Mathematics and Computer Science: Research Updates Vol. 6

Gestalt Principles and Blast Domination Patterns in Neural Networks: A Conceptual Exploration

2025-07-11T11:09:58+00:00

This research expounds an advanced integration of the Blast Domination Number, a graph-theoretic parameter quantifying control over network nodes, with neural network architectures to enhance learning efficiency and healthiness. By modelling neural networks as dynamic graphs, we identify optimal blast-dominating sets that influence neuron activations with minimal energy and maximum coverage. This novel approach enables targeted activation strategies, leading to improved convergence rates, reduced computational overhead, and superior performance in classification and pattern recognition tasks. Experimental results across standard datasets validate the method’s effectiveness, establishing a new paradigm in the application of combinatorial domination theory to adaptive neural computation and intelligent systems.

A Standardized Threshold Approach for Outlier Detection Using Python Algorithms

2025-07-11T11:14:54+00:00

This study aims to standardise threshold techniques and dataset preprocessing steps to improve outlier detection using Python-based algorithms. The objective of the study is to get results that are more precise in the algorithm for most types of datasets. The methodology involved using samples datasets and testing the results when using the normal thresholds of the python outliers detection algorithm, and comparing that results with the results have been done by using the generalize threshold which is mean-median. The results obtained from the supervised results showed that when standardising the threshold using the formula (Mean-Med) produced more precise and more generalised outcomes. The study also applied algorithms that use quartiles (Q1, Median, Q3) and found that adjusting the first quartile to 15% instead of the standard 25% helped to better isolate outliers in the lower range. Similarly, modifying the third quartile threshold to 80% instead of 75% provided more effective detection of upper-range outliers. More precise and more generalize results were obtained when using the formula in the python algorithms use threshold or normal thresholds, which are 0.1 to 2.5 as datasets threshold, compare to use the formula (Mean-Med). The study used some sample datasets for analysis and indicated the potential for applying the method to many other unsupervised datasets in future research.

Locally Optimal Designs for Generalised and Exponentiated Pareto Models

2025-07-11T11:17:22+00:00

Pareto functions are very versatile and a variety of uncertainties can be usefully modelled by them, such as lifetime models in actuarial sciences, survival analysis and growth models in economics, finance, etc. Pareto models play an important role in modelling extreme events. Hosking and Wallis (1987) discussed the parameter and quantile estimation for the generalised Pareto distribution. Optimal experimental designs are used for accurately estimating the unknown parameters of the model. In this study, locally D-, A- and E-optimal designs with two and three support points having equal weights for homoscedastic generalised Pareto, exponentiated Pareto II, and generalised exponentiated Pareto models are obtained. It has also proven that these designs are minimally supported. The results are illustrated through Norwegian fire insurance claim data for the generalised Pareto model. It is found that the D-optimal design with two and three support points is almost the same for equal and unequal weights. It is also observed that the support points for A- and E-optimal designs are the same for all the cases. The designs obtained in the paper can be used in practice.

New Addition Formulae Using Hypergeometric Functions Via Gamma Function Representations

2025-07-11T11:21:32+00:00

The discovery of a hypergeometric function has provided an intrinsic stimulation in the world of mathematics. It has also motivated the development of several domains such as complex functions, Riemann surfaces, differential equations, difference equations, arithmetic theory and so forth. The global structure of the Gauss hypergeometric function as a complex function, i.e., the properties of its monodromy and the analytic continuation, has been extensively studied by Riemann. His method is based on complex integrals. Moreover, when the parameters are rational numbers, its relation to the period integral of algebraicm curves became clear, and a fascinating problem on the uniformization of a Riemann surface was proposed by Riemann and Schwarz. On the other hand, Kummer has contributed a lot to the research of arithmetic properties of hypergeometric functions. But there, the main object was the Gauss hypergeometric function of one variable. The solution of many problems In this chapter we have developed certain addition formulae using Hypergeometric function in the form of Gamma function. The formulae which are developed here are all new.

Convex Combination of Finite Mixture Probability Models: Properties and Application

2025-07-11T11:27:01+00:00

In statistics, data is expressed as a frequency distribution function that displays the range of potential values for a variable together with its frequency. Not all real data sets can be well-fitted by standard probability distributions. Such type of data sets creates a necessity for developing a new class of flexible probability distributions. To efficiently model lifetime data, this study develops a novel continuous probability distribution by building a finite combination of the exponential and Rayleigh distributions. Compared to current mixing models, the new distribution exhibits better performance and increases flexibility. The important distributional features derived include the probability density function (PDF), cumulative distribution function (CDF), and several statistical properties such as moments, incomplete moments, survival and hazard functions, mean residual life, stochastic ordering, order statistics, and stress strength reliability. To assess inequality and concentration, the Lorenz curve and Bonferroni index are also obtained. The maximum likelihood method is employed for parameterm estimation. An empirical study using real data further demonstrates the applicability and effectiveness of the proposed model.

Real-Time Object Detection via Cloud-Enabled Deep Learning: A Systematic Review

2025-07-17T08:03:54+00:00

Automated vehicles, advanced surveillance systems, AR, and robots are just a few of the many new uses for real-time object recognition. While deep learning models are becoming increasingly complex and accurate, they might be challenging to execute on edge devices with limited resources due to the computational demands. By offloading computationally intensive processes to scalable cloud infrastructure, cloud-enabled deep learning enables real-time processing without sacrificing detection accuracy, offering an effective alternative. This study takes a close look at the current setup of cloud-based object recognition methods that work in real time. When considering latency, bandwidth, privacy, and processing costs, the pros and cons of several architectural paradigms are evaluated, including hybrid methodology, distributed inference, and edge-cloud cooperation. Additionally, the developments of lightweight convolutional neural networks (CNNs), single-shot detectors, and model compression techniques are examined, all of which are aimed at real-time performance in cloud environments. Improving fault tolerance, optimizing data transmission, safeguarding data security and privacy, and developing more adaptive and efficient cloud resource management strategies for dynamic real-time object detection contexts are all areas that could be further explored in this review.

Ethical Challenges and Privacy Concerns Associated with Big Data in the Hiring Process: A Mixed-Methods Study

2025-08-22T07:15:22+00:00

Background: The advent of big data in recruitment processes has introduced more efficient, quicker, and scalable enhanced decision-making. Big data technologies enable recruiters to analyse vast amounts of candidate information, ostensibly improving the precision with which suitable candidates are identified. However, this technological advance also presents significant ethical challenges.

Aims: This study aims to explore the ethical challenges and privacy concerns associated with the use of big data in recruitment processes, focusing on algorithmic bias, data privacy, and fairness in hiring practices.

Methodology: The research employs a mixed-methods design, integrating qualitative interviews with HR professionals and quantitative data analysis to assess the implications of big data utilisation in recruitment. The study was conducted across various organisations, focusing on their recruitment practices, over six months. Qualitative interviews were conducted with HR professionals to gather insights on real-world experiences related to ethical challenges in recruitment. Additionally, a quantitative analysis of recruitment algorithms was performed to identify prevalent biases and their impact on hiring decisions, using statistical evidence to highlight significant findings. By triangulating these methods, the research robustly examined how big data applications alter recruitment landscapes, identifying ethical challenges and laying a foundation for potential solutions.

Results: The findings reveal that algorithmic bias is a profound issue in recruitment, with 62% of surveyed HR professionals acknowledging its influence on hiring decisions. Moreover, significant concerns regarding data privacy emerged, with 75% of respondents indicating that handling sensitive candidate information lacks adequate safeguards, increasing the risk of unauthorised access. Addressing ethical concerns in big data recruitment necessitates the collaboration of multiple stakeholders, including HR professionals, data scientists, and ethicists. Integrating fairness-aware algorithms is a pivotal strategy, as they aim to rectify biases at different stages of data processing, ensuring equitable decision-making. By encouraging collaboration and implementing comprehensive strategies, organisations can mitigate the ethical challenges associated with using big data in recruitment, ultimately fostering a more inclusive and fair hiring environment.

Conclusion: The study concludes that while big data enhances recruitment efficiency, it simultaneously raises critical ethical challenges that must be addressed. Organisations need to implement robust frameworks to ensure fairness and transparency, thereby safeguarding candidates' privacy and fostering equitable hiring practices. These insights provide crucial guidance for HR professionals seeking to navigate the complexities of big data in recruitment.

Certain Addition Formulae in the form of Gamma Function

2025-08-22T07:18:37+00:00

Special functions cover the indispensable appliance for scientific communication of synergy and conglutination. It implement the articulation of a geoscientific issue by contraction such that advanced, more tangible problem can be bombarded within a orderly framework, frequently in the context of differential equations. Special functions procure the sufficiency to diagnosticate causation between the obstructiveness of the geomathematical conceptualization and impact on, together with cross-sectional relevance to the geoscientific reality. Different scientists might not completely agree on which functions are to be included among the special functions, although there would certainly be very substantial overlap. Hypergeometric function is one of the special function which is used to gain of a antenna satellite. The aim of this chapter is to developed some addition formulae in association with Gamma function. The formulae are easy to understand and new in the field of special function.

Characteristics of the Solution of a Certain Riccati Differential Equation Using GeoGebra in Engineering Students through a Case Study

2025-09-12T11:40:51+00:00

The objective of this study was to explore how engineering students at the University of Sinú (Colombia) understand and solve a Riccati differential equation using GeoGebra during the first semester of 2024. A qualitative approach was adopted with a case study design, involving three students selected at the researchers’ discretion from a total of nine students in the differential equations course. Through semi-structured interviews, the study aimed to identify the usefulness of the software for visualising and verifying solutions, as well as to recognise students’ perceptions of its advantages and limitations. The results show that GeoGebra facilitated the graphical representation of the comparison between manual and computational solutions, and the analysis of conceptual errors, which strengthened the understanding of the behaviour of differential solutions. Among the advantages, participants highlighted the immediacy in verifying results and the visual clarity for the graphical interpretation of equations, while as limitations, they pointed out technological dependence and the initial difficulty in learning the digital environment. In conclusion, the use of GeoGebra not only increases students’ motivation but also constitutes a valuable pedagogical resource for improving the teaching and learning of differential equations, providing relevant implications for engineering education. Learning differential equations provides relevant implications for engineering education.