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Exploring Artificial Intelligence to Assist Kinematics Analysis of Rigid Bodies 10

Exploring Artificial Intelligence to Assist Kinematics Analy...

引用

10th international conference on engineering and Emerging Technologies, ICEET 2024

作者： Morano-Okuno, Hector Rafael Sandoval-Benitez, Guillermo School of Engineering and Science Tecnologico de Monterrey Mexico

ISBN: (纸本)9798331532895

Artificial Intelligence applications have spread to various areas of knowledge. Some of the applications of Machine Learning are Large language Models and natural language processing. An efficient way of communication between users and Artificial Intelligence information models is through Prompt engineering. This article explores using Artificial Intelligence to assist in the kinematics analysis of rigid bodies through Prompt engineering. The results obtained from the kinematic analysis of a Large language Model are evaluated through a case study. Among the findings, it is determined that the results of the analysis depend on the clarity and content of details provided by the user in the creation of the Prompts used to make the queries, as well as the user's expertise in the subject of kinematic analysis of rigid bodies since it must be determined the certainty of the answers provided by Artificial Intelligence. © 2024 IEEE.

关键词： engineering education

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Building End-to-end Dialogue System with Large language Models 15

Building End-to-end Dialogue System with Large Language Mode...

引用

15th international conference on Signal processing Systems, ICSPS 2023

作者： Fan, Jie Ma, Guojun Ocean Collage Jiangsu University of Science and Technology 666 Changhui Road Jiangsu Zhenjiang China

ISBN: (纸本)9781510675056

In order to solve the complex module dependencies of dialogue systems and improve the system's ability to understand deep knowledge in natural language and produce more coherent texts, this paper introduces an end-to-end dialogue system based on large language models. First, low-rank adaption is used to fine-tune sequence-to-sequence large language models, which reduces system complexity and model fine-tuning cost. Then, the training method of reinforcement learning from human feedback is adopted to make the generated responses more aligned with human expectations. Finally, in-context learning is used to adapt to specific tasks, improving model flexibility and adaptability. Experimental results show that the system performs well in both automatic evaluation and practical use and has strong application value. © 2024 SPIE.

关键词： Reinforcement learning

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KrNER : A Novel Named Entity Recognition Method Based on knowledge Enhancement and Remote Supervision 22

KrNER : A Novel Named Entity Recognition Method Based on Kno...

引用

IEEE 22nd international conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Du, Jinhua Yin, Hao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Difficulty in recognizing complex entities and scarcity of annotated corpus have become the two major difficulties constraining effective information extraction from electronic medical records. Recent research focuses on utilizing complex feature engineering or large-scale pre-trained models to indirectly reduce the degradation of entity recognition caused by these two difficulties. In an attempt to directly address the difficulties, we propose KrNER, a new healthcare-oriented approach to named entity recognition. It utilizes knowledge enhancement and remote supervision techniques to effectively deal with the problem of recognizing complex entities, while guaranteeing better recognition results even in corpus-scarce contexts. Experiments show that KrNER is very effective and significantly outperforms the state-of-the-art Chinese medical named entity recognition models.

关键词： chinese electronic medical record named entity recognition deep learning pre-trained models natural language processing

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Enhanced Database Interaction using Large language Models for Improved Data Retrieval and Analysis 2

Enhanced Database Interaction using Large Language Models fo...

引用

2nd international conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Usha, V Abhinash, Nalagarla Chiru Chowdary, Sakhamuri Nitin Sathya, V Reddy, Eeda Ramakrishna Priya, Sathiya S. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept CSE Chennai Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Chennai Tamil Nadu India Velammal Engn Coll Dept Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

One of the difficult task for many users on SQL is to write the SQL Query due to its syntax and structure. If a person needs to query a database, they should know everything about how data is distributed and what the internal dependencies are. For this reason, it is not easy for everyone to access data in database without proper knowledge. This paper presents a novel application that leverages generative AI and natural language processing (NLP) to enable users to interact with databases using natural language queries. Built on the Gemini API, the application translates user queries into SQL queries, simplifying database interactions for non-technical users. The Python-based backend, SQLite database management, and Streamlit frontend provide a comprehensive solution for database querying and analysis. This approach democratizes data retrieval and analysis, offering automated insights and visualizations to users of all skill levels. The app also features automated data analysis, which boosts insight generation for users of all skill levels. Further, the traditional ways of querying SQL generally require specialized knowledge and are only accessible to those with technical backgrounds. When the application takes charge of the query construction process and offers data as UI elements, it can invigorate users to have a higher degree of insight into what their data actually is and explore it even more efficiently. The Python software stack combines Python for backend processing, SQLite for database management and a web-based frontend for user interaction to provide an all-encompassing database querying and analysis solution.

关键词： Generative AI natural language processing Text to SQL Generation Gemini API Large language Model Machine Learning Deep Learning Google LLM SQLQuery Generation Prompt engineering Data Retrieval

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Geo-Qwen: A Geometry Problem-Solving Method Based on Generative Large language Models and Heuristic Reasoning 21

Geo-Qwen: A Geometry Problem-Solving Method Based on Generat...

引用

21st international Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2024

作者： Shengyuan, Yan Xiuqin, Zhong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Research on geometry problem solving is gaining increasing attention in the field of natural language processing (NLP). Currently, research in geometry problem solving often relies on formal logical language systems and traditional rule-based reasoning. However, the transformation from natural language to formal language inevitably leads to information loss, which impacts the accuracy and completeness of reasoning. In recent years, with the development of large language models (LLM) exemplified by the GPT architecture and significant improvements in natural language comprehension, research on purely natural language-based geometric reasoning has become increasingly popular. This paper presents a novel heuristic method for geometric reasoning, called Geo-Qwen, which enables the automatic generation of geometry problem-solving steps from natural language to natural language. To support reasoning, we introduce a geometric theorem library composed of 38 theorems as the reasoning knowledge base. Geo-Qwen employs a heuristic single-step reasoning strategy, automatically generating the next solution step based on a selected theorem, previously generated steps, and the problem information. The generation process of the solution steps can be viewed as constructing a reasoning tree. Additionally, to address the combinatorial explosion issue during reasoning, we utilize the T5 model as a theorem predictor to limit the number of theorems used at each step of reasoning. Finally, extensive experiments conducted on the UniGeo dataset and the Geometry3K dataset demonstrate that Geo-Qwen significantly outperforms existing methods. © 2024 IEEE.

关键词： Trees (mathematics)

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The Implementation of natural language processing on Chatbot as Academic Information System: A Case Study of IC2IE 2024 7

The Implementation of Natural Language Processing on Chatbot...

引用

7th international conference of Computer and Informatics engineering, IC2IE 2024

作者： Purba, Christian Nataniel Yosua Parulian, Pratama Varian Andika Iswara, Ratna Widya Suhandana, Ariawan Andi Sari, Risna Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798331505530

The implementation of natural language processing (NLP) in chatbots has significantly enhanced the efficiency and user experience of academic information systems. This paper presents a case study of the international conference on Computing, Communication, and Information engineering (IC2IE) 2024, exploring how NLP-driven chatbot can serve as a robust academic information system. By employing advanced NLP techniques, the chatbot effectively understands and processes user inquiries, providing accurate and timely information about the conference schedules, speakers, submission guidelines, etc. Accuracy testing demonstrated an overall performance of 84.533%, with perfect accuracy in the 0.75-1.0 similarity range and 96.15% accuracy in the 0.5-0.74 range. The findings suggest that integrating NLP in academic information systems can streamline operations and enhance communication within academic events. © 2024 IEEE.

关键词： Information use

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SCF-Stega: Controllable Linguistic Steganography Based on Semantic Communications Framework

SCF-Stega: Controllable Linguistic Steganography Based on Se...

引用

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

作者： Long, Yilin Yang, Zhongliang Wang, Zhuang Zhou, Zhili Huang, Yongfeng Zhou, Linna School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Beijing China School of Artificial Intelligence Guangzhou University Guangzhou China

ISBN: (纸本)9798350368741

Linguistic steganography is a key information hiding technique but faces challenges like abrupt content shifts, detection risks, and high training resource demands. To address these, this paper introduces SCF-Stega, a controllable method based on Semantic Communications Framework. By using a knowledge graph to guide secret encoding and dynamically adjusting large language model outputs, SCF-Stega enhances text imperceptibility and semantic coherence. Experiments show improved text quality and strong resistance to steganalysis, without needing additional training data. © 2025 IEEE.

关键词： knowledge graph large language models Linguistic steganography semantic communications

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knowledge Graph Indexing Enhanced Q&A System Based on Large language Model 4

Knowledge Graph Indexing Enhanced Q&A System Based on Large ...

引用

4th international conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Sun, Jiawei Zhang, Quan Zhi, Yongqi Ling, Weiqing Cims Research Center School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798331531409

With the advent of the digital age, the explosive growth of data has brought unprecedented opportunities and challenges to societal development. The rapid evolution of data science is driving innovation and progress across various industries. Both the business and academic communities recognize the immense potential hidden within vast amounts of data. Extracting truly valuable information from this data has become a critical issue that needs to be addressed. As technology advances swiftly and data accumulates rapidly, traditional processing and analysis methods can no longer keep up with the current volume and velocity of data. This has prompted data professionals to seek innovative solutions to handle increasingly complex and massive data relationships and quantities. Against this backdrop, two key technologies have emerged due to their exceptional performance-knowledge graphs and large language models © 2024 IEEE.

关键词： knowledge graph

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Automated Disease Diagnosis Using natural language processing and Deep Learning 4

Automated Disease Diagnosis Using Natural Language Processin...

引用

4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Garg, Devesh Mani, Chandra Srivastava, Sanjay Jayant, Khel Prakash Kumar Saraswat, Birendra Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350364729

Current advances in natural language processing (NLP) and deep studying have made it possible to increase automatic structures for ailment analysis. By leveraging NLP strategies, such systems can procedure unfastened-form text, spotting precious patterns to extract capabilities from scientific information that may be used to appropriately identify disease. Deep learning algorithms can take those features and learn to recognize the presence of a sickness and appropriately perceive the most possibly diagnosis. Such mechanically generated diagnoses can then be used to guide clinicians in making appropriate decisions. This paper gives a top level view of automatic disorder diagnosis systems that use NLP and deep learning, highlighting key additives of those structures, and discussing present challenges and destiny guidelines of such systems. Computerized sickness analysis the usage of natural language processing and Deep studying is an rising field that leverages advances in herbal language processing (NLP) and deep getting to know to discover diagnosable diseases from patient-provided information. Moreover, such models can discover ways to interpret clinical information from herbal language sources which includes scientific records and different scientific files. Via applying NLP and deep gaining knowledge of techniques to patient facts, an automated diagnosis set of rules may want to discover affected person statistics this is indicative of a diagnosable disorder. In addition, such algorithms have capacity to similarly enhance the accuracy and speed of analysis so one can make sure patient health outcomes. Moreover, these algorithms will be included with current healthcare systems to be able to mechanically recommend remedies and in addition save charges related to healthcare shipping. © 2024 IEEE.

关键词： Diseases

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Faithful Fake News Detection Based on natural language Explanation Generation 24

Faithful Fake News Detection Based on Natural Language Expla...

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8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Yoshida, Ko Zhang, Jianwei Graduate School of Information Sciences Tohoku University Miyagi Sendai Japan Faculty of Science and Engineering Iwate University Iwate Morioka Japan

ISBN: (纸本)9798400717383

In this paper, we propose a fake news detection model that generates natural language explanations and leverages these explanations for prediction. Deep learning models, particularly Large language Models like BERT, are effective for fake news detection. However, interpreting and explaining predictions from these complex models remains challenging. Algorithms like LIME and SHAP provide numerical explanations, but these are often insufficient for users without technical knowledge. Our model generates text-based explanations, making them more user-friendly. Experimental results show our model improves binary classification accuracy and F1-score. Additionally, we demonstrate that the explanations contribute to classification, ensuring the model's faithfulness to the generated explanations. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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