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Accurate diagnosis of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting brain malignancies, but interpreting MRI data can be challenging and time-consuming for healthcare professionals.

An innovative method is presented that combines deep learning (DL) models with natural language processing (NLP) from ChatGPT to enhance the accuracy of brain tumor detection in MRI scans. The method generates textual descriptions of brain tumor regions, providing clinicians with valuable insights into tumor characteristics for informed decision-making and personalized treatment planning.

The evaluation of this approach demonstrates promising outcomes, achieving a notable Dice coefficient score of 0.93 for tumor segmentation, outperforming current state-of-the-art methods. Human validation of the generated descriptions confirms their precision and conciseness.

While the method showcased advancements in accuracy and understandability, ongoing research is essential for refining the model and addressing limitations in segmenting smaller or atypical tumors.

These results emphasized the potential of this innovative method in advancing neuroimaging practices and contributing to the effective detection and management of brain tumors.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

ChatGPT is a revolution in the field of lifelong learning in the digital era of higher education. This chapter explores ChatGPT's enormous influence on universities, showcasing its revolutionary potential for individualized, captivating and cooperative learning environments. With ChatGPT's exceptional adaptability to individual learning needs, students can navigate complex subjects with unparalleled ease and speed. ChatGPT is a sophisticated artificial intelligence (AI) language model. It enhances the learning process overall and creates a sense of community by enabling lively peer discussions and exchanges across geographic boundaries. However, alongside its potential benefits, ChatGPT presents ethical dilemmas that demand necessitate careful consideration. Issues such as algorithmic bias and data privacy need to be properly addressed in order to ensure responsible and equitable use of this technology is used in educational settings. The trajectory ChatGPT in higher education may be significantly influenced by forthcoming innovations like augmented and virtual reality, promising a new era of immersive and inclusive lifelong learning experiences. The chapter underscores the importance of a balanced strategy that leverages ChatGPT's benefits while proactively addressing associated challenges, supporting the fundamental transformation of higher education.