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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20691 条记录，以下是621-630 订阅

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Who Is to Err? A Limited Effect of data Labeling in Prediction of Users' Subjective Impressions of Web Designs 3

Who Is to Err? A Limited Effect of Data Labeling in Predicti...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Bortnikova, Anna V. Bakaev, Maxim A. Novosibirsk State Technical University Department of Automated Control Systems Novosibirsk Russia Novosibirsk State Technical University Department of Data Collection and Processing Systems Novosibirsk Russia

ISBN: (纸本)9798331516321

User Experience (UX) researchers and designers who seek to predict users' subjective impressions nowadays turn to Machine learning (ML) models trained on marked-up data. Labeling of graphical user interfaces (UIs) is a rather time-consuming process that is also prone to errors and inaccuracies, particularly if motivation of the human labelers is low. Correspondingly, the size of datasets in Human-Computer Interaction (HCI) rarely exceeds the order of 10,000, and the field largely relies on feature-based models. In our paper, we compare two principal ways of obtaining the features, with respect to their effect on the quality of the models that predict users' impressions of web pages' Complexity, Aesthetics and Orderliness. First, we calculated several layout-related metrics - balance, equilibrium, symmetry - for 368 website screenshots labeled by 40 human volunteers. Second, we obtained the similar metrics from ML-based online services working with raw web UI images. Our results suggest that despite the limitations of the current automated web UI parametrization tools, their metrics can actually yield superior models (having R2s higher by 107% on average). Our key implication for UI/UX designers and ML researchers is that the expected ROI should be carefully weighted before committing to the tedious manual web UI data labeling. © 2024 IEEE.

关键词： Web Design

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intelligent Diagnosis Research of Motor Drive Systems Based on Neural Networks 3

Intelligent Diagnosis Research of Motor Drive Systems Based ...

引用

3rd international conference on Energy and Electrical Power Systems (ICEEPS)

作者： Su, Yi Gao, Lei Lu, Yufeng Li, Baofeng Huang, Wei Rao, Xiajin Guangxi Power Grid Co Ltd Guangxi Key Lab Intelligent Control & Maintenance Elect Power Res Inst Nanning Guangxi Peoples R China

ISBN: (纸本)9798350375145;9798350375138

Servo motors, with their strong load capacity, reliable operation, and high efficiency, are widely used in electrical production and daily life. As one of the main power sources in modern industry, a motor failure can paralyze operating mechanisms and even threaten life safety. Therefore, this paper conducts intelligent diagnosis research on motor drive systems based on neural networks. Firstly, finite element method simulation is used to obtain fault data of the servo motor and collect sufficient samples. Secondly, addressing the slow convergence speed caused by the deepening of traditional convolutional neural networks (CNN), this paper combines residual learning with convolutional neural networks, proposing a residual learning-based convolutional neural network with Wide Kernel (R-WDCNN) model for fault classification. Finally, compared with traditional convolutional neural networks, the R-WDCNN algorithm achieves higher recognition accuracy and faster convergence speed.

关键词： Motor drive system intelligent diagnosis convolutional neural network

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MetaLIRS: Meta-learning for Imputation and Regression Selection 25th

MetaLIRS: Meta-learning for Imputation and Regression Select...

引用

25th international conference on intelligent data engineering and automated learning

作者： Erez, Ill Baysal Flokstra, Jan Pod, Mannes van Keulen, Maurice Univ Twente EEMCS NL-7500 AE Enschede Netherlands

ISBN: (纸本)9783031777301;9783031777318

Missing data is a prevalent problem in data science for many fields such as natural, social, and health sciences. Since most regression methods can not handle missing data directly, imputation methods are used in data pre-processing. Finding the best imputation method is non-trivial, however. Moreover, our results show that an independent choice for a best imputation method does not always result in the best predictive performance in the end;the combination matters. Furthermore, search-based approaches for finding a best-fitting imputer/regressor-pair can be computationally intensive. In this paper, we propose the MetaLIRS (Meta learning Imputation and Regression Selection) framework for developing resource-friendly ML-based recommendation models for method selection. With MetaLIRS, we constructed a proof-of-concept recommendation model based on 12 meta-features that achieves an accuracy of 63% for selecting the best-fitting imputer/regressor-pair. A data scientist can use this model for a quick resource-friendly recommendation on which imputation and regression method to use for their particular data set and task without the need for an expensive grid search among methods.

关键词： Missing data Imputation Regression

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Melanoma Detection with Transfer learning 3

Melanoma Detection with Transfer Learning

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Vishnu, Pragadeshwar Krishvadana, S. Rani, C. Khoodeeram, Rajeev Bouridane, Ahmed Rajesh Kumar, M. Vellore Institute of Technology School of Electronics Engineering Vellore India Université Des Mascareignes Department of Emerging Technologies Mauritius University of Sharjah Centre for Data Analytics Sharjah United Arab Emirates

ISBN: (纸本)9798350372120

The abnormal growth of skin cells that are exposed to the sun is identified as skin cancer. Even though skin cancer is curable, late diagnosis and improper treatment can lead to severe effects and death. Melanoma has been identified as the deadliest type of skin cancer and it is responsible for 85 \% of skin cancer deaths. Even though research on melanoma identification is prominent, still it has not been perfected. automated Melanoma recognition is a challenging task due to various artifacts such as low-contrast dermoscopic images, and large and similar varieties of other skin cancers. In this study, we take on this challenge using neural networks. We implement an approach in which two trained neural networks are combined to give a higher accuracy output. With the lack of training data for classification, we employ data augmentation techniques. The proposed Model makes use of two types of learning approaches by combining their weights and predicting the output. The combined or ensemble model categorizes the output into 6 different categories with an accuracy of up to 89 \%. The approach is simple to implement and surpasses other avant-garde techniques in terms of accuracy. © 2024 IEEE.

关键词： Diseases

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Enhancing IoT-Enabled Electric Vehicle Battery Performance Monitoring and Prediction using AI Techniques 3

Enhancing IoT-Enabled Electric Vehicle Battery Performance M...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tabassum, Saleha Bharathi, M. Peramasani, Narasimharaju Nagadivya, Nagadivya Busetty, Bhargavi Syed, Afrin Sulthana Vfstr Guntur India K.S.R.M. College of Engineering Kadapa India Mohan Babu University Thirupathi India K.S.R.M. College of Engineering Kadapa India

ISBN: (纸本)9798331515911

Strong battery management systems (BMS) are required to guarantee the best possible performance, safety, and longevity of battery packs due to the quick development of electric vehicles (EVs). This study introduces an Internet of Things (IoT)-enabled battery performance monitoring and control system that uses artificial intelligence (AI) methods to improve EV batteries' dependability and efficiency. In order to gather data on vital battery parameters like voltage, current, temperature, and state of charge (SoC) in real time, the suggested system combines a number of sensors. Through the use of a Wi-Fi communication module, this data is sent to a cloud-based platform for analysis, where artificial intelligence (AI) algorithms more precisely, a machine learning model are used to accurately forecast possible faults and monitor battery health. The technology maximizes battery life and energy efficiency by optimizing charging and discharging cycles through the use of predictive analytics. In order to prevent critical failures and guarantee safe operation, the system is also built to deliver alerts when battery characteristics surpass safe thresholds. The usefulness of the suggested strategy and its potential for use in smart EV technologies in the future are validated by simulation results showing notable increases in battery performance. © 2025 IEEE.

关键词： State of charge

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A Comparative Analysis of Alzheimer's Disease Detection using Deep learning 3

A Comparative Analysis of Alzheimer's Disease Detection usin...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Sharma, Garvit Gupta, Dinki Bhardwaj, Priyanshu Ramneet Verma, Pawan Sharda University Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331528201

Alzheimer's is one of the progressively debilitating conditions, and it currently affects millions of people worldwide with no definitive medication for treatment. Understanding the nature of this disease is very important and stands to achieve early intervention and management. The development of models to recognize individuals having Alzheimer's based on neuroimaging data, particularly MRI scans, has thus become accessible through recently advanced machine learning and deep learning algorithms. This paper does a comparison of some of the leading algorithms. Models used for research in this study are Support Vector Machines, Convolutional Neural Networks, 3D Convolutional Neural Networks, and hybrid pre-trained models. There's model differentiation which resulted in pointing out the gap in the system and weaknesses of each approach. Apart from this, the discovery of the Explainable AI (XAI) concept regarding insight in the predictions of the algorithms has also been emphasized recently. As a result, XAI can improve the interpretability of AI systems. With this perspective, the degree of insight could lead toward better outcomes as far as diagnosis and responses in clinical environments are concerned. Given the importance of the paper, it will suffice to contribute to further refinements of diagnostic methodologies for AD and inform future studies on the subject. © 2024 IEEE.

关键词： Convolutional neural networks

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AI-Enhanced HR Interview Simulation for Realistic Candidate Assessment 3

AI-Enhanced HR Interview Simulation for Realistic Candidate ...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sarumathi, S. Gowthaman, R.L. Sabareesh, M.B. Sultana, A. Department of Artificial Intelligence and Data Science K.S.Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research study proposes an AI application for a HR interview simulation system to improve candidate assessment. The proposed system is based on the recent AI technologies that generate questions out of the candidate’s resume and job description as in the case of a real interview with the employer. AI technology not only do the questioning but also get the emotional analysis of the candidate through the interview by analyzing the tone of the candidate’s voice and other factors. AI-based assessment captures both cognitive and affective performance, where the candidate’s merit and quality are given a true measure. This means that the proposed system greatly enhances the capabilities of the HR teams to investigate massive numbers of candidates while reducing the biases associated with conventional pure human interviews. Further, this research study aims to explain the system’s architecture, where facial expression analysis and voice sentiment analysis remain as the main components alongside reinforcement learning for question adaptation. With LLM-based approach, organizations can increase the efficiency of the hiring process, enrich candidates’ experience, and make better decisions. © 2025 IEEE.

关键词： Reinforcement learning

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Development and Application of New Energy Vehicle Testing and Evaluation System 24

Development and Application of New Energy Vehicle Testing an...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Xing, Ruofei Huang, Xiaoyan Zhang, Yufei Tian, Chuanyin Catarc Intelligent & Connected Technology Co. Ltd. Tianjin China

ISBN: (纸本)9798400712692

With the rapid development of the global new energy vehicle industry and the growing demand for professional technical talents, this paper introduces an online course and assessment system for new energy vehicles, which improves the efficiency of education and evaluation through digital resource sharing and intelligent examination functions, and provides a feasible solution for the digital transformation of education. The system is based on the B/S architecture, using Microsoft. NET Framework technology, combined with Microsoft SQL Server database, to achieve a 5-layer architecture of web and mobile application technology stack. The system provides corresponding permission configuration for different roles through data permission management design to meet personalized teaching and learning needs. The system not only improves the efficiency of resource utilization, but also provides a platform for the digitization of training resources and promotes the innovation of education models. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Curricula

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An Ontology-Lexicon-Driven Approach for Refining Sentiment Analysis Processes 25th

An Ontology-Lexicon-Driven Approach for Refining Sentiment A...

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25th international conference on intelligent data engineering and automated learning

作者： Toldy, Francisco Belo, Orlando Univ Minho ALGORITMI Res Ctr LASI P-4710059 Braga Portugal

ISBN: (纸本)9783031777370;9783031777387

The high number of sentiment analysis systems and applications developed over the last few years provided companies with very sophisticated analysis tools, allowing them to establish preferences, trends and patterns of customer behavior. This is quite important for companies intending to change their way of being, promoting work actions aimed at specific customer segments, to obtain business advantages and improve their image and performance in the market in which they work. In this paper, we present and describe a sentiment analysis system that combine techniques based on ontologies and domain lexicons, to provide relevant indicators to support the evaluation of the degree of user satisfaction and know the influence of each ontological element incorporated in opinion texts in sentiment classification.

关键词： Sentiment Analysis Opinion Analysis Ontologies Lexical Classification data Preparation Tagging Texts Marketing Applications

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Face Mask Detection in Real Time using Machine learning Algorithms 3

Face Mask Detection in Real Time using Machine Learning Algo...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, R. Sultania, Tanishq Padhmashri, V. Shreya, A. Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331509675

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16's deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time. © 2025 IEEE.

关键词： Image segmentation

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