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共 27256 条记录，以下是241-250 订阅

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Enhancing Chest X-ray Classification through knowledge Injection in Cross-Modality Learning

Enhancing Chest X-ray Classification through Knowledge Injec...

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

作者： Yan, Yang Yue, Bingqing Li, Qiaxuan Huang, Man Chen, Jingyu Lan, Zhenzhong Zhejiang University Zhejiang Hangzhou China School of Engineering Westlake University Zhejiang Hangzhou China Westlake University Research Center for Industries of the Future Westlake University Zhejiang Hangzhou China Zhejiang Hangzhou China

ISBN: (纸本)9798350368741

The integration of artificial intelligence in medical imaging has shown tremendous potential, yet the relationship between pre-trained knowledge and performance in cross-modality learning remains unclear. This study investigates how explicitly injecting medical knowledge into the learning process affects the performance of cross-modality classification, focusing on Chest X-ray (CXR) images. We introduce a novel Set Theory-based knowledge injection framework that generates captions for CXR images with controllable knowledge granularity. Using this framework, we fine-tune CLIP model on captions with varying levels of medical information. We evaluate the model's performance through zero-shot classification on the CheXpert dataset, a benchmark for CXR classification. Our results demonstrate that injecting fine-grained medical knowledge substantially improves classification accuracy, achieving 72.5% compared to 49.9% when using human-generated captions. This highlights the crucial role of domain-specific knowledge in medical cross-modality learning. Furthermore, we explore the influence of knowledge density and the use of domain-specific Large language Models (LLMs) for caption generation, finding that denser knowledge and specialized LLMs contribute to enhanced performance. This research advances medical image analysis by demonstrating the effectiveness of knowledge injection for improving automated CXR classification, paving the way for more accurate and reliable diagnostic tools. © 2025 IEEE.

关键词： Chest X-ray Cross-modality Learning knowledge Injection Phenotype Zero-shot Classification

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Text Summarization of Indo-Aryan languages Using Self-attention Mechanism 5th

Text Summarization of Indo-Aryan Languages Using Self-attent...

引用

5th international conference on Electrical and Electronics engineering, ICEEE 2024

作者： Hadawle, Shubham Kotkar, Pranav Bhatia, Om Dongre, Siddhant Singh, Amit R. Mumbai India

ISBN: (纸本)9789819791118

Text summarization plays a crucial role in distilling relevant information from large textual documents. However, there are negligible language models available for working on texts available in local or indigenous languages. Regional languages often suffer from a scarcity of digital content, resource limitations, inflections in the nature of the language and variation in the structure of the language. This paper tries to bridge the same gaps allowing us to model regional language text summarizers. In this study, we explore the effectiveness of self-attention mechanisms, specifically leveraging a Conventional Transformer, for abstractive text summarization in two Indo-Aryan languages: Hindi and Gujarati. Our approach involves training a custom-built Transformer model on the ILSUM dataset. The ILSUM (Indian language Summarization) consists of corpora curated using articles, headlines and summaries from several leading newspapers of the country. Our study, thus, proposes how Transformers are well-suited for text summarization across languages due to their self-attention mechanism, which captures context effectively, their ability to not reply on sequential processing and incorporation of positional encodings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

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Empowering Recommendations: Survey of LLM-Based Recommendation Systems 8

Empowering Recommendations: Survey of LLM-Based Recommendati...

引用

8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kumar, Rishav Dey, Titas Das, Soma University of Engineering & Management Kolkata India

ISBN: (纸本)9798331520762

The rise of e-commerce has emphasized the importance of recommendation systems in enhancing user experiences. Traditional methods, like collaborative filtering and content-based techniques, often struggle with understanding user preferences. Large language Models (LLMs), such as ChatGPT and GPT-4, offer a transformative approach by leveraging advanced natural language processing. This survey explores LLM-driven strategies in recommendation systems, focusing on their role in feature encoding, user-item interaction modeling, and explainability. By highlighting their ability to generate contextually relevant recommendations, we provide insights into their advantages over conventional methods and their potential for future advancements. © 2025 IEEE.

关键词： Recommender systems

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Augmenting Human Decision-Making: Exploring AI-Driven Automation in IT Operations (AIOps) for Enhanced Efficiency and Innovation 4

Augmenting Human Decision-Making: Exploring AI-Driven Automa...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Lakshmi Kumar, Tukkisetty Vasantha Naresh, Tippa Ganesh, Chunduru Saran Varma, Chekuri Tarun Raju, K.V. Hema, Chindu Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798331523923

This article analyzes how artificial intelligence (AI) has changed the IT industry, ushering in an age of unmatched technical growth and new potential for human-AI communication. Through a detailed research, it explores the complex dynamics of AI integration in IT operations, examining the likelihood that AI may supplant people in the workforce and highlighting the complexities and challenges that accompany this paradigm change. By examining present AI technologies, such as machine learning, deep learning, natural language processing, and data science, and emphasizing both the benefits and imminent obstacles, this article discusses the complex environment of AI adoption in IT. It also digs thoroughly into the legal frameworks, social repercussions, and ethical challenges that must be overcome in order to govern the AI-driven revolution of the IT industry. Through this research, the article wants to peacefully cohabit human knowledge with AI talents, opening up new paths for creativity, productivity, and sustainable development in the digital age. © 2025 IEEE.

关键词： Adversarial machine learning

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Autonomous Agents in Software Development: A Vision Paper

Autonomous Agents in Software Development: A Vision Paper

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25th international conference on Agile Software Development (XP)

作者： Rasheed, Zeeshan Waseem, Muhammad Sami, Malik Abdul Kemell, Kai-Kristian Ahmad, Aakash Anh Nguyen Duc Systae, Kari Abrahamsson, Pekka Tampere Univ Tampere Finland Univ Jyvaskyla Jyvaskyla Finland Univ Helsinki Helsinki Finland Lancaster Univ Leipzig Leipzig Germany Univ South Eastern Norway Notodden Norway

ISBN: (纸本)9783031727801;9783031727818

Large language Models (LLM) are reshaping the field of Software engineering (SE). They enable innovative methods for executing many SE tasks, including automation of entire process of Software Development Life Cycle (SDLC). However, only a limited number of existing works have thoroughly explored the potential of LLM based AI agents to automate the entire lifecycle in SE. In this paper, we demonstrate the success of our initial efforts in automating the entire lifecycle autonomously based on given software specification as input, which has shown remarkable efficiency and significantly reduced development time. Our preliminary results suggest that the careful implementation of AI agents can enhance the development lifecycle. We aim to streamline the SDLC by integrating all phases into an AI-driven chat interface, enhancing efficiency and transparency. Furthermore, we seek to enhance collaboration, creating an environment where stakeholders from various backgrounds can contribute, review, and refine ideas and requirements in real-time. This forward-looking direction guarantees to redefine the paradigms of SE and also make software creation more inclusive, collaborative, and efficient.

关键词： OpenAI AutoGPT Artificial Intelligence natural language processing Generative AI Software engineering Large language Model

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Sentiment-Based natural language processing for Automotive Industry 5th

Sentiment-Based Natural Language Processing for Automotive I...

引用

5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Padmanabhan, P. Bhuvana, J. Naval, Preeti Faujdar, Pramod Kumar Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mechanical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

The automobile industry is an unexpectedly changing sector that has embraced the modern era to take advantage of competitive benefits. natural language processing (NLP) performs a crucial position in information patron sentiment, as it can hit upon diffused nuances in textual content that people can also leave out or misinterpret. This paper explores how NLP can be used to tune and analyze client sentiment inside the automotive industry. We describe the exclusive types of sentiment analysis processes currently being applied in this enterprise, along with sentiment dictionaries, device learning-primarily based methods, and deep getting-to-know-primarily based strategies. We then offer numerous examples of how sentiment evaluation may be applied in the car industry, such as enhancing customer service, detecting and responding to patron reviews, and growing marketing campaigns. Finally, we speak of some of the challenges associated with sentiment evaluation inside the automotive enterprise and provide pointers for overcoming those demanding situations. Sentiment-primarily based natural language processing (NLP) is a modern utility of NLP techniques for extracting sentiment from text-based resources. The utility of NLP for sentiment evaluation can provide automotive enterprise stakeholders with critical insights into customer sentiment concerning their emblem, competition, product lines, and different marketplace factors. Using sentiment-based NLP, automotive industry stakeholders can benefit from essential insights into patron sentiment and leverage them to serve their customers better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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SSCM: Self-Supervised Critical Model for Reducing Hallucinations in Chinese Financial Text Generation

SSCM: Self-Supervised Critical Model for Reducing Hallucinat...

引用

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

作者： Jin, Keyan Wang, Yapeng Santos, Leonel Fang, Tao Yang, Xu Im, Sio Kei Faculty of Applied Sciences Macao Polytechnic University China Computer Science and Communication Research Centre Polytechnic of Leiria Leiria Portugal Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China Macao Polytechnic University China

ISBN: (纸本)9798350368741

Large language Models (LLMs) show strong performance in natural language processing tasks, but their application in the financial domain is limited. Current methods rely on large datasets and manual prompt engineering, resulting in high data demands, long inference times, and frequent hallucinations. To address these limitations, we propose a novel self-supervised prompt optimization framework tailored for the financial domain. Our approach involves training a critical model that evaluates and ranks generated outputs using both good and bad answers generated from various revised prompts. Experiments on a large Chinese financial corpus show that our framework significantly improves performance on tasks such as summarization and event-based question answering, as evidenced by higher scores on both automated metrics like ROUGE, BLEU, and BERTScore, and also through human evaluations. These results validate the effectiveness of our method in reducing hallucinations and improving the quality of financial text generation. © 2025 IEEE.

关键词： Critical Model Financial Applications Hallucination Large language Models (LLMs)

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

Joint Automatic Speech Recognition And Structure Learning Fo...

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

作者： Hu, Jiliang Li, Zuchao Shen, Mengjia Ai, Haojun Li, Sheng Zhang, Jun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

ISBN: (纸本)9798350368741

Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets. © 2025 IEEE.

关键词： Information Extraction Speech Recognition Spoken language Understanding

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Comparative Analysis of Contextual AI for Political Tweets 1

Comparative Analysis of Contextual AI for Political Tweets

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Khatavkar, Vaibhav Petkar, Sneha Vaidya, Archana Institute of Management Computer Engineering Department MH Pune India Computer Engineering GES Sapat COE MH Nashik India

ISBN: (纸本)9798350355611

This paper provides a comprehensive survey of Contextual AI applications for analyzing political discourse on Twitter, focusing on the latest advancements in natural language processing (NLP) and machine learning models. Analyzing Twitter data, with its informal language and rapid content generation, presents challenges like sarcasm, slang, and polarization. Contextual AI models, such as BERT and GPT-based architectures, offer significant improvements in political sentiment detection, emotion analysis, and bias identification. Key studies surveyed in this paper report high performance: the PoliBERT model achieved an F1 score of 0.95 and a precision of 0.96, while the sentiment analysis of political opinions showed balanced metrics with an F1 score of 0.89 and an accuracy of 0.88. A study on detecting political leanings achieved a 99% confidence level with an accuracy of 0.96 and ROC AUC of 0.95. These results demonstrate the potential of Contextual AI for accurately classifying political biases, real-time sentiment analysis, and handling complex language nuances. Future research should focus on real-time multilingual analysis, ethical considerations, and bias mitigation. This survey highlights the importance of Contextual AI in providing more precise, nuanced, and inclusive insights into political discourse on social media. © 2025 IEEE.

关键词： Tweets

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Evaluation of Classification Techniques for News Data

Evaluation of Classification Techniques for News Data

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanusree, Uppalapati Gatla, Anitha Abhishek, Baja Jason Kiran, Duvvapu Sai Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798331529833

In the digital world, there is a large amount of online news sources which creates a problem to users to navigate and get the updated information from the News content management. This is a machine learning project which is creating a way to allow articles to be intelligently categorized, so it's simpler to locate those news pieces that are important to you. Additionally, the scalability of each approach will be investigated to determine the suitability for processing large scale news datasets. The purpose of our approach is to classify news article to a relevant topic such that the news articles are grouped and users have a better experience in news platform. Using these performance metrics, this technology has real potential to streamline news management and increase user engagement to the next level. Furthermore, the project also investigates the idea of leveraging natural language processing (NLP) methods to further augment classification accuracy and to better extract the true semantic meaning of the news articles. We preprocessing the data by using natural language processing (NLP) techniques such as cleaning, tokenization, lemmatization and word embedding using TF- IDF vectorizer. Then we split the data and train an ANN and Gaussian Naive Bayes. Overall, this work was meant to make towards the development of a safe and effective device for automated grouping of on the web news articles so that it will certainly be simpler for individuals to access appropriate information in the dense environment of online media. © 2025 IEEE.

关键词： Semantics

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