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Automated software design using Machine Learning With natural language processing 14

Automated software design using Machine Learning With Natura...

引用

14th international conference on Computer and knowledge engineering, ICCKE 2024

作者： Khedmatkon, Fahimeh Hossein Hasheminejad, Seyed Mohammad Malak, Jaleh Shoshtarian Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Information Technology Department Tehran University of Medical Sciences Tehran Iran

ISBN: (纸本)9798331511272

Designing is the most difficult task in software production and development. Researchers are trying to add automation to the software design and development process. This is because of the daily increase of demanding software production;the quality growth of automotive software design makes designers respond more quickly to the refreshed demands. One of the good designing methods is to use of data mining. In this research, the decision tree algorithm method is used to identify classes and detect relationships between classes automatically. The data input is the proposed method is the use case descriptions software and its output is the list of classes with relationships between them. This method has three 3 stages: First, the use case description scenarios are analyzed by natural language processing tools. In the next step, by setting rules, the data set is created, and then the decision tree algorithm is executed on the dataset. Output is the list of classes along with the type of relationship between them. Extracting of association relations one-to-one, one-to-many, and many-to-many and association relationships. ATM, CBS, GDP, and SC case studies have been used. In this research, an attempt has been made to provide a method that has better results than previous methods. The accuracy and F-score of proposed method on the ATM dataset are 90% and 0.93 which is a value of accuracy and F-score is better than similar research and has better results. ©2024 IEEE.

关键词： Input output programs

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A Tertiary Study on AI for Requirements engineering 30th

A Tertiary Study on AI for Requirements Engineering

引用

30th international Working conference on Requirements engineering - Foundation for Software Quality (REFSQ)

作者： Mehraj, Ali Zhang, Zheying Systa, Kari Tampere Univ Tampere Finland

ISBN: (纸本)9783031573262;9783031573279

Context and Motivation: Rapid advancements in Artificial Intelligence (AI) have significantly influenced requirements engineering (RE) practices. Problem: While many recent secondary studies have explored AI's role in RE, a thorough understanding of the use of AI for RE (AI4RE) and its inherent challenges remains in its early stages. Principal Ideas: To fill this knowledge gap, we conducted a tertiary review on understanding how AI assists RE practices. Contribution: We analyzed 28 secondary studies from 2017 to September 2023 about using AI in RE tasks such as elicitation, classification, analysis, specification, management, and tracing. Our study reveals a trend of combining natural language process techniques with machine learning models like Latent Dirichlet Allocation (LDA) and Naive Bayes, and a surge in using large language models (LLMs) for RE. The study also identified challenges of AI4RE related to ambiguity, language, data, algorithm, and evaluation. The study gives topics for future research, particularly for researchers who want to start new research in this field.

关键词： Tertiary study Requirements engineering Artificial intelligence Machine learning natural language processing

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knowledge of language and natural language processing 22

Knowledge of Language and Natural Language Processing

引用

22nd international conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2023

作者： Di Sciullo, Anna Maria University of Naples Federico II Italy

ISBN: (纸本)9781643684307

Using knowledge rather than data is key in knowledge science and enables artificial systems to solve novel problems. We distinguish the knowledge of language internal to the mind from the externalized language. We differentiate the Generative Model of language from Large language Models. We take Structure Dependency to be a First Principle of the internal language. We address the question whether Large language Models provide reliable natural language processing. We identify limits of ChatGPT for answering queries including sentence embeddings, covert constituents, and pronominal anaphora, which rely on Structure Dependency. We draw consequences for reliable natural language processing systems. © 2023 IOS Press. All rights reserved.

关键词： Machine learning

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Backdoor Attack Detecting and Removing Based on knowledge Distillation for natural language Translation Model 9

Backdoor Attack Detecting and Removing Based on Knowledge Di...

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9th international conference on Computer and Communications, ICCC 2023

作者： Chen, Mao Pang, Lihui Tan, Qingyi Tang, Yilong Liu, Yulang Zhang, Wenwei University of South China School of Electrical Engineering Hengyang China Shenzhen Technology University Sino-German College of Intelligent Manufacturing Shenzhen China

ISBN: (纸本)9798350317251

The lack of interpretability in Deep Neural Networks makes them susceptible to backdoor attacks. The attacker mixes the poisoned data with triggers into a clean dataset, and uses it to train a backdoor model. This model maintains high accuracy on clean data while outputting the attacker's desired target for the poisoned data. Due to the serious threat of backdoor attacks on DNN, backdoor defense on DNN is particularly important. In our work, we apply the knowledge distillation method in the visual field to natural language processing. The method of knowledge distillation involves removing toxic data from the poisoned training dataset and restoring the accuracy of the distilled model. In a defensive scenario, one assumption is that the defender can collect clean data without labels. We evaluated the effectiveness of knowledge distillation on strategies through two application scenarios in natural language processing and multiple models. By distilling and fine-tuning to disable backdoors, we further improved the classification accuracy of the distilled models. The experimental results indicate that the method of knowledge distillation can also effectively defend against backdoor attacks in natural language processing. © 2023 IEEE.

关键词： Distillation

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Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi-Domain Stance Detection 30

Target-Adaptive Consistency Enhanced Prompt-Tuning for Multi...

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Joint 30th international conference on Computational Linguistics and 14th international conference on language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Shaokang Pan, Li Shanghai Key Laboratory of Integrated Administration Technologies for Information Security China School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9782493814104

Stance detection is a fundamental task in natural language processing (NLP). It is challenging due to diverse expressions and topics related to the targets from multiple domains. Recently, prompt-tuning has been introduced to convert the original task into a cloze-style prediction task, achieving impressive results. Many prompt-tuning-based methods focus on one or two classic scenarios with concrete external knowledge enhancement. However, when facing intricate information in multi-domain stance detection, these methods cannot be adaptive to multi-domain semantics. In this paper, we propose a novel target-adaptive consistency enhanced prompt-tuning method (TCP) for stance detection with multiple domains. TCP incorporates target knowledge and prior knowledge to construct target-adaptive verbalizers for diverse domains and employs pilot experiments distillation to enhance the consistency between verbalizers and model training. Specifically, to capture the knowledge from multiple domains, TCP uses a target-adaptive candidate mining strategy to obtain the domain-related candidates. Then, TCP refines them with prior attributes to ensure prediction consistency. The Pre-trained language Models (PLMs) in prompt-tuning are with large-scale parameters, while only changing the verbalizer without corresponding tuning has a limited impact on the training process. Target-aware pilot experiments are conducted to enhance the consistency between the verbalizer and training by distilling the target-adaptive knowledge into prompt-tuning. Extensive experiments and ablation studies demonstrate that TCP outperforms the state-of-the-art methods on nine stance detection datasets from multiple domains. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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A Novel AI-based chatbot Application for Personalized Medical Diagnosis and review using Large language Models

A Novel AI-based chatbot Application for Personalized Medica...

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Akilesh, S. Sheik Abdullah, A. Abinaya, R. Dhanushkodi, S. Sekar, Rajeev School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305708

This research showcases a groundbreaking application in the medical industry by leveraging the cutting-edge OpenAI service's GPT-3.5 model, powered by large language models (LLMs). Through the implementation of a Java-based Android app, it introduces a medical chatbot that redefines the way individuals interact with healthcare systems. By capitalizing on the advanced capabilities of LLMs, the app enables users to effortlessly input their symptoms and receive accurate insights, personalized diagnoses, and actionable medical advice. The utilization of natural language processing and AI algorithms ensures a seamless and intuitive user experience. The significance of this innovation lies in the revolutionary impact of LLMs on the entire medical industry. The integration of LLMs empowers the app to comprehend complex medical queries, interpret contextual information, and deliver reliable and up-to-date medical knowledge to users. This transformative technology facilitates improved access to healthcare information, enhances decision-making processes, and ultimately contributes to the optimization of healthcare services. © 2023 IEEE.

关键词： GPT 3.5 Model Large language Models Medical Chatbot natural language processing OpenAI Service

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Clinical Text Analysis with natural language processing: A BERT-based Approach

Clinical Text Analysis with Natural Language Processing: A B...

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2024 international conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Tiwari, Shashank Shekhar Goyal, Himanshu Rai Adnan, Myasar Mundher Shrivastava, Anurag Yadav, Kanchan Rao, A.L.N. Department Of Information Technology Rec U.P Ambedkar Nagar India Dr. Abdul Kalam Technical University Lucknow India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Gla University Department Of Electrical Engineering Mathura India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This research investigates the transformative potential of natural language processing (NLP) procedures, especially utilizing a BERT-based approach, for the comprehensive investigation of unstructured clinical content. The ponder centres on three key assignments: Named Entity Recognition (NER), Clinical Content Classification, and Subject Modeling, each contributing to an all-encompassing understanding of accounts inside electronic health records. The BERT-based algorithm illustrates predominant execution, accomplishing exactness, review, and F1-score values of 0.87, 0.89, and 0.88, individually, near an amazing AUC-ROC score of 0.94. The NER calculation shows accuracy and review values of 0.92 and 0.88, highlighting its adequacy in recognizing and classifying substances in clinical content. The Clinical Content Classification algorithm, fine-tuned with BERT, achieves precision, recall, and F1-score values of 0.85, 0.87, and 0.86, with an AUC-ROC score of 0.92. The Subject Modeling calculation, based on Latent Dirichlet Allocation (LDA), accomplishes a coherence score of 0.75, advertising profitable bits of knowledge into latent themes inside clinical accounts. This research contributes to the headway of data-driven healthcare by displaying the flexibility and viability of NLP strategies in extracting significant data from complex clinical content information. © 2024 IEEE.

关键词： natural language processing systems

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Research and Practice on the Construction of Course Ideological and Political Education Based on knowledge Graphs and Large language Models 24

Research and Practice on the Construction of Course Ideologi...

引用

2024 international Symposium on Artificial Intelligence for Education, ISAIE 2024

作者： Yang, Da Liu, Shutian Fu, Haoyang Shen, Jiayi School of Intelligent and Electronic Engineering Dalian Neusoft University of Information Liaoning Dalian China

ISBN: (纸本)9798400707100

knowledge graphs and large language models (LLMs) have become important tools for educational innovation. This paper explores the application of these two technologies in the construction of ideological and political education in university courses. The paper begins by analyzing the importance of course-based ideological and political education and the challenges currently faced. It then introduces the role of knowledge graphs in integrating educational resources and constructing knowledge systems, as well as the potential and current status of LLMs in natural language processing and providing personalized educational content. This study presents a method that integrates the use of knowledge graphs and LLMs to construct resources and application systems for course-based ideological and political education. The results of practical case studies demonstrate that the proposed method improves the efficiency of constructing ideological and political education content, enhances the effectiveness of moral education within courses, and contributes to the innovative development of ideological and political education. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： knowledge graph

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Transforming Adolescent Healthcare in Rwanda: Sustainable and Scalable Features in Digital Health

Transforming Adolescent Healthcare in Rwanda: Sustainable an...

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IEEE-EMBS international conference on Biomedical and Health Informatics (BHI)

作者： Dasari, Tejaswi Samrat Satish, Pratima Kayitesi, Laetitia Mbuvi, Mary Dias, Russell Ngamije, Fabiola Ishimwe Kwesiga, Joseph On, Robert Woodbridge, Diane Myung-Kyung Univ San Francisco San Francisco CA 94117 USA Univ Calif Berkeley Berkeley CA 94720 USA YLabs Berkeley CA USA Agcy Fund Albuquerque NM USA

ISBN: (纸本)9798350310504

CyberRwanda is a digital health intervention designed to increase knowledge of family planning and reproductive health (FP/RH) and access to youth-friendly services in Rwanda. Previous studies showed high acceptability and feasibility through user surveys, success in combating social stigma, and an effective educational impact on adolescents. In order to further enhance user experience and overcome resource limitations, the authors designed and developed new features, including an integrated dashboard and chatbot, leveraging data engineering and natural language processing. The experiments showed that new features substantially reduce the time required to access information for user groups, potentially leading to an improved user experience.

关键词： Big Data applications Data engineering natural language processing Public healthcare Smart healthcare

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Advancing Legal Decision Making: A Unique Method for Classifying Offense Incidents under the Indian Penal Code using Word Embedding and Labeled Searching

Advancing Legal Decision Making: A Unique Method for Classif...

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2024 international conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Srivastav, Ambrish Prajapat, Shaligram Acropolis Institute of Technology and Research Cse Department Indore India Iips Devi Ahilya Vishwavidyalya Cs Department Indore India

ISBN: (纸本)9798350388800

The advancements in artificial intelligence (AI) have propelled the domain of text similarity, where sophisticated algorithms harness the power of natural language processing to analyze and compare textual content. In the legal domain, the judicial system constitutes a vast landscape generating copious amounts of daily textual data, encompassing an array of documents such as court records, investigative reports, and First Information Reports (FIR). Moreover, a substantial amount of written content within this domain is found in legal articles and regulatory manuals. The similarity between these texts aids us in some way in making decisions. The Indian Penal Code (IPC) serves as the principal legislative framework in India for delineating criminal offenses. In the context of computer programs, maintaining information in a well-organized and structured format is crucial. This underscores the need for a suitable knowledge base that accurately represents the sections of the Indian Penal Code. Through evaluating various word embedding techniques and cosine similarity for measuring text similarity between crime complaint report and offense terms, this research reveals a method for categorizing IPC Sections. In addition, this study seeks to present a novel approach for creating a specialized knowledge base tailored to IPC documents. It also aims to introduce a labeled searching technique to augment the precision and efficacy of the acquired results. © 2024 IEEE.

关键词： natural language processing systems

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