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.

Axial Force Influence on Transverse Displacement and Rotation Under Moving Load of Elastically Supported Damped Shear Beam

2025-04-11T09:56:42+00:00

This research investigates the influence of axial force on the transverse displacement and rotation of a damped shear beam resting on an elastic foundation and subjected to moving load traveling at a constant velocity. The governing equations are coupled second-order partial differential equations. To simplify these equations, the finite Fourier series method was employed, transforming the coupled second-order partial differential equations into a set of coupled second-order ordinary differential equations. The simplified equations that describe the motion of the beam-load system were subsequently solved using Laplace transformation in conjunction with convolution theory to obtain the solutions. Comprehensive analyses are conducted to investigate the effects of axial force on the transverse displacement and rotation of damped shear beams of different length sizes when subjected to the moving load traversing the beam at different velocities respectively. Additionally, the study examines the effect of axial force on the critical velocities of the vibrating system. The findings reveal that the transverse displacement and rotation of the beam are noticeably reduced with increasing axial force. It is also found that as the value of axial force increases, the critical velocity increases indicating a safer dynamical system. From a practical perspective, this clearly indicates that axial force significantly enhances the dynamic stability of the beam when subjected to the moving load.

SIFT and its Applications in Medical Imaging: Advancements, Challenges, and Future Prospects

2025-04-11T09:59:57+00:00

The Scale-Invariant Feature Transform (SIFT) has become a foundational technique in the field of image processing, offering a robust and efficient method for detecting and describing local features in images. This manuscript explores the theoretical foundation, algorithmic steps, and applications of SIFT, with a particular focus on its use in medical imaging. The paper discusses how SIFT has been leveraged for tasks such as image registration, feature matching, and tumor detection, highlighting its significance in enhancing diagnostic accuracy. Additionally, the manuscript examines the advantages and limitations of SIFT in healthcare contexts, considering factors like computational costs and real-time processing capabilities. It also explores recent enhancements to the SIFT algorithm, including PCA-SIFT and Dense-SIFT, and discusses future directions, such as the integration of deep learning techniques, that promise to extend its utility in medical imaging. Through a detailed analysis, this paper provides insights into the continuing relevance of SIFT, offering a roadmap for future advancements in the field of medical image analysis.

Portfolio Formation Using the Mean-Semi-Variance Method on Jakarta Islamic Index (JII)

2025-04-14T11:43:56+00:00

Indonesians are increasingly drawn to sharia-compliant stock investments. Investors ought to strive to enhance their profits to effectivelyattenuate existing risks. The difficulties faced by investors can be reduced through careful portfolio construction. The mean-semivariance method acts as an advanced portfolio optimization technique that bypasses distribution assumptions and is proficientat creating portfolios marked by minimal risk. Prior to entering the realm of investment, it is essential for investors to assess the performance of their portfolios. This study employs the Sharpe Index as an insightful gauge for performance evaluation. The analysis will focus on five stocks that have consistently been included in the Jakarta Islamic Index (JII) over the last decade, namely: ADRO, AKRA, ICBP, KLBF, TLKM, UNTR, and UNVR. Drawing from stock returns between November 17, 2022, and November 17,2023, the ideal risk-minimized portfolio is formulated using two stocks: AKRA, with a weight of 24.23%, and ICBP, with a weight of 75.77%. This carefully curated composition results in an expected return of 0.00035 and a risk measure of 0.0000748. In addition, the Sharpe index of 0.02295 indicates that the constructed portfolio showcases commendable performance, establishing it as a judicious option for investors seeking reliable investment assets

Reduced Temperature Indices

2025-04-16T11:11:13+00:00

In this paper, we introduce the first and second reduced temperature indices and their corresponding polynomials of a graph. Also we compute these reduced temperature indices for some standard graphs, wheel graphs, friendship graphs, HC₅C₇[p, q] nanotubes and tetrameric 1,3-adamantane.

Optimizing Transportation Problems using the Substitution Method and Zero-Cut Technique

2025-04-16T11:12:55+00:00

Objectives: To find the initial basic feasible solution of a transportation problem in real life problem.

Methods: We proposed a new method to find the initial basic feasible solution to get optimal solution.

Findings: This study introduce the zero-cut method for finding the initial basic feasible solution for transportation problem. The attractive feature of this method is that it requires very simple, so it is very easy to understand. Also, we find a minimum transportation cost.

Novelty: The numerical illustration demonstrates with an example for comparing the existing methods.

Relaxed Skolem Mean Labeling of 4 - Star Graph with Partition (3,1)

2025-04-16T11:16:37+00:00

To prove that the 4 - star graph where is a relaxed skolem mean graph if ꞵ - α_{1 –}α_{2 –}α_{3 =}6 is the core objective of this article.

Bias in Content-Generating AI Algorithms: Technical Analysis, Detection, And Mitigation with Python

2025-04-19T11:36:18+00:00

Generative artificial intelligence models, such as GPT-4, DALL·E and Stable Diffusion are now essential tools for automated text and image production. However, these models are influenced by algorithmic biases resulting from training data , learning mechanisms and user interactions . These biases, often unconscious, can have significant consequences by reinforcing social stereotypes, excluding certain populations and altering the diversity of generated content.

This article provides an in-depth technical analysis of the biases present in generative AI. We first explore their origins , highlighting the biases of corpora training , biases introduced by learning algorithms and those induced by users . Then, we present methods for detecting and evaluating biases , using natural language processing (NLP), computer vision and data modeling tools . Experiments in Python illustrate how these biases manifest themselves in text and image models.

Finally, we propose bias mitigation strategies based on several technical approaches: data rebalancing , embedding debiasing , adjustment of cost functions , regulation of outputs And Model auditability . Integrating these techniques helps make AI models fairer and more transparent. The goal is to provide a pragmatic and rigorous approach to designing responsible generative AI models that respect the principles of fairness and diversity.

Stability Analysis of the Improved Analytic Solution of the Mathematical Model for the Complications of Diabetes Mellitus

2025-04-19T11:05:45+00:00

Diabetes mellitus has become a global silent epidemic rising at an alarming rate throughout the world due to increase in life expectancy, obesity and sedentary lifestyles. It is a chronic disease that can lead to complications over time. These complications can be prevented or minimized with the support of government, private sectors and medical expertise.

This paper proposed an improvement in the existing mathematical model based on number of diabetics with complications and number of diabetics with and without complications. Stability of the proposed model is analyzed and observed that the complications can be controlled during diabetes persists. Analytic solution of the system of ordinary differential equation is achieved.

In this paper, the proposed model precise the understanding of diabetes, its complications and how it can be controlled. The stability analysis of the model showed that the models were stable asymptotically at various parameter values. The model showed that the rate at which complications are controlled is very important parameter in controlling the diabetes and its complications. If the value of the rate at which complications are controlled is high, then the number of diabetics with complications is decreased. Numerical solution of the proposed models were obtained using Euler method. Numerical simulations of the analytic solutions were obtained at various values of the parameters.

Hasse diagrams of basic blocks containing four comparable reducible elements and having nullity four

2025-04-22T11:10:26+00:00

In $2020$, Bhavale and Waphare introduced the concepts of fundamental basic block and basic
block, associated to dismantlable lattices.
Further, they have provided the recursive formulae of the number of non-isomorphic fundamental basic
blocks as well as basic blocks, containing $r$ comparable reducible elements and having nullity $l$.
In this paper, we actually obtain the Hasse diagrams of the basic blocks containing four
comparable reducible elements and having nullity four.

Impact of Activation Function on the Performance of Convolutional Neural Network in Identifying Oil Palm Fruit Ripeness

2025-04-22T11:13:57+00:00

Activation functions play a crucial role in Convolutional Neural Networks (CNN), particularly in enabling the model to recognize and represent complex patterns in digital images. In image classification tasks, the choice of activation function can significantly impact the accuracy and overall performance of the model. The Rectified Linear Unit (ReLU) is the most commonly used activation function due to its simplicity; however, it has a limitation in discarding information from negative input values. To address this issue, alternative functions such as Leaky ReLU and Gaussian Error Linear Unit (GELU) have been developed, designed to retain information from negative inputs. This study presents a comparative analysis of three activation functions ReLU, Leaky ReLU, and GELU on a CNN model for classifying oil palm fruit ripeness levels. The results show that although all three activation functions achieved high training accuracy ReLU at 100%, Leaky ReLU at 99.93%, and GELU at 99.49%—the performance on testing data varied significantly. Leaky ReLU outperformed the others, achieving the highest test accuracy of 95.35%, an F1-score of 95.39%, and a Matthews Correlation Coefficient (MCC) of 93.28%. It also exhibited the smallest gap between training and testing accuracy (4.58%), indicating better generalization capability and a lower risk of overfitting compared to ReLU and GELU. Moreover, the model using Leaky ReLU was able to classify all three classes more evenly, particularly excelling in identifying the 'ripe' class, which tends to be more challenging. These findings highlight that Leaky ReLU is a more optimal activation function for oil palm fruit image classification, as it maintains high accuracy while effectively reducing overfitting. This study contributes to the selection of appropriate activation functions for CNN-based classification systems and opens opportunities for exploring more adaptive activation functions in future research.

Certain q-Kober fractional integral operator of generalized basic hypergeometric functions and q-polynomials

2025-04-23T11:30:26+00:00

The object of this paper is to established the Kober fractional integral operator of the generalized basic hypergeometric function.Interestingly Kober fractional integral operator of various q-polynomials have been expressed in terms of the basic analogue of Kamp˙e de F˙eriet function.Some special cases have been deduced as an application of main result.