Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and biotechnological value. In this study, we developed a novel deep learning-based framework for the classification of seven macrofungi species from the genera Mycena and Marasmius, leveraging their unique ecological and morphological characteristics. The proposed approach integrates a custom convolutional neural network (CNN) with a self-organizing map (SOM) adapted for supervised learning and a Kolmogorov–Arnold Network (KAN) layer to enhance classification performance. The experimental results demonstrate significant improvements in classification metrics when using the CNN-SOM and CNN-KAN architectures. Additionally, advanced pretrained models such as MaxViT-S and ResNetV2-50 achieved high accuracy rates, with MaxViT-S achieving 98.9% accuracy. Statistical analyses using the chi-square test confirmed the reliability of the results, emphasizing the importance of validating evaluation metrics statistically. This research represents the first application of SOM in fungal classification and highlights the potential of deep learning in advancing fungal taxonomy. Future work will focus on optimizing the KAN architecture and expanding the dataset to include more fungal classes, further enhancing classification accuracy and ecological understanding.

Sign language functions as an indispensable interaction method for a certain portion of people in society, offering a unique way of communication. A significant challenge in advancing towards this objective is the difficulty in obtaining suitable training data for each sign in supervised learning. This challenge comes from the complex process of labeling signs and the limited number of skilled people available to do this job. This work introduces a new approach to the problem of Zero-Shot Sign Language Recognition (ZSSLR). We basically utilize and model hand and landmark data streams extracted from the body of the signer. Based on these extracted and modeled features, we employ a data grading approach to facilitate visual embedding with the self-attention mechanism. We utilize textual sign description features along with visual embedding in the Zero-Shot Learning (ZSL) settings. We assess the efficacy of our methodology in two of the suggested ZSL benchmarks.