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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3597 条记录，以下是31-40 订阅

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What Students Should Learn and Teachers Must Know About Artificial Intelligence 25th

What Students Should Learn and Teachers Must Know About Arti...

引用

25th international conference on intelligent data engineering and automated learning

作者： Opel, Simone Linxen, Andrea Beecks, Christian Fern Univ Hagen D-58097 Hagen Germany

ISBN: (纸本)9783031777370;9783031777387

Systems based on artificial intelligence have become prominent in nearly all domains. However, knowledge of the inner workings of these intelligent systems is not as widespread, partly because the associated issues have been discussed only to a limited extent in computer science education. In order to gain an overview of AI in curricula and to see what competencies teachers need to teach this content, the AIrelated content of the computer science curricula of the German federal states was analysed and compared with existing approaches. Proposals for further training courses are derived from this to enable teachers to teach AI competently.

关键词： Curriculum AI Secondary School Teacher Education learning Students Further Education Curriculum Analysis

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Hotel's Price Prediction Based on Country Specific data 25th

Hotel's Price Prediction Based on Country Specific Data

引用

25th international conference on intelligent data engineering and automated learning

作者： Balan, Andrei Popescu, Paul Stefan Mihaescu, Marian Cristian Univ Craiova Craiova Romania

ISBN: (纸本)9783031777370;9783031777387

Online hotel booking became increasingly popular as time passed, and with its popularity, the data that can be collected based on customer actions has increased. this data can serve to build intelligent systems that can provide knowledge for both customers and hotel owners. In this paper, we focus on hotel owners who can benefit from the collected data by adjusting the prices to optimise the profit of their accommodations. To accomplish this, we built a system that collected the data from *** and gathered a helpful dataset for price prediction. We used five regression algorithms and an optimization technique to obtain the best results, leading us to a 9% error for price prediction. this result allows accommodation owners to predict the room price to keep the rooms fully occupied.

关键词： price prediction linear regression optimization

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Towards Reliable Drift Detection and Explanation in Text data 25th

Towards Reliable Drift Detection and Explanation in Text Dat...

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25th international conference on intelligent data engineering and automated learning

作者： Feldhans, Robert Hammer, Barbara Bielefeld Univ Bielefeld Germany

ISBN: (纸本)9783031777301;9783031777318

When delivered to the market, machine learning models face new data which are possibly subject to novel characteristics - a phenomenon known as concept drift. As this might lead to performance degradation, it is necessary to detect such drift and, if required, adapt the model accordingly. While a variety of drift detection and adaptation methods exists for standard vectorial data, a suitable treatment of text data is less researched. In this work we present a novel approach which detects and explains drift in text data based on their representation via transformer embeddings. In a nutshell, the method generates suitable statistical features from the original distribution and the possibly shifted variation. Based on these representations, drift scores can be assigned to individual data points, allowing a visualization and human-readable characterization of the type of drift. We demonstrate the approach's effectiveness in reliably detecting drift in several experiments.

关键词： Drift Explanation Text data Transformer Visualization

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

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

引用

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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Structural and Semantic data Layers in Time Series Analyses 25th

Structural and Semantic Data Layers in Time Series Analyses

引用

25th international conference on intelligent data engineering and automated learning

作者： Grass, Alexander Beecks, Christian Decker, Stefan Fraunhofer Inst Appl Informat Technol FIT St Augustin Germany Rhein Westfal TH Aachen Aachen Germany Univ Hagen Hagen Germany

ISBN: (纸本)9783031777301;9783031777318

Understanding the inherent complexity of temporal data is crucial for effective time series analytics. One dimension of complexity is the level of structural depth at which analysis methods operate. these levels range from entire time series collections down to individual sequences of reduced dimensionality and length. Complementary to this type of complexity, is the quantity and expressiveness of knowledge associated with time series data, including labels and other features that provide valuable information. Both, the structural as well as the semantic layer, define the suitability and effectiveness of different analysis methods. In this paper we introduce a conceptual framework to support the automated selection of analytical time series approaches. To this end, we specify a context-free grammar to describe hierarchies and compositions of time series data, while also defining different classes of semantic information, resulting in a data-specific classification of time series analysis methods. Along with a demonstration via concrete examples, we provide a discussion on challenges, opportunities and future work associated with the proposed approach.

关键词： Time Series data Analytics Knowledge Discovery

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Smart Sign Language Decoder 25th

Smart Sign Language Decoder

引用

25th international conference on intelligent data engineering and automated learning

作者： Costescu, Constantin Marius Popescu, Paul Stefan Mihaescu, Marian Cristian Univ Craiova Fac Automat Comp Sci & Elect Craiova Romania

ISBN: (纸本)9783031777370;9783031777387

Sign language recognition and understanding are challenging tasks for many people who are not familiar with it, which limits communication between deaf-mute people and others. the system presented in this paper lowers the communication barrier, introducing an automatic translation layer that facilitates sign language understanding. the system uses a deep-learning model for sign language detection and a separate library for hand joint mapping. the application's architecture was designed to allow users to access the system from desktop and mobile devices. the model's results revealed an 82% accuracy, and after several tweaks on the activation function in our tests, we achieved perfect classification in our real word tests. the results of the system offered excellent accuracy, and its usability lowers the communication barrier between people, providing flexibility as the application is available for any device with a browser.

关键词： Sign Language Recognition deep learning dataset

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A Deep-learning Approach for the Identification of New Subtypes of Lung Cancer 25th

A Deep-Learning Approach for the Identification of New Subty...

引用

25th international conference on intelligent data engineering and automated learning

作者： Banerjee, Tuhin Corradini, Andrea Canon EMEA Amstelveen Netherlands MCI Innsbruck Austria

ISBN: (纸本)9783031777301;9783031777318

Lung cancer is the deadliest cancer in the world. It is caused by unchecked cell division of damaged cells in the lungs forming tumors that eventually prevent the lung from functioning properly. Identification of novel unsupervised subtypes of lung cancer is critical to reveal new insights into the underlying biology of cancer and ensure that patients receive specialized precision treatment based on the subtype of cancer they are suffering from. the ability of modern sequencing tools to produce patient-specific RNA sequencing (RNA-seq) gene expression data has transformed cancer research by offering in-depth understanding of the molecular landscape of cancer. this paper reports on a pipeline that comprise of a Deep Autoencoder (DAE) model coupled with hierarchical agglomerative clustering (H-Clust). It aims to identify new unsupervised lung cancer subtypes from RNA-seq expression samples collected from a publicly available dataset. Further, a deep learning (DL) model, Artificial Neural Network (ANN) is used to classify a patient's data into one of the newly identified subtypes.

关键词： lung cancer subtype TCGA RNA-seq deep learning DAE encoder-decoder agglomerative clustering ANN

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Preliminary Investigation on Machine learning and Deep learning Models for Change of Direction Classification in Running 25th

Preliminary Investigation on Machine Learning and Deep Learn...

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25th international conference on intelligent data engineering and automated learning

作者： Jaiswal, Pranay Kaushik, Abhishek Lawless, Fiona Malaquias, Tiago McCaffery, Fergal Dundalk Inst Technol Dundalk Ireland STATSports Newry North Ireland

ISBN: (纸本)9783031777301;9783031777318

the ability to detect, define, and classify Change of Direction (COD) movements during running plays a crucial role in sports science, as it has been widely used to assess athlete performance. Automating the process of COD classification during live games or training can provide real-time feedback. In this study, we evaluated Machine learning (ML) and Deep learning (DL) models for the classification of COD using accelerometers and gyroscope sensor data, and speed data were calculated from the Global Positioning System (GPS) sensor data. We hypothesized that DL algorithms classify COD better than ML classification algorithms. Comparative analysis showed that the best-performing DL and ML models showed similar behavior. Similarly, the statistical analysis observed no significant difference. this emphasized the importance of accurate model selection.

关键词： Change of Direction Machine learning Deep learning Sports Running Classification

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Model-Based Meta-reinforcement learning for Hyperparameter Optimization 25th

Model-Based Meta-reinforcement Learning for Hyperparameter O...

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25th international conference on intelligent data engineering and automated learning

作者： Albrechts, Jeroen Martin, Hugo M. Tavakol, Maryam Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9783031777301;9783031777318

Hyperparameter Optimization (HPO) plays a significant role in enhancing the performance of machine learning models. However, as the size and complexity of (deep) neural architectures continue to increase, conducting HPO has become very expensive in terms of time and computational resources. Existing methods that automate this process still demand numerous evaluations to find the optimal hyperparameter configurations. In this paper, we present a novel approach based on model-based reinforcement learning to effectively improve sample efficiency while minimizing resource consumption. We formulate the HPO task as a Markov decision process and develop a predictive dynamics model for efficient policy optimization. Additionally, we employ the Deep Sets framework to encode the state space, which is then leveraged in meta-learning for transfer of knowledge across multiple datasets, enabling the model to quickly adapt to new datasets. Empirical studies demonstrate that our approach outperforms alternative techniques on publicly available datasets in terms of sample efficiency and accuracy.

关键词： Deep reinforcement learning

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Comparative Study of Federated learning Algorithms Based on SPADE Agents 25th

Comparative Study of Federated Learning Algorithms Based on ...

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25th international conference on intelligent data engineering and automated learning

作者： Enguix, Francisco Peris, Saul Cerda Rincon, J. A. Carrascosa, C. Univ Politec Valencia UPV Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain Univ Burgos Dept Digitalizac Escuela Politec Super Ctra Oron 28 Burgos 09200 Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning (FL) is a prominent method in machine learning, that ensures privacy by enabling distributed devices to collaboratively learn a shared model without exchanging local data. this paper provides a comparative analysis of various FL algorithms implemented on the Smart Python Agent Development Environment (SPADE) framework. We focus on evaluating the performance, scalability, and resilience of these algorithms across different network setups and data distribution scenarios. Our results highlight the differential impacts of decentralized versus centralized approaches, particularly under non-IID data conditions, common in real-world applications. By leveraging SPADE agents and consensus algorithms, this study not only tests algorithmic efficiency and system robustness but also explores advanced strategies like asynchronous updates and coalition-based learning, which show promise in enhancing model accuracy and reducing communication overhead.

关键词： Federated learning Multi-agent systems Consensus

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