Smart Agile Prioritization and Clustering: An AI-Driven Approach for Requirements Prioritization by Aya M. Radwan, Manal A. Abdel-Fattah, Wael Mohamed

“…This paper proposes Smart Agile Prioritization and Clustering (SAPC), an AI-driven approach that enhances requirements prioritization by leveraging Natural Language Processing (NLP), BERT embeddings, graph-based dependency modeling, and optimization techniques. …”
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Medical short text classification via Soft Prompt-tuning by Xiao Xiao, Han Wang, Feng Jiang, Tingyue Qi, Wei Wang

“…Inspired by the recent success of prompt- tuning, which has been extensively explored to enhance semantic modeling in various natural language processing tasks with the appearance of GPT-3, our method incorporates an automatic template generation method to address the issues related to short length and feature sparsity. …”
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Deep Learning in Written Arabic Linguistic Studies: A Comprehensive Survey by Manar Almanea

“…Considering the success of neural networks and DL in natural language processing tasks, the effectiveness of applying these methods to Arabic-based linguistic studies must be investigated. …”
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Exploring Named Entity Recognition via MacBERT-BiGRU and Global Pointer with Self-Attention by Chengzhe Yuan, Feiyi Tang, Chun Shan, Weiqiang Shen, Ronghua Lin, Chengjie Mao, Junxian Li

“…Named Entity Recognition (NER) is a fundamental task in natural language processing that aims to identify and categorize named entities within unstructured text. …”
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AI and consumer behavior: Trends, technologies, and future directions from a scopus-based systematic review by Aditya Nanda Riandhi, Muhammad Rifqi Arviansyah, Mery Citra Sondari

“…Findings report exponential publication surges after 2020, driven by machine learning and natural language processing applications. Consumer attitudes (trust, privacy) and behavioral intentions emerge as primary mediators of AI adoption, while ethical concerns underline the need for open algorithmic architectures. …”
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Development of an Artificial Intelligence-Based Text Sentiment Analysis System for Evaluating Learning Engagement Levels in STEAM Education by Chih-Hung Wu, Kang-Lin Peng

“…We developed an AI-based text sentiment analysis system to assess learning engagement, integrating speech recognition, natural language processing techniques, keyword analysis, and text sentiment analysis. …”
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A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade by Attila Kovari

“…Artificial intelligence tools, in particular machine learning, natural language processing and recommender algorithms, facilitate collaborative learning by enabling personalized learning through feedback and group work. …”
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Development of OCR Service for Page-Level Recognition for Camera-Captured Document Images by Junyoung Park, Wonjun Kang, Seonji Park, Keuntek Lee, Hyung Il Koo, Nam Ik Cho

“…The emergence of Large Language Models (LLMs) has driven significant advancements in Natural Language Processing (NLP) and introduced new text-related applications, such as Visual Question Answering (VQA). …”
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A diagnostic system for detecting COVID-19 patients depending on lexicon semantic and Biterm Topic Model-Based Feature Selection on WhatsApp Messages Classification by Raghad Hatem, Noor Hussein Eliwe

“…This study presents a novel approach to identifying potential COVID-19 patients by analyzing their WhatsApp messages using advanced natural language processing techniques. Our methodology combines Word2Vec embeddings with lexical-semantic enrichment using ConceptNet, creating a comprehensive system that can detect subtle linguistic patterns associated with COVID-19 symptoms and experiences. …”
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Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development by Tahir Rashid, Inam Illahi, Qasim Umer, Muhammad Arfan Jaffar, Waheed Yousuf Ramay, Hanadi Hakami

“…Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. …”
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Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network for Aspect-Level Sentiment Analysis by Hongwei Tang, Haining Yan, Ran Song

“…Aspect-level Sentiment Analysis (ASA) constitutes a critical task in natural language processing, aimed at detecting and classifying sentiment polarities associated with specific aspects in textual data. …”
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Knowledge Graph Completion With Pattern-Based Methods by Maryam Sabet, Mohammadreza Pajoohan, Mohammad Reza Moosavi

“…The first phase utilizes multi-source information and expands the KG using entity description as external information with efficient Natural Language Processing (NLP) techniques. In the second phase, we mine frequent patterns from the expanded KG, extract connections between them and assign entities to the patterns that construct the abstraction layer. …”
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Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

“…In particular, the emergence of a novel natural language processing (NLP) architecture called the transformer has improved the state-of-the-art performance of existing AI technologies. …”
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Optimizing an LSTM Self-Attention Architecture for Portuguese Sentiment Analysis Using a Genetic Algorithm by Daniel Parada, Alexandre Branco, Marcos Silva, Fábio Mendonça, Sheikh Mostafa, Fernando Morgado-Dias

“…A sentiment analysis is a Natural Language Processing (NLP) task that identifies the opinion or emotional tone of documents such as customer reviews, either at the general or detailed level. …”
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Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis by Emre Bilgin

“…For PsA, AI has also allowed for early detection among psoriasis patients using genetic markers, immune profiling, and EHR-based natural language processing systems. Overall, AI models may predict diagnosis, disease severity, treatment response, and comorbidities to improve care in patients with RA, axSpA, and PsA. …”
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The application of large language models in ophthalmology by ZHANG Wencheng, SHE Lingbing, HE Jingjing

“…Through natural language processing technology, LLMs can assist doctors in summarizing and analyzing clinical data. …”
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Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study. by Giuliano Lorenzoni, Cristina Tavares, Nathalia Nascimento, Paulo Alencar, Donald Cowan

“…Recent research has evidenced that machine learning (ML) and natural language processing (NLP) tools and techniques have significantly benefited the diagnosis of depression. …”
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A novel approach towards the robustness of centrality measures in networks by Jens Dörpinghaus, Vera Weil, Robert Rockenfeller, Meetkumar Pravinbhai Mangroliya

“…For example, historical and narrative texts in ancient languages are usually challenging for natural language processing methods and artificial intelligence technologies developed for modern languages due to their complexity and missing models. …”
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Toward Deep Semi-Supervised Continual Learning: A Unified Survey for Scalable and Adaptive AI by Mona Ebadi Jalal, Adel Elmaghraby

“…Our analysis highlights several critical applications including image classification, cybersecurity, and natural language processing, while also identifying key challenges that prevent its broader adoption in real-world scenarios. …”
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Development of a Knowledge Base for Construction Risk Assessments Using BERT and Graph Models by Wonjong Lee, Seulki Lee

“…Research on automatically extracting and managing predefined knowledge from various forms of data through natural language processing (NLP) is ongoing. This study proposes a novel method that uses NLP and graph models to automatically extract predefined knowledge from unstructured construction data and build an entity-relationship-based risk-assessment knowledge base. …”
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