Toward the Development of Large-Scale Word Embedding for Low-Resourced Language by Shahzad Nazir, Muhammad Asif, Shahbaz Ahmad Sahi, Shahbaz Ahmad, Yazeed Yasin Ghadi, Muhammad Haris Aziz

“…Word embedding is possessed by Natural language processing as a key procedure for semantically and syntactically manipulating the unlabeled text corpus. …”
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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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Improving Sentiment Analysis Performance on Imbalanced Moroccan Dialect Datasets Using Resample and Feature Extraction Techniques by Zineb Nassr, Faouzia Benabbou, Nawal Sael, Touria Hamim

“…Sentiment analysis is a crucial component of text mining and natural language processing (NLP), involving the evaluation and classification of text data based on its emotional tone, typically categorized as positive, negative, or neutral. …”
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Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review by Dipak Gautam, Philipp Kellmeyer

“… BackgroundThe rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. …”
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A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network by Shuang Liu, Zeng Zhuang, Yanfeng Zheng, Simon Kolmanic

“…To address this problem, the Transformer from the field of natural language processing (NLP) was introduced to the image segmentation field. …”
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Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning by Jingteng Li, Kimberley R. Zakka, John Booth, Louise Rigny, Samiran Ray, Mario Cortina-Borja, Payam Barnaghi, Neil Sebire

“…Abstract Introduction Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR). …”
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Supervised learning and resampling techniques on DISC personality classification using Twitter information in Bahasa Indonesia by Ema Utami, Irwan Oyong, Suwanto Raharjo, Anggit Dwi Hartanto, Sumarni Adi

“…Analyzing profile data from personal social media accounts reduces data collection time, as this method does not require users to fill any questionnaires. A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies). …”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…Transformers are becoming dominant deep learning backbones for both computer vision and natural language processing. While extensive experiments prove its outstanding ability for large models, transformers with small sizes are not comparable with convolutional neural networks in various downstream tasks due to its lack of inductive bias which can benefit image understanding. …”
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Using machine learning-based models for personality recognition by Fatemeh Mohades Deilami, Hossein Sadr, Mozhdeh Nazari

“…Among various deep neural networks, Convolutional Neural Networks (CNN) have demonstrated profound efficiency in natural language processing and especially personality detection. …”
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Community-engaged artificial intelligence research: A scoping review. by Tyler J Loftus, Jeremy A Balch, Kenneth L Abbott, Die Hu, Matthew M Ruppert, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Philip A Efron, Patrick J Tighe, William R Hogan, Parisa Rashidi, Michelle I Cardel, Gilbert R Upchurch, Azra Bihorac

“…Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. …”
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Distribution of trial registry numbers within full-text of PubMed Central articles: implications for linking trials to publications and indexing trial publication types by Arthur M. Holt, Ang Michael Troy, Neil R. Smalheiser

“…A variety of natural language processing (NLP)-based and machine learning-based models have been developed to assist users in identifying these connections. …”
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Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach by John Andoh, Louis Asiedu, Anani Lotsi, Charlotte Chapman-Wardy

“…Gathering public opinions on the Internet and Internet-based applications like Twitter has become popular in recent times, as it provides decision-makers with uncensored public views on products, government policies, and programs. Through natural language processing and machine learning techniques, unstructured data forms from these sources can be analyzed using traditional statistical learning. …”
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ChatGPT in healthcare education: a double-edged sword of trends, challenges, and opportunities by Michael Agyemang Adarkwah, Samuel Anokye Badu, Evans Appiah Osei, Enoch Adu-Gyamfi, Jonathan Odame, Käthe Schneider

“…The scoping review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, utilized search terms such as “ChatGPT,” “GPT,” “natural language processing,” “large language models,” and “health education”. …”
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Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks by Junyi Yang, Ruibin Mao, Mingrui Jiang, Yichuan Cheng, Pao-Sheng Vincent Sun, Shuai Dong, Giacomo Pedretti, Xia Sheng, Jim Ignowski, Haoliang Li, Can Li, Arindam Basu

“…However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. …”
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Applying the language acquisition model to the solution small language processing tasks by Dz. I. Kachkou

“…The relevance of this task is due to the following considerations: the need to eliminate the information inequality between speakers of different languages; the need for new tools for the study of poorly understood languages, as well as innovative approaches to language modeling in the low-resource context; the problem of supporting and developing small languages.There are three main objectives in solving the problem of small natural language processing at the stage of describing the problem situation: to justify the problem of modeling language in the context of resource scarcity as a special task in the field of natural languages processing, to review the literature on the relevant topic, to develop the concept of language acquisition model with a relatively small number of available resources. …”
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Exploring Topic Coherence With PCC-LDA and BERT for Contextual Word Generation by Sandeep Kumar Rachamadugu, T. P. Pushphavathi, Surbhi Bhatia Khan, Mohammad Alojail

“…In the field of natural language processing (NLP), topic modeling and word generation are crucial for comprehending and producing texts that resemble human languages. …”
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Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model by Ram Chandra Bhushan, Rakesh Kumar Donthi, Yojitha Chilukuri, Ulligaddala Srinivasarao, Polisetty Swetha

“…Results This research has proposed a novel model to address the challenges in natural language processing. The Improved Green anaconda-assisted Bi-GRU based Hierarchical ResNet BNER model (IGa-BiHR BNERM) is the model. …”
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Impact of Off-Time on Quality of Life in Parkinson’s Patients and Their Caregivers: Insights from Social Media by Philippe Damier, Emily J. Henderson, Jesús Romero-Imbroda, Laura Galimam, Nick Kronfeld, Tobias Warnecke

“…A random sample of these posts was then analyzed using natural language processing (NLP), and quantitative, qualitative,in-depth contextual analysis was also performed. …”
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Nonparametric analysis of inter‐individual relations using an attention‐based neural network by Takashi Morita, Aru Toyoda, Seitaro Aisu, Akihisa Kaneko, Naoko Suda‐Hashimoto, Ikuma Adachi, Ikki Matsuda, Hiroki Koda

“…In this study, researchers explored a nonparametric analysis of inter‐individual relations using a neural network with the attention mechanism, which plays a central role in natural language processing. The high interpretability of the attention mechanism and flexibility of the entire neural network allow for automatic detection of inter‐individual relations included in the raw data, without requiring prior knowledge/assumptions about what modes/types of relations are included in the data. …”
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Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning by Ashagrew Liyih, Shegaw Anagaw, Minichel Yibeyin, Yitayal Tehone

“…Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. …”
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