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...

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,...

In July 2024, a new Theme Taxonomy was launched for all IBRD/IDA (the Bank) lending operations and Advisory Services and Analytics (ASA) products, refreshing and building on the Theme Taxonomy structure that was launched in July 2016. Theme codes are aligned with World Bank Group goals and...

Emotion recognition from electroencephalogram (EEG) signals has garnered significant attention owing to its potential applications in affective computing, human-computer interaction, and mental health monitoring. This paper presents a comparative analysis of different machine learning methods...

The prevailing consensus in statistical literature is that multiple imputation is generally the most suitable method for addressing missing data in statistical analyses, whereas a complete case analysis is deemed appropriate only when the rate of missingness is negligible or when the missingness...

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...

Financial sentiment analysis is crucial for making informed decisions in the financial markets, as it helps predict trends, guide investments, and assess economic conditions. Traditional methods for financial sentiment classification, such as Support Vector Machines (SVM), Random Forests, and...

The utilization of data presents significant opportunities for enhancing business models and generating value. Consequently, the integration of data into value creation processes has given rise to the emergence of data-driven business models. This study aims to delineate and introduce diverse...

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...

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...

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.

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...

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...

This study examines the use of artificial neural networks (ANNs) to classify financial risks in micro-, small-, and medium-sized enterprises (MSMEs) in Montenegro and the wider Western Balkan region. The economies in this region share structural similarities, such as a high concentration of...