Carbon emissions disclosure (CED) has become a pivotal aspect of corporate sustainability efforts, reflecting a company’s commitment to environmental responsibility and accountability. This study delves into the complex connection between CED and …

Partial label learning (PLL) is a particular problem setting within weakly supervised learning. In PLL, each sample corresponds to a candidate label set in which only one label is true. However, in some practical application scenarios, the …

In this paper, a nonparametric kernel method is introduced to estimate the well-known overlapping coefficient, Matusita $$\rho (X,Y)$$ ρ ( X , Y ) , between two random variables $$X$$ X and $$Y$$ Y . Due to the complexity of finding the formula …

In this paper we first define the class of Generalized Inflated Power Series Distributions (GIPSDs) which contain the inflated discrete distributions most often seen in practice as special cases. We describe the hitherto unkown exponential family …

Agriculture is the primary source of food, fuel, and raw materials and is vital to any country’s economy. Farmers, the backbone of agriculture, primarily rely on instinct to determine what crops to plant in any given season. They are comfortable …

With the widespread use of social networks, detecting the topics discussed on these platforms has become a significant challenge. Current approaches primarily rely on frequent pattern mining or semantic relations, often neglecting the structure of …

In this article, we propose the quadratic rank transmutation map approach on shifted Lindley distribution to improve the existing distribution further. An additional skewness parameter $$\lambda $$ λ is incorporated to transmute the distribution.

Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant health, reduced production …

In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approach is founded on the principle …

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this …

In this paper, we propose a new model by adding an additional parameter to the baseline distributions for modeling claim and risk data used in actuarial and financial studies. The new model is called alpha power transformed exponential Poisson …

Alcohol's dehydrating effects can cause vocal cords to dry out, potentially causing temporary voice changes and increasing the risk of vocal strain or damage. Short-term changes in pitch, volume, and alcohol consumption can cause voice clarity …

Metric learning consists of designing adaptive distance functions that are well-suited to a specific dataset. Such tailored distance functions aim to deliver superior results compared to standard distance measures while performing machine learning …

The Inverse Rayleigh distribution has many applications in the area of reliability studies. It is regarded as a model for a lifetime random variable. It is essential to develop an efficient goodness-of-fit test for this distribution. In this …

The Medical Imaging Query Response System is among the most challenging concepts in the medical field. It requires a significant amount of effort to organize and comprehend the various representations of the human body. Additionally, the system …

In this work, we propose a novel hybrid method for the estimation of regression models, which is based on a combination of LASSO-type methods and smooth transition (STR) random forests. Tree-based regression models are known for their flexibility …

In recent years, generative artificial intelligence has been developing rapidly. In the image domain, image generation models based on deep learning have made remarkable achievements. Early frameworks for image generation models were dominated by …

Nowadays, with the growth of emerging technologies, increased attention has been paid to the classification of privacy-preserved medical data and development of various privacy-preserving models for the promotion of online medical pre-diagnosis …

In this study, we use a novel approach to explore possible connections between foreign exchange and stock returns using Turkish financial data from 2005 to 2022. Our method involves a two-stage technique. The first stage begins by decomposing …

Modernization in the healthcare industry is happening with the support of artificial intelligence and blockchain technologies. Collecting healthcare data is done through any Google survey from different governing bodies and data available on the …