For columns made of reinforced cement concrete (RCC), conventional steel-tied reinforcement could not offer the best containment. based on many experimental findings and theoretical works evaluation a variety of materials, including WWF and FRP, were used as reinforcements to boost the ductility and load-bearing capability of the components of the structure. So, welded wire mesh is one such material. (WWM) that we employed for this project in an effort to enhance the restriction and malleability. The entire effort seeks to specify how compression will be aided by confinement and improved confinement. members to make them more ductile (Circular column). Thus, we employed an added to the prior reinforcement using welded wire mesh. An example used were divided into two groups, with samples contained using both traditional techniques and more advanced confinement.

In recent times, stock prices have exhibited considerable random fluctuations on a daily basis. These unpredictable variations have posed significant challenges for investors, making the task of predicting stock market trends increasingly complex. This unpredictability has, in turn, rendered stock market prediction a fertile and dynamic area of ongoing research. As part of this endeavor, this paper aims to explore the prediction of stock market closing prices by leveraging a neural network (NN)-based approach. The proposed methodology begins by validating the fundamental principles of a neural network-based stock market prediction (SMP) model. It establishes the effectiveness of using neural networks for such predictive tasks, given their ability to model complex, non-linear relationships present in financial data. A crucial part of this methodology involves experimenting with varying ranges of moving average features. These features are key indicators that can help smooth out price fluctuations over time, and their variability provides an insightful evaluation of the prediction performance. To assess the effectiveness of the predictions, this study employs two key performance metrics: the Mean Absolute Error (MAE) and error histograms. The MAE provides a straightforward measurement of the average magnitude of errors in the predictions, without considering their direction. Error histograms, on the other hand, offer a graphical representation of the distribution of errors, enabling a deeper analysis of the model's prediction accuracy and its deviations. The results of the proposed approach demonstrate a high degree of accuracy in predicting stock market closing prices. Specifically, the methodology achieves an impressive accuracy rate of 99.987% when tested using Microsoft stock price data. This remarkable result underscores the potential of neural networks in providing highly reliable stock price forecasts.

Noise removal in Magnetic Resonance Imaging (MRI) is crucial for ensuring high-quality images, which are essential for accurate medical diagnoses and the study of anatomical structures. MRI images are often compromised by various types of noise, which can degrade image quality and obscure critical diagnostic details. In this study, a Modified Median Filter is proposed to address the challenge of de-noising MRI images. The technique improves upon traditional median filtering by adapting the filter window size based on local noise characteristics within the image, ensuring both effective noise suppression and preservation of fine anatomical structures. The filtering process is optimized using a 3x3 window to maintain image sharpness and contrast. The performance of the proposed method is evaluated using Peak Signal-to-Noise Ratio (PSNR) and contrast ratio metrics, which demonstrate significant improvements in image clarity and detail retention. The results show that the Modified Median Filter is highly effective in removing noise while preserving important image features, making it a valuable tool for enhancing the quality of MRI images in clinical settings.