A pathway from fragmentation to interoperability through standards-based enterprise architecture to enhance patient safety by Zoie Shui-Yee Wong, Yang Gong, Shin Ushiro

“…Abstract Creating an ontology is the essential step in natural language processing (NLP). To improve patient safety in this era of generative AI, it is crucial to develop a standards-driven, ontology-based architecture for patient safety that can seamlessly integrate with health systems, thereby facilitating effective detection and monitoring potentially preventable harms in healthcare. …”
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Key technologies of AI in customer service system by Zheng WANG, Hua REN, Xuhai LU

“…In the customer service system structure,manual seats,work order processing and other links require a lot of manual operations and participation,the cost is high,the efficiency is relatively low,and there will still be a certain percentage of problems and failures that can’t be found and resolved.In the customer service system,the introduction of artificial intelligence technology can solve these problems to a certain extent,reduce costs and improve the overall efficiency and effectiveness of customer service.The key technologies which could be applied to the customer service system in AI were analyzed,including speech recognition,speech synthesis,and natural language processing.The application of new technology in the customer service system of the operator was discussed.The effect in practical application was illustrated by an example.…”
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Benchmark for Evaluation of Danish Clinical Word Embeddings by Martin Sundahl Laursen, Jannik Skyttegaard Pedersen, Pernille Just Vinholt, Rasmus Søgaard Hansen, Thiusius Rajeeth Savarimuthu

“… In natural language processing, benchmarks are used to track progress and identify useful models. …”
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Disease prediction using NLP techniques by Hamza Ouabiba, Farah Sniba

“…By leveraging Natural Language Processing (NLP), the system offers automated analysis, enabling quicker and more accurate diagnoses based on symptoms provided by users. …”
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An XML Approach of Coding a Morphological Database for Arabic Language by Mourad Gridach, Noureddine Chenfour

“…Optimizing the production, maintenance, and extension of morphological database is one of the crucial aspects impacting natural language processing (NLP). For Arabic language, producing a morphological database is not an easy task, because this it has some particularities such as the phenomena of agglutination and a lot of morphological ambiguity phenomenon. …”
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Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders by Muhammad Sajid, Kaleem Razzaq Malik, Ateeq Ur Rehman, Tauqeer Safdar Malik, Masoud Alajmi, Ali Haider Khan, Amir Haider, Seada Hussen

“…Abstract Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. …”
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Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method by Robert Stewart, Hitesh Shetty, Matthew Broadbent, Angus Roberts, Rashmi Patel, Philip McGuire, Richard D Hayes, Nishamali Jayatilleke, Genevieve Gorrell, Chin-Kuo Chang, Richard Jackson, Nadia Foskett, Caroline Johnston

“…Objectives To identify negative symptoms in the clinical records of a large sample of patients with schizophrenia using natural language processing and assess their relationship with clinical outcomes.Design Observational study using an anonymised electronic health record case register.Setting South London and Maudsley NHS Trust (SLaM), a large provider of inpatient and community mental healthcare in the UK.Participants 7678 patients with schizophrenia receiving care during 2011.Main outcome measures Hospital admission, readmission and duration of admission.Results 10 different negative symptoms were ascertained with precision statistics above 0.80. 41% of patients had 2 or more negative symptoms. …”
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The future of oral cancer care: Integrating ChatGPT into clinical practice by Sirwan Khalid Ahmed

“…By utilizing ChatGPT's natural language processing and information synthesis capabilities, healthcare practitioners can enhance decision-making, personalize patient treatment, and improve patient education and support. …”
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Big Data Deep Learning: Challenges and Perspectives by Xue-Wen Chen, Xiaotong Lin

“…It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. …”
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A dataset dedicated to the training of large- language models for agronomic management practices and production in Norwegian agricultureGithubKaggle by Olena Bugaiova, Kristian Nikolai Jæger Hansen

“…The cleaned text data is valuable for training or evaluating Natural Language Processing (NLP) Models in an experimental context in Norway or adapting Large-Language Models (LLM) to the domain of Norwegian agriculture within the Norwegian language.…”
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Special Issue in Artificial Intelligence by Dora Maria Ballesteros

“…Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. …”
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Resources for assigning MeSH IDs to Japanese medical terms by Yuka Tateisi

“…Medical Subject Headings (MeSH), a medical thesaurus created by the National Library of Medicine (NLM), is a useful resource for natural language processing (NLP). In this article, the current status of the Japanese version of Medical Subject Headings (MeSH) is reviewed. …”
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Steganalysis for stegotext based on text redundancy by LUO Gang, SUN Xing-ming

“…Targeted at the text steganography with mimic model,a steganalysis method based on source redundancy for stegotexts was proposed.This method processed the text and the words in it as the m-order Markov source and the source symbols respectively,then computed the redundancy of the source.Through analyzing the relationship between the re-dundancy and the size of the text,the existence of hidden information could be determined.With testing of 8 000 ste-gotexts produced by the four main softwares NiceText,Texto,Stego and Sams Big Play Maker,and 2 400 normal texts randomly sampled from innocuous texts downloaded from the Internet,the results show that the false positive rate of our steganalysis method is 0.5% and the false negative rate is 3.9%.Experiments and analyzing results indicate that the steganalysis method can effectively detect stegotexts based on natural language processing with mimic model.…”
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Machine learning security and privacy:a survey by Lei SONG, Chunguang MA, Guanghan DUAN

“…As an important method to implement artificial intelligence,machine learning technology is widely used in data mining,computer vision,natural language processing and other fields.With the development of machine learning,it brings amount of security and privacy issues which are getting more and more attention.Firstly,the adversary model was described according to machine learning.Secondly,the common security threats in machine learning was summarized,such as poisoning attacks,adversarial attacks,oracle attacks,and major defense methods such as regularization,adversarial training,and defense distillation.Then,privacy issues such were summarized as stealing training data,reverse attacks,and membership tests,as well as privacy protection technologies such as differential privacy and homomorphic encryption.Finally,the urgent problems and development direction were given in this field.…”
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A Survey on Hardware Accelerators for Large Language Models by Christoforos Kachris

“…Large language models (LLMs) have emerged as powerful tools for natural language processing tasks, revolutionizing the field with their ability to understand and generate human-like text. …”
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Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study by Heng Wang, Danni Zheng, Mengying Wang, Hong Ji, Jiangli Han, Yan Wang, Ning Shen, Jie Qiao

“…Case extraction was performed at a hospital’s case data center, and the best-matching cases were differentiated through natural language processing, word segmentation, synonym conversion, and sorting. …”
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Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation by Yosuke Yamagishi, Yuta Nakamura, Shouhei Hanaoka, Osamu Abe

“… Abstract BackgroundThe application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. …”
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Research on structure and defense of adversarial example in deep learning by Guanghan DUAN, Chunguang MA, Lei SONG, Peng WU

“…With the further promotion of deep learning technology in the fields of computer vision,network security and natural language processing,which has gradually exposed certain security risks.Existing deep learning algorithms can not effectively describe the essential characteristics of data or its inherent causal relationship.When the algorithm faces malicious input,it often fails to give correct judgment results.Based on the current security threats of deep learning,the adversarial example problem and its characteristics in deep learning applications were introduced,hypotheses on the existence of adversarial examples were summarized,classic adversarial example construction methods were reviewed and recent research status in different scenarios were summarized,several defense techniques in different processes were compared,and finally the development trend of adversarial example research were forecasted.…”
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A survey of efficient deep neural network by Rui MIN

“…Recently,deep neural network (DNN) has achieved great success in the field of AI such as computer vision and natural language processing.Thanks to a deeper and larger network structure,DNN’s performance is rapidly increasing.However,deeper and lager deep neural networks require huge computational and memory resources.In some resource-constrained scenarios,it is difficult to deploy large neural network models.How to design a lightweight and efficient deep neural network to accelerate its running speed on embedded devices is a great research hotspot for advancing deep neural network technology.The research methods and work of representative high-efficiency deep neural networks in recent years were reviewed and summarized,including parameter pruning,model quantification,knowledge distillation,network search and quantification.Also,vadvantages and disadvantages of different methods as well as applicable scenarios were analyzed,and the future development trend of efficient neural network design was forecasted.…”
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Research on intelligent computing network technology for large-scale pre-trained models by WANG Xuecong, JI Siwei, LI Cong

“…With the development of artificial intelligence, significant achievements are made in various fields such as natural language processing and computer vision through the utilization of large-scale pre-trained models,which promotes the construction of intelligent computing centers. …”
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