Developing Ship Electronic Lookout Using LoRA Fine-Tuned Large Language Model

Lecture notes in electrical engineering, Год журнала: 2025, Номер unknown, С. 157 - 164

Опубликована: Янв. 1, 2025

Язык: Английский

---

Application of large language models in healthcare: A bibliometric analysis

Digital Health, Год журнала: 2025, Номер 11

Опубликована: Янв. 1, 2025

The objective is to provide an overview of the application large language models (LLMs) in healthcare by employing a bibliometric analysis methodology. We performed comprehensive search for peer-reviewed English-language articles using PubMed and Web Science. selected were subsequently clustered analyzed textually, with focus on lexical co-occurrences, country-level inter-author collaborations, other relevant factors. This textual produced high-level concept maps that illustrate specific terms their interconnections. Our final sample comprised 371 journal articles. study revealed sharp rise number publications related LLMs healthcare. However, development geographically imbalanced, higher concentration originating from developed countries like United States, Italy, Germany, which also exhibit strong inter-country collaboration. are applied across various specialties, researchers investigating use medical education, diagnosis, treatment, administrative reporting, enhancing doctor-patient communication. Nonetheless, significant concerns persist regarding risks ethical implications LLMs, including potential gender racial bias, as well lack transparency training datasets, can lead inaccurate or misleading responses. While promising, widespread adoption practice requires further improvements standardization accuracy. It critical establish clear accountability guidelines, develop robust regulatory framework, ensure datasets based evidence-based sources minimize risk reliable use.

Язык: Английский

---

Leveraging large language models through natural language processing to provide interpretable machine learning predictions of mental deterioration in real time

Arabian Journal for Science and Engineering, Год журнала: 2024, Номер unknown

Опубликована: Авг. 27, 2024

Based on official estimates, 50 million people worldwide are affected by dementia, and this number increases 10 new patients every year. Without a cure, clinical prognostication early intervention represent the most effective ways to delay its progression. To end, Artificial Intelligence computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, treatment. However, traditional approaches need more semantic knowledge management explicability capabilities. Moreover, using Large Language Models (LLMs) cognitive decline diagnosis is still scarce, even though these models advanced way clinical-patient communication intelligent systems. Consequently, we leverage an LLM latest Natural Processing (NLP) techniques in chatbot solution provide interpretable Machine Learning prediction of real-time. Linguistic-conceptual features appropriate analysis. Through explainability, aim fight potential biases improve their help workers decisions. More detail, proposed pipeline composed (i) data extraction employing NLP-based prompt engineering; (ii) stream-based processing including feature engineering, selection; (iii) real-time classification; (iv) explainability dashboard visual descriptions outcome. Classification results exceed 80 % all evaluation metrics, with recall value mental deterioration class about 85 %. sum up, contribute affordable, flexible, non-invasive, diagnostic system work.

Язык: Английский

---

Rapid Guessing Behavior Detection in Microlearning: Insights into Student Performance, Engagement, and Response Accuracy

IEEE Access, Год журнала: 2024, Номер 12, С. 157996 - 158024

Опубликована: Янв. 1, 2024

Язык: Английский

---

Web-Enhanced Vision Transformers and Deep Learning for Accurate Event-Centric Management Categorization in Education Institutions

Systems, Год журнала: 2024, Номер 12(11), С. 475 - 475

Опубликована: Ноя. 7, 2024

In the digital era, social media has become a cornerstone for educational institutions, driving public engagement and enhancing institutional communication. This study utilizes AI-driven image processing Web-enhanced Deep Learning (DL) techniques to investigate effectiveness of King Faisal University’s (KFU’s) strategy as case study, particularly on Twitter. By categorizing images into five primary event management categories subcategories, this research provides robust framework assessing content generated by KFU’s administrative units. Seven advanced models were developed, including an innovative integration Vision Transformers (ViTs) with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, VGG16, ResNet. The ViT-CNN hybrid model achieved perfect classification accuracy (100%), while “Development Partnerships” category demonstrated notable (98.8%), underscoring model’s unparalleled efficacy in strategic classification. offers actionable insights optimization communication strategies data collection processes, aligning them national development goals Saudi Arabia’s 2030, thereby showcasing transformative power DL event-centric broader higher education landscape.

Язык: Английский

---