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Effective Model for Improving Symptoms-Based Disease Prediction by BiMM - BERT Algorithm

Effective Model for Improving Symptoms-Based Disease Predict...

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2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Jayasudha, V. Deepa, N. Devi, T. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350348217

Nowadays, symptoms-based disease prediction is more helpful for predicting the correct type of disease. These predictions are more helpful to the medical industry and doctors;they can reduce the time taken to check up and accurately predict the disease type. Combining the Bidirectional Encoder Representations from Transformers (BERT), natural language processing (NLP), Hidden Markov Model (HMM) and Bi-directional Maximal Matching algorithm (BiMM), a unique word classification algorithm, BERT-BiMM, is presented. It can use quick word segmentation to match sub-dictionaries. The knowledge hidden in many research topics can be found using natural language processing methods. The knowledge hidden in many research topics can be found using NLP methods. The remaining text is joined by the remaining specific words BiMM post text segmentation;the remaining text is connected by the remaining single words using Hidden Markov Model. We compared the real-world clinical text. We took some symptoms of the disease. HMM, BiMM, achieves maximum accuracy. BERT is used to connect the remaining text with these methods, and high accuracy in medical text segmentations will be obtained if this model outperforms provincial models by achieving strict scores of 91.29% on the BiMM model. © 2023 IEEE.

关键词： Hidden Markov models

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Improving Cybersecurity Named Entity Recognition with Large language Models 6

Improving Cybersecurity Named Entity Recognition with Large ...

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6th international conference on Software engineering and Computer Science, CSECS 2023

作者： Qiao, Zhe Zhang, Chen Du, Gang Information Security Management and Operation Center China Mobile Ltd Beijing10053 China China Mobile Group Design Institute Co. Ltd Beijing100080 China

ISBN: (纸本)9798350306378

A lot of attention has been paid to cybersecurity threat intelligence analysis based on security knowledge graphs because they can evaluate multi-source threat intelligence data at a fine-grained level. The primary effort in building knowledge graphs is named entity recognition (NER). However, the retrieved features from classic NER algorithms are insufficient to identify novel security entities in the cybersecurity field. Currently, the natural language processing(NLP) field has made unprecedented progress, driven by large language models(LLMs). In this paper, we present a BERT- BiLSTM-CRF cybersecurity NER method based on BiLSTM-CRF that combines the pre-trained large language model BERT. The preprocessed annotated corpus is fed into the Bi-LSTM and CRF models, which employ the word vectors to extract contextual features and annotate 9 different categories of named entities. The proposed cyberecurity NER method is effective in identifying cybersecurity entities on large-scale cybersecurity datasets, and the relevant evaluation indexes are superior to other algorithms, according to comparative experiments carried out within the cyberspace security corpus. © 2023 IEEE.

关键词： Cybersecurity

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Gamification with natural language processing for Educational Videogame Modeling

Gamification with Natural Language Processing for Educationa...

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2023 IEEE international conference on engineering Veracruz, ICEV 2023

作者： Hernandez-Romero, Diana L. Garcia, E. Tzitzihua Arguijo, Pedro Melendez-Armenta, Roberto Angel Tecnológico Nacional de México /Instituto Tecnólogico Superior de Misantla Veracruz Misantla Mexico

ISBN: (纸本)9798350312959

Traditional education has been based for many years on monotonous teaching techniques and practices to assess students' skills and knowledge in the classroom, which interferes with the evolution and adaptation of education using recent technologies. To create new learning methods using intelligent and digital tools, this paper presents an architecture that uses gamification and natural language processing techniques as an alternative to propose new learning and feedback methods for high school students. The main objective is to motivate students to obtain new knowledge or study what they have already learned in the classroom while playing video games, and for teachers to obtain relevant data on the learning level of each student. As a result of the proposed architecture, a platform video game for mobile devices has been developed, which performs quizzes on specific topics of biology to high school level education. Users give answers through voice commands, which are processed with the VOSK library, and then compared with other options using the cosine similarity method. Finally, an algorithm leverages the scores acquired from video game actions on scenes and questionnaires to synergistically derive a comprehensive metric that assesses the user's proficiency in learning. This methodology was validated according to several experiments, and in future work, it will be applied to control and test groups of students to validate the level of learning obtained using the proposed platform. © 2023 IEEE.

关键词： Students

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A NOVEL RECOMMENDATION SYSTEM FOR TOURIST PLACES AND ITS SPECIALITY

A NOVEL RECOMMENDATION SYSTEM FOR TOURIST PLACES AND ITS SPE...

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jyothi, E.V.N. Rama Chaithanya Tanguturi Shirdinath, Yarramsetty Venkata Lalith, Pasupuleti Yashwanth Gopi, Nanganam Sravan, Ch. Department of CSE PACE Institute of Technology & Sciences AP Valluru India

ISBN: (纸本)9798350305708

By tremendous extension of Internet, information is the most important forms of tourism Big Data. The powerful way to express touristthinking, text mining information has great capability to stimulate innovation in tourism professionals. Over the past decades, various text mining methods areproposed and implemented in tourism to enhance value describing techniques, construct tourism proposal models, generate tourism profiles, and establish process to monitor tourist's markets. The success of these technologies has been accelerated by advances in natural language processing (NLP), ML, and DL. Understanding the complexities by these different approaches and tourism information sources, this analysis explores text mining methods which has been implemented to recent tourism big data analysis and tries to provide an up-to-date overview. Various data analyzing designs, content-based NLP methods oftopic removal, text categorization, description of emotions, text clustering related to tourists' text mining and requests in tourism profiles, picture description of travel destinations, market requests, etc. The upcoming years will provide development rules for new tourism big data applications. © 2023 IEEE.

关键词： Deep Learning (DL) MachineLearning (ML) natural language processing (NLP) Text Mining

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A Survey of Punjabi language Translation using OCR and ML 10

A Survey of Punjabi Language Translation using OCR and ML

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Khepra, Shaveta Kumari, Priya Gupta, Raj Abhishek Bramhe, Vijendra Singh School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9789380544472

The use of natural language processing (NLP) in machine translation has grown in significance as technologies as well as computers have become more prevalent in our daily lives. This essay's goal is to evaluate the work done in the field of natural language processing (NLP) to translate the Gurmukhi script, which is primarily used to represent the Punjabi language. Together with Hindi and Punjabi, the languages written throughout the Gurumukhi script has more than 1.5 billion native users, making it among the most used language worldwide. However, there is limited research available on the Gurmukhi script. The study presented in this paper focuses on using machine learning and AI techniques to translate written Gurmukhi text into Hindi and English languages. The input for the translation process will be in the form of machine-printed Gurmukhi text, while the output will be provided in both Hindi and English languages. The goal of this study is to enhance how well global languages absorb information and technology, making it easier and more accurate to translate from regional languages. In conclusion, the paper provides a comprehensive review of the work related to the translation of Gurmukhi script using NLP techniques, with a focus on the use of machine learning and AI. The findings of this study can contribute to the development of more advanced and accurate translation systems, making the exchange of information and knowledge more accessible for people who speak different languages. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning

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CONCEPTVIS: GENERATING AND EXPLORING DESIGN CONCEPTS FOR EARLY-STAGE IDEATION USING LARGE language MODEL

CONCEPTVIS: GENERATING AND EXPLORING DESIGN CONCEPTS FOR EAR...

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ASME 2024 international Design engineering Technical conferences and Computers and Information in engineering conference, IDETC-CIE 2024

作者： Duan, Runlin Karthik, Nachiketh Shi, Jingyu Jain, Rahul Yang, Maria C. Ramani, Karthik Purdue University West LafayetteIN United States Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9780791888377

Large language models (LLMs) are capable of generating cross-domain design knowledge, opening up new possibilities for creating a myriad of design concepts for early-stage design ideation. The current interfaces and interaction capabilities of LLMs, however, pose challenges in controlling the ideation process in terms of its diversity and quality. To enhance human guidance over the LLM-driven ideation process, we have developed ConceptVis, a system that organizes and symbiotically coordinates the LLM-generated design space through an interactive knowledge graph. In ConceptVis, designers can easily control the breadth and depth of the design space by intuitively prompting the LLM from the graph nodes. natural language processing (NLP) algorithms extract concept keywords and related design knowledge from LLM responses, which are then added to the knowledge graph for visualization. We conducted a user study with 24 novice designers and compared the performance of ConceptVis with that of a chat-based LLM interface for concept generation. With ConceptVis, designers can explore the design space with a balance of breadth and depth. This approach prevents them from merely prompting the LLM to generate random concepts, struggling to keep track of what has been generated in long linear lists, or fixating on early ideas. Supporting users to interact with LLMs through an interactive visual interface significantly improves both the efficiency and quality of concept generation. This result highlights the importance of developing user-centered design systems to facilitate human-LLM collaboration during the early stages of design. Copyright © 2024 by ASME.

关键词： knowledge graph

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Few-Shot Named Entity Recognition Based on Self-Descriptive Network and knowledge Graph Enhancer

Few-Shot Named Entity Recognition Based on Self-Descriptive ...

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2024 IEEE international conference on Data, Information, knowledge and Wisdom, DIKW 2024

作者： Hou, Yongze Zhou, Zili Wang, Yanna Liu, Zhenchao School of Cyber Science and Engineering Qufu Normal University Jining China

ISBN: (纸本)9798331540548

Named Entity Recognition (NER) is a critical task in natural language processing (NLP), focusing on extracting entities from text and classifying them into predefined categories. However, traditional NER methods are limited by their reliance on large, annotated datasets, making it challenging in scenarios with limited data. Few-shot NER has emerged as a promising approach to address these challenges, but existing methods still face limitations in handling unseen entity types due to knowledge mismatch and information insufficiency. To address these gaps, this paper proposes a novel few-shot NER model that integrates self-descriptive mechanisms with knowledge graph enhancement. The self-descriptive mechanism provides consistent descriptions of entity types across schemas, while the knowledge graph dynamically incorporates domain-specific knowledge to improve the model's understanding of unseen or low-resource entities. The proposed model was evaluated on several benchmark datasets, demonstrating significant improvements in entity recognition performance across various domains. The model achieved a notable F1 score of 74.7 on the Movie2 dataset, outperforming existing models such as SDNet and TaGuProto. Ablation studies further confirmed the effectiveness of knowledge integration in enhancing the model's robustness and adaptability in few-shot scenarios. The research contributes to the field by offering a robust solution to the knowledge mismatch problem in few-shot NER, and by providing a model that is both adaptable and generalizable across diverse domains. The integration of knowledge graphs with self-descriptive mechanisms represents a significant advancement in NER, especially in low-resource settings. © 2024 IEEE.

关键词： knowledge graph

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Development of a Movie AI Question Answering System Based on knowledge Graph 4

Development of a Movie AI Question Answering System Based on...

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4th international Signal processing, Communications and engineering Management conference, ISPCEM 2024

作者： Xu, Yihan Xie, Nan Hu, Xinjun Chen, Weimin College of Computer Science and Technology Zhejiang University of Water Resources and Electric Power Zhejiang Hangzhou China College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou China

ISBN: (纸本)9798331528676

With the increasing influence of the internet, information retrieval technology needs to be continuously upgraded. The development of the internet drives the transformation of the film industry, with an increase in the number of online movies, a flourishing of professional film websites and forums, and an increasing influence of online comments. There are still shortcomings in the relationship between the internet and film evaluation, and further exploration is needed. The development of this system adopts a front-end and back-end separation mode, with Vue technology architecture used in the front-end and Django framework used in the back-end. The knowledge graph can organize and express movie information, providing data support for intelligent question answering and comment systems. The system constructs a graph by collecting movie data, achieving storage, management, and querying, providing intelligent and personalized services, and designing movie review functions to provide comprehensive information and evaluation references;The question answering function is implemented through natural language processing technology (NLP) and graph database technology, which can recognize the user's query intention and retrieve relevant information, generate accurate and concise answers. Its core module is the movie AI question answering function based on Neo4j. After the user raises a question, the comment system will parse the question and construct a Cypher query statement. After execution, the result will be returned and displayed to the user, achieving intelligent answers and personalized services. In system design, the connection between the movie AI question answering module and the Neo4j database is crucial. This connection enables effective communication and information exchange between user questions and movie knowledge graphs, providing users with convenient and accurate movie related information. Through deep integration with the Neo4j database, the system can bette

关键词： Information management

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Recent Progress on Named Entity Recognition Based on Pre-trained language Models 35

Recent Progress on Named Entity Recognition Based on Pre-tra...

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35th IEEE international conference on Tools with Artificial Intelligence (ICTAI)

作者： Yang, Binxia Luo, Xudong Guangxi Normal Univ Sch Comp Sci & Engn Guilin Peoples R China Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Minist Educ PRC Key Lab Educ Blockchain & Intelligent Technol Guilin Peoples R China

ISBN: (纸本)9798350342734

This paper presents an overview of the latest developments in Named Entity Recognition (NER) using Pretrained language Models (PLMs). First, it discusses how PLMs have evolved and their impact on NER, including various pre-training techniques. The paper then explores strategies to adapt PLMs to NER, such as fine-tuning and transfer learning, while also considering the challenges in selecting the suitable model. Recent advancements in NER techniques using PLMs, including integrating external knowledge sources, are also covered. Finally, the paper comprehensively evaluates the state-of-theart NER systems based on PLMs, comparing their performance and discussing their strengths and limitations. Its goal is to provide a thorough understanding of NER progress, insights into ongoing advancements, and potential avenues for further exploration. This paper is a valuable resource for researchers and practitioners seeking to use PLMs in their NER applications.

关键词： natural language processing Named Entity Recognition PLM BERT GPT

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knowledge-Guided Prompt Learning for Deepfake Facial Image Detection

Knowledge-Guided Prompt Learning for Deepfake Facial Image D...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wang, Hao Deng, Cheng Zhao, Zhidong School of Cyberspace Security Hangzhou Dianzi University Hangzhou China School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9798350368741

Recent generative models demonstrate impressive performance on synthesizing photographic images, which makes humans hardly to distinguish them from pristine ones, especially on realistic-looking synthetic facial images. Previous works mostly focus on mining discriminative artifacts from vast amount of visual data. However, they usually lack the exploration of prior knowledge and rarely pay attention to the domain shift between training categories (e.g., natural and indoor objects) and testing ones (e.g., fine-grained human facial images), resulting in unsatisfactory detection performance. To address these issues, we propose a novel knowledge-guided prompt learning method for deepfake facial image detection. Specifically, we retrieve forgery-related prompts from large language models as expert knowledge to guide the optimization of learnable prompts. Besides, we elaborate test-time prompt tuning to alleviate the domain shift, achieving significant performance improvement and boosting the application in real-world scenarios. Extensive experiments on DeepFakeFaceForensics dataset show that our proposed approach notably outperforms state-of-the-art methods. © 2025 IEEE.

关键词： deepfake facial image detection domain shift knowledge-guided prompt learning test-time prompt tuning

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