RAGing ahead in rheumatology: new language model architectures to tame artificial intelligence by Diego Benavent, Vincenzo Venerito, Xabier Michelena

“…The advent of generative models and large language models (LLMs) has expanded AI’s capabilities, particularly in natural language processing (NLP) tasks such as question-answering and medical literature synthesis. …”
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Student Performance Prediction Using Machine Learning Algorithms by Esmael Ahmed

“…Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. …”
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AI-Driven Chatbot for Real-Time News Automation by Fahim Sufi, Musleh Alsulami

“…This study presents an AI-driven chatbot designed for real-time news automation, integrating advanced natural language processing techniques, knowledge graphs, and generative AI models to improve news summarization and correlation analysis. …”
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TIBW: Task-Independent Backdoor Watermarking with Fine-Tuning Resilience for Pre-Trained Language Models by Weichuan Mo, Kongyang Chen, Yatie Xiao

“…Pre-trained language models such as BERT, GPT-3, and T5 have made significant advancements in natural language processing (NLP). However, their widespread adoption raises concerns about intellectual property (IP) protection, as unauthorized use can undermine innovation. …”
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Transformer-based ensemble model for dialectal Arabic sentiment classification by Omar Mansour, Eman Aboelela, Remon Talaat, Mahmoud Bustami

“…Sentiment analysis is crucial for assessing public sentiment toward specific issues; however, applying it to dialectal Arabic presents considerable challenges in natural language processing. The complexity arises from the language’s intricate semantic and morphological structures, along with the existence of multiple dialects. …”
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Leveraging Grammatical Roles for Measuring Semantic Similarity Between Texts by Md. Atabuzzaman, Md Shajalal, M. Elius Ahmed, Masud Ibn Afjal, Masaki Aono

“…Assessing the textual similarity is a prerequisite in almost all applications in the field of language processing and information retrieval. However, the diversity in the sentence structure makes it formidable to estimate the similarity. …”
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Probing Public Perceptions of Antidepressants on Social Media: Mixed Methods Study by Jianfeng Zhu, Xinyu Zhang, Ruoming Jin, Hailong Jiang, Deric R Kenne

“…MethodsData were collected from AskaPatient and Reddit, analyzed using natural language processing and large language models. Analytical techniques included sentiment analysis, emotion detection, personality profiling, and topic modeling. …”
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Airline reviews processing: Abstractive summarization and rating-based sentiment classification using deep transfer learning by Ayesha Ayub Syed, Ford Lumban Gaol, Alfred Boediman, Widodo Budiharto

“…In recent years, deep transfer learning has opted to solve many challenging tasks in Natural Language Processing (NLP) relieving the hassles of exhaustive training and the requirement of extensive labelled datasets. …”
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Breaking Away From AI: The Ontological and Ethical Evolution of Machine Learning by Enrico Barbierato, Alice Gatti, Alessandro Incremona, Andrea Pozzi, Daniele Toti

“…Unlike AI’s pursuit of general intelligence and symbolic reasoning, ML focuses on data-driven performance optimization, with impactful applications in computer vision, natural language processing (NLP), and autonomous systems. The study highlights ethical challenges—such as addressing algorithmic bias (50 occurrences), fairness (2,778 publications), and environmental sustainability (283 related works)—which emphasize the need for dedicated ethical frameworks tailored to ML. …”
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AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions by Zohra Dakhia, Mariateresa Russo, Massimo Merenda

“…This review highlights AI applications, including computer vision for food recognition and quality assessment, Natural Language Processing for recipe analysis, and predictive modeling for dietary recommendations. …”
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Small Language Models for Speech Emotion Recognition in Text and Audio Modalities by José L. Gómez-Sirvent, Francisco López de la Rosa, Daniel Sánchez-Reolid, Roberto Sánchez-Reolid, Antonio Fernández-Caballero

“…Speech emotion recognition has become increasingly important in a wide range of applications, driven by the development of large transformer-based natural language processing models. However, the large size of these architectures limits their usability, which has led to a growing interest in smaller models. …”
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Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems by Salam Fraihat, Qusai Shambour, Mohammed Azmi Al-Betar, Sharif Naser Makhadmeh

“…Deep learning (DL) models demonstrated outstanding performance across different domains: computer vision, natural language processing, image analysis, pattern recognition, and recommender systems. …”
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EEG reveals brain network alterations in chronic aphasia during natural speech listening by Ramtin Mehraram, Jill Kries, Pieter De Clercq, Maaike Vandermosten, Tom Francart

“…Abstract Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. …”
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Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT by Fahim Sufi

“…Leveraging recent advancements in machine learning and natural language processing (NLP), this study utilizes large language models (LLMs) such as GPT and BERT, known for their exceptional comprehension and generation of human-like text. …”
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Learning High-Order Features for Fine-Grained Visual Categorization with Causal Inference by Yuhang Zhang, Yuan Wan, Jiahui Hao, Zaili Yang, Huanhuan Li

“…Recently, causal models have gained significant attention in natural language processing (NLP) and computer vision (CV) due to their capability of capturing features with causal relationships. …”
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Comparing Word Representation BERT and RoBERTa in Keyphrase Extraction using TgGAT by Novi Yusliani, Aini Nabilah, Muhammad Raihan Habibullah, Annisa Darmawahyuni, Ghita Athalina

“…These systems are crucial for supporting various natural language processing tasks, such as text summarization, information retrieval, and representation. …”
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Predicting accrual success for better clinical trial resource allocation by Sisi Ma, Yinzhao Wang, John Wagner, Steve Johnson, Serguei Pakhomov, Constantin Aliferis

“…Features of the dataset were informed by prior literature and constructed using data-driven natural language processing methods. We built predictive models for accrual failure using state-of-the-art supervised machine learning protocols and methods. …”
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An Assessment of Measuring Local Levels of Homelessness Through Proxy Social Media Signals by Yoshi Bird, Sarah Grobe, Michael Arnold, Sean Rogers, Mikaela Fudolig, Julia Zimmerman, Christopher Danforth, Peter Sheridan Dodds

“…Recent studies suggest social media activity can function as a proxy for measures of state-level public health, detectable through straightforward applications of natural language processing. We present the results of our efforts to apply this approach to estimate homelessness at the state level throughout the US during the period 2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets containing the substring “homeless.” …”
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Akrivia Health Database—deep patient characterisation using a secondary mental healthcare dataset in England and Wales: cohort profile by Ana Todorovic, Philip Craig, Simon Pillinger, Panagiota Kontari, Sophie Gibbons, Luke Bryden, Tarso Franarin, Ceyda Uysal, Gloria Roque, Benjamin Fell

“…Akrivia Health also uses a bespoke natural language processing model to further extract the research-relevant information from free-text progress notes, including diagnoses, medications and clinical symptoms. …”
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Exploring the Potentials of Large Language Models in Vascular and Interventional Radiology: Opportunities and Challenges by Taofeeq Oluwatosin Togunwa, Abdulquddus Ajibade, Christabel Uche-Orji, Richard Olatunji

“…LLMs, designed for natural language processing, exhibit promising capabilities in clinical decision-making, workflow optimization, education, and patient-centered care. …”
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