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检索条件"任意字段=23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022"

共 102 条记录，以下是31-40 订阅

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Topological Analysis of Credit data: Preliminary Findings 23rd

Topological Analysis of Credit Data: Preliminary Findings

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Cooper, James Mitic, Peter Reinert, Gesine Temcinas, Tadas Santander US Boston MA 02109 USA UCL Dept Comp Sci London England Santander UK London England Univ Oxford Dept Stat Oxford England

ISBN: (纸本)9783031217524;9783031217531

Intuitively, similar customers should have similar credit risk. Capturing this similarity is often attempted using Euclidean distances between customer features and predicting credit default via logistic regression. Here we explore the use of topological data analysis for describing this similarity. In particular, persistent homology algorithms provide summaries of point clouds which relate to their topology. This approach has been shown to be useful in many applications but to the best of our knowledge, applying topological data analysis to prediction of credit risk is novel. We develop a pipeline which is based on the topological analysis of neighbourhoods of customers, with the neighbourhoods determined by a geometric network construction. We find a modest signal using three data sets from the Lending Club, and the Japan Credit Screening data set. The Cleveland oncological data set is used to validate the pipeline. The results have high variance, but they indicate that including such topological features could improve credit risk prediction when used as additional explanatory variable in a logistic regression.

关键词： Credit risk Topological data analysis Barcode Landscape Logistic regression

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Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis 23rd

Federating Unlabeled Samples: A Semi-supervised Collaborativ...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Launet, Laetitia del Amor, Rocio Colomer, Adrian Mosquera-Zamudio, Andres Moscardo, Anais Monteagudo, Carlos Zhao, Zhiming Naranjo, Valery Univ Politecn Valencia I3B Valencia Spain Univ Valencia Hosp Clin Univ Valencia Dept Pathol Valencia Spain Univ Amsterdam Multiscale Networked Syst Amsterdam Netherlands

ISBN: (纸本)9783031217524;9783031217531

Over the last decades, deep learning-based algorithms have witnessed tremendous progress in the medical field to assist pathologists in clinical decisions and reduce their workload. For these models to reach their full potential, access to large and diverse datasets is essential, but collaborations between hospitals are highly limited by privacy-related regulations. At the same time, medical institutions do not always have specialized pathologists to diagnose biopsies and label local data. To address these limitations, federated learning gained traction to enable multi-institution model training without sharing sensitive patient data. However, this technique is still in its infancy when it comes to digital pathology applications, and does not consider institutions with unlabeled data in federations. In this paper, we introduce a novel semi-supervised federated learning approach that promotes multi-institutional training of deep learning models while integrating unlabeled collaborating data sources into the federated setup. The experimental results show a better performance for models trained under a federated setting with both labeled and unlabeled data. Optimally, this framework will also bring the promise of assisting clinical decisions in hospitals that do not have specialized pathologists.

关键词： Federated learning Self-training Whole slide images Multiple instance learning

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A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting 23rd

A Sequence to Sequence Long Short-Term Memory Network for Fo...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Santos, Luis Matos, Luis Miguel Ferreira, Luis Alves, Pedro Viana, Mario Pilastri, Andre Cortez, Paulo CCG ZGDV Inst EPMQ Guimaraes Portugal KYAIA SOLUCOES INFORMAT LDA Guimaraes Portugal OVERCUBE SA Guimaraes Portugal Univ Minho Dept Informat Syst LASI ALGORITMI Res Centre Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

Footwear sales forecasting is a critical task for supporting product managerial decisions, such as the management of footwear stocks and production levels. In this paper, we explore a recently proposed Sequence to Sequence (Seq2Seq) Long Short-Term Memory (LSTM) deep learning architecture for multi-step ahead footwear sales Time Series Forecasting (TSF). The analyzed Seq2Seq LSTM neural network is compared with two popular TSF methods, namely ARIMA and Prophet. Using real-world data from a Portuguese footwear company, several computational experiments were held. Focusing on daily sales, we analyze data recently collected during a 3-year period (2019-2021) and related with seven types of products (e.g., sandals). The evaluation assumed a robust and realistic rolling window scheme that considers 28 training and testing iterations, each related with one week of multi-step ahead predictions. Overall, competitive predictions were obtained by the proposed LSTM model, resulting in a weekly Normalized Mean Absolute Error (NMAE) that ranges from 5% to 11%.

关键词： Time series forecasting ARIMA Prophet Deep learning

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Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active learning 23

Accelerating the Training of Convolutional Neural Networks f...

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23rd IEEE international conference on intelligent Transportation Systems (ITSC)

作者： Chen, Weitao Salay, Rick Sedwards, Sean Abdelzad, Vahdat Czarnecki, Krzysztof Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781728141497

Semantic segmentation is an important perception function for automated driving (AD), but training a deep neural network for the task using supervised learning requires expensive manual labelling. Active learning (AL) addresses this challenge by automatically querying and selecting a subset of the dataset to label with the aim to iteratively improve the model performance while minimizing labelling costs. This paper presents a systematic study of deep AL for semantic segmentation and offers three contributions. First, we compare six different state-of-the-art querying methods, including uncertainty-estimate, Bayesian, and out-of-distribution methods. Our comparison uses the state-of-the-art image segmentation architecture DeepLab on the Cityscapes dataset. Our results demonstrate subtle differences between the querying methods, which we analyze and explain. We show that the differences are nevertheless robust by reproducing them on architecture-independent randomly generated data. Second, we propose a novel way to aggregate the output of a query, by counting the number of pixels having acquisition values above a certain threshold. Our method outperforms the standard averaging approach. Finally, we demonstrate that our findings remain consistent for whole images and image crops.

关键词： Semantic Segmentation

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Machine-learning Models for Software Quality: a Compromise Between Performance and Intelligibility

Machine-Learning Models for Software Quality: a Compromise B...

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23rd IEEE international conference on Tools with Artificial Intelligence (ICTAI)

作者： Lounis, Hakim Gayed, Tamer Fares Boukadoum, Mounir Univ Quebec Dept Informat UQAM Montreal PQ H3C 3P8 Canada

ISBN: (纸本)9780769545967

Building powerful machine-learning assessment models is an important achievement of empirical software engineering research, but it is not the only one. Intelligibility of such models is also needed, especially, in a domain, software engineering, where exploration and knowledge capture is still a challenge. Several algorithms, belonging to various machine-learning approaches, are selected and run on software data collected from medium size applications. Some of these approaches produce models with very high quantitative performances;others give interpretable, intelligible, and "glass-box" models that are very complementary. We consider that the integration of both, in automated decision-making systems for assessing software product quality, is desirable to reach a compromise between performance and intelligibility.

关键词： machine-learning software product quality assessment models metrics maintainability reusability

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Multi-Scale Deep Ensemble learning for Melanoma Skin Cancer Detection 23

Multi-Scale Deep Ensemble Learning for Melanoma Skin Cancer ...

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23rd IEEE international conference on Information Reuse and Integration for data Science (IEEE IRI)

作者： Guergueb, Takfarines Akhloufi, Moulay A. Univ Moncton Dept Comp Sci Percept Robot & Intelligent Machines Res Grp PRIM Moncton NB Canada

ISBN: (纸本)9781665466035

Skin cancer is the most prevalent type of cancer in the world. Melanoma, specifically is among the deadliest variants of cancer, But if detected early, most melanomas can be cured with minor surgery. In this study we propose an innovative melanoma detection pipeline which utilizes ensemble learning to combine the predictive power of several deep convolutional neural network models. All experiments are performed using image data acquired from the Society for Imaging Informatics in Medicine and the international Skin Imaging Collaboration SIIM-ISIC 2020. The proposed approach achieved a high performance with an Area Under Curve (AUC) of 99.02% outperforming many state-of-the-art algorithms.

关键词： Pipelines Imaging Training data Surgery Melanoma Predictive models Prediction algorithms

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Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring 23rd

Synonym-Based Essay Generation and Augmentation for Robust A...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Tashu, Tsegaye Misikir Horvath, Tomas Eotvos Lorand Univ Fac Informat Dept Data Sci & Engn Telekom Innovat Labs Pazmany Peter Setany 1-C H-1117 Budapest Hungary Wollo Univ Kombolcha Inst Technol Coll Informat Kombolcha 208 Ethiopia Pavol Jozef Safarik Univ Fac Sci Inst Comp Sci Jesenna 5 Kosice 04001 Slovakia

ISBN: (纸本)9783031217524;9783031217531

Automatic essay scoring (AES) models based on neural networks (NN) have had a lot of success. However, research has shown that NN-based AES models have robustness issues, such that the output of a model changes easily with small changes in the input. We proposed to use keyword-based lexical substitution using BERT that generates new essays (adversarial samples) which are lexically similar to the original essay to evaluate the robustness of AES models trained on the original set. In order to evaluate the proposed approach, we implemented three NN-based scoring approaches and trained the scoring models using two stages. First, we trained each model using the original data and evaluate the performance using the original test and newly generated test set to see the impact of the adversarial sample of the model. Secondly, we trained the models by augmenting the generated adversarial essay with the original data to train a robust model against synonym-based adversarial attacks. The results of our experiments showed that extracting the most important words from the essay and replacing them with lexically similar words, as well as generating adversarial samples for augmentation, can significantly improve the generalization of NN-based AES models. Our experiments also demonstrated that the proposed defense is capable of not only defending against adversarial attacks, but also of improving the performance of NN-based AES models.

关键词： Adversarial attack data augmentation Automatic essay scoring

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Deep learning Transforming the Manufacturing Industry: A Case Study 23

Deep Learning Transforming the Manufacturing Industry: A Cas...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ahmad, Hafiz Mughees Rahimi, Afshin Hayat, Khizer University of Windsor Mechanical Automotive and Materials Engineering WindsorON Canada Ifiveo Canada INC. WindsorON Canada

ISBN: (纸本)9781665494571

The traditional manufacturing industry uses decades-old technology and manual processes that are expensive, laborious, redundant, and a slight human error can cost millions of dollars. With the industrial revolution 4.0, industries are adopting intelligent additions and modifications into their systems involving robotics and internet of things (IoT) based technology, which can help in their operations. Artificial intelligence and machine learning have revolutionized every field, and the manufacturing industry has also started to reap its benefits. In this case study, we have explained the applications of deep learning-based approaches contributing to the automation of surveillance of production lines. Several cameras are installed to collect live feeds on the floor lines where the manually operated cranes and different tasks it performs are detected and tracked by state-of-the-art Convolutional Neural Network-based YOLOv3, an object detection model, and DeepSORT tracking algorithm. The post-processing is done on the matrices generated. It varies based on the use case and helps the manufacturers with automated inspection, quality control, and labor productivity. This process will save millions of dollars per year just in the crane maintenance operation for our industry partner's client. The developed technology can be replicated in almost every manufacturing industry using traditional methods and provide them better key insights into their production lines. © 2021 IEEE.

关键词： Manufacturing industries Deep learning Cranes Tracking Surveillance Object detection Maintenance engineering

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Performance/Resources Comparison of Hardware Implementations on Fully Connected Network Inference 23rd

Performance/Resources Comparison of Hardware Implementations...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Lozada, Randy Ruiz, Jorge Gonzalez, Manuel L. Sedano, Javier Villar, Jose R. Garcia-Vico, Angel M. Skibinsky-Gitlin, E. S. Inst Tecnol Castilla & Leon Burgos Spain Univ Oviedo Oviedo Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18071 Spain

ISBN: (纸本)9783031217524;9783031217531

Fully Connected Network inference is a complex algorithm that can be accelerated using edge devices like Field Programmable Gate Array (FPGA). One commonly known performance improvement for Fully Connected Network inference is quantization. This technique replaces the floating points weights of the network by integers. Frameworks like Open Neural Network Exchange (ONNX) and Tensorflow Lite provide solutions for this procedure. However, these frameworks have different inference algorithms with different operations and data types. In this article inference algorithms of common Fully Connected Networks in ONNX and Tensorflow Lite have been analysed. A performance and resource usage comparison is tested on Xilinx((R)) Zynq UltraScale+(TM) PSoC. Results show that to achieve lower latency is better to avoid floating point operations in the inference algorithm. In terms of FPGA resource usage, an increase is observed when the neural network becomes more complex regardless of the algorithm. This growth in resource usage is framework-dependent.

关键词： FPGA Fully connected network Quantization

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The Black-Box Syndrome: Embracing Randomness in Machine learning Models 23rd

The Black-Box Syndrome: Embracing Randomness in Machine Lear...

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23rd international conference on Artificial Intelligence in Education (AIED)

作者： Anthis, Z. UCL Gower St London WC1 6BT England

ISBN: (纸本)9783031116476;9783031116469

Acknowledging the 'curse' of dimensionality, the educational sector has reasonably turned to automated (in some cases autonomous) solutions, in the process of extracting and communicating patterns in data, to promote innovative teaching and learning experiences. As a result, learning Analytics (LA) and Educational dataMining (EDM) have both been relying on various Machine learning (ML) techniques to project novel and meaningful predictions. This inclusion has led to the need for developing new professional skills in the teaching community, that go beyond digital competence and data literacy. This paper seeks to address the issue of ML adoption in educational settings by using an interactive Exploratory learning Environment (ELE) to test the potential impact of randomness on explainability. The goal is to investigate how misconceptions about stochasticity can lead to distorted projections or expectations, and potentially expose any lack of transparency (to teachers), indirectly affecting the overall trust in artificially intelligent tools.

关键词： learning Analytics (LA) Educational data Mining (EDM) Machine learning (ML) Exploratory learning Environment (ELE)

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