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Empowering a Nutritional Lifestyle through Soaked and Streamed Nuts Water Reuse using Novel RNN and NLP

Empowering a Nutritional Lifestyle through Soaked and Stream...

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2nd international conference on IT Innovations and knowledge Discovery, ITIKD 2024

作者： Lekhya, K. Surendran, R. Logapriya, E. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This study evaluates the effectiveness of a Novel Recurrent Neural Network (RNN) against natural language processing (NLP) methods for nutrient extraction from soaked and steamed nut water. Research Instruments and Methodologies: Ten nutrients were retrieved utilizing advanced RNN and NLP approaches. The iteration value was estimated using G-power analysis with a power of 0.8 and a 95% confidence range. The findings indicated that the Novel RNN method attained an accuracy of 84.10%, surpassing the NLP approach's 71.48%. A statistical analysis indicated that the two approaches exhibited a substantial difference, evidenced by a two-tailed p-value of 0.000 (p < 0.05). The results indicate that the Novel RNN surpasses NLP methods in nutrient extraction accuracy, rendering it a more dependable option for this work. © 2025 IEEE.

关键词： Biochemical analysis Circular Economy Food Production natural language processing Novel Recurrent Neural Network Nutrient Extraction

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MLLMIE: Multiple Large language Model Information Extraction in Ancient Medical Texts

MLLMIE: Multiple Large Language Model Information Extraction...

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Machine Learning and natural language processing (MLNLP), international conference on

作者： Feilong Wang Donghui Shi Xinyi Cui School of Electronics and Information Engineering Anhui Jianzhu University Mass Spectrometry Key Technology R&D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments Hefei China School of Electronics and Information Engineering Anhui Jianzhu University Hefei China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Ancient medical texts, which reflect traditional cultural practices, can play an important role in the promotion of medical traditions. Nevertheless, the complexity of extracting information from classical Chinese medical texts and the potential inconsistencies and hallucinations in large language models during information extraction present significant challenges. In domains such as medicine, where the precision of information extraction is of paramount importance, the paper augmented existing methodologies for information extraction from large language models and proposed a novel approach, designated as Multiple Large language Model Information Extraction (MLLMIE). The method initially defined a pattern layer to explicitly delineate the concepts, terms, and their interrelationships within the text. Subsequently, it employed prompt engineering to extract information from multiple models, thereby obtaining candidate medical ancient text knowledge results. Finally, the model validation layer was employed to filter the candidate results, thereby obtaining the final medical ancient text knowledge. The method attains F1 scores of 92.68% for named entity recognition and 94.91% for relation extraction in ancient medical texts, thereby demonstrating high-quality medical ancient text information extraction. This method effectively enhances the accuracy of ancient medical text information extraction and facilitates the discovery and application of ancient medical text knowledge.

关键词： knowledge engineering Large language models Instruments Named entity recognition Machine learning Information retrieval Information filters Data mining Cultural differences Prompt engineering

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Multi-label Masked language Modeling on Zero-shot Code-switched Sentiment Analysis 22

Multi-label Masked Language Modeling on Zero-shot Code-switc...

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45th international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Li, Zhi Gao, Xing Zhang, Ji Zhang, Yin Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781450387323

In multilingual communities, code-switching is a common phenomenon and code-switched tasks have become a crucial area of research in natural language processing (NLP) applications. Existing approaches mainly focus on supervised learning. However, it is expensive to annotate a sufficient amount of code-switched data. In this paper, we consider zero-shot setting and improve model performance on code-switched tasks via monolingual language datasets, unlabeled code-switched datasets, and semantic dictionaries. Inspired by the mechanism of code-switching itself, we propose multilabel masked language modeling and predict both the masked word and its synonyms in other languages. Experimental results show that compared with baselines, our method can further improve the pretrained multilingual model's performance on code-switched sentiment analysis datasets.

关键词： Code-switched Zero-shot Learning Sentiment Analysis Multi-label Masked language Modeling

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Multimodal Learning in natural language processing: Fact-Checking, Ethics and Misinformation Detection

Multimodal Learning in Natural Language Processing: Fact-Che...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), international conference on

作者： Himanshu Sanjay Joshi Hamed Taherdoost College of Business Westcliff University CA USA R&D Department Hamta Business Corporation Vancouver Canada College of Technology & Engineering Westcliff University CA USA University Canada West Vancouver Canada GUS Institute | Global University Systems London UK Q Minded Quark Minded Technology Inc. Vancouver Canada

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Multimodal learning (MML) is a subtype of deep learning that improves the capabilities of machine learning models by integrating modalities from multiple data sources such as text, images, audio, and video. This integration of modalities produces a more comprehensive understanding of complex data and nuanced outputs. Applications of this approach in real-time decision-making, information retrieval, and knowledge representation can improve the insights derived from the natural language processing (NLP) system. Moreover, in a time of information overload, there are concerns regarding the credibility of the content. The role of NLP systems is central to processing textual data in MML systems. In this environment, the ethical development of multimodal systems for fact-checking is crucial. This paper addresses an existing gap in use of multimodal learning in the context of fact-checking by investigating issues related to integration of NLP, Question-Answering (QA) systems, and knowledge graphs to combat misinformation. This research presents multimodal data based frameworks based on knowledge graph integration. The exploration of the frameworks enables to enhance the accuracy, reliability, and ethical soundness of fact-checking mechanisms. This study also highlights new directions for overcoming challenges related to modality alignment, computational efficiency, and ethical transparency.

关键词： Ethics Technological innovation Accuracy Soft sensors Scalability knowledge graphs Streaming media Reliability theory natural language processing Fake news

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A Multi-Agent Collaborative Framework for Constructing knowledge Graphs from Text

A Multi-Agent Collaborative Framework for Constructing Knowl...

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IEEE international conference on Big knowledge (ICBK)

作者： Gui Chen Xianhui Liu College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Recent advancements in large language models (LLMs) have significantly improved natural language understanding and generation, making them valuable tools for knowledge graph construction. However, a single LLM often struggles with the complexity of this task, leading to suboptimal results. To address this challenge, we propose a robust multi-agent collaborative framework for constructing knowledge graphs from text. This framework leverages dynamic interactions among specialized agents, including knowledge graph experts, knowledge extraction experts, data processing experts, and domain-specific experts, to effectively build accurate knowledge graphs from text. Additionally, we introduce a novel prompt construction method tailored for knowledge extraction and a revision mechanism to revise preliminary knowledge graphs. These innovations address common issues in knowledge extraction and enhance the quality of model-generated content. Experimental results on four datasets across two tasks (Named Entity Recognition and Relation Extraction) demonstrate that our approach achieves superior performance in the F1 score compared to baseline methods, highlighting its effectiveness and robustness.

关键词： knowledge engineering Technological innovation Large language models Collaboration knowledge graphs Named entity recognition Ontologies Data processing Robustness Data mining

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Chinese Text Simplification Based on Large language Models

Chinese Text Simplification Based on Large Language Models

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Computational Linguistics and natural language processing (CLNLP), international conference on

作者： Hongying Huo Zetong Fu Haochen Yu Shengguang Yang Gongbo Tang School of Information Science Beijing Language and Culture University Beijing China

ISBN: (数字)9798350392098

ISBN: (纸本)9798350392104

Chinese is a complex language, creating a demand for text simplification in various contexts. Traditional text simplification models have primarily relied on pre-trained models. With the advancement of large language models (LLMs), researchers have begun leveraging their robust text comprehension and generation capabilities for text simplification tasks. However, these implementations are often simplistic, merely instructing the LLM to simplify text without addressing inherent challenges. One notable issue is the hallucination phenomenon where LLMs introduce inaccuracies, especially in fine-grained knowledge. This paper proposes an approach to interact with LLMs by embedding the text’s underlying knowledge through carefully designed prompts. These prompts provide clearer guidance to the LLMs, thereby improving the quality of text simplification. In addition, LLMs may be further improved on specific tasks after instruction fine-tuning. Thus, we constructed a text simplification training dataset with semantic knowledge from Chinese Parataxis graph, and fine-tuned existing large models specifically for the text simplification task. Experimental results demonstrate that fine-tuning LLMs can significantly enhance the quality of text simplification. Finally, we provide the proposed methods as a service via a web interface and a browser extension.

关键词： Training Large language models Computational modeling Semantics natural language processing Computational linguistics Browsers Prompt engineering

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AI-Enhanced Learning Assistant Platform: An Advanced System for Q&A Generation from Provided Content, Answer Evaluation, Identification of Students' Weak Areas, Recursive Testing for Strengthening knowledge, Integrated Query Forum, and Expert Chat Support 2

AI-Enhanced Learning Assistant Platform: An Advanced System ...

引用

2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Redhu, Vivek Singh, Abhishek Kumar Saravanan, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question-and-answer generation, answer evaluation, identification of weak areas, recursive testing, an integrated query forum, and expert chat support. This platform makes use of artificial intelligence (AI) technology to try to satisfy the many needs that students and teachers have. Using natural language processing and machine learning, the platform's question and answer generating feature generates relevant questions on its own from the provided content. This encourages participation and in-depth subject understanding. The answer evaluation section provides quick feedback for improvement by utilizing AI algorithms to assess the accuracy and caliber of student responses. One of this platform's key advantages is its capacity to identify students' areas of weakness. Through the analysis of performance patterns and root causes, the system can generate customized recommendations and learning materials to help overcome those constraints. The property of recurring testing facilitates continuous assessment and reinforcement of knowledge. Through repeated practice, the program gradually pushes students to increase their understanding of the material by creating adaptive exams. Through' the integrated query forum, students can collaborate and ask for assistance from others by asking questions and receiving answers from teachers and their peers. Furthermore, by enabling real-time communication between users and subject matter experts, the expert chat support tool fosters an engaging and motivating learning environment. To sum up, the AI-Enhanced Learning Assistant Platform offers a wide range of features designed to maximize learning. With AI technology, it helps students learn more effectively and retain what they have learned, promotes active learning, and provides the support they need for a good educational experience. © 2024 IEEE.

关键词： Students

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Enhancing Information Extraction from Building Standards with ChatGPT-4: A Multimodal Approach 20th

Enhancing Information Extraction from Building Standards wit...

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20th international conference on Computing in Civil and Building engineering, ICCCBE 2024

作者： Zentgraf, Sven König, Markus Chair of Computing in Engineering Ruhr-Universität Bochum Universitätsstraße 150 Bochum44801 Germany

ISBN: (纸本)9783031842078

The AECO (Architectural, engineering, Construction, and Operations) industry is increasingly benefiting from the advantages of advanced information management, particularly through the use of Building Information Modeling (BIM) methods. BIM integrates geometric and alphanumeric data, such as dimensions and spatial relationships, into digital building models. These elements are subject to specific constraints defined by industry standards and guidelines. The issue with these constraints embedded in regulatory documents is that they are mostly only available in non-machine readable formats which hinders the exchange and usage of data within the building’s lifecycle. Currently, the required rules and requirements are extracted manually by experts from the regulatory documents and integrated manually and labor-intensively into corresponding BIM processes. Thus, many research projects are currently addressing the issue of how the textual components of regulatory documents can be automatically analyzed to extract rules and requirements using natural language processing or other approaches. In addition to this textual information, many standards also contain figures and illustrations that explain the information described in the text in more detail or contain additional knowledge. Converting this graphical content into machine-readable information is just as challenging as analyzing the textual components. This paper aims to develop an approach for extracting information related to building information requirements from figures contained in standards using the two state-of-the-art MLLMs Kosmos 2 and Chat GPT-4. An MLLM is an AI system that can process text, images, audio, and video, unlike traditional models that are limited to text. MLLMs trained on large data sets recognize complex patterns in different types of data. With the help of the two MLLMs, the images, and their corresponding texts and captions from the standards will first be analyzed, and then the information

关键词： Concretes

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Evaluating the Contribution of AI-Powered knowledge and Digital Literacy to the Improvement of Fintech Operations in the Electronics Sector 1

Evaluating the Contribution of AI-Powered Knowledge and Digi...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Choudhari, Amey Adinath Moholkar, Nilambari Hanmant Ghadage, Avinash Bhoite, Dashrath Kumari, Sujata Patil, Mangalgouri S JSPM's Rajashi Shahu College of Engineering Pune India Dr. D.Y. Patil Institute of Technology Pimpri Pune India D.Y. Patil Vidyapeeth's Global Business School and Research Centre Pune India International School of Management and Research Pune India Shri Balaji University Pune India Pune India

ISBN: (纸本)9798331518578

The financial services sector is undergoing a change because to artificial intelligence (AI) particularly in the field of financial technology (FinTech). FinTech's incorporation of artificial intelligence (AI) has fundamentally altered traditional financial services by increasing security, accuracy, and efficiency. Artificial intelligence (AI) technology, financial organizations can benefit from technologies like ML algorithms, natural language processing (NLP), including predictive analytics in making better decisions, streamlining operations, and offering customized customer experiences. While cutting edge capability and advanced education enables both staff and purchasers to capably connect with fintech courses of action, recreated insight-controlled data helps associations with overhauling heading, further foster client experiences, and limit money related risks. Since technology has made it easier to share information and lower transaction costs, financial services production could be broken down. Because specialized players offer unbundled financial services, customers can locate and put together the product suites that they want. This study sees how cutting-edge capability and man-made brainpower-controlled data could chip away at financial undertakings, advance safe trades, and addition proficiency in the contraptions business. This study assesses how these advances add to the augmentation and goodness of fintech applications in equipment by dissecting context-oriented examinations, ongoing crazes, and inconveniences. © 2025 IEEE.

关键词： Sales

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Implementation of Qur’anic Question Answering System Based on the BERT Model 2nd

Implementation of Qur’anic Question Answering System Based ...

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2nd international Libyan conference on Information and Communication Technologies, ILCICT 2023

作者： Alarabi, Ebtihal Ellabib, Issmail Computer Engineering Department University of Tripoli Tripoli Libya

ISBN: (纸本)9783031626234

The Holy Qur'an is the oldest comprehensive Arabic book of recommendation for Muslims worldwide. Provide knowledge and information used in various ways. People often utilize the Holy Qur'an, considered a reliable and trustful legislated text, for education and to meet their needs and inquiries of the Muslim community. Like extracting an answer span from the provided passage, The Qur'an has the potential to captivate the curiosity of non-Muslims and propel them towards exploring a vast array of topics and pursuing answers. Over the past few years, Question Answering (Q.A.) has drawn much attention from the NLP community. Researchers and experts have developed various Qur'anic Question Answering (QAA) systems. Nevertheless, the main challenge in the Arabic language is the need for more resources, making it difficult to provide highly accurate Arabic QA systems. The first Qur’an Question Answering shared task workshop, "Qur’an QA 2022," aims to promote state-of-the-art research on Qur'anic question answering QA in general and machine reading comprehension MRC in particular. It aims to develop models to extract questions answering the holy Qur’an passages. This research paper motivated by this task, suggests an ensemble learning model based on Arabic-supported versions of BERT, which will be implemented using the KNIME platform. We aim to use this model for Arabic Question Answering.;as a result, we get 0.488 for the AUC and 0.946 for accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： natural language processing systems

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