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Digitizing Old Nubian Dictionary: Optical Character Recognition for Multi-Lingual and Multi-Script Text from Medieval Africa

Digitizing Old Nubian Dictionary: Optical Character Recognit...

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international conference on information Science and technology (ICIST)

作者： So Miyagawa Research Department National Insitute for Japanese Language and Linguistics (NINJAL) Tokyo Japan

this paper explored the application of the Transkribus software, a Handwritten Text Recognition (HTR) and Optical Character Recognition (OCR) tool, to multi-lingual and multi-script text from Medieval Nubia in Africa. Transkribus leverages machine learning algorithms using Recurrent Neural Networks (RNNs) to enhance accuracy, specifically targeting mixed publications in Old Nubian, Coptic, Greek, Arabic, and English. the output of the Transkribus processing facilitates the analysis, search, and translation of historical documents, ensuring their preservation and accessibility for future researchers and scholars worldwide. We aim to underscore the growing importance of accurate OCR technology in safeguarding and disseminating invaluable historical resources while highlighting Transkribus’s role in this process.

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Experimental Disease Prediction Research on Combining Natural language processing and Machine Learning

Experimental Disease Prediction Research on Combining Natura...

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international conference on Computer Science and Network technology (ICCSNT)

作者： Hong Qing Yu School of Computer Science and Technolgy University of Bedfordshire Luton United Kingdom

Nowadays Artificial Intelligent (AI) technologies are applied widely in many different areas to assist knowledge gaining and decision-making tasks. Especially, health information system can get most benefits from the AI advantages. In particular, symptoms based disease prediction research and production became increasingly popular in the healthcare sector recently. Various researchers and organizations have turned their interest in using modern computational techniques to analyze and develop new approaches that can efficiently predict diseases with reasonable accuracy. In this paper, we propose a framework to evaluate the efficiency of applying both Machine Learning (ML) and Nature language processing (NLP) technologies for disease prediction system. As an example, we scraped a disease- symptom dataset with NLP features from one of the UK most trustable National Health Service (NHS) website. In addition, we will exam our data in depth having symptom frequency, similarity and clustering analysis. As result, we can see that the prediction can have a very positive efficient rate but still open issues need to be addressed.

关键词： Diseases Prediction algorithms Machine learning algorithms Classification algorithms Data mining Support vector machines Machine learning

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Praise or Insult? Identifying Cyberbullying Using Natural language processing

Praise or Insult? Identifying Cyberbullying Using Natural La...

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international Multi-Topic ICT conference (IMTIC)

作者： Nimra Tariq Zafi Sherhan Syed Erum Saba Department of Information Technology Mehran University of Engineering and Technology Jamshoro Pakistan Department of Telecommunication Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Information Technology Centre Sindh Agricultural University Tandojam Pakistan

Cyberbullying has become a prevalent issue in the current era of technology. the prevalence of such content on online platforms not only perpetuates harmful behavior but also poses a threat to online communities and individuals' mental health. To address this issue there is a growing need for automated systems to detect toxic comments on social media. In this work, our focus is on utilizing natural language processing (NLP) techniques to distinguish between praise and insult in online comments on Reddit, a popular social media platform. In particular, we first benchmarked the classification performance using Term-Frequency Inverse Document Frequency (TF-IDF) features and then employed two different deep-learning-based approaches, Keras Embedding, and Global vectors for word representation (GloVe). Our results show that whereas the TF-IDF features achieve an accuracy of 83%, GloVe achieves an accuracy of 89%, but Keras Embeddings achieved an accuracy of 93%. Although these results are relatively poorer than the baseline classification accuracy of 97.8%, the size of our proposed solution is around 28 MB compared to 340 MB of the baseline model XLNet model, thereby making our solution more viable for real-time applications. these results suggest that there is ample opportunity for further research and development in this area, with the potential to make a real difference in the fight against cyberbullying.

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Design and Optimization of an Intelligent English Interaction System Based on Speech Recognition and Natural language processing technology

Design and Optimization of an Intelligent English Interactio...

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Education, Network and information technology (ICENIT), international conference on

作者： Bingbing Wang Jilin Animation Institute Changchun China

ISBN: (数字)9798350374919

ISBN: (纸本)9798350374926

With the rapid development of globalization, traditional learning methods have significant limitations in speaking practice and struggle to meet the personalized needs of learners. Innovative educational tools are needed to enhance students' speaking skills. this paper proposes a design and optimization plan for an intelligent English interactive system based on speech recognition and natural language processing technologies. It explores the adaptability of speech recognition technology under different accents, speech rates, and noise environments. By optimizing deep learning models, the accuracy of recognition is further improved. Meanwhile, by integrating natural language processing technology, the system understands user intentions and generates natural responses, enhancing the fluency of human-computer dialogue. In the system design, a modular architecture is adopted to facilitate later optimization, implementing core functional modules for speech recognition, natural language understanding, dialogue management, natural language generation, and speech synthesis. Experimental validation shows that the optimized system significantly improves recognition accuracy in multiple scenarios, performing particularly well in processing movie dialogues and everyday conversations. the research results indicate that the system designed in this paper not only enhances the English learning experience but also provides an effective technical path for the development of intelligent interactive systems.

关键词： Accuracy Interactive systems Noise Speech recognition Oral communication Motion pictures Natural language processing Stability analysis Optimization System analysis and design

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When textual information becomes spatial information compatible with satellite images

When textual information becomes spatial information compati...

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international Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)

作者： Eric Kergosien Hugo Alatrista-Salas Mauro Gaio Fábio N. Güttler Mathieu Roche Maguelonne Teisseire GERiiCO Univ. Lille Lille France Pontificia Universidad Católica del Perú Lima Peru LIUPPA Univ Pau Pau France ICube Univ. Strasbourg Strasbourg France UMR TETIS & LIRMM Montpellier France

ISBN: (纸本)9781509019670

With the amount of textual data available on the web, new methodologies of knowledge extraction domain are provided. Some original methods allow the users to combine different types of data in order to extract relevant information. In this context, we present the cornerstone of manipulations on textual documents and their preparation for extracting compatible spatial information with those contained in satellite images. the term footprint is defined and its extraction is performed. In this paper, we describe the general process and some experiments conducted in the ANIMITEX project, which aims to match the information coming from texts with those of satellite images.

关键词： Satellites Planning Remote sensing Natural language processing Context Feature extraction Roads

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Attribute Based Service Customization and Selection

Attribute Based Service Customization and Selection

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IEEE international conference on Service-Oriented Computing and Applications (SOCA)

作者： Jun Yan Hao Gao Yi Mu School of Information Systems and Technology University of Wollongong Wollongong Australia School of Computer Science and Software Engineering University of Wollongong Wollongong Australia

ISBN: (纸本)9781479968343

In a highly competitive global service market, service consumers tend to select a provider from a large pool of functionally equivalent services based on Quality of Service (QoS). To gain competitive advantages, it is desirable for a service provider to adopt service customization, particularly QoS customization, as one of the key strategies to differentiate itself from the competitors. this, however, has raised new challenges in describing and publishing QoS information, identifying users, and adjusting QoS offerings. this research borrows the concept of attribute based access control and presents an attribute based service customization model. the attribute based access control policy language is extended to enable definition of QoS customization rules. An algorithm is proposed to support consumer identification and QoS customization. this paper also discusses a case study to illustrate the proposed approach.

关键词： Quality of service Access control Context Educational institutions web services Semantics

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Integrating Artificial Intelligence and Natural language processing in E-Learning Platforms: A Review of Opportunities and Limitations

Integrating Artificial Intelligence and Natural Language Pro...

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international conference on information Science and technology (ICIST)

作者： Mouna Kaouni Fatima Lakrami Ouidad Labouidya Departement of Physics Laboratory of Sciences and Technologies of Information and Communication Faculty of Science Chouaib Doukkali University El Jadida Morocco

In recent decades, the world has witnessed an explosion of digitalization in various sectors, notably in education. Online learning platforms such as Moodle, Canvas, Claroline, Open edX, and Blackboard, have made significant strides in improving the learning process by enhancing flexibility and efficiency. Nevertheless, these tools do not fully meet the needs of learners and educators, primarily due to the static design and presentation of pedagogical content. Recent advances in Artificial Intelligence (AI) and Natural language processing (NLP) present promising solutions to adapt content, navigation, and presentation in a more diverse and personalized manner for each learner. these advancements involve the deployment of techniques such as text analysis, automatic content translation, text classification, sentiment analysis, voice recognition, and recommendation systems. Consequently, this work aims to reveal the contributions and challenges of AI devices in education, focusing on the importance of using NLP in educational platforms to improve and adapt them to the needs of educators in content design and those of learners in terms of navigation and learning dynamics.

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Towards GPU Accelerated Computation for Stock Prediction Based on News Headlines 7th

Towards GPU Accelerated Computation for Stock Prediction Bas...

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7th international conference on information and Communication technology for Competitive Strategies , ICTCS 2022

作者： Sarkar, Anish Bera, Riddhirup Bhattacharyya, Soham Sriraman, Harini Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9789811996375

the stock market movement has the potential to tell the future worth of a company’s stock. the foresight of a stock’s future price is very crucial as it could yield significant profit. this paper has compared different modern machine learning models for predicting the direction of stock movement of a company for a given date. there has been the use of models that do not involve neural networks for training, as well as those that do, and their performance have been compared. It was found that neural networks by far bested the other methods in terms of validation accuracy, but not at execution time, despite the use of parallelism in both varieties of models. On the other hand, this paper has also explored the prospect of parallel web scraping and its variation of speed when parallelized on a variable number of cores, as well as the extraction of the sentiment of scraped data using pre-trained NLP modules, done in parallel. the authors of this paper tried to utilise the GPU environment provided, and successfully parallelised a subset of the models on the said environment. In this paper, we have separately trained models and scraped news sources from the Internet with the vision to marry the two and predict real-time stock movement based on news articles. Further, this proposed work has explored the prospect of applying the best model for prediction in a rudimentary demonstration of the same, in a real-world scenario. the ‘best model’ here refers to the model that produced the highest validation accuracy irrespective of whether parallelization was applied or not, and a very promising model based on BERT qualified for this, which produced a validation accuracy of 99.08%, and because the GPU acceleration reduced the training time from more than 6 h to less than 20 min. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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the Implementation of Natural language processing on Chatbot as Academic information System: A Case Study of IC2IE 2024

The Implementation of Natural Language Processing on Chatbot...

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international conference of Computer and Informatics Engineering (IC2IE)

作者： Christian Nataniel Yosua Purba Pratama Varian Andika Parulian Ratna Widya Iswara Ariawan Andi Suhandana Risna Sari Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

the implementation of Natural language processing (NLP) in chatbots has significantly enhanced the efficiency and user experience of academic information systems. this paper presents a case study of the international conference on Computing, Communication, and information Engineering (IC2IE) 2024, exploring how NLP-driven chatbot can serve as a robust academic information system. By employing advanced NLP techniques, the chatbot effectively understands and processes user inquiries, providing accurate and timely information about the conference schedules, speakers, submission guidelines, etc. Accuracy testing demonstrated an overall performance of 84.533%, with perfect accuracy in the 0.75-1.0 similarity range and 96.15% accuracy in the 0.5-0.74 range. the findings suggest that integrating NLP in academic information systems can streamline operations and enhance communication within academic events.

关键词： Schedules Accuracy Databases Education conference management Chatbots User experience Informatics Testing Guidelines

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CAVI: A Contrastive Image Captioning Framework with the Contributions of All Visual information

CAVI: A Contrastive Image Captioning Framework with the Cont...

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international conference on Signal processing Proceedings (ICSP)

作者： Ziwei Shang Gaoyun An Shan Cao Institute of Information Science Beijing Jiaotong University Beijing China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China College of Information Engineering Capital Normal University Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Image captioning is a multimodal task combining computer vision (CV) and natural language processing (NLP). Contrastive language image pre-training has made significant progress by providing effective multimodal representation. In this paper, we propose a contrastive learning-based perceptual image captioning model that utilizes all visual *** make improvements in visual encoder, mainly reflected in first storing the visual information encoded by each layer of encoder, and obtaining aligned visual input by averaging to avoid the loss of key visual *** results show that CAVI improves the ability to describe the relationship between image objects and enhances the quality of generated descriptions. It performs well on the relevant indicators of the Flickr dataset, and also demonstrates certain cross-domain description capabilities on the COCO dataset as a validation set.

关键词： Visualization Computer vision Image databases Computational modeling Contrastive learning Signal processing Natural language processing Decoding web sites Multimedia communication

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