Latest Publications on Natural Language Processing (NLP)
This is our latest selection of worldwide publications on Natural Language Processing (NLP), between many scientific online journals, classified and focused on natural language processing, tokenization, stemming, lemmatization, part-of-speech, named entity recognition and sentiment analysis.
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A Novel Approach Assessed in the Caribbean Sea
On 2025-02-02 by David Francisco Bustos Usta, Lien Rodríguez-López, Rafael Ricardo Torres Parra, Luc Bourrel @MDPI
Keywords: forecasting, sea surface temperature, Chronos, upwelling, foundational models
AI summary: Novel approach evaluated in Caribbean Sea. Models compared for SST forecasting. Chronos outperforms Lag-Llama. Highlights benefits and limitations of foundational models for accurate predictions. Benchmark established for future research.
Abstract: Sea surface temperature (SST) plays a pivotal role in air&ndash;sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. This study evaluates the performance of foundational models, Chronos and Lag-Llama, in forecasting SST using 22 years (2002&ndash;2023) of high-resolution satellite-derived and in situ da[...]
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A Decarbonization Strategy for the Civil and Construction Industry
On 2025-01-31 by Widjaja, Rachmawati, Kim @MDPI
Keywords: decarbonization, construction industry, rebar consumption, waste, optimization
AI summary: Strategy to minimize rebar consumption and waste in construction industry, integrating special-length-priority algorithm and lap splice adjustments or couplers. Study shows efficiency of reducing consumption and minimizing waste, emphasizing importance of sustainable practices.
Abstract: The growing demand for reinforced concrete (RC) structures, driven by population growth, significantly contributes to carbon emissions, particularly during the construction phase. Steel rebar production, a major contributor to these emissions, faces challenges due to high material consumption and waste, often stemming from market-length rebar and conventional lap splices, impeding decarbonization efforts. This study introduces a comprehensive strategy to minimize rebar consumption and waste, adv[...]
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Identifying Human Factors in Aviation Accidents with Natural Language Processing and Machine Learning Models
On 2025-01-31 by Flávio L. Lázaro, Tomás Madeira, Rui Melicio, Duarte Valério, Luís F. F. M. Santos @MDPI
Keywords: machine learning, natural language processing, aviation accidents, classifier models, incident reports
AI summary: Machine learning techniques used to analyze aviation incident reports, identify contributing factors, and improve air safety. Classifier models such as LS, KNN, Random Forest, Extra Trees, and XGBoost are applied to uncover hidden patterns. Metrics like precision, recall, F1-score, and accuracy are used to assess predictive models. KNN, Random Forest, and Extra Trees are identified as top performers in identifying root causes of incidents.
Abstract: The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. In this paper, classifier models such as LS, KNN, Random Forest, Extra Trees, and XGBoost, which have proven effective in classification tasks, are used to analyze incident reports parsed with natural language processing (NLP) techniques, to uncover hidden patterns and prevent future incidents. Metrics such as precisio[...]
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Semi-Supervised Chinese Word Segmentation in Geological Domain Using Pseudo-Lexicon and Self-Training Strategy
On 2025-01-29 by Bo Wan, Zhuo Tan, Deping Chu, Yan Dai, Fang Fang, Yan Wu @MDPI
Keywords: semi-supervised, Chinese word segmentation, geoscience domain, deep learning, self-training
AI summary: Key challenges in CWS for geoscience domain include lack of labeled data and complex geological terms. GeoCWS framework utilizes pseudo-lexicon and self-training strategy. Backbone model with BERT-based features outperforms baseline methods. Self-training strategy improves generalization to unlabeled data.
Abstract: Chinese word segmentation (CWS), which involves splitting the sequence of Chinese characters into words, is a key task in natural language processing (NLP) for Chinese. However, the complexity and flexibility of geologic terms require that domain-specific knowledge be utilized in CWS for geoscience domains. Previous studies have identified several challenges that have an impact on CWS in the geoscience domain, including the absence of abundant labeled data and difficult-to-delineate complex geol[...]
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A Novel Approach
On 2025-01-28 by Saad Alqithami @MDPI
Keywords: reinforcement learning, natural language processing, gamification, resource allocation, sentiment analysis
AI summary: Dynamic resource allocation framework using AI for disaster response, Optimizing distribution of resources during disasters, Leveraging social media data for sentiment analysis, Gamified simulation platform for stakeholder interaction.
Abstract: Efficient disaster response requires dynamic and adaptive resource allocation strategies that account for evolving public needs, real-time sentiment, and sustainability concerns. In this study, a sentiment-driven framework is proposed, integrating reinforcement learning, natural language processing, and gamification to optimize the distribution of resources such as water, food, medical aid, shelter, and electricity during disaster scenarios. The model leverages real-time social media data to cap[...]
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Decentralized Public Transport Management System Based on Blockchain Technology
On 2025-01-28 by Stanislav Trofimov, Leonid Voskov, Mikhail Komarov @MDPI
Keywords: blockchain technology, decentralized, intelligent transportation system, data processing, fleet management
AI summary: Development of decentralized public transport system based on blockchain technology. Innovative tokenization approach for precise condition monitoring. Mathematical model for vehicle state assessment. Optimizing data transmission via satellite communication.
Abstract: The development of intelligent transportation systems (ITSs) is penetrating many economies around the globe. This paper presents three key innovations in the field of intelligent transportation systems, as follows: (1) a novel tokenization approach where each vehicle is represented as a macro-token subdivided into 500,000 micro-tokens for precise condition monitoring, (2) a comprehensive mathematical model for vehicle state assessment incorporating multiple operational factors, and (3) the GDEPZ[...]
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