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Automatic search of machine learning models based on intelligent computing 4

Automatic search of machine learning models based on intelli...

引用

4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Zhao, Zhijian Jiangsu Union Technical Institute Nanjing Engineering Branch Nanjing China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361643

this paper focuses on the research of automatic search of machine learning models using intelligent computing technology. In the current era of big data and artificial intelligence, selecting and optimizing the right machine learning model is critical to improving the accuracy of data analysis and prediction. However, traditional manual methods are time-consuming and inefficient when dealing with a large number of models and hyperparameter combinations. In this paper, an automatic search strategy for machine learning models based on intelligent computing is proposed. By integrating reinforcement learning, evolutionary algorithm and meta-learning, the model structure and hyperparameter space are effectively explored. this strategy aims to find the optimal model configuration adaptively according to the specific task requirements and data characteristics, thus breaking through the limitations of manual model setting and significantly improving the efficiency and performance of model training. the experimental results show that the automatic search method proposed in this study can automatically discover highly optimized machine learning models on multiple public data sets, and outperforms traditional manual parameter tuning and some existing automatic tuning methods on multiple evaluation indicators. this research achievement not only helps to promote the popularization and deepening of machine learning model application, but also provides a new idea and practice path for the construction of intelligent and automated machine learning system in the future. At the same time, it also reveals the great potential and broad application prospect of intelligent computing in solving complex optimization problems. © 2024 IEEE.

关键词： Reinforcement learning

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data Mining in Credit Card Approval: Feature Importance Testing Comparison 25th

Data Mining in Credit Card Approval: Feature Importance Test...

引用

25th international conference on intelligent data engineering and automated learning

作者： Ye, Qingyu Fong, Simon Yu, Jiahui Tallon-Ballesteros, Antonio J. Univ Macau Dept Comp & Informat Sci Zhuhai Macau Peoples R China Univ Huelva Dept Elect Engn Comp Syst & Automat Huelva Spain

ISBN: (纸本)9783031777370;9783031777387

Understanding the significance of features in data mining is crucial for accurately analyzing customer behavior, constructing reliable credit scoring models, and detecting fraud within the credit card approval process. this paper explores the application of data mining techniques in the credit industry, with a specific focus on credit card approval classification. We investigate seven feature importance testing techniques and three classification methods, assessing their performance through various metrics. the research demonstrates that FLOFO with linear regression and ShapFlex with agnostic causal relations substantially improve the performance of all classifiers, with SVM emerging as the most effective classifier across all feature selection techniques. Feature importance testing is pivotal as it not only enhances model accuracy but also provides deeper insights into the factors driving credit card approval decisions. the findings underscore the essential role of data mining in financial risk analysis and credit approval processes, offering valuable perspectives for advancing research and practices in financial technology. the results emphasize the potential of specific feature importance testing techniques and classificationmethods in refining credit card approval classification tasks.

关键词： Feature Importance data Mining Credit Card Approval Classification Methods Financial Risk Analysis

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Translation Systems: A Synoptic Survey of Deep learning Approaches and Techniques 11

Translation Systems: A Synoptic Survey of Deep Learning Appr...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kumar, Kirti Faiz, Anas Shruti, Kumari Chhabra, Megha Setia, Sonia School of Engineering and Technology Sharda University Department of Computer Science & Engineering U.P. Greater Noida India

ISBN: (纸本)9798350350357

Within artificial intelligence, deep learning is a subset that emulates the information processing mechanism of the human brain. allowing machines to learn and make decisions on their own. this technology has revolutionized the field of language translation. Deep learning models analyze vast amounts of linguistic data, allowing visualization of context, and idiomatic expressions. this results in translations that read more naturally and capture the true essence of the source text. In order to accomplish the goal of higher accuracy, this study explores a variety of methodologies, including rule-based, neural-based, hybrid-based, and statistical-based machine translation systems, along with associated pre-processing strategies. It details the process of data collection, pre-processing, model building, training, and evaluation. the paper concludes by highlighting its key contribution in the field. © 2024 IEEE.

关键词： Deep learning

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Towards Synthetic Trace Generation of Modeling Operations using In-Context learning Approach 24

Towards Synthetic Trace Generation of Modeling Operations us...

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39th ACM/IEEE international conference on automated Software engineering (ASE)

作者： Muttillo, Vittoriano Di Sipio, Claudio Rubei, Riccardo Berardinelli, Luca Dehghani, MohammadHadi Univ Teramo Teramo Italy Univ Aquila Laquila Italy Johannes Kepler Univ Linz Linz Austria

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Producing accurate software models is crucial in model-driven software engineering (MDE). However, modeling complex systems is an error-prone task that requires deep application domain knowledge. In the past decade, several automated techniques have been proposed to support academic and industrial practitioners by providing relevant modeling operations. Nevertheless, those techniques require a huge amount of training data that cannot be available due to several factors, e.g., privacy issues. the advent of large language models (LLMs) can support the generation of synthetic data although state-of-the-art approaches are not yet supporting the generation of modeling operations. To fill the gap, we propose a conceptual framework that combines modeling event logs, intelligent modeling assistants, and the generation of modeling operations using LLMs. In particular, the architecture comprises modeling components that help the designer specify the system, record its operation within a graphical modeling environment, and automatically recommend relevant operations. In addition, we generate a completely new dataset of modeling events by telling on the most prominent LLMs currently available. As a proof of concept, we instantiate the proposed framework using a set of existing modeling tools employed in industrial use cases within different European projects. To assess the proposed methodology, we first evaluate the capability of the examined LLMs to generate realistic modeling operations by relying on well-founded distance metrics. then, we evaluate the recommended operations by considering real-world industrial modeling artifacts. Our findings demonstrate that LLMs can generate modeling events even though the overall accuracy is higher when considering human-based operations. In this respect, we see generative AI tools as an alternative when the modeling operations are not available to train traditional IMAs specifically conceived to support industrial practitioners.

关键词： data privacy Accuracy Large language models Training data data models Software Regulation Context modeling Software engineering Synthetic data

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the effects of gender bias in word embeddings on patient phenotyping in the mental health domain 11

The effects of gender bias in word embeddings on patient phe...

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11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Sogancioglu, Gizem Kaya, Heysem Salah, Albert Ali Univ Utrecht Informat & Comp Sci Utrecht Netherlands Bogazici Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9798350327434

Word embeddings, renowned for their role as superior semantic feature vector representation in diverse NLP tasks, can exhibit an undesired bias for stereotypical categories. the bias arises from the statistical and societal biases within the datasets used for training. In this study, we analyze the gender bias in four different pre-trained word embeddings for a range of affective computing tasks in the mental health domain including the detection of psychiatric disorders such as depression, and alcohol/substance abuse. We incorporate both contextual and non-contextual embeddings, which are trained not just on general domain data but also on data specific to the clinical domain. Our findings indicate that the bias in embeddings is towards different gender groups, depending on the type of embeddings and the training dataset. Furthermore, we highlight how these existing associations transfer to subsequent tasks and might even be amplified during supervised training for patient phenotyping. We also show that a simple method of data augmentation - swapping gender words - noticeably reduces bias in these subsequent tasks. the scripts to reproduce the results are available at: https://***/gizemsogancioglu/gender-bias-mental-health.

关键词： fairness bias mitigation gender bias bias in mental health fairness in machine learning

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Analysis of Students Performance in Flipped Classroom-based Vocational Education Using data Mining Tools 11

Analysis of Students Performance in Flipped Classroom-based ...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Kamal Eldeen, Yousra O. Abdelraheem, Mohamed Soliman, Taysir Hassan A. Misr International Technological University IT Department Egyptian-German College of Technology Assiut Egypt Assiut University Electrical Engineering Department Assiut Egypt Assiut University Faculty of Computers and Information Information Science Department Assiut Egypt

ISBN: (纸本)9798350322101

this research investigates the impact of applying a Flipped Classroom Approach (FCA) in Vocational Education and proposes an improved approach that integrates Cognitive theory of Multimedia learning (CTML) with flipped learning based on Revised Bloom's taxonomy. Educational data mining (EDM) using statistics and data analysis was applied to transform large educational raw data into useful and meaningful information. this information can be applied to gain insights into students and their learning environments, enhance teaching support, and optimize decision-making in educational systems. EDM was performed on the student activities recorded on online learning management system (Moodle) including, pre-class activities, attendance, and after-class assignments. A correlation analysis was performed to explore the relationship between their activities in Moodle and academic performance. Results show that students who followed the flipped learning strategy outperformed those who did not, and the video designed according to CTML had a positive impact on student outcomes. the study also revealed that after-class assignments had a significant influence on students' results, and that the students' final results could be predicted through their assignment performance. © 2023 IEEE.

关键词： Students

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Smart University: A Bibliometric Analysis 11th

Smart University: A Bibliometric Analysis

引用

11th international conference on Smart Education and E-learning

作者： Singh, Abhishek Bakken, Jeffrey P. Uskov, Vladimir L. Velampalli, Madhu Mitha Bradley Univ Dept Comp Sci & Informat Syst Peoria IL 61625 USA Bradley Univ Dept Educ Counseling & Leadership Peoria IL 61625 USA Bradley Univ InterLabs Res Inst Peoria IL 61625 USA

ISBN: (纸本)9789819749560;9789819749546;9789819749539

this paper presents a comprehensive study of the evolution and trends in the smart university research area. this study methodically compiles and examines data sourced from various academic publications in the Scopus database and affiliated sources, ranging from journal articles to conference proceedings. the bibliometric analysis was conducted using a set of the following keywords: "Smart University", "Smart School", "intelligent University", and "Smart Classroom". Limit-To-Language parameter was assigned as "English". A total of 1520 documents, related to the smart university area were identified in the Scopus database and analyzed for this project. the publications cover the time period from 1989 to 2023. A bibliometric analysis was carried out using the Scopus toolkit. VOSviewer was applied as a visualization tool to generate visualization network maps, including visualization network maps of co-authorship, countries and keywords.

关键词： intelligent University Smart Classroom Smart School Smart University

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CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care data Interoperability 25th

CSSDH: An Ontology for Social Determinants of Health to Oper...

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25th international conference on intelligent data engineering and automated learning

作者： Das, Subhashis Naskar, Debashis Rodriguez Gonzalez, Sara Univ Salamanca Dept Comp Sci & Automat BISITE Res Grp Salamanca 37007 Spain

ISBN: (纸本)9783031777301;9783031777318

the rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monitor their health and share information with healthcare professionals as needed. However, creating an efficient care plan management system requires more than just analyzing hospital summaries and Electronic Health Records (EHRs). Factors such as individual user needs and social determinants of health, including living conditions and the flow of healthcare information between different settings, must also be considered. Challenges in this complex healthcare network involve schema diversity (in EHRs, personal health records, etc.) and terminology diversity (e.g., ICD, SNOMED-CT) across ancillary healthcare operations. Establishing interoperability among various systems and applications is crucial, with the European Interoperability Framework (EIF) emphasizing the need for patient-centric access and control of healthcare data. In this paper, we propose an integrated ontological model, the Common Semantic data Model for Social Determinants of Health (CSSDH), by combining ISO/DIS 13940:2024 ContSys with WHO Social Determinants of Health. CSSDH aims to achieve interoperability within the Continuity of Care Network.

关键词： Continuity of care EHR OWL Social Determinants of Health

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Deep learning Inference on Edge: A Preliminary Device Comparison 25th

Deep Learning Inference on Edge: A Preliminary Device Compar...

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25th international conference on intelligent data engineering and automated learning

作者： Gonzalez, Manuel L. Ruiz, Jorge Andres, Lidia Lozada, Randy Skibinsky, Erik S. Fernandez, Jorge Sedano, Javier Garcia-Vico, Angel M. Inst Tecnol Castilla & Leon Burgos Spain Univ Jaen Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain

ISBN: (纸本)9783031777301;9783031777318

the deployment and inference of Deep learning models are investigated in this article on seven different Edge Computing (EC) devices. this study addresses the features, performance and limitations of the NVIDIA Jetson Orin NX and Nano, Google Coral DevBoard and USB, Intel Neural Compute Stick 2, NXP ***8 Plus and Xilinx Zynq UltraScale+ MPSoC ZCU104 on Deep learning inference. Fully Connected, Convolutional and Long Short-Term Memory (LSTM) neural networks are implemented to test these EC devices. the benchmarking focuses on the performance metrics: inference latency, increase in error metric, and power consumption. the results show considerable variability among devices, with the ZCU104 and Jetson Orin achieving the lowest latencies across most models without any increase in the error metric. At the same time, Coral devices exhibit increased latency and error for complex convolutional models. NVIDIA Jetson devices, ZCU104 and Neural Compute Stick 2 are the only devices that support LSTM inference. the study also highlights differences in power consumption, with USB accelerators being the most energy-efficient.

关键词： Edge Computing Deep learning Real-Time Inference

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Exploring data Symbion EI Deep learning and Model Sharing Modules 25th

Exploring Data Symbion EI Deep Learning and Model Sharing Mo...

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25th international conference on intelligent data engineering and automated learning

作者： Huszcza, Rafael Mendes, Amanda Lopes, Jeferson Borges, Eduardo N. Lucca, Giancarlo B. Guilherme, Pablo D. B. Pereira, Leandro A. Univ Fed Rio Grande FURG Ctr Ciencias Computac C3 Rio Grande Brazil Univ Catolica Pelotas Ctr Ciencias Sociais & Tecnol Mestrado Engenharia Eletron & Computacao Pelotas RS Brazil Univ Estadual Parana UNESPAR Ctr Educ Humanidades Ciencias Biolog Paranagua Brazil Inst Fed Parana IFPR Eixo Tecnolog Meio Ambiente Paranagua Brazil

ISBN: (纸本)9783031777370;9783031777387

Considering the growing volume of available information, storing and managing data is an increasing challenge. Leveraging these raw datasets to extract meaningful knowledge and make informed decisions requires data mining methods, especially when combined with Machine learning algorithms to uncover hidden patterns in data. Considering this context, this work describes a project to contribute to datadriven initiatives by seamlessly integrating a data repository system with Machine learning (ML) tools. this integration allows users to implement pipelines within a single system-from storing and managing datasets to training and deploying ML models, making easier, more efficient, and intuitive data science workflow accessible to all, not just computer scientists. Furthermore, the system aims to enhance knowledge sharing among researchers and practitioners by enabling users to share datasets and trained machine learning models within the platform. this work highlights the system's key features, particularly the Machine learning module, which includes classic ML algorithms for tabular data and the newly introduced Deep learning algorithms for image classification. the new Model Sharing module also emphasizes promoting collaboration by allowing users to share and manage trained models effectively.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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