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检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

共 2202 条记录，以下是81-90 订阅

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The IoT Service Agent Model based on Federated learning to Improve Service Quality 22

The IoT Service Agent Model based on Federated Learning to I...

引用

22nd IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Hsu, Tse-Chuan Chu, William Cheng-Chung Chen, Shyh-Wei Soochow Univ Dept Comp Sci & Informat Management Taipei Taiwan Tunghai Univ Dept Comp Sci Tiachung Taiwan

ISBN: (纸本)9798350319910

The combination of learning analysis technology and terminal equipment can provide a rapidly imitating learning method between devices, and cognitive intelligence affects the learning effect. To enhance automated learning services, we can use federated learning methods to enable the training of different devices and enhance each other through devices. Using the training database to improve the quality of automated learning services. In this study, a novel agentassisted active detection and data collection framework is designed. Monitoring agents can learn from each other to establish intelligent models, and through mutual communication between devices. Can check if established data can be applied to machine data model to get data. It can be used for intelligent manufacturing in the future. The agent may learn methods of learning and managing between devices having different properties. Obtaining experimental simulation and control data, and using machine learning to analyze growth progress and results allow for a deeper analysis of associated adjustments and anticipated changes.

关键词： component Federated learning Dectection Design Agent automated Learining IoT

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Neural Complexity Assessment: A Deep learning Approach to Readability Classification for European Portuguese Corpora 22nd

Neural Complexity Assessment: A Deep Learning Approach to Re...

引用

22nd international conference on intelligent data engineering and automated learning

作者： Correia, Joao Mendes, Rui Univ Minho Ctr Algoritmi Dept Informat Braga Portugal

ISBN: (纸本)9783030916077;9783030916084

The following paper describes a Deep learning model capable of classifying the inherent readability complexity of a piece of text in European Portuguese. The model was developed using modern Natural Language Processing techniques, featuring a highly-fine-tuned Neural Network which takes as input both the text as well as multiple metrics relating to it. This classifier was trained on a dataset featuring texts divided in 5 CEFR categories, obtaining an accuracy of 73%, a top 2 accuracy of 90% and an adjacent accuracy of 94%.

关键词： Deep learning Natural language processing Textual complexity

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automated Classification of Metamodel Repositories: A Machine learning Approach 22

Automated Classification of Metamodel Repositories: A Machin...

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22nd ACM/IEEE international conference on Model Driven engineering Languages and Systems (MODELS)

作者： Nguyen, Phuong T. Di Rocco, Juri Di Ruscio, Davide Pierantonio, Alfonso Iovino, Ludovico Univ Aquila Laquila Italy Gran Sasso Sci Inst Laquila Italy

ISBN: (纸本)9781728125350

Manual classification methods of metamodel repositories require highly trained personnel and the results are usually influenced by the subjectivity of human perception. Therefore, automated metamodel classification is very desirable and stringent. In this work, Machine learning techniques have been employed for metamodel automated classification. In particular, a tool implementing a feed-forward neural network is introduced to classify metamodels. An experimental evaluation over a dataset of 555 metamodels demonstrates that the technique permits to learn from manually classified data and effectively categorize incoming unlabeled data with a considerably high prediction rate: the best performance comprehends 95.40% as success rate, 0.945 as precision, 0.938 as recall, and 0.942 as F-1 score.

关键词： Metamodels WAREHOUSE Taxonomy Artificial Intelligence Machine learning Automation Classification Methods Subjectivity recall Mental Recall dataset

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Finding Local Explanations Through Masking Models 22nd

Finding Local Explanations Through Masking Models

引用

22nd international conference on intelligent data engineering and automated learning

作者： Angiulli, Fabrizio Fassetti, Fabio Nistico, Simona Univ Calabria DIMES Dept Arcavacata Di Rende Italy

ISBN: (纸本)9783030916077;9783030916084

Among the XAI (eXplainable Artificial Intelligence) techniques, local explanations are witnessing increasing interest due to the user need to trust specific black-box decisions. In this work we explore a novel local explanation approach appliable to any kind of classifier based on generating masking models. The idea underlying the method is to learn a transformation of the input leading to a novel instance able to confuse the black-box and simultaneously minimizing dissimilarity with the instance to explain. The transformed instance then highlights the parts of the input that need to be (de-)emphasized and acts as an explanation for the local decision. We clarify differences with existing local explanation methods and experiment our approach on different image classification scenarios, pointing out advantages and peculiarities of the proposal.

关键词： eXplainable Artificial Intelligence Local explanations for machine learning Deep learning

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Combining Encoplot and NLP Based Deep learning for Plagiarism Detection 22nd

Combining Encoplot and NLP Based Deep Learning for Plagiaris...

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22nd international conference on intelligent data engineering and automated learning

作者： Amzuloiu, Ciprian Mihaescu, Marian Cristian Rebedea, Traian Univ Craiova Craiova Romania Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9783030916077;9783030916084

This paper tackles the classical problem of plagiarism detection by employing current state-of-the-art NLP methods based on Deep learning. We investigate whether transformer models may be used along with existing solutions for plagiarism detection, such as Encoplot and clustering, to improve their results. Experimental results show that transformers represent a good solution for capturing the semantics of texts when dealing with plagiarism. Further efficiency improvements are needed as the proposed method is highly effective but also requires high computational resources. This prototype approach paves the way to further fine-tuning such that we may obtain a solution that scales well for a large number of source documents and large-sized documents.

关键词： Transformers Document embeddings Plagiarism detection Encoplot

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Multimodal Semi-supervised Bipolar Disorder Classification 22nd

Multimodal Semi-supervised Bipolar Disorder Classification

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22nd international conference on intelligent data engineering and automated learning

作者： AbaeiKoupaei, Niloufar Al Osman, Hussein Univ Ottawa Sch Elect & Comp Engn Ottawa ON Canada

ISBN: (纸本)9783030916077;9783030916084

The objective of this study is to classify the states of individuals with bipolar disorder. We employ a dataset that uses the Young Mania Recall Scale to distinguish the manic states of patients as: Mania, Hypo-Mania, and Remission. The dataset comprises audio-visual recordings of bipolar disorder patients undergoing a structured interview. Having a small dataset and confidential test labels have motivated us to train a classifier using a semi-supervised ladder network, which benefits from unlabeled data during training. The key advantage of developing a semi-supervised model is removing the manual annotation training data, which is an expensive and time-consuming. We collect informative audio, visual, and textual features from the recordings to realize a multi-model classifier of the manic states. The proposed model achieved a 53.7% UAR and 60.0% UAR on the test and development sets, respectively.

关键词： Bipolar disorder automated detection Semi-supervide learning

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Semi-Supervised learning for RGB-D Object Recognition 22

Semi-Supervised Learning for RGB-D Object Recognition

引用

22nd international conference on Pattern Recognition (ICPR)

作者： Cheng, Yanhua Zhao, Xin Huang, Kaiqi Tan, Tieniu Chinese Acad Sci Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp Beijing 100864 Peoples R China

ISBN: (纸本)9781479952083

Conventional supervised object recognition methods have been investigated for many years. Despite their successes, there are still two suffering limitations: (1) various information of an object is represented by artificial features only derived from RGB images;(2) lots of manually labeled data is required by supervised learning. To address those limitations, we propose a new semi-supervised learning framework based on RGB and depth (RGB-D) images to improve object recognition. In particular, our framework has two modules: (1) RGB and depth images are represented by convolutional-recursive neural networks to construct high level features, respectively;(2) co-training is exploited to make full use of unlabeled RGB-D instances due to the existing two independent views. Experiments on the standard RGB-D object dataset demonstrate that our method can compete against with other state-of-the-art methods with only 20% labeled data.

关键词： Accuracy Cameras Feature extraction Object recognition Semisupervised learning Shape Training

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Toward an intelligent Tutoring System for Tone Languages: learning of Tone Levels in Moore 22

Toward an Intelligent Tutoring System for Tone Languages: Le...

引用

22nd IEEE international conference on Advanced learning Technologies (ICALT)

作者： Zongo, Pengwende Ouedraogo, Tounwendyam Frederic Univ Norbert Zongo Lab Math Informat & Applicat Koudougou Burkina Faso

ISBN: (纸本)9781665495196

This paper presents preliminary results of a research project that aims to build an intelligent Tutoring System (ITS) to learn the tones of the Moore language, the most spoken language in Burkina Faso. This interactive learning system is a solution that we propose to meet the needs for local language learning expressed by the staff of government and Non-Governmental Organizations. We use CommonKADS, a knowledge engineering methodology to specify the knowledge and processes of our system, particularly the inference models of the learning activities involved in the domain and pedagogical modules of the ITS.

关键词： intelligent Tutoring System Tone Language Inference Knowledge engineering

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On Artificial Intelligence for Simulation and Design Space Exploration in Gas Turbine Design 22

On Artificial Intelligence for Simulation and Design Space E...

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ACM/IEEE 22nd international conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Pilarski, Sebastian Staniszewski, Martin Villeneuve, Frederic Varro, Daniel McGill Univ Elect & Comp Engn Montreal PQ Canada Siemens Canada Ltd Montreal PQ Canada Siemens Energy Inc Orlando FL USA

ISBN: (纸本)9781728151250

Gas turbine design is a process that requires designing many interrelated subsystems, e.g., performance, secondary air system, air compression, or combustion. Subsystem models are created by various engineering design tools. During the design process there exists an extraordinary amount of generated data resulting from created models, simulation, and engine field tests. This data can be leveraged by artificial intelligence techniques such as machine learning to help accelerate the exploration of the large design spaces existing in the complex system of a gas engine. This paper presents a vision and road map of integrating such AIs and preliminary ideas on relevant AI models for such use cases. We explore increasing the realistic nature of existing simulations, approximating simulations to avoid excess computation, and cumulative effect modeling.

关键词： Artificial intelligence computer aided engineering computational intelligence intelligent systems internal combustion engines knowledge engineering learning systems machine learning prediction methods

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Improving Maximum Likelihood Estimation Using Marginalization and Black-Box Variational Inference 22nd

Improving Maximum Likelihood Estimation Using Marginalizatio...

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22nd international conference on intelligent data engineering and automated learning

作者： Shalileh, Soroosh HSE Univ Lab Methods Big Data Anal LAMBDA Pokrovsky Blvd 11 Moscow Russia

ISBN: (纸本)9783030916077;9783030916084

Based upon Black Box Variational Inference, a new set of classification algorithms has recently emerged. The goals of this set of algorithms are twofold: 1) increasing generalization power;2) decreasing computational and implementation complexity. To this end, we assume a set of latent variables during the generation of data points. We subsequently marginalize the conventional classification likelihood objective function w.r.t this set of latent variables and then apply black-box variational inference to estimate the marginalized likelihood. We evaluate the performance of the proposed method by comparing the results obtained from the application of our method to real-world datasets with those obtained using several classification algorithms. The experimental results prove that our proposed method is competitive.

关键词： Black box variational inference classification Variational inference classification Variational inference Classification

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