a modified densely connected networks (DenseNet) technique for learning complex and intrinsic feature patterns. Besides, the hyperparameter range of the modified DenseNet system takes place utilizing a SSA. At last, the NSVM technique is employed for the detection and identification of leukemia. The performance validation of the SSA-NSVM algorithm is verified utilizing a benchmark medicinal image dataset. The simulation values emphasized that the SSA-NSVM model reaches better detection outcomes than other existing approaches.

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