Transformers

As data and context continue to expand, a vast amount of textual content, including books, blogs, and papers, is produced and distributed electronically. Analyzing such large amounts of content manually is a time-consuming task. Automatic detection of feelings and emotions in these texts is crucial, as it helps to identify the emotions conveyed by the author, understand the author's writing style, and determine the target audience for these texts. The Qur’an, regarded as the word of God and a divine miracle, serves as a comprehensive guide and a reflection of human life. Detecting emotions and feelings within the content of the Qur’an contributes to a deeper understanding of God's commandments. Recent advancements, particularly the application of transformer-based language models in natural language processing, have yielded state-of-the-art results that are challenging to surpass easily. In this paper, we propose a method to enhance the accuracy and generality of these models by incorporating syntactic features such as Parts Of Speech (POS) and Dependency Parsing tags. Our approach aims to elevate the performance of emotion detection models, making them more robust and applicable across diverse contexts. For model training and evaluation, we utilized the Isear dataset, a well-established and extensive dataset in this field. The results indicate that our proposed model achieves superior performance compared to existing models, achieving an accuracy of 77% on this dataset. Finally, we applied the newly proposed model to recognize the feelings and emotions conveyed in the Itani English translation of the Qur’an. The results revealed that joy has the most significant contribution to the emotional content of the Holy Qur’an. 

Satellite imagery provides valuable data to address the growing demand for agricultural production. However, analyzing such vast amounts of data requires advanced artificial intelligence methods, such as deep learning. The primary challenge lies in the scarcity of labeled training data, as its preparation is both costly and time-consuming. To address this issue, this study integrates remote sensing data, deep neural networks, and transfer learning techniques to estimate the cultivated area of strategic crops in Iran. Given the diverse climates and topographies across Iran’s provinces, in addition to Sentinel-1 and Sentinel-2 satellite data, MODIS sensor and SRTM elevation data were also utilized. To compensate for data limitations, transfer learning was employed to enhance model performance in data-deficient regions (Kermanshah and Markazi). This approach resulted in an approximate 10% improvement in Cohen’s Kappa coefficient. Furthermore, the study investigated the minimum data required for fine-tuning the models. The results demonstrated that even with a reduction of over 60% in the target province's training data, transfer learning still achieved model performance comparable to scenarios where it was not applied.