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Research on Improving Rural Tourism Experience Model Using natural language processing Technology

Research on Improving Rural Tourism Experience Model Using N...

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Electronics, Energy Systems and Power engineering (EESPE), ieee international conference on

作者： Shifeng Huang School of Economics and Management Guangzhou Huaxia Vocational College Guangzhou China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper proposes a set of algorithm frameworks based on natural language processing (NLP) technology, aiming to improve the accuracy and adaptability of rural tourism experience models. This algorithm framework is mainly used for tasks such as sentiment analysis, topic modeling and trend prediction. By deeply mining tourist feedback data, a rural tourism experience model that can accurately reflect tourists' subjective feelings and changes in demand is constructed. In terms of sentiment analysis, this paper combines the sentiment dictionary method with the latent Dirichlet allocation (LDA) model to analyze the sentiment tendency of tourists' text feedback, thereby revealing tourists' emotional fluctuations and preference trends. This paper also introduces a data fusion method to integrate data from multiple sources to reduce the interference of noise on the results and improve the reliability of prediction. The simulation results show that the rural tourism experience model based on NLP technology can effectively identify changes in tourist needs in practical applications, adjust tourism services and facilities in a timely manner, and thus improving the overall satisfaction of tourists. Specific experimental data show that the experience design after model optimization has increased tourist satisfaction by about 15% compared with the traditional method. Through real-time adjustment of the algorithm and personalized recommendation, the overall experience of tourists has been significantly improved.

关键词： Analytical models Sentiment analysis Adaptation models Accuracy Simulation Prediction algorithms Market research Data models Resource management Sustainable development

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Blind Modulation Recognition of UWA Signals with Semi-Supervised Learning

Blind Modulation Recognition of UWA Signals with Semi-Superv...

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2022 ieee international conference on Signal processing, Communications and Computing, ICSPCC 2022

作者： Xu, Zehong Wu, Xiurong Gao, Daqing Su, Wei Xiamen University Dept. of Information and Communication Engineering Xiamen China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

Blind modulation recognition is one of the most important component of the intelligent communication. Recent years, supervised learning is proved to be an effective way to solve this problem. But in time space varying underwater acoustic (UWA) channels, high quality labeled data is very difficult to obtain. To solve this problem, we put forward a semi-supervised learning-based blind modulation recognition scheme SSLUWA for UWA signals. This scheme linearly interpolates the unlabeled signal as fake label, and trains it by interpolation consistency. It can test the knowledge learned from the labeled signals by the accuracy of training on unlabeled signals, to improve the recognition accuracy when the labeled signals are insufficient. In order to verify the performance of SSLUMA, extensive pool and simulation experiments have been conducted. The results show that, compared with 100% label samples, the recognition accuracy of the SSLUWA decreases slightly at low SNR when the label samples are only 10%. © 2022 ieee.

关键词： Underwater acoustic communication

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PersianRAG: A Retrieval-Augmented Generation System for Persian language

PersianRAG: A Retrieval-Augmented Generation System for Pers...

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conference on Information and knowledge Technology

作者： Hossein Hosseini Mohammad Sobhan Zare Amir Hossein Mohammadi Arefeh Kazemi Zahra Zojaji Mohammad Ali Nematbakhsh Department of software engineering University of Isfahan Isfahan Iran ADAPT Research Centre Dublin City University Dublin Ireland Department of software engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (数字)9798331522254

ISBN: (纸本)9798331522261

Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) tasks. However, applying RAG models in Persian language as a low-resource language, poses distinct challenges. These challenges primarily involve the preprocessing, embedding, retrieval, prompt construction, language modeling, and response evaluation of the system. In this paper, we address the challenges towards implementing a real-world RAG system for Persian language called PersianRAG. We propose novel solutions to overcome these obstacles and evaluate our approach using several Persian benchmark datasets. Our experimental results demonstrate the capability of the PersianRAG framework to enhance question answering task in Persian.

关键词： Analytical models Computational modeling Retrieval augmented generation Benchmark testing Hyperparameter optimization Question answering (information retrieval) Data models Prompt engineering Optimization

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Artificial Intelligence and natural language processing for Quality Control and Management

Artificial Intelligence and Natural Language Processing for ...

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ieee international conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Haiyan Sally Xie Sai Ram Gandla Mangolika Bhattacharya Pranshoo Solanki Dingnan Zheng Department of Technology Illinois State University Normal IL USA Illinois State University Normal IL USA University of Illinois Urbana-Champaign Champaign IL USA

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

Established engineering standards are facing challenges in adapting to the evolution of novel material properties, designing entirely new materials, and uncovering new mechanisms that transcend intuitive understanding. This research aims to investigate a new framework of domain-specific language models to automatically generate feasible engineering designs based on requirements. The work is anchored in the understanding of natural language processing and the fidelity requirements of machine-learning models for the civil and construction engineering domain. Relevant reports from the Transportation Research Information Service and standard specifications from various departments of transportation were included in the data collection. Another dataset was the summarized literature of 36 highway agencies. After the comparison of Support Vector Machines (SVM), Long Short-Term Memory network, and linear regression, the SVM algorithm was implemented in the framework to support decision-making through textual and tabular communication, and the results showed improvements in accuracy (130%) and F1 Score (65%). In this research, utilizing case-based content extraction alongside reliable statistical uncertainty estimation has shown the potential to generate valuable decision-support tools and recommendation systems for both engineers and managers. The applications encompassing textual content extraction alongside reliable statistical uncertainty estimation demonstrate the potential to create valuable decision-support tools and recommendation systems for engineers and managers.

关键词： Support vector machines Road transportation Uncertainty Transportation Estimation Virtual environments Reliability engineering

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Bebert: Efficient And Robust Binary Ensemble Bert

Bebert: Efficient And Robust Binary Ensemble Bert

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

作者： Jiayi Tian Chao Fang Haonan Wang Zhongfeng Wang Nanjing University Nanjing China University of Southern California Los Angeles USA

Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks. However, their excessive amount of parameters hinders them from efficient deployment on edge devices. Binarization of the BERT models can significantly alleviate this issue but comes with a severe accuracy drop compared with their full-precision counterparts. In this paper, we propose an efficient and robust binary ensemble BERT (BEBERT) to bridge the accuracy gap. To the best of our knowledge, this is the first work employing ensemble techniques on binary BERTs, yielding BEBERT, which achieves superior accuracy while retaining computational efficiency. Furthermore, we remove the knowledge distillation procedures during ensemble to speed up the training process without compromising accuracy. Experimental results on the GLUE benchmark show that the proposed BEBERT significantly outperforms the existing binary BERT models in accuracy and robustness with a 2× speedup on training time. Moreover, our BEBERT has only a negligible accuracy loss of 0.3% compared to the full-precision baseline while saving 15× and 13× in FLOPs and model size, respectively. In addition, BEBERT also outperforms other compressed BERTs in accuracy by up to 6.7%.

关键词： Training Computational modeling Bit error rate Benchmark testing Signal processing Robustness natural language processing

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Enhancing Document Summarization with Machine Learning Approaches for Single and Multiple Documents 8th

Enhancing Document Summarization with Machine Learning Appro...

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8th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Deshmukh, Gopal B. Takale, Dattatray G. Gawali, Piyush P. Kashid, Shraddha S. Mahalle, Parikshit N. Sule, Bipin Patil, Sambhaji A. Lahve, Ramesh Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune Pune India Department of Computer Engineering MIT ADT University Pune India Department of AI & DS Vishwakarma Institute of Information Technology SPPU Pune Pune India and Humanities Vishwakarma Institute of Technology Pune India E&TC PGM College of Engineering Pune Wagholi India Department of Information Technology Anantrao Pawar COE Pune SPPU Pune Pune India

ISBN: (纸本)9789819766802

The purpose of this research paper is to enhance the effectiveness of document summarization by integrating advanced deep learning methodologies into the process. In this study, neural networks, attention mechanisms, transformer-based models, and attention mechanisms are investigated in light of both single and multiple document scenarios. This paper provides valuable insights into how well these approaches work in producing concise and informative summaries by conducting comprehensive experiments and evaluations. As part of this paper, an innovative approach is introduced to document summarization by combining the strengths of Ensemble RNNs and Long Short-Term Memory (LSTM) networks. Having to summarize multiple documents at the same time is challenging. A nuanced approach is required to accomplish this task. The proposed method utilizes the power of RNN-LSTM networks in order to capture long-range dependencies and contextual information and then enhances this data by utilizing Ensemble techniques so that summarizing performance is improved. To demonstrate our approach's effectiveness, we conduct comprehensive experiments and evaluations on a variety of datasets. According to the results of this study, the Ensemble LSTM-based hybrid summarization technique achieves superior results when compared to individual LSTM models, setting a new standard in document summarization for single or multiple documents. In addition to contributing significantly to the advancement of document summarization, this research provides valuable insights for practical applications in information retrieval and knowledge extraction that could be applied in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： natural language processing systems

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Securing Against Deception: Exploring Phishing Emails Through ChatGPT and Sentiment Analysis

Securing Against Deception: Exploring Phishing Emails Throug...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Shahrzad Sayyafzadeh Mark Weatherspoon Jie Yan Hongmei Chi Dept. of Comp. & Info Sciences Florida A&M University Tallahassee FL FAMU-FSU College of Engineering Tallahassee FL Dept. of Computer Science Bowie State University Bowie Maryland USA

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

The origin of large language models (LLMs) has left an indelible mark on various domains, most notably in natural language processing and artificial intelligence. While extensive research has delved into LLMs like ChatGPT 4 for tasks such as code generation and text synthesis, their application in identifying malicious web content, particularly phishing web sites, still needs to be explored. To confront the growing wave of automated cyber threats facilitated by LLMs, automating the detection of malicious email content. This paper investigates the utilization of natural language processing (NLP) techniques, including VADER (valence aware dictionary and sentiment Reasoner) sentiment analysis and Large language Models (LLMs) to strengthen the detection of phishing emails, offering enhanced defense mechanisms against cyber threats by analyzing the diverse attributes of phishing emails, including sender details, URLs, textual content, and linguistic characteristics. In this study, we leverage the power of ChatGPT's 4 state-of-the-art language models and employ natural language processing (NLP) techniques. We develop an intelligent system that identifies and flags potential phishing attempts in spam emails and URLs. Our experiments using GPT-4 on our dataset demonstrated promising results, achieving an accuracy of 92%. These findings underscore LLMs' potential, including VADER sentiment analysis, to detect rapidly and accurately phishing emails, safeguarding cybersecurity challenges and protecting users from online fraud and phishing attempts.

关键词： Sentiment analysis Phishing Large language models Unsolicited e-mail Linguistics Chatbots Electronic mail

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Social Media Event Summarization using Neural Networks 3

Social Media Event Summarization using Neural Networks

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3rd international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Yadala, Sucharitha Gurram, Anitha Yamunadevi, S. Veeresh, U. Latha, S Bhargavi Chitti, T.N. VNR Vignana Jyothi Institute of Engineering and Technology Computer Science and Engineering Hyderabad India CMR Institute of Technology Computer Science and Engineering Hyderabad India Panimalar Engineering College Computer Science and Engineering Tamilnadu India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9781665456647

Micro blogging sites have become extremely popular, with a huge number of tweets being posted daily on a broad assortment of subjects. Recent studies have exposed that a significant part of these tweets are regarding 'events', and the discovery of new events in the tweet-stream has pulled in a lot of attention. In the natural language processing (NLP) field, automatic text summarization is viewed as an extremely tricky problem. The content substance on the internet, specifically, is rising at an outstanding level. The capacity to decode through such immense volumes of data, to facilitate mine the require knowledge, is a foremost undertaking and needs an automatic system to serve with the present warehouse of data. A decent text summarization framework should comprehend the entire content, reorganize information, and create coherent, useful and amazingly tiny summaries to pass on the significant information of the innovative content. In this article, an inventive content summarization framework has been built, which combines RNN with LSTM and encoder-decoder structure. Our framework is assessed on the LCSTS dataset, which is an excellent corpus of Chinese short content summarization dataset created from 'SinaWeibo'. Empirical outcomes exhibit the adequacy of the proposed framework, which outperforms all the existing approaches. Particularly, the proposed framework enhances the semantic consistency by 5% in terms of human assessment. © 2022 ieee.

关键词： Semantics

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PNetGPT: Proprietary Protocol Network Traffic Generation with Pre-trained Transformer

PNetGPT: Proprietary Protocol Network Traffic Generation wit...

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

作者： Zedong Li Shijie Li Dongliang Fang Xin Chen Zhanwei Song Zhi Li Shichao Lv Limin Sun Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative pre-trained transformers are exceedingly effective as generative models and classifiers, widely used in natural language processing and computer vision. This work contributes to the exploration of generative pre-trained transformer-based models in the proprietary protocol network traffic. However, building a pre-trained model for proprietary protocol network traffic is non-trivial due to the heterogeneous unknown formats and the extreme scarcity of proprietary protocol network traffic datasets. In this paper, we present PNetGPT, a pre-trained transformer-based model for generating proprietary protocol network traffic. We have constructed the inaugural dataset of 2 real-world proprietary protocols. After training on this dataset, PNetGPT possesses the capacity to generate high-quality proprietary protocol network traffic to support various applications of proprietary protocols, including reverse analysis, protocol fuzzy testing, intrusion detection, etc. We evaluated PNetGPT with two real proprietary protocols and demonstrated state-of-the-art (SOTA) performance in handling heterogeneous unknown formats. The code and datasets are available at: https://***/Snail1502/PNetGPT

关键词： Training Protocols Computational modeling Scalability Intrusion detection Telecommunication traffic Signal processing Transformers natural language processing Speech processing

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natural language processing in Healthcare: Intelligent System for Extracting Insights from Clinical Text data

Natural Language Processing in Healthcare: Intelligent Syste...

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2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Rashmi, C. Srinivas, Dava Prabu, S. Santosh Kumar, P.N. Siddiqua, Ayesha Malla Reddy Engineering College for Women It Department Hyderabad India Jyothishmathi Institute of Technology & Science Department of Computer Science and Engineeing Karimnagar India Mahendra Institute of Technology Department of Ece Namakkal India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The World Health Organization identified COVID-19 to be a new pandemic in March 2020. This fatal virus spread over the world, affecting many different countries. Massive volumes of useful data were provided by social media platforms like Twitter on the outbreak to help evaluate health-related decision-making. To predict disease occurrence and issue early warnings, thus implement that user's attitudes be evaluated through the use of efficient deep learning. The gathered tweets were pre-processed and divided into three categories like neutral, positive, and negative. In the second step, various features were extracted from the articles using a number of popular techniques to capture feature dataset, including TF-IDF, FastText, and Glove. The implemented TFIDF has combination of semantic features (Glove and FAsText) to accurately describe posts and enhance the classification process utilizing the deep learning method. The implemented TF-IDF-CNN method's performance is evaluated using Twitter dataset. Implemented method achieved better performance than the existing intrusion detection methods including NLP-BiLSTM, TF-IDF, SVM and CF and the metrics are 96% of accuracy, 0.89 of f-measure, 0.93 of precision, and 0.90 of recall. © 2023 ieee.

关键词： COVID-19 Deep learning Feature extraction Sentiment analysis Twitter

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