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Augmenting Human Decision-Making: Exploring AI-Driven Automation in IT Operations (AIOps) for Enhanced Efficiency and Innovation 4

Augmenting Human Decision-Making: Exploring AI-Driven Automa...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Lakshmi Kumar, Tukkisetty Vasantha Naresh, Tippa Ganesh, Chunduru Saran Varma, Chekuri Tarun Raju, K.V. Hema, Chindu Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798331523923

This article analyzes how artificial intelligence (AI) has changed the IT industry, ushering in an age of unmatched technical growth and new potential for human-AI communication. Through a detailed research, it explores the complex dynamics of AI integration in IT operations, examining the likelihood that AI may supplant people in the workforce and highlighting the complexities and challenges that accompany this paradigm change. By examining present AI technologies, such as machine learning, deep learning, natural language processing, and data science, and emphasizing both the benefits and imminent obstacles, this article discusses the complex environment of AI adoption in IT. It also digs thoroughly into the legal frameworks, social repercussions, and ethical challenges that must be overcome in order to govern the AI-driven revolution of the IT industry. Through this research, the article wants to peacefully cohabit human knowledge with AI talents, opening up new paths for creativity, productivity, and sustainable development in the digital age. © 2025 IEEE.

关键词： Adversarial machine learning

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Research on the Application of AI in Personalized Education

Research on the Application of AI in Personalized Education

引用

2023 international conference on Information Education and Artificial Intelligence, ICIEAI 2023

作者： Zhu, Guibin Zhao, Bo Tang, Jianbo Communication Nco Academy Army Engineering University Chongqing China

ISBN: (纸本)9798400716157

Smart education uses advanced information technology, combined with educational theories and teaching methods, to achieve automatic, intelligent, and efficient teaching process. Personalized education represents the core content and goal of smart education because traditional classroom or remote teaching can't accurately grasp every student's individual knowledge and understanding of the content being taught. The development of artificial intelligence technology has provided technical support for smart education, particularly for personalized education. natural language processing models such as chatGPT and knowledge graph technology have made personalized education increasingly practicable. This article describes the technological framework of smart education, its potential applications, and emphasizes the use of AI technology in personalized education. The article covers topic areas, including learning situation analysis, implementing personalized instruction, personalized teaching management. © 2023 ACM.

关键词： knowledge graph

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Interview Bot with Automatic Question Generation and Answer Evaluation 9

Interview Bot with Automatic Question Generation and Answer ...

引用

9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Pandey, Raj Chaudhari, Divya Bhawani, Sahil Pawar, Omkar Barve, Sunita Savitribai Phule Pune University School of Computer Engineering MIT Academy of Engineering Pune India

ISBN: (纸本)9798350397376

Resume screening and interview chatbots are the basic requirements for today's hiring processes for a given job post. There are a large number of candidates who apply and manually sorting these resumes as per the job requirement is a tedious task for shortlisting the candidates. The motivation for the project lies in solving the challenge of automating the process of recruitment from resume screening to the first technical test of the candidate. The challenge for screening candidates' resumes would be to get structured data from differently structured resumes using natural language processing and get all the keywords consisting of programming languages. The other challenge lies in the generation of questions and evaluation of answers given by the candidates using natural language processing techniques. The challenge of proctoring the candidate during the test is the detection of eye movements of candidates during the interview and lip reading to check for any malpractice. The idea here is that the candidate's resume would be scanned for all the keywords consisting of programming languages to the concept words extracted from the resume. Then based on that, the candidate would be assigned a job role that suits his/her skill set which reduces the time required to find the relevant candidate for a given Job Description. The candidate's interview would be conducted to know the candidate's technical skills to some extent. Multiple Choice Questions based tests could be used for the initial screening. Still, they don't assess the in-depth knowledge of the candidate as much as the short answer questions can. Hence generation of short answer questions and evaluation is important. The question generated for the candidate would be either based on sentence-level information or paragraph-level information;the sentences and paragraphs would be of different technical questions on the programming languages and concepts. The questions would be selected both randomly and based on cand

关键词： natural language processing systems

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natural language processing of Messages from a Social Network for Inflation Analysis 3rd

Natural Language Processing of Messages from a Social Networ...

引用

3rd EAI international conference on Data and Information in Online Environments (DIONE)

作者： Vianna, Leonardo Silva Antunes, Lizyane Rosa Pinto, Rafael Maia Goncalves, Alexandre Leopoldo Univ Fed Santa Catarina Grad Program Knowledge Engn & Management Florianopolis Brazil

ISBN: (纸本)9783031223235;9783031223242

Inflation is a progressive increase in the average price of goods and services. It can be measured using different inflation rates, which can vary according to the time, but also according to the region or country. Depending on the variation of product and service prices, users of social networks could discuss and express their private opinions about inflation. We questioned if there is a correlation between themessages transmitted by Twitter((c)) users and the monthly variation of a specific inflation rate. Consequently, this research aims to examine Twitter((c)) messages with content about inflation in Brazil through natural language processing and network analysis. The Twitter((c)) messages from users in Brazil, obtained through the Application Programming Interface of this social network platform, were analyzed. The steps performed included querying the API for data acquisition, processing themessages using natural language processing techniques, and executing a network analysis. We concluded that inflation influences the behavior of social network users and, additionally, natural language processing of Twitter((c)) messages can reveal relevant knowledge for inflation analysis and have potential for prediction purposes.

关键词： Inflation natural language processing Network analysis

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Enhancing LLMs Contextual knowledge with Ontologies for Personalised Food Recommendation 25th

Enhancing LLMs Contextual Knowledge with Ontologies for Pe...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Bagozi, Ada Bianchini, Devis Melchiori, Michele Rula, Anisa University of Brescia Brescia Italy

ISBN: (纸本)9789819605729

Food recommendation systems help consumers make sustainable and nutritionally complete choices, promoting healthy eating habits and addressing the growing interest in food sustainability and waste reduction. Large language Models (LLMs), such as ChatGPT, are increasingly used for food recommendations due to their natural language processing capabilities. However, providing personalised and contextually relevant suggestions remains challenging because of the lack of a robust conceptualisation of healthy and sustainable food aligned with users’ dietary and lifestyle preferences. Ontologies can address this by offering a structured and semantically rich framework for organising information. In this paper, we propose a modular ontology to enhance the contextual knowledge of LLMs, enabling them to deliver personalised, contextually relevant food recommendations. The ontology’s modules are based on competency questions derived from a research project focused on sustainable and healthy food recommendations. To evaluate the effectiveness of this approach, we conducted experiments where ChatGPT-4 answered these competency questions with and without ontology integration. The answers were then assessed in a user study. Preliminary experimental results indicate significant improvements in the quality and relevance of recommendations when the ontology is employed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Consumer Empowerment Large language Models Multi-perspective Ontology engineering Sustainable Food Recommendation

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MFinBERT: Multilingual Pretrained language Model For Financial Domain 14

MFinBERT: Multilingual Pretrained Language Model For Financi...

引用

14th international conference on knowledge and Systems engineering, KSE 2022

作者： Nguyen, Duong Cao, Nam Nguyen, Son Ta, Son Dinh, Cuong Vps Securities Jsc Viet Nam

ISBN: (数字)9781665452816

ISBN: (纸本)9781665452809

There has been an increasing demand for good semantic representations of text in the financial sector when solving natural language processing tasks in Fintech. Previous work has shown that widely used modern language models trained in the general domain often perform poorly in this particular domain. There have been attempts to overcome this limitation by introducing domain-specific language models learned from financial text. However, these approaches suffer from the lack of in-domain data, which is further exacerbated for languages other than English. These problems motivate us to develop a simple and efficient pipeline to extract large amounts of financial text from large-scale multilingual corpora such as OSCAR and C4. We conduct extensive experiments with various downstream tasks in three different languages to demonstrate the effectiveness of our approach across a wide range of standard benchmarks. © 2022 IEEE.

关键词： natural language processing systems

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Bridging the knowledge Gap: Improving BERT models for answering MCQs by using Ontology-generated synthetic MCQA Dataset 37

Bridging the Knowledge Gap: Improving BERT models for answer...

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37th international Florida Artificial Intelligence Research Society conference, FLAIRS 2024

作者： Sahil, Sahil Kumar, P. Sreenivasa Department of Computer Science and Engineering Indian Institute of Technology Madras India

BERT-based models possess impressive language understanding capabilities but often lack domain-specific knowledge, limiting their performance on specialised tasks such as medical multiple-choice question answering (MCQA). In this paper, we study how biomedical ontologies, rich repositories of medical knowledge, can be harnessed to enhance BERT-based models for medical MCQA task. Our contributions include OntoMCQA-Gen, a system which leverages different biomedical ontologies to construct BioOntoMCQA, a large synthetic MCQA dataset. OntoMCQA-Gen exploits the subclass-class relationships, definitions of concepts, and also synonym relationships from the ontologies to create this dataset of MCQs automatically. We then use this synthetic dataset to fine-tune various BERT-based models to answer medical MCQs. We evaluated these fine-tuned BERT models on the challenging MedMCQA and MedQA datasets of questions from admission examinations for medical degrees in India and USA, respectively. Our evaluation study on these datasets shows that fine-tuning the BERT-based models on BioOntoMCQA results in significantly improved accuracy scores. BioBERT and PubMedBERT, pretrained on the large medical corpus, have also shown significant improvements with our technique of fine-tuning ontology-generated synthetic data. This finding highlights the effectiveness of incorporating biomedical ontologies to enhance the BERT-based model in the medical domain. Moreover, our results underscore the importance of using ontology-generated data along with model adaptation for specialised domains, contributing to a novel advancement in natural language processing. Copyright © 2024 by the authors.

关键词： Ontology

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Answer Type Prediction: Unveiling the Power of Deep Learning Models 2

Answer Type Prediction: Unveiling the Power of Deep Learning...

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2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Swathi, B.P. Geetha, M. Shenoy, Manjula Suhas, M.V. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information & Communication Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal India

ISBN: (纸本)9798350308846

Answer type prediction plays a crucial role in natural language question answering systems, enabling the generation of relevant SPARQL queries, a query language for retrieving data from RDF databases. This research paper focuses on answer type prediction for SPARQL query generation. Answer type prediction involves identifying the expected type of the answer to a given question, such as a person's name, a location, or a numerical value. By accurately predicting the answer type, the subsequent SPARQL query generation process can be tailored to retrieve the desired information from a knowledge base. The proposed work evaluates the performance of four deep learning models, which include GRU (Gated Recurrent Unit), Bi-GRU, LSTM (Long Short-Term Memory), and Bi-LSTM. The study's conclusion highlights GRU as the top-performing model for predicting the answer type based on the analysis of input natural language queries. The performance of the answer type prediction model is evaluated on a publicly available dataset, demonstrating its effectiveness in achieving an accuracy of 81% using GRU. The results obtained from the study emphasize the importance of accurate answer type prediction and provide promising outcomes. © 2023 IEEE.

关键词： Answer type prediction Bi-GRU Bi-LSTM GRU LSTM natural language processing natural language Query Question Answering Systems SPARQL

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Sequence-to-Sequence Multi-Hop knowledge Reasoning Based on Retentive Network 7

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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7th international conference on Computer Information Science and Application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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Chinese Named Entity Recognition Base on Multi-Metadata Embedding and Global Pointer 3

Chinese Named Entity Recognition Base on Multi-Metadata Embe...

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3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Wang, Shulai Liu, Fang Huang, Biao Li, Jialun Feng, Ping Changchun University College of Cybersecurity Jilin Province Changchun China Baicheng Medical College Clinical Medicine Jilin Province Baicheng China Changchun University Jilin Province Changchun China

ISBN: (纸本)9798350316674

Named Entity Recognition (NER) aims to identify entities from unstructured text and is an important subtask in natural language processing and knowledge graph construction. Existing NER methods mostly use Conditional Random Fields (CRF) as the label decoder or employ pointer networks for entity recognition. However, when the number of labels is large, CRF-based methods incur high computational costs and fail to address the issue of nested entities. Pointer networks use two modules to identify the first and last entity, respectively, where each module can only focus on the information of either the first or the last entity, lacking the ability to consider global information about the entities. Furthermore, recent pointer network models have neglected two important aspects that are specific to the Chinese language: structure and pronunciation. These two aspects carry essential syntactic and semantic information, which are crucial for language comprehension. To address these challenges, the paper proposes a Chinese NER model called Multi-Embedding Global Pointer (MEGP) that incorporates radical and Pinyin features to enhance performance. This model combines these features with a global pointer network to improve Chinese NER. The model employs a simple feature enhancement approach by merging radical and pronunciation information into the vector representation of each character, followed by entity recognition using the global pointer method. Experimental results demonstrate that compared to existing mainstream models, this model achieves improved F1 scores on multiple public datasets such as Weibo, Resume, and People's Daily. © 2023 IEEE.

关键词： Chinese named entity recognition component Deep Neural Networks natural language processing

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