Metaphor interpretation in Jordanian Arabic, Emirati Arabic and Classical Arabic: artificial intelligence vs. humans by Aseel Zibin, Nabeeha Binhaidara, Hala Al-Shahwan, Haneen Yousef

“…These findings emphasize the need for improvements in AI models to boost their language processing abilities, as they often miss the cultural aspects required for accurately interpreting figurative language. …”
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An Exploration of the Association Between Residents’ Sentiments and Street Functions During Heat Waves—Taking the Five Core Urban Areas of Chengdu City as an Example by Tianrui Hua, Yufei Ru, Sining Zhang, Shixian Luo

“…Taking the five core urban areas of Chengdu as an example, this study used natural language processing technology to quantify the sentiments in social media texts and combined traditional geographical information for spatial analysis and correlation analysis, to explore the spatial distribution pattern of sentiments during heat waves (SDHW), as well as the correlation between SDHW and the functional categories of streets (FCS). …”
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Leveraging generative AI for course learning outcome categorization using Bloom's taxonomy by Omaima Almatrafi, Aditya Johri

“…Overall, this article demonstrates the potential of leveraging large language models to advance both theoretical understanding and practical application within the field of education and natural language processing.…”
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Dark Surfer-A Dark Pattern Analyzer & Classifier by Babu K. Srinivas, Nithin Bangari Sai, Raghuram E., Bharath A.

“…Leveraging natural language processing techniques and visual analysis, our classifier identifies deceptive design elements based on linguistic cues, visual attributes, and interaction patterns. …”
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Large Language Model‐Based Chatbots in Higher Education by Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan

“…Large language models (LLMs) are artificial intelligence (AI) platforms capable of analyzing and mimicking natural language processing. Leveraging deep learning, LLM capabilities have been advanced significantly, giving rise to generative chatbots such as Generative Pre‐trained Transformer (GPT). …”
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Longitudinal observational cohort study: Speech for Intelligent cognition change tracking and DEtection of Alzheimer’s Disease (SIDE-AD) by Craig W Ritchie, Saturnino Luz, Graciela Muniz Terrera, Stina Saunders, Fasih Haider

“…The speech biomarkers will be analysed longitudinally, and we will use machine learning and natural language processing technology to automate the assessment of the respondents’ speech patterns.Ethics and dissemination The SIDE-AD study has been approved by the NHS Research Ethics Committee (REC reference: 23/WM/0153, protocol number AC23046, IRAS Project ID 323311) and received NHS management approvals from Lothian, Fife and Forth Valley NHS boards. …”
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GBsim: A Robust GCN-BERT Approach for Cross-Architecture Binary Code Similarity Analysis by Jiang Du, Qiang Wei, Yisen Wang, Xingyu Bai

“…To address these challenges, we propose GBsim, a novel approach that combines graph neural networks with natural language processing. GBsim employs a cross-architecture language model to transform binary functions into semantic graphs, leverages a multilayer GCN for structural feature extraction, and employs a Transformer layer to integrate semantic information, generates robust cross-architecture embeddings that maintain high performance despite significant distribution shifts. …”
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USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION by Oleksandr Tymoshchuk

“…AI tools, including natural language processing models for text analysis and machine learning algorithms for misconceptions classification, were used to provide personalised feedback and targeted corrective information. …”
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Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding by Dheya Mustafa, Safaa M. Khabour, Mousa Al-kfairy, Ahmed Shatnawi

“…It does this by utilizing word embedding models, deep learning techniques, and natural language processing to extract subjective opinions, determine polarity, and recognize customers’ feelings in the FDS domain. …”
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Analysis and Recognition Method of Internet Image Public Opinion Based on Partial Differential Equation by Yang Zhang

“…Opinion mining is the use of natural language processing technology to automatically extract the emotional tendencies and evaluation objects contained in the subjective text. …”
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Unveiling construction accident causation: a scientometric analysis and qualitative review of research trends by Haoyu Zang, Ming Li, Zhiyao Jin, Jingfei Huang

“…Findings reveal a shift toward data-driven, intelligent approaches, with artificial intelligence techniques—such as large models (capable of understanding complex patterns from massive datasets), graph neural networks (suitable for modeling relationships between contributing factors), and natural language processing (for extracting insights from textual accident reports)—enhancing accident prevention and risk prediction. …”
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Enhanced Semantic Retrieval with Structured Prompt and Dimensionality Reduction for Big Data by Donghyeon Kim, Minki Park, Jungsun Lee, Inho Lee, Jeonghyeon Jin, Yunsick Sung

“…Large language models (LLMs) have become critical tools because of their advanced language processing capabilities. However, their static nature limits their ability to incorporate real-time and domain-specific knowledge. …”
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Usability of ChatGPT in the english essay writing proficiency of the BEED students by Karl Alvin Aglibot, Edrian Mark Tomines, Janel Talaman, Janabeth Soguilon, Rowena Bongolto, Rhona Marielle Abayon

“…The advancement of AI technology, especially in language processing systems like ChatGPT, creates opportunities for students to elevate their writing skills. …”
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Larger models yield better results? Streamlined severity classification of ADHD-related concerns using BERT-based knowledge distillation. by Ahmed Akib Jawad Karim, Kazi Hafiz Md Asad, Md Golam Rabiul Alam

“…This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural language processing (NLP) applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task-classifying severity levels of Attention Deficit Hyperactivity Disorder (ADHD)-related concerns from social media text data. …”
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Leveraging large language models for automated depression screening. by Bazen Gashaw Teferra, Argyrios Perivolaris, Wei-Ni Hsiang, Christian Kevin Sidharta, Alice Rueda, Karisa Parkington, Yuqi Wu, Achint Soni, Reza Samavi, Rakesh Jetly, Yanbo Zhang, Bo Cao, Sirisha Rambhatla, Sri Krishnan, Venkat Bhat

“…Despite the advancements of Large Language Models (LLMs), quantifying self-reported experiences with natural language processing has resulted in imperfect accuracy. …”
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Triple dimensional psychology knowledge encouraging graph attention networks to exploit aspect-based sentiment analysis by Xuefeng Shi, Weiping Ding, Min Hu, Xin Kang, Fuji Ren

“…Abstract As a fine-grained sentiment analysis subtask in the natural language processing (NLP) community, aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of the aspect terms according to the given sentence. …”
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The Rise of Artificial Intelligence Phobia! Unveiling News-Driven Spread of AI Fear Sentiment Using ML, NLP, and LLMs by Jim Samuel, Tanya Khanna, Julia Esguerra, Srinivasaraghavan Sundar, Alexander Pelaez, Soumitra S. Bhuyan

“…Nearly 70,000 AI-related news headlines were analyzed using natural language processing (NLP), machine learning (ML), and large language models (LLMs) to identify dominant themes and sentiment patterns. …”
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Recorded poor insight as a predictor of service use outcomes: cohort study of patients with first-episode psychosis in a large mental healthcare database by Robert Stewart, Neha Ramu, Anna Kolliakou, Jyoti Sanyal, Rashmi Patel

“…Objectives To investigate recorded poor insight in relation to mental health and service use outcomes in a cohort with first-episode psychosis.Design We developed a natural language processing algorithm to ascertain statements of poor or diminished insight and tested this in a cohort of patients with first-episode psychosis.Setting The clinical record text at the South London and Maudsley National Health Service Trust in the UK was used.Participants We applied the algorithm to characterise a cohort of 2026 patients with first-episode psychosis attending an early intervention service.Primary and secondary outcome measures Recorded poor insight within 1 month of registration was investigated in relation to (1) incidence of psychiatric hospitalisation, (2) odds of legally enforced hospitalisation, (3) number of days spent as a mental health inpatient and (4) number of different antipsychotic agents prescribed; outcomes were measured over varying follow-up periods from 12 months to 60 months, adjusting for a range of sociodemographic and clinical covariates.Results Recorded poor insight, present in 46% of the sample, was positively associated with ages 16-35, bipolar disorder and history of cannabis use and negatively associated with White ethnicity and depression. …”
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A simple interpolation-based data augmentation method for implicit sentiment identification by Yuxia Zhao, Mahpirat Mamat, Alimjan Aysa, Kurban Ubul

“…Abstract Implicit sentiment identification tasks occupy an important place in the field of natural language processing (NLP). In recent years, deep learning models have made significant progress in most tasks in NLP, but implicit sentiment identification still faces two major challenges. …”
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Semantics-driven improvements in electronic health records data quality: a systematic review by Yirong Wu, Mudan Ren, Na Chen, Liu Yang

“…Six semantic techniques were identified as instrumental in improving EHR DQ: EHR standardization, controlled vocabulary, ontology, semantic web, knowledge graph, and natural language processing (NLP). These technologies were further mapped to 16 core data quality indicators and the FAIR principles (Findable, Accessible, Interoperable, and Reusable), highlighting their contributions across both technical and governance dimensions. …”
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