Convolutional Neural Networks (CNNs) are a specialized class of deep neural networks tailored for processing high-dimensional data, excelling in tasks like image classification, object detection, and facial recognition. Their architecture is built on convolutional layers interspersed with...

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...

This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

In this study, we address the ambiguity in portfolio optimization, particularly focusing on the uncertainty related to the statistical parameters governing asset returns. We propose a novel method that combines robust optimization with artificial neural networks (ANNs). Our approach effectively...

This study examines the application of machine learning models to predict financial performance in various sectors, using data from 21 companies listed in the BIST100 index (2013-2023). The primary objective is to assess the potential of these models in improving financial forecast accuracy and...

This study presents novel deep neural networks (DNNs) that integrate a thorough data preparation process, including dollar bar sampling, trend scanning labeling, and piecewise aggregate approximation (PAA), to extract features for predicting the directional returns of leading technology stocks...

This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few in its field, and the only one...

In recent years, the actuarial literature involving machine learning in insurance pricing has flourished. However, most actuarial machine learning research focuses on property and casualty insurance, while using such techniques in health insurance is yet to be explored. In this paper, we discuss...

Purpose: Efficient scheduling in Operating Rooms (ORs) is essential for optimizing corresponding costs and enhancing customer satisfaction in healthcare systems.Design/methodology/approach: Conventional static scheduling methods rely on fixed historical surgery times and often lead to...

The increasing use of intelligent data processing and its capacity to handle vast data sets enhance efficiency and effectiveness in production management. Consequently, machine learning models have become essential for decision-making in this domain. Previous literature reviews have not...

Predicting the outcome of a sports game is a favourite pastime for sports fans and researchers. The interest has intensified in recent years due to data availability, the development and successful implementation of machine learning algorithms, and the proliferation of internet gaming. This...

This study presents a prescriptive analytics framework for optimal intelligent predictive maintenance of railway tracks. We use machine learning and Graph Convolutional Networks (GCNs) to optimize the maintenance schedules for railway infrastructure and enhance operational efficiency and safety....

Ride-hailing services are part of intermodal transport systems, allowing passengers to use various transport modes for their trip. The optimal choice for a request in the intermodal system depends on the passenger's waiting time for the ride-hailing service. Estimating this waiting time is...

Short-term electricity load forecasting is a critical factor in optimizing power systems, minimizing operating costs, and securing reliable energy resources. There are various approaches for short-term electricity load forecasting, but handling complex dependencies and sudden changes in load...

This study aims to develop nine different consumer bankruptcy forecasting models with the help of three types of artificial neural networks and to verify the usefulness of new, innovative ratios for implementation in personal finance. A learning sample comprising 200 consumers, and a testing...

Market turnover levels and liquidity changes across various territories significantly influence currency prices, leading to continuous fluctuations. Consequently, traders and investors constantly seek strategies to mitigate exchange rate risks. This study aimed to measure and assess foreign...

This research article presents a comparative analysis between logistic regression as a traditional method, artificial neural networks (ANNs), and decision tree as machine learning techniques for predicting credit risk. It meticulously examines and evaluates these three methods, elucidating their...

Background: Despite the resurgence of interest in augmented reality (AR) due to Industry 4.0 and its ability to resolve several challenges faced by current business models, comprehensive research examining the capabilities of AR in supply chain management (SCM) and logistics remains limited....