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Microservices Enterprise Architecture Design with AI-Powered OCR for Improving Fixed Broadband Complaint Handling: A Telco Company Case Study

Microservices Enterprise Architecture Design with AI-Powered...

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2025 international conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Puspadarma, Inka Sharatu Mukti, Iqbal Yulizar Ramadani, Luthfi School of Industrial Engineering Telkom University Department of Information System Bandung Indonesia

ISBN: (纸本)9798331513320

The rapid growth of internet users in Indonesia has increased the demand for efficient broadband services. This study proposes a microservices-based enterprise architecture integrating AI-powered Optical Character Recognition (OCR) and natural language processing (NLP) with the Integrated Customer Management System (ICMS) and knowledge Management System (KMS). The architecture addresses challenges arising from Fixed Mobile Convergence (FMC), which introduced complexities in customer service. Automating complaint categorization and solution retrieval through AIbased recommender systems improves accuracy, response times, and operational efficiency, supporting digital transformation goals. The current customer service processes rely heavily on manual intervention, causing inefficiencies like long wait times, especially on digital platforms. Leveraging TOGAF, this research designs a scalable architecture where OCR extracts data from unstructured sources, and NLP enables intelligent categorization and recommendations. Using Design Science Research Methodology, the study demonstrates the architecture's potential to enhance customer experience, streamline workflows, and optimize telco operations in a rapidly evolving technological landscape. © 2025 IEEE.

关键词： Efficiency

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Transfer Learning Approach to Multilabel Biomedical Literature Classification using Transformer Models 8

Transfer Learning Approach to Multilabel Biomedical Literatu...

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8th IEEE international conference for Convergence in Technology, I2CT 2023

作者： Thushari, Pahalage Dona Niazi, Sakina Meena, Shweta Delhi Technological University Department of Software Engineering New Delhi India

ISBN: (纸本)9798350334012

The recently developed transformer models have a significant influence on natural language processing (NLP) research. Transformer-based models have attained innovative results on numerous NLP benchmarks in linguistics. Researchers have also investigated transformer models for a variety of applications in the biomedical field. To facilitate information classification, this work aims to thoroughly investigate four state-of-the-art transformer-based models (BERT, Roberta, SciBERT, and ClinicalBERT). Large language model training, however, requires a lot of time and processing resources. As a result, pre-trained language models like BERT are advantageous because they (1) offer cutting-edge performance, (2) The knowledge gained can be used to do several tasks, such as categorization, and (3) free up practitioners from having to gather enough resources (including the hardware, data and time) to train models. However, pre-trained models possibly will perform poorly in some domains since they are generic. Here, we inspect the case of multilabel classification for biomedical Literature, which is a domain that has received relatively minor attention in the literature assessing pre-trained language models. Based on our study, using SciBERT as the baseline model gave the best results compared to the rest of the models with an 89.35% micro F1 score. In the context of classifying Biomedical Literature, domain-specific pre-trained models tend to perform better compared to generic models. © 2023 IEEE.

关键词： Feedforward neural networks

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Review on the Entity Extraction Methods for Low-resource languages 14

Review on the Entity Extraction Methods for Low-resource Lan...

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14th international conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Yilahun, Hankiz Hamdulla, Askar Institute of Information Science and Engineering Xinjiang University Urumqi830046 China

ISBN: (纸本)9781665499781

With the economy developing as one of the key technologies of the construction knowledge graph, entity extraction has attracted more and more researchers' attention. To understand the latest developments, this paper introduced the emergence and development of entity extraction firstly;Then, the related methods are systematically sorted out and summarized. Finally, explained the current improved methods of entity extraction that are researched in low-resource language in natural language processing. © 2022 IEEE

关键词： natural language processing systems

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Hierarchical Network with Decoupled knowledge Distillation for Speech Emotion Recognition 48

Hierarchical Network with Decoupled Knowledge Distillation f...

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48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhao, Ziping Wang, Huan Wang, Haishuai Schuller, Bjorn Tianjin Normal University College of Computer and Information Engineering Tianjin China Zhejiang University College of Computer Science China University of Augsburg Embedded Intelligence for Health Care and Wellbeing Germany Imperial College London Glam - Group on Language Audio & Music United Kingdom

ISBN: (纸本)9781728163277

The goal of Speech Emotion Recognition (SER) is to enable computers to recognize the emotion category of a given utterance in the same way that humans do. The accuracy of SER is strongly dependent on the validity of the utterance-level representation obtained by the model. Nevertheless, the "dark knowledge"carried by non-target classes is always ignored by previous studies. In this paper, we propose a hierarchical network, called DKDFMH, which employs decoupled knowledge distillation in a deep convolutional neural network with a fused multi-head attention mechanism. Our approach applies logit distillation to obtain higher-level semantic features from different scales of attention sets and delve into the knowledge carried by non-target classes, thus guiding the model to focus more on the differences between sentiment features. To validate the effectiveness of our model, we conducted experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. We achieved competitive performance, with 79.1 % weighted accuracy (WA) and 77.1 % unweighted accuracy (UA). To the best of our knowledge, this is the first time since 2015 that logit distillation has been returned to state-of-the-art status. © 2023 IEEE.

关键词： decoupled knowledge distillation multi-head attention speech emotion recognition

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Leveraging Deep Learning in Implementing Efficient Healthcare Processes 15

Leveraging Deep Learning in Implementing Efficient Healthcar...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nomula, Varun Kumar Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Dhanasekaran, S. Department of Analytics/Industrial & Systems Engineering Georgia Institue of Technology United States Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky United States Department of Information Technology University of the Cumberlands Kentucky United States Department of Electronics and Communication engineering Sri Eshwar College of engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

deep learning is a quick-developing subfield of machine studying. It has made big improvements in diverse areas consisting of computer imagination and prescient, natural language processing, and self-reliant driving. Recently, it has additionally been used to improve healthcare strategies, and this has caused greater efficient and price-effective remedies for conditions and diseases. For instance, deep getting to know has been efficiently used to classify medical photographs, together with X-rays and CAT scans, and hit upon diseases. It can also be used to expect affected person results and examine large datasets of scientific statistics and different clinical statistics. In addition, deep learning is being used to improve the accuracy and speed of diagnosis, prevent medical events, and recommend remedies. It has additionally been used to broaden personalized medicinal drug models by gaining knowledge of affected persons' clinical records and other health-related statistics. Deep getting to know can, as a result, be used to enhance and automate healthcare strategies, main to higher care, and step forward affected person outcomes. ©2024 IEEE.

关键词： Deep reinforcement learning

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PFMRC: A Unified MRC Framework for Chinese Named Entity Recognition via Semantic Feature Fusion 4

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Deng, Zhenrong Zhang, Jinglin Xiao, Yannan Wei, Shiwei Guilin University of Electronic Technology Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798331531225

The landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. The integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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HPV Vaccine Hesitancy in Rural America and Exploring Artificial-Intelligence Interventions 58

HPV Vaccine Hesitancy in Rural America and Exploring Artific...

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58th Hawaii international conference on System Sciences, HICSS 2025

作者： Argyris, Young Anna Nelson, Victoria R. Kwon, Kyungeun Kim, Yongsuk Tan, Pang-Ning Lehto, Georgia Department of Media and Information Michigan State University United States Department of Advertising and Public Relations Michgan State University United States Kookmin University Korea Republic of Department of FinTech Sungkyunkwan University Korea Republic of Department of Computer Science and Engineering Michigan State University United States College of Human Medicine Michigan State University United States

ISBN: (纸本)9780998133188

This study illuminates human papillomavirus (HPV) vaccine hesitancy in rural America and explores the potential of using chatbot-enabled social media interventions. The results from our nationally representative survey among US parents of adolescents (n = 1,196) show the differences in HPV vaccine uptake between rural, suburban, and urban areas. Our results indicate that rural parents' hesitancy in initializing or continuing the HPV vaccine regimen with their children appears to be a result of a lack of knowledge about the necessity, safety, and efficacy of the vaccine. Chatbots with natural language processing abilities are proposed to educate rural parents about the preventive benefits of HPV vaccines and alleviate their hesitancy. With the increasing broadband Internet connectivity in rural communities, chatbots can create effective and economical interventions. The opportunities created by chatbots, their integration into the existing clinical workflow for vaccine deliveries, and their benefits to healthcare providers are discussed. © 2025 IEEE Computer Society. All rights reserved.

关键词： natural language processing systems

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Construction and Application of knowledge Map of French Corpus Based on Graph Embedding Algorithm

Construction and Application of Knowledge Map of French Corp...

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Electrical Drives, Power Electronics & engineering (EDPEE), international conference on

作者： Yuanyuan Song Xi'an Fanyi University Xi'an Shaanxi China

ISBN: (数字)9798350395631

ISBN: (纸本)9798350395648

In today's information age, natural language processing and semantic analysis technology are more and more widely used in various fields. As one of the most widely used languages in the world, the construction and application of French corpus is of great significance to the research in linguistics, computer science and other fields. However, there are still some challenges in the construction and application of knowledge map for French corpus. Therefore, this paper aims to combine graph embedding algorithm to construct a knowledge map of French corpus and explore its application in natural language processing and semantic analysis. Finally, the effectiveness of the proposed model is verified by experiments, and empirical analysis is carried out in application scenarios such as semantic search and intelligent question answering. The experimental results show that the knowledge map of French corpus based on graph embedding algorithm has obvious advantages in semantic representation and relationship mining, which provides strong support for natural language processing and semantic analysis.

关键词： Computer science Analytical models Semantic search Semantics Linguistics Information age natural language processing

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A Study of Cyber Bullying Classification Using Social Media and Texual Analysis Based on Machine Learning Approches 14

A Study of Cyber Bullying Classification Using Social Media ...

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rahman Aronno, Md. Shafiur Zumma, Md.Thoufiq Prodhan, Rashed Zohora, Fatema Tuz Sakib, Nazmus Tahmiduzzaman, K.B.M. Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Wilmington University Department of Information Systems Technologies DE United States Bangladesh University of Business &technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

In today's world, cyberbullying is a problem that is becoming more and more common, especially among teenagers and young people. The prevalence of social networking sites and other digital communication tools has made it simpler for offenders to harass their victims in secret and without repercussions. natural language processing (NLP) methods have been used in recent years to categorize instances of cyberbullying and assist identify them. The language used in online conversations is examined using these approaches to look for trends and signs of cyberbullying behavior. The purpose of this study is to investigate how well NLP approaches can be used to recognize and categorize cyberbullying behavior. To provide a thorough knowledge of the many types of cyberbullying, the study will use a variety of data sources, including social media posts, chat logs, and other online conversations. Overall, this research will further our knowledge of the intricate nature of cyberbullying and shed light on the potential applications of NLP approaches to lessen its negative impacts. © 2023 IEEE.

关键词： Digital communication systems

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Examining ChatGPT's Impact: Challenges, Opportunities, and Future Directions for Enhancing Educational Experiences 3rd

Examining ChatGPT's Impact: Challenges, Opportunities, and F...

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Ravindar, B. Ramachandran, K.K. Bansod, Suyash Premendra Sheena Joy, Varghese Sudheer Muhammad, K.M. School of Sciences SR University Telangana Warangal India Jesselton University College Jalan Lintas Sabah Kota Kinabalu88300 Malaysia Computer Science & Engineering Indian Institute of Information Technology Pune India School of Management National Institute of Technology Karnataka Surathkal Mangalore India Department of Commerce Rajagiri College of Social Sciences Kerala Cochin India VIT Business School Vellore Institute of Technology - VIT Chennai Tamilnadu Chennai India

ISBN: (纸本)9789819727155

ChatGPT, a natural language processing giant whose generation goes as GPT series, has caught the eye of educators because it can converse in language understood by humans and because of its skills to infer meaning. The research results evince that introduction of ChatGPT disrupts the mechanism of people getting access to the data in its nature. It has substantially diminished obstacles to joint work among multiple disciplines and information has never been so accessible before. This is the dawn of a different era of Digital Age that probably has the power to reconstruct people's lives. It will turn out that ChatGPT will be one of the factors that will change the face of education too. It can be by cutting and sowing between the dangers and positive sides of technology, so that education survives and prospers. This paper offers a summarizing approach of the major research outlined in order to determine the working fundamentals of ChatGPT, its pedagogical ramifications, and the predictions of its future usage. This text is meant for educators and academics who want more knowledge about the consequences and possible uses of ChatGPT for education;they will gather some helpful information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： natural language processing systems

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