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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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Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes 23rd

Using Design of Experiments to Support the Commissioning of ...

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

作者： Voigt, Tim Schoene, Marvin Kohlhase, Martin Nelles, Oliver Kuhn, Martin Bielefeld Univ Appl Sci Fac Engn & Math Ctr Appl Data Sci Gutersloh Bielefeld Germany Univ Siegen Automat Control Mechatron Siegen Germany Miele & Cie KG Prod Qual Gutersloh Germany

ISBN: (纸本)9783031217524;9783031217531

Ensuring high product quality is an important success factor in modern industry. data-driven models are increasingly used for this purpose and need to be integrated into industrial processes as early as possible. As these models require high-quality training data, strategies for obtaining such data in the commissioning phase of a process are needed. Design of experiments (DoE) methods can be used for this task, but are not directly applicable for all types of processes. A common class of processes, to which most DoE methods cannot be applied, are assembly processes. The approach described in this paper aims to gather as much information as possible from an assembly process with minimal effort. It makes use of given sets of components from which optimal combinations are created, in order to achieve a space-filling design. Further design improvements are achieved by additionally specifying different mounting position of parts in the design and by using a subset selection procedure to remove less informative design points. The approach was successfully applied to an assembly process of washing machine drums to predict the radial deviation of assembled drums.

关键词： Design of experiments Optimal design Quality prediction Assembly processes

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Microbial Taxonomy: An Artful Exploration of Microbes with Neural Networks 3

Microbial Taxonomy: An Artful Exploration of Microbes with N...

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3rd international conference on intelligent Computing, Communication and Convergence, ICI3C 2023

作者： Abhishek, S. Anjali, T. Prakash, Prathibha Bentov, Rina Barouch Rolls-Royce Inc Bangalore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Stanford University School of Medicine Department of Competative Medicine 300 Pastuer Drive Edward building Rm R320 StanfordCA United States

ISBN: (纸本)9798350343922

Microorganisms, which include a wide range of living forms such as fungi and algae, are essential to human health, biotechnological uses, and ecological equilibrium. Accurate taxonomic categorization of these minuscule organisms is crucial in comprehending their ecological importance and capitalizing on their possibilities in several scientific fields. Our research explores the nexus between microbiology and AI, thoroughly examining our novel method for classifying microorganisms. Our approach is the precise training of sophisticated neural networks on large data sets selected to capture the complex genetic and morphological characteristics unique to every microbe. This work highlights the potential revolutionary effect of deep learning in microbiology and demonstrates the high classification accuracy reached. Our research produced an astounding accuracy of 99%, highlighting the resilience of our method in distinguishing the complex morphological and genetic subtleties unique to every bacterium. Our method's resilience provides possibilities for automated taxonomic categorization, which may have a wide range of applications, from biotechnological advancements to medical diagnostics and environmental monitoring. © 2023 IEEE.

关键词： Biotic

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An Integrated Platform for Educational and Research Management Using Institutional Digital Resources 3rd

An Integrated Platform for Educational and Research Manageme...

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3rd international conference on Novel and intelligent Digital Systems, NiDS 2023

作者： Chytas, Konstantinos Tsolakidis, Anastasios Triperina, Evangelia Karanikolas, Nikitas N. Skourlas, Christos University of West Attica department of Informatics and Computer Engineering Athens12243 Greece

ISBN: (纸本)9783031441455

Currently, universities not only offer technologically enhanced education and automated processes, but also provide a wide spectrum of online services, especially after COVID19. We propose e-EDURES, an integrated platform for educational and research management in tertiary education to further benefit the stakeholders by relying on the data of university online services to enable strategic planning and decision making. More specifically, in this paper, we focus on the presentation of an interactive system that utilizes the data derived by university’s synchronous and asynchronous e-learning services to gain insights about the educational performance of the students. It employs Educational data Mining techniques to ultimately boost the educational process, by assessing the previous performance, monitoring the provided services and predicting future performance of the students, in order to avoid potential pitfalls. The proposed solution merges the current advances in smart education and educational data mining to offer an advanced and updated version of online services provided by universities that respond better to the current needs in academia. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Zero-Shot Knowledge Graph Completion for Recommendation System 23rd

Zero-Shot Knowledge Graph Completion for Recommendation Syst...

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

作者： Wang, Zhiyuan Chen, Cheng Tang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Key Lab Brain Inspired Intelligent Comp Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031217524;9783031217531

Knowledge graphs are structured representations of actual entities and relations. They are widely used to improve the performance of downstream tasks such as recommendation systems and semantic searching. Knowledge graph completion (KGC) is a technology for discovering the missing relations between the entities in a knowledge graph (KG). Existing methods leverage known relations on a KG to build a model to predict missing relations. Such methods implicitly require a substantial number of relations to be known in advance, which might not be available in practice. To cope with the cold-start scenario for KGC, i.e., no relation is known in advance, we propose a zero-shot approach in this paper. Our approach converts the KGC process to an optimization problem. It uses the Evolutionary Strategy (ES) algorithm to optimize a model used to complete the KG according to the performance of the recommendation system constructed based on the completed KG. Experiments on a movie dataset demonstrate that our approach can complete the KG in the cold-start scenario and improve the performance of the recommendation system built based on the completed KG.

关键词： Knowledge graph completion Zero-shot Recommendation system Evolutionary strategy

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Ensemble Stack Architecture for Lungs Segmentation from X-ray Images 23rd

Ensemble Stack Architecture for Lungs Segmentation from X-ra...

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

作者： Lasker, Asifuzzaman Ghosh, Mridul Obaidullah, Sk Md Chakraborty, Chandan Goncalves, Teresa Roy, Kaushik Aliah Univ Dept Comp Sci & Engn Kolkata 700160 India Shyampur Siddheswari Mahavidyalaya Dept Comp Sci Howrah 711312 India Natl Inst Tech Teachers Training & Res Dept Comp Sci & Engn Howrah 700106 India Univ Evora Dept Comp Sci Evora Portugal Univ Evora ALGORITMI Ctr Evora Portugal West Bengal State Univ Dept Comp Sci Barasat 700126 India

ISBN: (纸本)9783031217524;9783031217531

In healthcare, chest X-rays are an inexpensive medical imaging diagnostic tools. The lung images segmentation from chest X-rays (CXRs) is important for screening and diagnosing diseases. The lungs are opacified in many patients' CXRs, making it difficult to segment them. A segmentation algorithm based on U-Net is proposed in this paper to address this problem. The proposed architecture was developed using three pre-trained models: MobileNetV2, InceptionResNetV2, and EfficientNetB0. In this architecture, we designed a ensemble stacked framework which is based on the pre-trained models to improve segmentation performance. Compared with the conventional U-Net model, our method improves by 3.02% dice coefficient and 3.43% IoU experimenting on the three public lung segmentation datasets.

关键词： Deep learning Lung segmentation Unet Medical imaging Chest X-rays

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Go-Around Prediction in Non-Stabilized Approach Scenarios Through a Regression Machine-learning Model Trained from Pilots' Expertise 23rd

Go-Around Prediction in Non-Stabilized Approach Scenarios Th...

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

作者： Cantero, Jesus Colomer, Adrian Launet, Laetitia Duchevet, Alexandre De La Hogue, Theo Imbert, Jean-Paul Naranjo, Valery Univ Politecn Valencia Inst Res & Innovat Bioengn Valencia Spain ENAC Ecole Natl Aviat Civile 7 Ave Edouard Belin CS 54005 F-31055 Touluse France

ISBN: (纸本)9783031217524;9783031217531

To face the pilots' shortage that could hit the aeronautical world in the future, single-pilot operations are envisaged as a solution. Even if the single pilot will have to assume the tasks that are today done by two pilots in the cockpits, the safety level of the flight should remain the same compared to standard operations. The challenge will be significant during high workload phases when complex decisions are to be made. We focused our work on the final approach phase during which the decision to perform a go-around or to land must be taken. This decision reveals to be particularly difficult as studies show that 97% of the time, the choice to land is made whereas a go-around was needed. In this paper, we propose a machine learning regression algorithm based on expert users classification to predict the need for a go-around during the final approach. An average precision of 0.96, a sensitivity of 0.84 and an F1-score of 0.88 shows a promising behaviour for the GA automatic identification task.

关键词： Go-around prediction Non-stabilized approach Pilot expertise Regression neural network Aircraft parameters

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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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Real-Time 6-DoF Object Pose Estimation Network Based on 2D-3D Coordinate Correspondence 3rd

Real-Time 6-DoF Object Pose Estimation Network Based on 2D-3...

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

作者： Li, Shuai Chen, Jinlong Yang, Minghao Su, Jianhua Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guangxi Guilin541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guangxi Guilin541004 China Beijing100190 China HuaiRou Beijing101400 China Beijing100190 China

ISBN: (纸本)9789819721993

Object 6D pose estimation is essential for a variety of applications, including autonomous driving, robotic manipulation, and automated harvesting. Due to the influence of different lighting conditions and occlusions, objects with different poses may exhibit significant variations in appearance in different views. This makes the estimation of the 6D pose of objects from a single RGB image a challenging task. This paper proposes a deep learning network capable of simultaneously detecting and estimating the 6D pose (3D rotation + 3D translation) of target objects. As establishing a large-scale dataset using real data is extremely challenging, this paper introduces an automatic collection scheme based on virtual data generated from real images. We conducted experiments on the generated astronaut dataset using our data collection scheme for 6D pose estimation. The experimental results show that the accuracy of 6D pose estimation based on the ADD metric is 96%. Therefore, this method has significant potential and can be extended to other downstream tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep 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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