Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.

Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.

The rapid advancement of Artificial Intelligence (AI) in education has created new opportunities and challenges for English language teaching. While AI-driven tools offer innovative ways to enhance learning experiences, their effective use depends largely on teachers’ preparedness and confidence in integrating them into classroom practice. This study explores the role of Training of Trainers (ToT) programs in preparing English language teachers for AI integration. Using a qualitative research design, data were collected through semi-structured interviews and focus group discussions with trainers and teacher participants involved in AI-focused ToT initiatives. Thematic analysis was employed to examine participants’ perceptions, experiences, and challenges in adopting AI for pedagogical purposes. Findings reveal that ToT programs serve as a critical bridge between technological innovation and classroom application by equipping teacher educators with both technical knowledge and pedagogical strategies. Participants highlighted the importance of practical demonstrations, collaborative learning, and continuous mentoring in building AI literacy. However, challenges such as limited access to resources, varying levels of digital competence, and resistance to change emerged as barriers. Despite these constraints, the study found that ToT programs fostered a supportive professional community that encouraged experimentation and innovation in teaching practices. The research underscores the need for sustained and context-sensitive ToT models that go beyond technical training to address pedagogical integration and reflective practice. Ultimately, the study contributes to understanding how professional development structures can empower English language teachers to harness AI effectively, enhancing both teacher agency and student learning outcomes.