Advanced Deep Learning Approaches for Automated Recognition of Cuneiform Symbols

This paper presents a thoroughly automated method for identifying and interpreting cuneiform characters via advanced deep-learning algorithms. Five distinct deep-learning models were trained on a comprehensive dataset of cuneiform characters and evaluated according to critical performance metrics, including accuracy and precision. Two models demonstrated outstanding performance and were subsequently assessed using cuneiform symbols from the Hammurabi law acquisition, notably Hammurabi Law 1. Each model effectively recognized the relevant Akkadian meanings of the symbols and delivered precise English translations. Future work will investigate ensemble and stacking approaches to optimize performance, utilizing hybrid architectures to improve detection accuracy and reliability. This research explores the linguistic relationships between Akkadian, an ancient Mesopotamian language, and Arabic, emphasizing their historical and cultural linkages. This study demonstrates the capability of deep learning to decipher ancient scripts by merging computational linguistics with archaeology, therefore providing significant insights for the comprehension and conservation of human history.

SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convolutional neural networks (CNNs) play a crucial role in capturing PolSAR image characteristics by leveraging kernel capabilities to consider local information and the complex-valued nature of PolSAR data. In this study, a novel three-branch fusion of complex-valued CNN, named the Shallow to Deep Feature Fusion Network (SDF2Net), is proposed for PolSAR image classification. To validate the performance of the proposed method, classification results are compared against multiple state-of-the-art approaches using the airborne synthetic aperture radar (AIRSAR) datasets of Flevoland and San Francisco, as well as the ESAR Oberpfaffenhofen dataset. The results indicate that the proposed approach demonstrates improvements in overallaccuracy, with a 1.3% and 0.8% enhancement for the AIRSAR datasets and a 0.5% improvement for the ESAR dataset. Analyses conducted on the Flevoland data underscore the effectiveness of the SDF2Net model, revealing a promising overall accuracy of 96.01% even with only a 1% sampling ratio.

Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image Classification

This research work presents a novel dual-branch model for hyperspectral image classification that combines two streams: one for processing standard hyperspectral patches using Real-Valued Neural Network (RVNN) and the other for processing their corresponding Fourier transforms using Complex-Valued Neural Network (CVNN). The proposed model is evaluated on the Pavia University and Salinas datasets. Results show that the proposed model outperforms state-of-the-art methods in terms of overall accuracy, average accuracy, and Kappa. Through the incorporation of Fourier transforms in the second stream, the model is able to extract frequency information, which complements the spatial information extracted by the first stream. The combination of these two streams improves the overall performance of the model. Furthermore, to enhance the model performance, the Squeeze and Excitation (SE) mechanism has been utilized. Experimental evidence show that SE block improves the models overall accuracy by almost 1\%.