The World Bank Group supports the Ministry of Economy of Armenia in developing a green taxonomy (GT) as a key step toward a sound system of identifying, selecting, and financing green and sustainable development projects and actions, and as an essential milestone toward establishing an...

The rapid evolution of technology is reshaping labor markets by altering skill demands and job profiles. This paper introduces a novel skill-based measure of occupational technology intensity - the Occupational Technology Skill Share (OTSS) - that distinguishes between manual, digital, and...

Recidivism is a persistent challenge for criminal justice systems worldwide, yet evidence from Latin America remains scarce. This study addresses that gap through three contributions. First, it reviews the individual, institutional, and contextual determinants of recidivism, with special...

This study develops an interpretable machine learning framework for type 1 motor insurance renewal classification using 70,290 real-world Thai policies, providing essential insights for pricing, customer retention, and operational decision making. The dataset was partitioned into a 70% training...

In insurance portfolios, classifying customers without a prior history at a given company is particularly challenging due to the absence of historical behavior, extreme class imbalance, heavy-tailed loss distributions, and strict operational constraints. Traditional machine learning approaches,...

This study aims to predict stock market risk and improve preparedness for potential economic crises by identifying sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data (2015 to 2024), a daily return series was constructed. A...

Development finance increasingly funds entrepreneurship in developing countries, but evidence of its impact on entrepreneurship is mixed. Existing studies analyze total development finance flows as entrepreneurship-specific development finance data did not previously exist. By training and...

Accurate prediction of customer repurchase behaviour is vital for businesses aiming to boost customer retention. This study introduces an advanced approach that merges classification algorithms with RFM analysis, a widely adopted framework in customer relationship management. The proposed models...

Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage...

Social networks are becoming vital for people to interact with each other, which in turn represent the production of a huge amount of information that can be useful in many contexts (e.g., medicine, natural disasters, commercial purposes, tourism). Nevertheless, analyzing such Big Data for...

Seaweed is a globally significant aquaculture commodity with increasing economic and environmental importance. However, inconsistent quality standards and subjective procurement practices continue to hinder efficiency in its value chain, particularly for dried Eucheuma seaweed. This study...

Sustainable supply chain management plays a pivotal role in shaping corporate reputation and enhancing customer loyalty in the contemporary market. This study uniquely integrates regional, demographic, and psychographic data with advanced machine learning methodologies, including clustering,...

This work introduces a novel method for sentiment analysis in the stock market by combining Deep Neural Networks (DNN) and an improved Firefly Algorithm (FA). It is essential to comprehend investor sentiment in financial markets in order to forecast stock price movements and make wise investment...

This paper presents a computationally efficient method for binary classification using Manski's (1975,1985) maximum score model when covariates are discretely distributed and parameters are partially but not point identified. We establish conditions under which it is minimax optimal to allow for...

This publication provides guidance to producers of statistics about the classification of economic activities according to NACE Rev. 2.1. At the same time, it enables statistics users to better understand official statistics based on NACE Rev. 2.1.

Purpose: To develop a controlled vocabulary of less than 30 terms that represents level 1 of a taxonomy for the field of operations management, integrating previous available work to improve efficiency in searching and retrieving scientific information in the area.Design/methodology/approach: A...

Background: The utilization of satellite images has become increasingly popular for detecting land usage, focusing on agricultural land classification in recent years, due to the significant decline in bees. Objectives: This paper seeks to address these challenges by applying several machine...

Poor households are heterogeneous in the circumstances preventing an improvement in their welfare. It is important to understand the nuances within different types of poor households so that critical pathways out of poverty that remedy the variegated sets of constraints they face can be...

Classifying household water-consumption behaviors is crucial for providing targeted suggestions for water-saving behaviors and enabling effective resource management and conservation. Although it is common knowledge that energy consumption is closely coupled with household water consumption, the...

Purpose - The contemporary landscape of supply chains necessitates a comprehensive integration of multiple components encompassing production, distribution and customer engagement. The pursuit of supply chain harmony underscores the significance of pricing strategies within the framework of...

Machine learning algorithms are typically evaluated using benchmark datasets under the assumption that these datasets are clean. However, recent studies have revealed the presence of label noise in many benchmark datasets, indicating a biased evaluation to date. Confident learning (CL), an...

Customer churn is a recurring problem in bank marketing, and there are several machine learning approaches that can help. This paper compares two of them: a Multilayer Perceptron (MLP) neural network and logistic regression. We used the UCI Bank Marketing dataset, which has 41,188 client records...

Due to the increasing importance of sustainability and ESG concepts in global business, companies strive to operate in an environmentally, socially, and economically responsible manner, integrating the fundamental principles of sustainability into their business processes, operations, and...