Recent Advances in Algorithms for Neural Networks

Special Issue Editors


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Guest Editor
Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Belgrade, Serbia
Interests: machine learning; deep learning; natural language processing/machine learning; computer vision; medical image processing

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Guest Editor
1. Research, Development and PhD Studies Division, Estonian Business School, A. Lauteri 3, 10114 Tallinn, Estonia
2. 3S Holding OÜ, Uus-Veeriku tee 1, 62220 Tartu, Estonia
Interests: multimodal emotion recognition; applied machine learning; computer vision; human-robot interaction
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Special Issue Information

Dear Colleagues,

The field of neural networks has seen significant strides over the past few years, driven by advancements in both theoretical understanding and practical implementations. These innovations continue to reshape various domains, including computer vision, natural language processing, healthcare, finance, and robotics. The Special Issue "Recent Advances in Algorithms for Neural Networks" aims to collect research papers that highlight cutting-edge developments in neural network architectures, algorithms, and applications.

This Special Issue will encompass a diverse range of topics that reflect the multi-faceted nature of neural networks:

  • new architectures and learning paradigms;
  • optimization techniques;
  • regularization and generalization;
  • explainability and interpretability;
  • applications and real-world impact;
  • theoretical insights.

Overall, this Special Issue will underscore the diverse and evolving landscape of neural network research. It will showcase the state-of-the-art techniques that are pushing the boundaries of what is possible, while also highlighting remaining challenges that the field must address. We hope that this collection will inspire further exploration, innovation, and collaboration in neural network research.

Prof. Dr. Marina Marjanovic
Prof. Dr. Anbarjafari Gholamreza
Guest Editors

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Keywords

  • neural networks
  • deep learning
  • optimization algorithms
  • neural network
  • architectures
  • explainability
  • generalization
  • regularization techniques
  • machine learning
  • artificial intelligence
  • real-world applications

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Published Papers (1 paper)

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Research

36 pages, 2892 KB  
Article
Bridging Behavioral and Emotional Intelligence: An Interpretable Multimodal Deep Learning Framework for Customer Lifetime Value Estimation in the Hospitality Industry
by Milena Nikolić, Marina Marjanović and Žarko Rađenović
Math. Comput. Appl. 2026, 31(2), 39; https://doi.org/10.3390/mca31020039 - 3 Mar 2026
Viewed by 587
Abstract
Customer Lifetime Value (CLV) estimation over the observed transactional horizon is a fundamental challenge in hospitality analytics, supporting revenue management, personalization, and long-term customer relationship strategies. However, existing models predominantly rely on structured behavioral data while overlooking the emotional intelligence embedded in guest [...] Read more.
Customer Lifetime Value (CLV) estimation over the observed transactional horizon is a fundamental challenge in hospitality analytics, supporting revenue management, personalization, and long-term customer relationship strategies. However, existing models predominantly rely on structured behavioral data while overlooking the emotional intelligence embedded in guest narratives. This study proposes an interpretable multimodal deep learning (DL) framework that bridges behavioral and emotional intelligence for CLV estimation by integrating structured booking records with unstructured hotel review text. Model interpretability is ensured through SHAP analysis for structured attributes, LIME for local textual explanations, and attention visualization for modality interaction analysis. Experimental evaluation on large-scale hospitality datasets demonstrates that the proposed multimodal framework outperforms traditional machine learning models, unimodal deep learning baselines, and classical ensemble learners, yielding consistent improvements across multiple error metrics and a notable increase in goodness of fit. The results confirm that emotional intelligence extracted from guest reviews significantly enhances CLV estimation and provides actionable insights for hospitality decision-making, supporting the deployment of transparent and explainable artificial intelligence (XAI) systems for strategic customer value management. Full article
(This article belongs to the Special Issue Recent Advances in Algorithms for Neural Networks)
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