The IJCNN 2025 Review Process

TL;DR

This paper reviews the review process of IJCNN 2025, highlighting the growth in submissions, reviewers, and attendees, and introduces methods for scoring and bias removal in the review process.

Contribution

It presents a detailed description of the review process and introduces a score ranking strategy and bias calibration method for reviewer assessments.

Findings

Significant increase in submissions, reviewers, and attendees compared to previous years.

Development of a score index for ranking reviewer scores.

Implementation of a calibrated review score to reduce reviewer bias.

Abstract

The International Joint Conference on Neural Networks (IJCNN) is the premier international conference in the area of neural networks theory, analysis, and applications. The 2025 edition of the conference comprised 5,526 paper submissions, 7,877 active reviewers, 426 area chairs, 2,152 accepted papers, and more than 2,300 attendees. This represents a growth of about 100% in terms of submissions, 200% in terms of reviewers, and over 50% in terms of attendees as compared to the previous edition. In this paper, we describe several key aspects of the whole review process, including a strategy for ranking the scores provided by the reviewers by evaluating a score index and a calibrated version used experimentally to remove reviewer-specific bias from reviews.