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Analysis of 50 Years of Health Food Research Trends Using natural language processing and Generative AI 28th

Analysis of 50 Years of Health Food Research Trends Using Na...

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28th international conference on knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Kobayashi, Yoshiyuki Uchida, Takumi Inoue, Takahiro Iwasaki, Yusuke Ito, Rie Saito, Koichi Akiyama, Hiroshi Tsuda, Kazuhiko Yoshida, Kenichi School of Pharmacy and Pharmaceutical Sciences Hoshi University 2-4-41 Ebara Shinagawa-Ku Tokyo142-8501 Japan University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba-shi305-8577 Japan SCI-Analytics Kuwano Bldg. 2F 6-23-4 Jingumae Shibuya-ku Tokyo150-0001 Japan

This study investigates trends in health food research from 1975 to 2024 using text mining and generative AI. Analyzing 92,028 journal entries from Scopus revealed a significant increase in publications, with key topics including nutrients, bioactive compounds, disease prevention, and research methods. AI-assisted classification yielded 12 categories reflecting the field's multidisciplinary nature. Trends include a growing focus on nutrients, health conditions, study design, and food production and consumption context. The findings highlight the increasingly interdisciplinary landscape of health food research, informing future directions and evidence-based decision-making. © 2024 The Authors.

关键词： Food ingredients

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Resume Screening-Testing for Data Stability

Resume Screening-Testing for Data Stability

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2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Wahedna, Asim Vakil, Adit Shah, Somil Kelkar, Vishakha V. Shrivastava, Ishan Dwarkadas J.Sanghvi College of Engineering Department of Electronics and Telecommunication Mumbai400056 India

ISBN: (纸本)9798350319200

Resume parsing and evaluation are critical tasks for human resources teams, and with the surplus of candidates applying to jobs in today's time, the process of resume evaluation is both manual and time consuming. The process of resume parsing has been traditionally done manually, which is time-consuming and prone to errors. However, with the advancements in natural language processing (NLP), automated resume parsing has become a popular research area. In addition to parsing, the advent of machine learning has allowed resume screening to be automated, through the use of key words and named entities. In this technical paper, we provide a comprehensive analysis of pre-existing research on resume parsing and evaluation frameworks, and provide a detailed overview of the different NLP techniques used for resume parsing, such as Named Entity Recognition, along with the various machine learning algorithms used for screening resumes, such as cosine similarity. We then implement our own models using resume datasets provided by a third-party firm, and analyze the stability and consistency of output data for each model using frequency distribution graphs, statistical averages, etc. in order to determine the best model for our given dataset. This paper provides a number of valuable graphs and observations that can be helpful for researchers and practitioners in the field of recruitment, machine learning. © 2023 IEEE.

关键词： e-recruitment Hiring Information Retrieval Job Seekers Machine learning natural language processing Resume Analysis Resume Evaluation Resume quality assessment Resume Recommendation Resume Similarity

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Enhanced Graph-Based Model with Mathematical knowledge Embedding for Math Word Problem Solving

Enhanced Graph-Based Model with Mathematical Knowledge Embed...

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Intelligent Education and Intelligent Research (IEIR), international conference on

作者： Xin He Chao Sun Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China

ISBN: (数字)9798331519827

ISBN: (纸本)9798331519834

Math Word Problems (MWPs) represent a critical area of research in natural language processing and artificial intelligence, with the goal of translating natural language descriptions into mathematical expressions and deriving solutions. Despite recent progress with graph-based and tree-based neural models, many approaches still struggle to capture the implicit mathematical knowledge embedded in these problems. This limitation impedes their ability to accurately represent the problem-solving process, especially for complex MWPs that require an understanding of implicit mathematical relationships. To tackle this challenge, we propose integrating mathematical knowledge directly into graph neural network models to enhance their effectiveness in solving intricate MWPs. Specifically, we introduce a formula graph where each formula is abstracted into a graph structure and integrated into an enhanced model, thereby facilitating efficient resolution of MWPs involving implicit knowledge. Our approach aims to improve model performance on complex MWPs, as demonstrated through empirical evaluation on the Ape210K dataset.

关键词： knowledge engineering Accuracy Translation Large language models Mathematical models Graph neural networks natural language processing Libraries Problem-solving Physics

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Machine Learning Approaches for Suicidal Ideation Detection on Social Media 4

Machine Learning Approaches for Suicidal Ideation Detection ...

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4th international conference on Computation, Automation and knowledge Management, ICCAKM 2023

作者： Nandhini, B. Jayanthi Shrinivas, K. Vinod, Priya School of Management Studies Bannari Amman Institute of Technology Sathyamangalam India Hindusthan College of Engineering and Technology Department of Management Sciences Coimbatore India Bangalore India

ISBN: (纸本)9798350393248

Social media suicidal ideation has become a serious public health issue that requires creative solutions for early diagnosis and management. An extensive investigation of machine-learning techniques for the automated detection of suicidal thoughts in internet postings is presented in this research. We start off by talking about the concerning increase in information on social media about mental health issues and the pressing need to create efficient monitoring mechanisms. The research explores the several methods used to identify the subtleties of suicidal thought conveyed in text, photographs, and audio-visual information. These methods include sentiment analysis, natural language processing, and deep learning models. We look at the problems with unbalanced data, privacy issues, and the moral ramifications of keeping an eye on user-generated material. We also go over the research's practical ramifications, such as the creation of instruments for real-time monitoring and crisis response techniques. Through comprehensive experiments and benchmarking, we demonstrate the potential of machine learning in providing timely support for those in need, thereby reducing the impact of suicidal ideation on society. © 2023 IEEE.

关键词： Sentiment analysis

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natural language processing Based Intrusion Detection Interface For IoT Devices

Natural Language Processing Based Intrusion Detection Interf...

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Recent Advances in Science and engineering Technology (ICRASET), international conference on

作者： Ramakrishna Hegde S Keerthana R M Sripriya Sreesh Raghu K V Kavin Computer Science and Engg Vidyavardaka College of Engineering Affiliated to Visvesvaraya Technological University Mysuru India Computer Science and Engg Vidyavardaka College of Engineering Mysuru India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The “natural language processing based intrusion detection interface for IoT devices” project aims to develop a system to detect intrusions (security threats) in Internet of Things (IoT) devices that have been compromised. The technology analyzes data from Internet of Things (IoT) devices using natural language processing (NLP), making it easier and simpler to identify and explain threats. One big challenge with IoT devices is that they generate a lot of different types of data, which makes it hard in terms of threat detection. Our project solves this by processing this data using natural language processing (NLP) to process this data and provide clear, easy-to-understand instructions for users to respond to potential security threats. This way, even those without technical expertise can understand the system’s alerts and take action. The system is designed to operate on low-power Internet of Things(IoT) devices, which means it can function efficiently without consuming a lot of resources. Additionally, it will continue to pick up knowledge from the material it examines, keeping it informed about emerging risks. All things considered, the technology offers consumers effective and straightforward IoT device protection (Explainable IoT IDS)

关键词： Accuracy Intrusion detection natural language processing Threat assessment Internet of Things Security Object recognition Usability Protection Faces

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Sentiment Analysis of the Amazon Customers Using the BiGRU Neural Network Enhanced by Attention Mechanism 14

Sentiment Analysis of the Amazon Customers Using the BiGRU N...

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14th international conference on Information and knowledge Technology, IKT 2023

作者： Al-Abedi, Sara Sinan Salman Mohebbi, Keyvan Branch Department of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350349412

In order to effectively analyze the vast amount of information available on the Internet, including opinions, attitudes, and views, it is crucial to predict the emotions and behavior of users. While recurrent neural networks have shown promising results in natural language processing, they encounter challenges such as gradient vanishing in lengthy texts and limited text comprehension. Gated neural networks, on the other hand, excel in understanding long-Term dependencies between word sequences. This research introduces a novel approach that leverages a bidirectional gated recurrent unit neural network with an attention mechanism to analyze the sentiments of Amazon users. The model begins by training the initial word input using Word2vec and transferring the appropriate weights for each embedding through transfer learning from GloVe. The model consists of three layers of bidirectional GRU with the output of the third layer feeding into the attention mechanism. This mechanism assigns weights to the words in the sentence, giving importance to crucial words. The final part of the model employs three fully connected layers. Evaluation results on the Amazon review dataset demonstrate that the proposed approach significantly improves precision, recall, F-score, and accuracy metrics, achieving rates of 96.30%, 91.10%, 93.62%, and 95.82% respectively. © 2023 IEEE.

关键词： Sentiment analysis

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Chinese Medical Record Entity Recognition Based on Lexicon and Self-attention

Chinese Medical Record Entity Recognition Based on Lexicon a...

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2023 IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2023

作者： Wang, Le Zhao, Yahui Cui, Rongyi Jin, Guozhe Wang, Jinxiang Yanbian University Inst. of Intelligent Information Processing Yanji133002 China Yanbian University Computer Science and Technology College of Engineering Yanji China

ISBN: (纸本)9798350313734

Named Entity Recognition (NER) is a pivotal challenge in the field of natural language processing. In the medical field, NER plays a crucial role in identifying medical entities, such as diagnoses and drugs, from electronic medical records. To address the issue of incomplete and inaccurate word recognition in Chinese electronic medical records, we propose a BERT-BiLSTM-CRF model that incorporates both a lexicon and self-attention *** our approach, we first utilize the BERT model for preprocessing to generate word vectors. These vectors capture important semantic information about the words. Next, we employ a BiLSTM network to further capture contextual information from the input sequence. This helps to enhance the understanding of the text's meaning. To reinforce the significance of the sequence information, we introduce a self-attention layer that learns to prioritize relevant contextual details. Subsequently,the extracted features are fed into a CRF model for final training. The CRF layer takes into account the dependencies among the labels and improves the overall sequence labeling accuracy. Given the specialized nature of the medical domain, we integrate a medical lexicon, incorporating external knowledge features into the model. This inclusion enables better recognition of medical terms and enhances performance on medical-related *** extensive experiments on the CCKS2019 dataset, our proposed model demonstrates favorable performance. It get relatively excellent results in identifying medical entities accurately and effectively from Chinese electronic medical records. © 2023 IEEE.

关键词： Diagnosis

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In-depth Analysis of Information Overflows and its Role in Shaping Future knowledge Structures 3

In-depth Analysis of Information Overflows and its Role in S...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Paranthaman, V. Kamal, M.D. Raj Balaji, V. Sunil, G. Adhi College of Engineering and Technology Dept. of Mechanical Engineering TN Chennai India Velammal Institute of Technology Department of Mechanical Engineering TN Chennai India Loyola Institute of Technology Department of Mechanical Engineering TN Chennai India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

This study provides a thorough evaluation of data deluges and how they will play a significant role in the development of future knowledge architectures. The suggested methodology is adaptable and interdisciplinary since it combines cutting-edge data mining, NLP, and network analysis methods. Data mining, NLP, network analysis, content analysis, big data analytics, cognitive mapping, SNA, MNA, QR, and IV are all compared to the proposed method, as are machine learning algorithms, qualitative research methods, and data visualization. Accuracy, relevance, depth of analysis, innovation, practical consequences, and clarity are only a few of the areas where the suggested technique shines. Its greater analytical skills are shown by its persistent outperformance of conventional approaches. Additionally, the suggested approach better accords with ethical norms and responsible research procedures, thus they are all considered as well. In summary, this study demonstrates the value of looking at information floods from several angles of study. The suggested technique not only provides cutting-edge analytic instruments for negotiating the intricacies of the digital information ecosystem, but also gives a forward-looking viewpoint by anticipating new patterns and developments. It demonstrates how crucial it is to use cutting-edge methods and evolve with the information age. © 2023 IEEE.

关键词： natural language processing systems

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Analysis of Disaster Tweets Using natural language processing

Analysis of Disaster Tweets Using Natural Language Processin...

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international conference on Data Analytics, Intelligent Computing, and Cyber Security, ICDIC 2020

作者： Bikku, Thulasi Chandrika, Pathakamuri Kanyadari, Anuhya Prathima, Vuyyuru Sai, Borra Bhavana Department of Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Guntur India

ISBN: (纸本)9789811941610

Now-a-days social media has become a crucial part of life. Twitter is a social networking site on which people post and interact with messages renowned as tweets. Users registered officially can tweet, like and re-tweet messages. During the emergence of a disaster or crisis social media has become a significant means of communication. The widespread use of mobile phones and other forms of communication allows individuals to express and alert others about real-life disasters. Such knowledge relating to disasters spread over the media could save thousands of individuals by warning others and allowing them to take the required actions. It is being worked on by many firms to analyze tweets and observe tweets relating to disasters and emergencies using programming. Such efforts may be useful to loads of people using the internet. However, this effort has other problems, such as detecting and distinguishing catastrophe tweets from non-disaster tweets. Often the data available in twitter is not structured so processing is to be done on the data to classify data as ‘disaster’ and ‘non-disaster’. This paper deals with developing a model that can tell if a user is sharing data about a disaster. The data set used includes 10,000 tweets along with classifiers. This Optimized SVM model pre-processes the data using natural language processing (NLP) and then builds the classifier model that gives maximum accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Disasters

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Multiple-choice Question Generation for the Chinese language 14

Multiple-choice Question Generation for the Chinese Language

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14th international conference on knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2022

作者： Sun, Yicheng Chen, Hejia Wang, Jie Department of Computer Science University of Massachusetts LowellMA01854 United States School of Computer Science and Technology Xidian University Xi’an China

ISBN: (纸本)9789897586149

We present a method to generate multiple-choice questions (MCQs) from Chinese texts for factual, eventual, and causal answer keys. We first identify answer keys of these types using NLP tools and regular expressions. We then transform declarative sentences into interrogative sentences, and generate three distractors using geographic and aliased entity knowledge bases, Synonyms, HowNet, and word embeddings. We show that our method can generate adequate questions on three of the four reported cases that the SOTA model has failed. Moreover, on a dataset of 100 articles randomly selected from a Chinese Wikipedia data dump, our method generates a total of 3,126 MCQs. Three well-educated native Chinese speakers evaluate these MCQs and confirm that 76% of MCQs, 85% of question-answer paris, and 91% of questions are adequate and 96.5% of MCQs are acceptable. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： natural language processing systems

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