Special Sessions

SS-1: Appell Polynomials and Approximation Theory with Applications II (confirmed)

Organizers:

• Mehmet Ali Özarslan (Eastern Mediterranean University, mehmetali.ozarslan@emu.edu.tr)
• Bayram Çekim (Gazi University, bayramcekim@gazi.edu.tr)
• Mustafa Kara (Eastern Mediterranean University, mustafa.kara@emu.edu.tr)

Description: The aim of SS-1 is to bring together academics whose research topics are mainly focused on the Appell-type polynomials (Appell, Binomial, Sheffer, Boas–Buck polynomials, etc.), approximation theory, and their connections with applications. We welcome research papers, as well as review papers, covering, but not limited to, any of the following topics:

• General types of Appell polynomials with analytical properties and applications.
• Certain special cases of Appell (and/or special) polynomials and numbers (Bernoulli, Euler, Hermite, Charlier, Fibonacci, etc.).
• Approximation properties of linear, positive operators constructed based on general (and/or special cases of) Appell-type polynomials.
• Appell-type polynomials from the operational calculus point of view.
• Linear positive operators constructed based on certain special functions.

Contact: To participate in SS-1, please first contact the organizer(s) of the session via the email addresses above.

Note: This special session is a continuation of the session with the same title that was held at ATSF 2024, where 12 successful presentations were delivered (click here for details).

SS-2: Approximation by Nonlinear Operators and Their Applications II (confirmed)

Organizers:

• İsmail Aslan (Hacettepe University, ismail-aslan@hacettepe.edu.tr)
• Türkan Yeliz Gökçer Ellidokuz (Bahçeşehir University, ylzgkcr@gmail.com)

Description: The primary goal of SS-2 is to provide a high-level forum for researchers to present novel forms of nonlinear approximation operators and discuss the latest advancements in approximation theory. While classical linear operators provide a robust foundation, nonlinear approaches - including sublinear, pseudo-linear, max-product and max-min operators - have become indispensable for capturing complex behaviors in modern data science. This session aims to foster active discussions concerning efficient methodologies for achieving improved convergence rates and high-order approximation results. A key focus will be the bridge between abstract functional analysis and practical implementation. We particularly welcome contributions that carefully analyze the potential impact of these techniques across diverse application domains, such as digital image processing, signal filtering and reconstruction. The highlighted topics are as follows:

• Classical approximation, rates of convergence.
• Sampling operators and interpolation operators.
• Approximation by neural networks.
• Max-product operators and max-min operators.
• Nonlinear operators, pseudo-linear operators, sublinear operators.
• Summability on nonlinear operators.

Contact: To participate in SS-2, please first contact the organizer(s) of the session via the email addresses above.