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Journal articles on the topic 'Database, global network, natural language processing, error'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Kolesnikov, Alexey, Egor Plitchenko, and Maria Kropacheva. "Automation of data preparation for mapping using natural language processing systems." InterCarto. InterGIS 28, no. 1 (2022): 659–69. http://dx.doi.org/10.35595/2414-9179-2022-1-28-659-669.

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The current level of development of information technology makes it possible to automate the processing of those types of data that only a specialist could previously work with. One such example is natural language processing technologies that implement the functions of sentiment analysis, machine translation, and question-answer systems. For the processes of creating cartographic and geoinformation works, the methods of extracting named entities are of the greatest interest, which allows extracting geographical names from unstructured text and linking named entities, which make it possible to

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Gupta, B. M., S. M. Dhawan, and Ghouse Modin N. Mamdapur. "Research trends in the field of natural language processing : A scientometric study based on global publications during 2001-2020." COLLNET Journal of Scientometrics and Information Management 17, no. 1 (2023): 61–79. http://dx.doi.org/10.47974/cjsim-2022-0023.

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The study provides a quantitative and qualitative description of global research in “Natural Language Processing” ( NLP) using bibliometric methods. The analysis is based on publications data sourced from Scopus database for the period 2001-2020. The purpose of the study is to understand the status of NLP research at the global, national, institutional, and author level. The study highlights the productivity and performance of NLP research on a series of metrics as well as provides a visual view of collaborative network relationship between authors, research institutions, and leading countries

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Lo, Shaw-Hwa, and Yiqiao Yin. "Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network." Machine Learning and Knowledge Extraction 3, no. 4 (2021): 922–45. http://dx.doi.org/10.3390/make3040046.

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Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models are capable of making good predictions, yet there is a lack of connection between language semantics and prediction results. This paper proposes a novel influence score (I-score), a greedy search algorithm, called Backward Dropping Algorithm (BDA), and a novel feature engineering technique called the “dagger technique”. First, the paper proposes to use the novel influence score (I-score) to detect and search for the important language semantics in text documents that are useful for

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Hang, Ching-Nam, Pei-Duo Yu, Roberto Morabito, and Chee-Wei Tan. "Large Language Models Meet Next-Generation Networking Technologies: A Review." Future Internet 16, no. 10 (2024): 365. http://dx.doi.org/10.3390/fi16100365.

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The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including co

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Odisho, Anobel Y., Briton Park, Nicholas Altieri, et al. "Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation." JAMIA Open 3, no. 3 (2020): 431–38. http://dx.doi.org/10.1093/jamiaopen/ooaa029.

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Abstract Objective Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings. Materials and methods Our data comes from the Urologic Outcomes Database at UCSF which includes 3232 annotated prostate cancer pathology reports from 2001 to 2018. We approach 17 separate information extraction tasks, involving a wide range of pathologic features. To handle the diverse range of fields, we required 2 stat

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Fang, Yong, Jian Gao, Zhonglin Liu, and Cheng Huang. "Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM." Applied Sciences 10, no. 17 (2020): 5922. http://dx.doi.org/10.3390/app10175922.

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In the context of increasing cyber threats and attacks, monitoring and analyzing network security incidents in a timely and effective way is the key to ensuring network infrastructure security. As one of the world’s most popular social media sites, users post all kinds of messages on Twitter, from daily life to global news and political strategy. It can aggregate a large number of network security-related events promptly and provide a source of information flow about cyber threats. In this paper, for detecting cyber threat events on Twitter, we present a multi-task learning approach based on t

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Muravskyi, Volodymyr. "The impact of global technological trends on accounting." Herald of Ternopil National Economic University, no. 4 (86) (December 12, 2017): 138–48. http://dx.doi.org/10.35774/visnyk2017.04.138.

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The article points out that the pace of technological advance has led to integrating informaion and communication technology into accounting processes. Examples of advanced technologies for business that influence accounting management include computer-assisted learning and artificial intelligence, “smart” applications for telecommunication devices, “smart” things, complemented by virtual reality, digital twins, blockchain, chat communication systems, adaptive security systems, applications and network architecture, integrated electronic platforms. The aim of the research is to elucidate the i

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Tao, Jin, Kelly Brayton, and Shira Broschat. "Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database." Applied Sciences 11, no. 1 (2020): 24. http://dx.doi.org/10.3390/app11010024.

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Advances in genome sequencing technology and computing power have brought about the explosive growth of sequenced genomes in public repositories with a concomitant increase in annotation errors. Many protein sequences are annotated using computational analysis rather than experimental verification, leading to inaccuracies in annotation. Confirmation of existing protein annotations is urgently needed before misannotation becomes even more prevalent due to error propagation. In this work we present a novel approach for automatically confirming the existence of manually curated information with e

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Wang, Wenshuo. "Enhancing Multimodal Emotion Analysis through Fusion with EMT Model Based on BBFN." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 52–59. http://dx.doi.org/10.62051/gfeqm854.

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Sentiment analysis, as one of the key technologies of natural language processing, has been widely used in medical, film and television fields. In order to increase sentiment analysis's precision, it is particularly important to integrate multi-modal data. This paper presents a pioneering fusion strategy that amalgamates the cutting-edge Efficient Multimodal Transformer (EMT) model with the innovative Bi-Bimodal Fusion Network (BBFN) to revolutionize emotion analysis. By synergistically integrating these two state-of-the-art models, the research endeavors to enhance the efficiency and precisio

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Valcamonico, Dario, Piero Baraldi, Francesco Amigoni, and Enrico Zio. "Natural Language Processing method for the identification of the factors influencing road accident severity." PHM Society European Conference 6, no. 1 (2021): 12. http://dx.doi.org/10.36001/phme.2021.v6i1.2899.

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Although road safety has improved in the last decades, the rate of accidents with severe and fatal consequences is still exceeding the safety objectives (European Commission 2019; World Health Organization 2018).This work explores the possibility of using Natural Language Processing (NLP) techniques for the automatic extraction of knowledge from road accidents reports, with the objective of supporting the safety management of the road infrastructure system (Persia et al. 2016).To this aim, we consider databases of textual reports on road accidents, provided by the local public authorities. The

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Huang, Subin, Daoyu Li, Chengzhen Yu, Junjie Chen, Qing Zhou, and Sanmin Liu. "Empowering entity synonym set generation using flexible perceptual field and multi-layer contextual information." PLOS One 20, no. 4 (2025): e0321381. https://doi.org/10.1371/journal.pone.0321381.

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Automatic generation of entity synonyms plays a pivotal role in various natural language processing applications, such as search engines, question-answering systems, and taxonomy construction. Previous research on generating entity synonym sets has typically relied on approaches that involve sorting and pruning candidate entities or solving the problem in a two-stage manner (i.e., initially identifying pairs of synonyms and subsequently aggregating them into sets). Nevertheless, these approaches tend to disregard global entity information and are susceptible to error propagation issues. This p

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Somanathan Pillai, Sanjaikanth E. Vadakkethil, Srinivas A. Vaddadi, Rohith Vallabhaneni, Santosh Reddy Addula, and Bhuvanesh Ananthan. "TextBugger: an extended adversarial text attack on NLP-based text classification model." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 3 (2025): 1735. https://doi.org/10.11591/ijeecs.v38.i3.pp1735-1744.

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Recently, adversarial input highly negotiates the security concerns in deep learning (DL) techniques. The main motive to enhance the natural language processing (NLP) models is to learn attacks and secure against adversarial text. Presently, the antagonistic attack techniques face some issues like high error and traditional prevention approaches accurately secure data against harmful attacks. Hence, some attacks unable to increase more flaws of NLP models thereby introducing enhanced antagonistic mechanisms. The proposed article introduced an extended text adversarial generation method, TextBu

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Zhang, Huan, Yibin Yao, Chaoqian Xu, Wei Xu, and Junbo Shi. "Transformer-Based Global Zenith Tropospheric Delay Forecasting Model." Remote Sensing 14, no. 14 (2022): 3335. http://dx.doi.org/10.3390/rs14143335.

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Zenith tropospheric delay (ZTD) plays an important role in high-precision global navigation satellite system (GNSS) positioning and meteorology. At present, commonly used ZTD forecasting models comprise empirical, meteorological parameter, and neural network models. The empirical model can only fit approximate periodic variations, and its accuracy is relatively low. The accuracy of the meteorological parameter model depends heavily on the accuracy of the meteorological parameters. The recurrent neural network (RNN) is suitable for short-term series data prediction, but for long-term series, th

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Chacha Andrea, Marwa, Choong Kwon Lee, and Mijin Noh. "Analysis of Job Ads to Understand the IT Trend in Southeast Asia." Academic Society of Global Business Administration 20, no. 6 (2023): 152–71. http://dx.doi.org/10.38115/asgba.2023.20.6.152.

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In recent decades, Southeast Asian countries, including Indonesia, Malaysia, and Singapore, have witnessed substantial economic growth and enhancements in the quality of life. These nations have emerged as high-income economies and significant contributors to the global economic landscape. This study endeavors to illuminate the contemporary landscape of highly demanded occupations and technological proficiencies within Southeast Asian nations, with a specific focus on Indonesia, Malaysia, the Philippines, and Singapore. A comprehensive dataset comprising 137,434 job postings spanning from Janu

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Kanwal, Dr Preet. "EXPLORING GROWTH OF RESEARCH TRENDS IN ARTIFICIAL INTELLIGENCE: A BIBLIOMETRIC STUDY." international journal of advanced research in computer science 16, no. 2 (2025): 82–85. https://doi.org/10.26483/ijarcs.v16i2.7231.

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Bibliometric analysis is one of the major techniques applied to measure the literature output on research in any subject. This study identifies the global literature output on ‘Artificial intelligence or AI-related’ research based on data retrieved from ‘DOAJ’ indexing database over a longitudinal period 1950-2024. Classification of data has been done using a spreadsheet package. Quantitative analysis to evaluate the trends in AI-related research has been undertaken using bibliometric statistics of open access publications. It is observed from the research output that the AI-related research b

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Nakazato, Takeru. "Knowledge Extraction from Specimen-Derived Data from GenBank to Enrich Biodiversity Information." Biodiversity Information Science and Standards 5 (September 1, 2021): e73787. https://doi.org/10.3897/biss.5.73787.

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DNA barcoding and environmental DNA (eDNA) are increasing the need for the utilization of gene sequences in the field of biodiversity. GBIF (Global Biodiversity Information Facility) and GGBN (Global Genome Biodiversity Network) are taking action on the treatment of gene sequences in the field of biodiversity (Finstad et al. 2020). Gene sequences have been collected and published by INSDC (International Nucleotide Sequence Database Collaboration) for over 30 years (Arita et al. 2020). Biodiversity information has been collected using standards such as Darwin Core (Wieczorek et al. 2012), but I

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Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, et al. "Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study." Barw Medical Journal 3, no. 3 (2025): 6–12. https://doi.org/10.58742/bmj.v3i3.180.

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Abstract Introduction The exact manner in which large language models (LLMs) will be integrated into pathology is not yet fully comprehended. This study examines the accuracy, benefits, biases, and limitations of LLMs in diagnosing dermatologic conditions within pathology. Methods A pathologist compiled 60 real histopathology case scenarios of skin conditions from a hospital database. Two other pathologists reviewed each patient’s demographics, clinical details, histopathology findings, and original diagnosis. These cases were presented to ChatGPT-3.5, Gemini, and an external pathologist. Each

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El, Maazouzi Qamar, Asmaâ Retbi, and Samir Bennani. "Enhancing online learning: sentiment analysis and collaborative filtering from Twitter social network for personalized recommendations." Enhancing online learning: sentiment analysis and collaborative filtering from Twitter social network for personalized recommendations 14, no. 3 (2024): 3266–76. https://doi.org/10.11591/ijece.v14i3.pp3266-3276.

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Online learning presents a major challenge for learners, namely the diversification of courses and information overload. In response to this issue, recommender systems are widely used. Nowadays, social networks have become a global platform where individuals share a multitude of information. For instance, Twitter is a social network where users exchange messages and interact with various communities. These interactions on social networks have created a new dimension in the field of online learning. In this article, we propose a novel approach that combines se

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Gong, Liang Yu, and Xue Jun Li. "Deepfake Voice Detection: An Approach Using End-to-End Transformer with Acoustic Feature Fusion by Cross-Attention." Electronics 14, no. 10 (2025): 2040. https://doi.org/10.3390/electronics14102040.

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Deepfake technology uses artificial intelligence to create highly realistic but fake audio, video, or images, often making it difficult to distinguish from real content. Due to its potential use for misinformation, fraud, and identity theft, deepfake technology has gained a bad reputation in the digital world. Recently, many works have reported on the detection of deepfake videos/images. However, few studies have concentrated on developing robust deepfake voice detection systems. Among most existing studies in this field, a deepfake voice detection system commonly requires a large amount of tr

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Jeynes, Jonathan C. G., Matthew Corney, and Tim James. "A large-scale evaluation of NLP-derived chemical-gene/protein relationships from the scientific literature: Implications for knowledge graph construction." PLOS ONE 18, no. 9 (2023): e0291142. http://dx.doi.org/10.1371/journal.pone.0291142.

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One area of active research is the use of natural language processing (NLP) to mine biomedical texts for sets of triples (subject-predicate-object) for knowledge graph (KG) construction. While statistical methods to mine co-occurrences of entities within sentences are relatively robust, accurate relationship extraction is more challenging. Herein, we evaluate the Global Network of Biomedical Relationships (GNBR), a dataset that uses distributional semantics to model relationships between biomedical entities. The focus of our paper is an evaluation of a subset of the GNBR data; the relationship

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Arjun Varma, Jampana Venkata. "YOLOv8-Enabled Real-Time Crop Health Monitoring with Conversational Diagnosis and Geospatial Support." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47655.

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Abstract—Agriculture is a cornerstone of global economies, supplying food, employment, and raw materials for numerous industries. Yet, one of the sector’s enduring challenges is crop disease, which can drastically reduce yields and threaten food security. Traditional approaches to identifying plant diseases rely on manual inspections and expert evaluations, which are often slow, costly, and vulnerable to human error. Without early diagnosis, diseases can spread uncontrollably, leading to major economic setbacks for farmers and decreased crop output. To overcome these issues, this project intro

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Gu, Qianqian, Ben Scott, and Vincent Smith. "Enhancing Botanical Knowledge Graphs with Machine Learning." Biodiversity Information Science and Standards 6 (August 23, 2022): e91384. https://doi.org/10.3897/biss.6.91384.

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Integrating sparse and incomplete biodiversity data into a global, coherent data space and generating machine-readable data infrastructures is a challenge in biodiversity informatics. In recent years, biodiversity data researchers have started proposing Knowledge Graphs (KGs) as one approach to connecting biodiversity data worldwide (Page 2019), representing the connections between the what, when, and where of objects in natural history collections. At the Natural History Museum (NHM) we have constructed a KG of botanical specimens and collectors, encoded into numerical representations, and us

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Pachzelt, Adrian, Gerwin Kasperek, Andy Lücking, Giuseppe Abrami, and Christine Driller. "Semantic Search in Legacy Biodiversity Literature: Integrating data from different data infrastructures." Biodiversity Information Science and Standards 5 (September 10, 2021): e74251. https://doi.org/10.3897/biss.5.74251.

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Nowadays, obtaining information by entering queries into a web search engine is routine behaviour. With its search portal, the Specialised Information Service Biodiversity Research (BIOfid) adapts the exploration of legacy biodiversity literature and data extraction to current standards (Driller et al. 2020). In this presentation, we introduce the BIOfid search portal and its functionalities in a <em>How-To</em> short guide. To this end, we adapted a knowledge graph representation of our thematic focus of Central European, primarily German language, biodiversity literature of the 19th and 20th

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Novoa, Sepúlveda Carla, Stephan Biebl, Nadja Pöllath, et al. "GBIF-Compliant Data Pipeline for the Management and Publication of a Global Taxonomic Reference List of Pests in Natural History Collections." Biodiversity Information Science and Standards 7 (September 8, 2023): e112391. https://doi.org/10.3897/biss.7.112391.

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There is a growing demand for monitoring pests in natural history collections (NHCs) and establishing integrated pest management (IPM) solutions (Crossman and Ryde 2022). In this context, up-to-date taxonomic reference lists and controlled vocabularies following standard schemes are crucial and facilitate recording organisms detected in collections.The data pipeline described here results in the publication of a taxon reference list based on information from online resources and standard IPM literature. Most of the over 140 pest taxa on species level and above are insects, the rest belong to o

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Chen, Defu, Yunlong Zhou, Xianbao Wang, Sheng Xiang, Xiaohu Liu, and Yijian Sang. "Res2Former: Integrating Res2Net and Transformer for a Highly Efficient Speaker Verification System." Electronics 14, no. 12 (2025): 2489. https://doi.org/10.3390/electronics14122489.

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Speaker verification (SV) is an exceptionally effective method of biometric authentication. However, its performance is heavily influenced by the effectiveness of the extracted speaker features and their suitability for use in resource-limited environments. Transformer models and convolutional neural networks (CNNs), leveraging self-attention mechanisms, have demonstrated state-of-the-art performance in most Natural Language Processing (NLP) and Image Recognition tasks. However, previous studies indicate that standalone Transformer and CNN architectures present distinct challenges in speaker v

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Md, Owahedur Rahman, and Bilal Tariq Muhammad. "The Causes and Effects and Control Systems of Industrial Air Pollution." North American Academic Research 2, no. 6 (2019): 54–68. https://doi.org/10.5281/zenodo.3245566.

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<strong>Introduction</strong> Air pollution is the presentation into the atmosphere of synthetics, particulates, or organic materials that cause uneasiness, infection, or demise to people, harm other living life forms, for example, sustenance harvests, or harm the normal environment or constructed environment.A substance noticeable all around that can be unfriendly to people and the earth is known as an air poison. Poisons can be as strong particles, fluid beads, or gases. Likewise, they might be normal or man-made. Poisons can be named essential or optional. Generally, essential toxins are le

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Zang, Haoyu, Ming Li, Zhiyao Jin, and Jingfei Huang. "Unveiling construction accident causation: a scientometric analysis and qualitative review of research trends." Frontiers in Built Environment 11 (April 30, 2025). https://doi.org/10.3389/fbuil.2025.1602297.

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The construction industry, a cornerstone of global economic growth, faces frequent safety accidents due to its complex environments and multi-party collaboration, impeding sustainable development. These incidents arise from interlinked causal factors, including human error, management shortcomings, technical failures, and environmental conditions. This study systematically reviews construction accident causation research by integrating scientometric analysis and qualitative methods, using VOSviewer to analyze literature from Scopus and Web of Science databases, with 110 peer-reviewed articles

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Bellagha, Mohamed Lazhar, and Mounir Zrigui. "Speaker Naming in Arabic TV Programs." International Arab Journal of Information Technology 19, no. 6 (2022). http://dx.doi.org/10.34028/iajit/19/6/1.

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Automatic speaker identification is the problem of identifying speakers by their real identities. Previous approaches use textual information as a source of naming, try to associate names to neighbouring speaker segments using linguistic rules. However, these approaches have a few limitations that hinder their application on spoken text. Deep learning approaches for natural language processing have recently reached state-of-the-art results. However, deep learning requires a lot of annotated data which is difficult to obtain in the case of speaker identification task. In this paper, we present

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Wang, Zhuang, Qun Kong, Bingcai Wei, Liye Zhang, and Aikui Tian. "Radio map construction based on BERT for fingerprint-based indoor positioning system." EURASIP Journal on Wireless Communications and Networking 2023, no. 1 (2023). http://dx.doi.org/10.1186/s13638-023-02247-2.

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AbstractDue to the heavy workload of RSS collection, the instability of WLAN signal strength and the disappearance of signals caused by complex indoor environments, the construction of radio map for wireless local area network (WLAN) fingerprint-based indoor positioning system is time-consuming and laborious. In order to rapidly deploy indoor WLAN positioning system, the bidirectional encoder representation from transformers (BERT) model is used to fill the missing signal in radio map and quickly build radio map. The radio map is imported into the BERT model in the form of natural language tex

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Kejriwal, Mayank. "Link Prediction Between Structured Geopolitical Events: Models and Experiments." Frontiers in Big Data 4 (November 30, 2021). http://dx.doi.org/10.3389/fdata.2021.779792.

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Often thought of as higher-order entities, events have recently become important subjects of research in the computational sciences, including within complex systems and natural language processing (NLP). One such application is event link prediction. Given an input event, event link prediction is the problem of retrieving a relevant set of events, similar to the problem of retrieving relevant documents on the Web in response to keyword queries. Since geopolitical events have complex semantics, it is an open question as to how to best model and represent events within the framework of event li

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Gu, Qianqian, Ben Scott, and Vincent Smith. "Enhancing Botanical Knowledge Graphs with Machine Learning." Biodiversity Information Science and Standards 6 (August 23, 2022). http://dx.doi.org/10.3897/biss.6.91384.

Full text

Abstract:

Integrating sparse and incomplete biodiversity data into a global, coherent data space and generating machine-readable data infrastructures is a challenge in biodiversity informatics. In recent years, biodiversity data researchers have started proposing Knowledge Graphs (KGs) as one approach to connecting biodiversity data worldwide (Page 2019), representing the connections between the what, when, and where of objects in natural history collections. At the Natural History Museum (NHM) we have constructed a KG of botanical specimens and collectors, encoded into numerical representations, and us

APA, Harvard, Vancouver, ISO, and other styles

32

Pachzelt, Adrian, Gerwin Kasperek, Andy Lücking, Giuseppe Abrami, and Christine Driller. "Semantic Search in Legacy Biodiversity Literature: Integrating data from different data infrastructures." Biodiversity Information Science and Standards 5 (September 10, 2021). http://dx.doi.org/10.3897/biss.5.74251.

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Nowadays, obtaining information by entering queries into a web search engine is routine behaviour. With its search portal, the Specialised Information Service Biodiversity Research (BIOfid) adapts the exploration of legacy biodiversity literature and data extraction to current standards (Driller et al. 2020). In this presentation, we introduce the BIOfid search portal and its functionalities in a How-To short guide. To this end, we adapted a knowledge graph representation of our thematic focus of Central European, primarily German language, biodiversity literature of the 19th and 20th centurie

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33

Wang, Siyang, Dasheng Wu, and Xinyu Zheng. "TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection." Frontiers in Plant Science 14 (August 17, 2023). http://dx.doi.org/10.3389/fpls.2023.1223410.

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IntroductionAccurate grading identification of tea buds is a prerequisite for automated tea-picking based on machine vision system. However, current target detection algorithms face challenges in detecting tea bud grades in complex backgrounds. In this paper, an improved YOLOv7 tea bud grading detection algorithm TBC-YOLOv7 is proposed.MethodsThe TBC-YOLOv7 algorithm incorporates the transformer architecture design in the natural language processing field, integrating the transformer module based on the contextual information in the feature map into the YOLOv7 algorithm, thereby facilitating s

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Novoa Sepúlveda, Carla, Stephan Biebl, Nadja Pöllath, et al. "GBIF-Compliant Data Pipeline for the Management and Publication of a Global Taxonomic Reference List of Pests in Natural History Collections." Biodiversity Information Science and Standards 7 (September 8, 2023). http://dx.doi.org/10.3897/biss.7.112391.

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There is a growing demand for monitoring pests in natural history collections (NHCs) and establishing integrated pest management (IPM) solutions (Crossman and Ryde 2022). In this context, up-to-date taxonomic reference lists and controlled vocabularies following standard schemes are crucial and facilitate recording organisms detected in collections. The data pipeline described here results in the publication of a taxon reference list based on information from online resources and standard IPM literature. Most of the over 140 pest taxa on species level and above are insects, the rest belong to

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35

Loc, Tran. "Application of three graph Laplacian based semisupervised learning methods to protein function prediction problem." August 25, 2018. https://doi.org/10.5121/ijbb.2013.3202.

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International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.2, June 2013 DOI: 10.5121/ijbb.2013.3202 11 Application of three graph Laplacian based semisupervised learning methods to protein function prediction problem Loc Tran University of Minnesota tran0398@umn.edu Abstract: Protein function prediction is the important problem in modern biology. In this paper, the un-normalized, symmetric normalized, and random walk graph Laplacian based semi-supervised learning methods will be applied to the integrated network combined from multiple networks to predict the functions of all ye

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De Boisboissel, G. "Արհեստական բանականություն. կիրառման նոր ձևերը և ազդեցությունը զորքերի մարտական կառավարման վրա / Artificial intelligence: new uses and impacts on military command and control". Հայկական բանակ / Armenian Army, 2024, 36–70. https://doi.org/10.61760/18290108-ehb24.2-36.

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General information and background on AI 1.1 The three battlefield revolutions The digitisation of the battlefield is a major revolution in combat, which needs to be assessed on a long-term scale as it will profoundly change military operating methods. First of all, it will mean that all the equipment deployed in the field will be interconnected with a tactical bubble that enables secure data exchanges to reduce the fog of war. What is already true for many armoured vehicles* will be true in the future for the dismounted soldier himself, who will be carrying advanced technologies. Processing t

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