Search Results - subject_exact:"Kohonen map"

The impact of countries' artificial intelligence readiness levels on new business establishment : controlling for energy consumption, economic growth, inflation, and population

Zharkimbekova, Kamila S.; Niyazbekova, Roza K.; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015620788

Forecasting economic growth with traditional methods and a simple neural network model

Li, Shujie; Feng, Yuanhua - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015627073

Mood in the market : forecasting IPO activity with music sentiment and LSTM

Ding, Qinxu; Guan, Chong; Yu, Yinghui - 2026

We examine whether aggregate "music mood" derived from globally popular songs can help forecast primary equity issuance. We build a Friday-anchored weekly panel that merges SEC EDGAR counts of priced Initial Public Offerings (IPOs) with features from the Spotify Daily Top 200 (audio descriptors...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015628658

AI-driven bankruptcy prediction in manufacturing SMEs : comparing machine learning techniques with logistic regression

Letkovský, Stanislav; Jenčová, Sylvia; … - 2026

Bankruptcy prediction is currently a widely researched topic, as it typically results from a chain of negative events. Logistic Regression (LR) is one of the standard prediction tools; however, with advances in technology, machine learning (ML) methods are gaining prominence and demonstrating...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015634019

Randomized algorithms and neural networks for communication-free multiagent singleton set cover

He, Guanchu; Hill, Colton; Seaton, Joshua H.; Brown, … - 2026

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615288

Measuring economic outlook in the news

Beck, Elliot; Eckert, Franziska; Kühne, Linus; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015615555

Comparing GDP growth rates between countries with high and low renewable energy usage based on neural networks

Abdelsamiea, Abdelsamiea Tahsin; Abd El-Aal, Mohamed F. - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015616954

Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting

Das, Abhinav; Schlüter, Stephan; Schneider, Lorenz - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635119

Graph neural network model for cable tunnel cost prediction under high-dimensional construction data

Fang, Ming; Lu, Handong; Lai, Yifeng - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015635256

Natural hazards and financial activity : evidence from solar storms impact on BTC Mining

Michaēlidēs, Panagiōtēs G.; Prelorentzos, … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015609729

Large and deep factor models

Kelly, Bryan T.; Kuznetsov, Boris; Malamud, Semyon; Xu, … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015609789

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Large (and deep) factor models

Kelly, Bryan T.; Kuznetsov, Boris; Malamud, Semyon; Xu, … - 2024

Book / Working Paper

Pathways of climate variability, agricultural performance, and conflict : a machine learning approach to complex dependencies

Gattone, Tulia; Romano, Donato; Tiberti, Luca - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015611909

Forecasting stock market behavior in BRICS economies using artificial neural machine learning models

Panigrahi, Shrikant; Kukreja, Gagan; Kumaraswamy, Sumathi - 2026

Purpose - This study aims to forecast the stock market behavior of BRICS nations (Brazil, Russia, India, China and South Africa) using advanced machine learning models. The focus is on identifying market trends, predicting future index prices and analyzing returns. Design/methodology/approach -...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015592502

Graph attention networks in exchange rate forecasting

Landmesser-Rusek, Joanna; Orłowski, Arkadiusz - 2026

Exchange rate forecasting is an important issue in financial market analysis. Currency rates form a dynamic network of connections that can be efficiently modeled using graph neural networks (GNNs). The key mechanism of GNNs is the message passing between nodes, allowing for better modeling of...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015640548

Fine-Scale Spatial Disaggregation of Statistical Data via Graph Neural Networks

Lee, Kamwoo; Blankespoor, Brian; Newhouse, David - 2026

Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to environmental management. However, many statistics are only available for coarse administrative units, necessitating techniques for fine-scale spatial disaggregation. This paper...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015641144

A Bayesian learning approach for predictive resilience in engineer-to-order supply chains

Alaoua, Aicha; Karim, Mohammed - 2026

Accurate supplier lead time prediction is critical for maintaining resilience in Engineer-to-Order (EtO) supply chains, characterized by high customization and uncertainty. This study develops a simulation-based predictive framework combining log-normal sensitivity analysis, Internet of Things...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015640886

Critical regimes of systemic risk : flow network cascades in the U.S. banking system

Montañez Jacquez, Samuel; Quezada Téllez, Luis Alberto; … - 2026

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015639141

Aligned explanations in neural networks

Lobet, Corentin; Chiaromonte, Francesca - 2026

As artificial intelligence increasingly drives critical decisions, the ability to genuinely explain how neural networks make predictions is essential for trust. Yet, most current explanation methods offer post-hoc rationalizations rather than guaranteeing a true reflection of the model's...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015644225

Empirical analysis of industry 4.0 determinants in Moroccan supply chains : a neural network approach

2026

This paper examines the factors shaping the adoption of Industry 4.0 (I4.0) technologies in Moroccan supply chains (SCs), with a focus on how digitalization, management practices, strategic planning, and financial resources contribute to SC optimization. The study aims to explore how these...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015636782

Self-driving neural networks for term structure modeling

Kooiker, Sicco; Brummelen, Janneke van; Schaumburg, Julia; … - 2026

We propose a factor model with time-varying loadings for term structure modeling and fore casting. While maintaining the interpretation of the factors as level, slope, and curvature through explicit identification restrictions, we allow the loadings to take flexible shapes by specifying them as...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015611785

Exploring market efficiency with GRU-D neural networks : evidence from global stock markets

Ben Jbara, Abdelhamid; Rabah Gana, Marjene; Dakhlaoui, Mejda - 2026

This study revisits the Efficient Markets Hypothesis by employing a GRU-D neural network to predict stock return distributions across global equity markets, accounting for missing and irregular data. It examines whether stock returns exhibit statistically significant departures from purely...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015643213

Sequential solution for DSGE models with deep neural networks

Ferrari, Massimo Minesso; Frenzel, Carla - 2026

This paper develops a sequential deep learning algorithm for solving dynamic stochastic general equilibrium (DSGE) models. The algorithm trains a deep neural network to approximate the model's policy functions across four progressive phases: steady-state anchoring, exploration around the steady...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015650845

An expectile-based neural network approach for mixed-frequency economic forecasting

Saputra, Wisnowan Hendy; Prastyo, Dedy Dwi; … - 2026

Timely and accurate forecasting of Gross Domestic Product (GDP) is critical for economic policymaking, yet it is complicated by the need to integrate high-frequency financial indicators with low-frequency macroeconomic data and to capture complex nonlinear dynamics. This study introduces a novel...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015652276

Neural demand estimation with habit formation and rationality constraints

Grzeskiewicz, Marta - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015652614

Explainable neural algorithms for corporate sustainability forecasting : a layered predictive model anchored in executive awareness, green finance, and digital innovation

Ibrahim, Yara; Moubarak, Hosam; Badawy, Hebatallah Abd … - 2026

This study investigates how artificial intelligence (AI) capability drives sustainable performance through the mediating role of Digital Green Innovation (DGI). Grounded in the Resource-Based and Natural Resource-Based Views, survey data from 321 organizations are analyzed using a multi-method...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015651766

A computational approach to cognitive architecture with adaptive networks and entropy-driven dynamics

Arbaoui, Billel - 2026

This study presents a computational approach to cognitive architecture that integrates adaptive networks with entropy-driven dynamics to model the evolution and stabilization of mental states. The framework combines temporal-causal network modeling with entropy-based mechanisms inspired by...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015654358

Predicting shock propagation and uncovering heterogeneity with graph neural networks

Arata, Yoshiyuki - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015655641

Predicting stock market trends using convolutional neural networks : a deep learning approach

Özkul, Ege - 2026

Technical analysis aims to predict stock returns based on price and volume patterns and has seen growing adoption of machine learning methods. However, most approaches rely on hand-crafted features. This paper investigates whether deep learning applied to stock chart images can predict future...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015652027

Siamese networks for AI-powered automated banknote quality control

Gentile, Salvatore; Luciani, Andrea; Marchetti, Sabina; … - 2026

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015655714

Estimating parameters of structural models using neural networks

Wei, Yanhao; Jiang, Zhenling - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015197209

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Estimating Parameters of Structural Models Using Neural Networks

Wei, Yanhao 'Max' - 2020

Book / Working Paper

Predicting the length-of-stay of pediatric patients using machine learning algorithms

Boff Medeiros, Natália; Fogliatto, Flavio Sanson; … - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015197550

Robust portfolio selection under model ambiguity using deep learning

Miri, Sadegh; Salavati, Erfan; Shamsi, Mostafa - 2025

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015338300

Combining convolution neural networks with long-short term memory layers to predict Parkinson's disease progression

Frasca, Maria; La Torre, Davide; Pravettoni, Gabriella; … - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015338338

Optimising electric vehicle wireless charging systems using neural networks to enable free-position parking

Merrigan, Hannah; Wu, Yu-hsin; Lesage-Landry, Antoine; … - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015339243

The energy potential of residual biomass gasification integrated with internal combustion engine in Córdoba, Colombia using artificial neural network techniques

Rhenals-Julio, Jesús D.; Torres, Cristina Cogollo; … - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015339849

Estimating nonlinear heterogeneous agent models with neural networks

Kase, Hanno; Melosi, Leonardo; Rottner, Matthias - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015206737

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Estimating nonlinear heterogeneous agent models with neural networks

Kase, Hanno; Melosi, Leonardo; Rottner, Matthias - 2024

Book / Working Paper

Predicting bank distress in Europe : using machine learning and a novel definition of distress

Malikkidou, Despo; Strohbach, Wolfgang - 2025

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015185208

Thinking with many minds : using large language models for multi-perspective problem-solving

Park, Sanghyun; Maciejovsky, Boris; Puranam, Phanish - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015191524

Convolutional neural networks: key components and architectures

Fischer, Manfred M. - 2025

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...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015193130

Does money matter for predicting overall prices in Albania? : an analysis with recurrent neural network

Vika, Blerina; Vika, Ilir; Xhaja, Denada - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015193360

Enhancing stock price prediction using GANs and transformer-based attention mechanisms

Li, Siyi; Xu, Sijie - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015193781

Heterogeneity, climate change, and crop yield distributions : solvency implications for publicly subsidized crop insurance programs

Schuurman, Daniel; Ker, Alan P. - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015166748

Using natural language processing to identify monetary policy shocks

Piller, Alexandra; Schranz, Marc; Schwaller, Larissa - 2025

Identifying the causal effects of monetary policy is challenging due to the endogeneity of policy decisions. In recent years, high-frequency monetary policy surprises have become a popular identification strategy. To serve as a valid instrument, monetary policy surprises must be correlated with...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015484251

Dynamic distributed ambulatory care scheduling

Moosavi, Amirhossein; Ozturk, Onur; Patrick, Jonathan - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015484374

Optimizing solar photovoltaic power forecasting via multi-architecture machine learning framework with multiple hyperparameter optimization techniques

Tahir, Muhammad Faizan; Tzes, Anthony; El-Fouly, Tarek H. M. - 2025

The integration of solar photovoltaic systems into power grids has become crucial for sustainable energy development, particularly in extreme climate conditions where accurate power forecasting remains challenging. This study presents a novel multi-architecture machine learning framework with...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015485825

Operationalization of the construct "business model of a bank" : clustering analyses with deep neural networks

Herdt, Manfred; Schulte-Mattler, Hermann - 2025

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015486007

Hydrogen revolution : artificial intelligence and machine learning driven policies, feasibility, challenges and opportunities: Insights from Asian countries

Raghuvanshi, Sankalp; Singh, Kalpana; Chaudhary, Malvika; … - 2025

Green hydrogen, a zero-carbon emission fuel, has become a real competitor to transform the energy market, thanks to improvements in the electrolysis process, decreased costs, and the presence of renewable energy resources. Energy industries have shown considerable progress in hydrogen production...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015492632

A data-driven approach using recurrent neural networks for material demand forecasting in manufacturing

Orozco Torres, Jorge Antonio; Medina Santiago, Alejandro; … - 2025

Background: In the current context of increasing competitiveness and complexity in markets, accurate demand forecasting has become a key element for efficient production planning. Methods: This study implements recurrent neural networks (RNNs) to predict raw material demand using historical...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015492723

Understanding users' effective use of generative conversational AI from a media naturalness perspective : a hybrid structural equation modeling-artificial neural network (SEM-ANN) approach

Wang, Kun; Lu, Yaobin; Pan, Zhao - 2025

Although generative conversational artificial intelligence (AI) can answer questions well and hold conversations as a person, the semantic ambiguity inherent in text-based communication poses challenges to effective use. Effective use reflects the users' utilization of generative conversational...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015455580

Analysis of urban functional areas based on graph clustering neural networks

Bai, Huimin; Zhu, Xia; Liu, Yuanping; Zhang, Tiange; … - 2025

Urban functional areas, as an important fundamental component of urban planning, hold significant importance for optimizing urban layouts, resource allocation, and the rational organization of socio-economic activities. Urban functional area division using traditional methods often has...

Persistent link: https://www.econbiz.de, ebvufind01.dmz1.zbw.eu/10015458311