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Automated Disease Diagnosis Using natural language processing and Deep Learning 4

Automated Disease Diagnosis Using Natural Language Processin...

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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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natural language processing Algorithms for Academic Content Generation 4

Natural Language Processing Algorithms for Academic Content ...

引用

4th ieee international conference on Advanced Learning Technologies for Education and Research, ICALTER 2024

作者： Paredes-Vargas, Alfredo Salinas-Llorca, Piero Santisteban, Jose Information Systems Engineering Universidad Peruana de ciencias Aplicadas UPC Lima Peru Information Systems Engineering Universidad Peruana de Ciencias Aplicadas Lima Peru

ISBN: (纸本)9798331510244

This study has conducted several activities to analyze and compare the performance of five natural language processing (NLP) models: GPT-3, T5, ERNIE, BERT, and XLNet for generating academic content in universities located in Lima, Peru. Some NLP models, including GPT and BERT, were assessed based on accuracy, quality, speed, and customization capabilities. The research examined the use of various algorithms for academic tasks like essay and summary creation. The findings show that all models perform adequately;however, some excel in generating coherent content tailored to different academic styles, thereby optimizing the time of both students and teachers. In conclusion, the ERNIE model emerges as one of the best, as it enables the generation of diverse and high-quality content efficiently. © 2024 ieee.

关键词： natural language processing systems

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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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knowledge Graphs for Textbooks: Extraction and Completion Techniques 6

Knowledge Graphs for Textbooks: Extraction and Completion Te...

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6th ieee international conference on Artificial Intelligence and knowledge engineering, AIKE 2023

作者： Yao, Yutong Potikas, Petros Potika, Katerina Department of Computer Science San José State University San JoseCA United States School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798350331288

This paper aims to apply knowledge graph construction techniques to textbooks, explicitly focusing on the challenge of the absence of domain-specific schema for each textbook. Various entity and relation extraction models are utilized to capture logical and semantic information related to the textbook s topic. These models include a Text-Encoding-Initiative (TEI) model to extract hierarchical concepts, spaCy natural language processing (NLP), and Google Cloud natural language to extract semantic information from the main textual content. The study includes a case study on a cloud computing textbook, where each approach is evaluated and analyzed. Ultimately, the goal is to create knowledge graphs of textbooks, enabling the completion task of predicting missing entities or relations in a low-dimensional space. © 2023 ieee.

关键词： knowledge graph

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INTEGRATING language MODELS WITH SYMBOLIC FORMULAS FOR FIRST-ORDER LOGIC REASONING 49

INTEGRATING LANGUAGE MODELS WITH SYMBOLIC FORMULAS FOR FIRST...

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49th ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Sheng, Yu Li, Linjing Wang, Yifei Zeng, Daniel Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beijing Wenge Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Performing logical reasoning based on prior knowledge is a crucial human cognitive ability and has been a long-standing objective in the field of artificial intelligence. Large language models based on transformer architecture have been a common approach for logical reasoning over text. However, the current language models often struggle to learn semantic information from logical expressions, resulting in underwhelming performance on logical reasoning tasks. In this paper, we propose a novel method to convert first-order logic (FOL) expressions to the form of a graph and integrate it with embeddings from language models to enhance their reasoning ability. The proposed method is designed to learn directly from FOL formulas and is able to generalize to any scenarios involving logical expressions. Experimental results demonstrate that the proposed method enhances the model's ability of learning logical semantic representations, and thus it brings a significant improvement on the performance of complex reasoning tasks. The code is available at https://***/FOL-GNN.

关键词： Neural-symbolic Integration First-order Logic Reasoning Graph Neural Networks

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Enhancing NLP for Indic languages with Limited Resources: A Study of Transformer Models for Translation and Summarization

Enhancing NLP for Indic Languages with Limited Resources: A ...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bhati, Abhivyakt Gupta, Ved Kumar Shah, Hare Ram Nagar, Rajesh Nimawat, Sunil Computer Science & Engineering-AIML IPS Academy Institute of Engineering & Science Indore India Computer Science & Engineering IPS Academy Institute of Engineering & Science SAGE University Indore India Computer Science & Engineering Indore India IQAC Cell Indore India Computer Science & Engineering IPS Academy Institute of Engineering & Science Indore India

ISBN: (纸本)9798331512965

Significant advancements have been made in natural language processing (NLP), especially in numerous languages. The vast linguistic diversity of India presents unique challenges for automated language processing tasks as Indian languages other than Hindi are marginalized in both cases;datasets which incur a vast toll on summarization models that require huge datasets to be trained on. This paper presents a comparative and comprehensive analysis of multiple transformer-based models which have been trained and/or fine-tuned for lesser-used Indic languages. Purely focusing on natural language Translation (NLT) and evaluating its efficiency in generating contextually accurate summaries for languages such as Bengali and Marathi. Models such as mT5, IndicBART, mBART and other multilingual NLP models will be benchmarked and assessed using the ROUGE score. This study highlights the model's strengths and weaknesses in handling the limitations summarization of low-resource Indic languages. The findings will contribute heavily to the development of NLP tools aimed at improving summarization inclusivity across India's diverse languages. © 2025 ieee.

关键词： Translation (languages)

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An Empirical Study of License Conflict in Free and Open Source Software 23

An Empirical Study of License Conflict in Free and Open Sour...

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ieee/ACM 45th international conference on Software engineering - Software engineering in Practice (ICSE-SEIP)

作者： Cui, Xing Wu, Jingzheng Wu, Yanjun Wang, Xu Luo, Tianyue Qu, Sheng Ling, Xiang Yang, Mutian Chinese Acad Sci Inst Software Beijing Peoples R China Beijing ZhongKeWeiLan Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350300376

Free and Open Source Software (FOSS) has become the fundamental infrastructure of mainstream software projects. FOSS is subject to various legal terms and restrictions, depending on the type of open source license in force. Hence it is important to remain compliant with the FOSS license terms. Identifying the licenses that provide FOSS and understanding the terms of those licenses is not easy, especially when dealing with a large amount of reuse that is common in modern software development. Since reused software is often large, automated license analysis is needed to address these issues and support users in license compliant reuse of FOSS. However, existing license assessment tools can only identify the name and quantity of licenses embedded in software and thus cannot identify whether the licenses are being used safely and correctly. Moreover, they cannot provide a comprehensive analysis of the compatibility and potential risk that come with the term conflicts. In this paper, we propose DIKE, an automated tool that can perform license detection and conflict analysis for FOSS. First, DIKE extracts 12 terms under 3,256 unique open source licenses by manual analysis and natural language processing (NLP) and constructs a license knowledge base containing the responsibilities of the terms. Second, DIKE scans all licenses from the code snippet for the input software and outputs the scan results in a tree structure. Third, the scan results match the license knowledge base to detect license conflicts from terms and conditions. DIKE designs two solutions for software with license conflicts: license replacement and code replacement. To demonstrate the effectiveness of DIKE, we first evaluate with the term extraction and responsibility classification, and the results show that their F1-scores reach 0.816 and 0.948, respectively. In addition, we conduct a measurement study of 16,341 popular projects from GitHub based on our proposed DIKE to explore the conflict of license us

关键词： Free and Open Source Software license analysis license conflict natural language processing

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Investigating on the External knowledge in RAG for Zero-Shot Cross-language Transfer 4

Investigating on the External Knowledge in RAG for Zero-Shot...

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4th ieee international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Wang, Wenmin Zhang, Peilin Liu, Ge Wu, Ruihua Song, Guixiang China Mobile Online Service Co. Ltd. Zhengzhou450000 China

ISBN: (纸本)9798350361643

In the field of multilingual natural language processing (NLP), zero-shot cross-language transfer is an important research direction, which aims to enable models to effectively learn and reason without target language training data. This study explores the role of external knowledge in the Retrieval-Augmented Generation (RAG) model to improve the performance of zero-shot cross-lingual transfer. This paper proposes a new model architecture that enriches the knowledge base of the RAG model by integrating external knowledge bases, thereby enhancing its information bridging capabilities between source and target languages. In the experimental section, this paper conducts experiments using multiple cross-language tasks, including machine translation, question answering, and text summarization, to evaluate the performancen and domain of the model in different languages. The experimental results indicate that introducing external knowledge sources significantly improves the accuracy and robustness of the model, especially in resource-scarce language pairs. This research not only provides an effective solution for zero-shot cross-language transfer, but also provides new insights into understanding the role of external knowledge in improving the performance of NLP models. © 2024 ieee.

关键词： knowledge based systems

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Enhancing the Recognition of Hand Written Telugu Characters: natural language processing and Machine Learning Approach 2

Enhancing the Recognition of Hand Written Telugu Characters:...

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2nd ieee international conference on Contemporary Computing and Communications, InC4 2024

作者： Reedy, Baddala Govardhan Meghana, Gandham Subhadra, Thangadadona Mithun, B.N. Yogish, Deepa Christ University Karnataka Bangalore India A P S College of Engineering Dept of CSE Karnataka Bangalore India Christ University Dept of CSE Karnataka Bangalore India

ISBN: (纸本)9798350383652

Handwritten character recognition has wider application in many areas including heritage documents, education, document digitalization, language processing, and assisting the visually handicapped and other related areas. The paper tries to improve the accuracy and efficiency of recognizing handwritten letters of Telugu language scripts, a difficult task for computers. Telugu is most widely spoken language in southern part of India, it has rich cultural heritage. Using the natural language Toolkit (NLTK), this study investigates ways to enhance recognition accuracy by analyzing handwritten content and implementing methods such as feature extraction and classification. The purpose is to use NLTK's capabilities to develop handwritten character recognition. © 2024 ieee.

关键词： Adversarial machine learning

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Time-CoT for Enhancing Time Reasoning Factual Question Answering in Large language Models

Time-CoT for Enhancing Time Reasoning Factual Question Answe...

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international Joint conference on Neural Networks (IJCNN)

作者： Yin, Baosheng Hu, Naiyu Shenyang Aerosp Univ Res Ctr Human Comp Intelligence Shenyang Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Question-answering tasks are one of the most important tasks in natural language processing (NLP). Recently, large language models (LLMs) have demonstrated outstanding performance, but our experiments reveal that such factual questions involving time reasoning still pose a challenge for existing LLMs. This is primarily attributed to the insensitivity of LLMs to numbers and inability to comprehend the implicit meaning of time, leading to incorrect responses. In order to overcome these limitations, we propose Time Chain-of-Thought (Time-CoT). Specifically, Time-CoT represents the thought process in answering factual questions involving time reasoning and serves as a prompt for LLMs, including explicitation of the implicit "time parameter", the description of the "standard question entity" along the dimension of the timeline and reasoning based on the "time parameter". The aim is to stimulate its potential to answer such questions without external knowledge. We evaluate Time-CoT on three related question-answering datasets and achieve significant improvements, with the most significant improvement of up to 10.87% over strong baselines.

关键词： Large language Models Chain-of-Thought time reasoning

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