Latest Advances in Intelligent Computing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 719

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Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Interests: artificial intelligence; machine learning; intelligent control systems; robotics; automatic control; nanomechanics; nanosensors
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Guest Editor
Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Interests: internal combustion engines; gas and steam turbines; marine propulsion plants; power/energy systems; energy and exergy analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent computing has witnessed significant advancements in recent years, driven by breakthroughs in artificial intelligence, machine learning, evolutionary algorithms, and symbolic computation. These developments have paved the way for innovative applications in various fields, including robotics, healthcare, finance, cybersecurity, and engineering.

This Special Issue aims to highlight the latest theoretical and practical contributions to intelligent computing, with a particular emphasis on novel algorithms, optimization techniques, and explainable AI models. Topics of interest include, but are not limited to, deep learning architectures, reinforcement learning, genetic programming, symbolic regression, intelligent data analysis, and neuromorphic computing. Additionally, studies addressing interpretability, robustness, and ethical considerations in intelligent systems are encouraged.

We welcome original research articles, reviews, and case studies that present new methodologies, empirical findings, and real-world applications. Submissions demonstrating the integration of intelligent computing with emerging technologies such as edge computing, quantum computing, and the Internet of Things (IoT) are highly encouraged. This Special Issue will serve as a valuable resource for researchers, practitioners, and industry professionals seeking to explore the frontiers of intelligent computing.

Dr. Nikola Anđelić
Prof. Dr. Vedran Mrzljak
Guest Editors

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Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • evolutionary algorithms
  • genetic programming
  • symbolic computation
  • explainable AI
  • intelligent data analysis
  • reinforcement learning
  • neuromorphic computing
  • optimization techniques
  • quantum computing
  • edge computing
  • IoT and AI integration
  • cybersecurity in intelligent computing

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Published Papers (1 paper)

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Research

18 pages, 1277 KB  
Article
Ensemble Extreme Learning Machines for Uncertainty Quantification in Ordinary Differential Equations
by Sajad Ahmad Sheikh and Lateef Ahmad Wani
Mathematics 2026, 14(14), 2527; https://doi.org/10.3390/math14142527 - 14 Jul 2026
Viewed by 196
Abstract
This paper proposes an uncertainty-quantified surrogate framework for ordinary differential equations (ODEs) based on ensembles of extreme learning machines (ELMs). The method constructs M=100 independently randomized ELM surrogates for a given ODE trajectory, uses the ensemble mean as the predictor, and [...] Read more.
This paper proposes an uncertainty-quantified surrogate framework for ordinary differential equations (ODEs) based on ensembles of extreme learning machines (ELMs). The method constructs M=100 independently randomized ELM surrogates for a given ODE trajectory, uses the ensemble mean as the predictor, and defines a pointwise ensemble spread as a preliminary uncertainty measure. To obtain statistically valid prediction intervals, a split-conformal calibration procedure is applied to the ensemble spread, yielding finite-sample marginal coverage under exchangeability while preserving computational efficiency, since each ELM is trained via a single ridge-regression solve. Theoretical results establish exact satisfaction of the prescribed initial condition, stability of the ensemble mean and variance with respect to perturbations in the training data, and almost-sure convergence of the empirical ensemble variance to the corresponding random-feature prediction variance. The hidden-layer sampling hypothesis is made explicit: the experiments use bounded hyperbolic-tangent features with independent uniform draws as the default and independent normal draws in sensitivity tests, both of which satisfy the finite-moment assumptions required by the convergence theorem. Comparisons with capacity-matched Bayesian random-feature neural surrogates and Monte Carlo dropout clarify differences in uncertainty representation. Numerical experiments on exponential, logistic, and damped oscillator dynamics demonstrate accurate reconstruction and calibrated uncertainty quantification in sparse and noisy regimes. Additional ablation studies quantify the effect of the denominator safeguard, calibration-sample size, ensemble size, training time, noise level, and hidden-parameter distribution. Full article
(This article belongs to the Special Issue Latest Advances in Intelligent Computing)
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