International Journal Of Mathematics And Computer Research

Evaluating the Performance of Ensembled YOLOv8 Variants in Smart Parking Applications Under Varying Lighting Conditions

2025-04-02T11:32:03+00:00

With an emphasis on performance under various ambient illumination circumstances this paper explores the potency of YOLOv8 variants for vehicle and license plate detection. The suggested method will capture entire video frames, identify areas of interest with cars, and feed those regions into two distinct, pre-trained YOLOv8 models—one for license plate recognition and the other for vehicle detection. To make the photos easier for the Tesseract OCR engine to read, they are pre-processed using the OpenCV and Pillow libraries to make the images brighter and higher DPI. The four YOLOv8 models can be paired for vehicle and license plate identification tasks to produce sixteen possible combinations. We evaluate the performance of the chosen YOLOv8 combinations under various ambient light intensity levels (measured in lux) after they have been selected using TOPSIS analysis. Finding the most reliable model combinations that provide precise license plate and vehicle detection in the variety of illumination situations found in real-world settings is the goal of this evaluation.

Stability and Boundedness of Solutions to a Kind of Third Order Neutral Stochastic Differential Equations with Delay

2025-04-03T10:38:06+00:00

This study investigates the stability and boundedness of solutions to a specific type of third order neutral stochastic differential equations with delay. The analysis focuses on the stability, asymptotic stability and boundedness of solutions of the nonlinear neutral stochastic system. By employing appropriate Lyapunov-Krasovskii functional techniques, the study provides insights into the behavior of the solutions under various conditions. The results contribute to and extend the understanding of the dynamics of third order neutral stochastic differential equations with delay which have existed in the literature and have implications for applications in various fields. Examples are provided to show the effectiveness of the technique used and the reliability of the obtained theoretical results.

Modeling the Volatility-Return Relationship in the Indonesian Stock Market using the GARCH-M Framework

2025-04-03T10:47:18+00:00

The LQ45 Index was observed to be in the red zone, with a decline of 9.64% year-to-date (YTD), reaching the level of 877.02. The LQ45 Index became increasingly weakened following the announcement of Donald Trump's victory in the U.S. presidential election, which impacted the Indonesian capital market. It was recorded that the LQ45 Index fell by 5.3% during the final trading month of 2024. Nevertheless, there remains a potential for strengthening the stock prices of LQ45 constituent issuers in the remainder of this year, particularly in December 2024. One of the stocks recommended by IDX is PT Indofood CBP Sukses Makmur Tbk., which has also been one of the most liquid companies according to IDX throughout 2024. The return volatility of stocks in emerging markets is generally much higher than that of developed markets. High volatility reflects a higher level of risk faced by investors, as it indicates significant fluctuations in stock price movements. Therefore, equity investments in Indonesia carry a potentially high level of risk. A common characteristic of financial time series data, particularly return data, is that the probability distribution of returns exhibits fat tails and volatility clustering, often referred to as heteroscedasticity. Time series models that can be used to model these conditions include ARCH and GARCH models. One variation of the ARCH/GARCH models is the Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M) model, which incorporates the effect of volatility into the mean equation. The purpose of this study is to predict volatility using the GARCH-M model in the analysis of daily closing price return data of PT Indofood CBP Sukses Makmur Tbk. The best model used for volatility forecasting is ARIMA(2,0,1) GARCH(1,1)-M.