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17th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Batouche, Abderrahim O. Czeizler, Eugen Koskinen, Miika Mirtti, Tuomas Rannikko, Antti S. Doctoral Programme in Computer Science University of Helsinki Helsinki Finland Research Program in Systems Oncology University of Helsinki Helsinki Finland ICAN-Digital Precision Cancer Medicine Flagship Helsinki Finland HUS Helsinki University Hospital Helsinki Finland Department of Pathology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland

The presence of detailed clinical information in electronic health record (EHR) systems presents promising prospects for enhancing patient care through automated retrieval techniques. Nevertheless, it is widely acknowledged that accessing data within EHRs is hindered by various methodological challenges. Specifically, the clinical notes stored in EHRs are composed in a narrative form, making them prone to ambiguous formulations and highly unstructured data presentations, while structured reports commonly suffer from missing and/or erroneous data entries. This inherent complexity poses significant challenges when attempting automated large-scale medical knowledge extraction tasks, necessitating the application of advanced tools, such as natural language processing (NLP), as well as data audit techniques. This work aims to address these obstacles by creating and validating a novel pipeline designed to extract relevant data pertaining to prostate cancer patients. The objective is to exploit the inherent redundancies available within the integrated structured and unstructured data entries within EHRs in order to generate comprehensive and reliable medical databases, ready to be used in advanced research studies. Additionally, the study explores potential opportunities arising from these data, offering valuable prospects for advancing research in prostate cancer. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Electronic health record

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Active fault-tolerant control design for T-S fuzzy systems with application to a near space vehicle

Active fault-tolerant control design for T-S fuzzy systems w...

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IEEE International Symposium on intelligent Control (ISIC)

作者： Zhifeng Gao Bin Jiang Youmin Zhang Fuchun Sun College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Department of Mechanical and Industrial Engineering Concordia University Montreal QUE Canada State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China

Based on adaptive and ℋ ∞ control techniques, this paper deals with the problem of active fault-tolerant control for a class of T-S fuzzy systems with actuator faults. The presented approach is dependent on a fuzzy iterative learning observer (ILO), which produces a control input adjustment term in order to compensate for the effect of actuator faults. Based on Lyapunov theorem, a sufficient condition for the stability of error dynamic systems is derived. The active fault-tolerant control strategy proposed in the paper is finally demonstrated in simulation on the near space vehicle (NSV) reentry attitude dynamics to illustrate its effectiveness.

关键词： Fuzzy systems Actuators Vehicle dynamics Fault diagnosis Observers Fault tolerance Fault tolerant systems

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Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light

arXiv

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arXiv 2024年

作者： Song, Alexander Kottapalli, Sai Nikhilesh Murty Goyal, Rahul Schölkopf, Bernhard Fischer, Peer Max Planck Institute for Medical Research Heidelberg Germany Institute for Molecular Systems Engineering and Advanced Materials Universität Heidelberg Heidelberg Germany Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zürich Zürich Switzerland

Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. Our framework is designed for real-time, parallelized operations, leveraging 2D arrays of LEDs and photodetectors connected via independent analog electronics. We experimentally demonstrate this approach using a system with a three-layer network with two hidden layers and operate it to recognize images from the MNIST database with a recognition accuracy of 92% and classify classes from a nonlinear spiral data with 86% accuracy. By implementing multiple layers of a deep neural network simultaneously, our approach significantly reduces the number of read-ins and read-outs required and paves the way for scalable optical accelerators requiring ultra low energy. Copyright © 2024, The Authors. All rights reserved.

关键词： Multilayers

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LCINet: Local Cross-position Interaction Network for Oracle Bone Inscriptions Recognition

LCINet: Local Cross-position Interaction Network for Oracle ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qingyang Sun Xiaoqing Zhang Chenlu Gui Hanxi Sun Tingsheng Cai Yan Hu Jigen Tang Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Humanities and Social Sciences Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Oracle Bone Inscriptions (OBIs) are a type of ancient Chinese hieroglyphs, which are precursors of many Asian characters. Automatic OBIs recognition can assist archaeologists in understanding the history and evolution of hieroglyphs. Recently, deep learning methods have been gradually applied to OBIs recognition, but these methods often fail to leverage the glyphological feature information of oracle bone characters. In this paper, we propose a novel Local Cross-position Interaction (LCI) module, which dynamically adjusts the relative importance of feature maps in convolutional neural networks (CNNs) by exploiting the potential of the glyphological information. LCI extracts the glyphological context information by orientation pooling, then constructs the local dependencies between glyphological context features via a cross-position interaction. Subsequently, we combine the LCI module with the residual module to form the Residual-LCI module and then build an LCINet for automatic OBIs recognition by stacking multiple Residual-LCI modules. In addition, we construct an OBIs dataset named OBI316 to verify the effectiveness of LCINet, which will be released soon. The comprehensive experiments on the OBI316 dataset demonstrate that our LCINet outperforms baselines and state-of-the-art attention-based networks. The CIFAR datasets are used to further demonstrate the generalization ability of our method.

关键词： Deep learning Stacking Neural networks Bones Feature extraction Complexity theory Character recognition

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis

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Procedia computer science 2023年 225卷 4930-4942页

作者： Joanna Kołodziejczyk Yuliia Tarasenko Piotr Sulikowski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw 04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment.

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Social media emotion macroscopes reflect emotional experiences in society at large

arXiv

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arXiv 2021年

作者： Garcia, David Pellert, Max Lasser, Jana Metzler, Hannah Graz University of Technology Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz8010 Austria Complexity Science Hub Vienna Vienna1080 Austria Medical University of Vienna Center for Medical Statistics Informatics and Intelligent Systems Vienna1090 Austria Medical University of Vienna Department of Social and Preventive Medicine Center for Public Health Vienna1090 Austria

Social media generate data on human behaviour at large scales and over long periods of time, posing a complementary approach to traditional methods in the social sciences1. Millions of texts from social media can be processed with computational methods to study emotions2,3 over time and across regions4,5. However, recent research has shown weak correlations between social media emotions and affect questionnaires at the individual level6,7 and between static regional aggregates of social media emotion and subjective well-being at the population level8, questioning the validity of social media data to study emotions. Yet, to date, no research has tested the validity of social media emotion macroscopes to track the temporal evolution of emotions at the level of a whole society. Here we present a pre-registered prediction study that shows how gender-rescaled time series of Twitter emotional expression at the national level substantially correlate with aggregates of self-reported emotions in a weekly representative survey in the United Kingdom. A follow-up exploratory analysis shows a high prevalence of third-person references in emotionally-charged tweets, indicating that social media data provide a way of social sensing the emotions of others9 rather than just the emotional experiences of users. These results show that, despite the issues that social media have in terms of representativeness10 and algorithmic confounding11, the combination of advanced text analysis methods with user demographic information in social media emotion macroscopes can provide measures that are informative of the general population beyond social media users. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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An integrated framework for parameter-based optimization of scientific workflows

An integrated framework for parameter-based optimization of ...

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18th ACM International Symposium on High Performance Distributed Computing, HPDC 09, Co-located with the 2009 International Symposium on High Performance Distributed Computing Conference, HPDC'09

作者： Kumar, Vijay S. Sadayappan, P. Mehta, Gaurang Vahi, Karan Deelman, Ewa Ratnakar, Varun Kim, Jihie Gil, Yolanda Hall, Mary Kurc, Tahsin Saltz, Joel Department of Computer Science and Engineering Ohio State University Columbus OH 43210 United States Advanced Systems Division Information Sciences Institute University of Southern California Marina del Rey CA 90292 United States Intelligent Systems Division Information Sciences Institute University of Southern California Marina del Rey CA 90292 United States School of Computing University of Utah Salt Lake City UT 84112 United States Center for Comprehensive Informatics Emory University Atlanta GA 30307 United States

ISBN: (纸本)9781605585871

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework. Copyright 2009 ACM.

关键词： Data handling

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A Watershed Based Segmentation Method for Multispectral Chromosome Images Classification

A Watershed Based Segmentation Method for Multispectral Chro...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Petros S. Karvelis Dimitrios I. Fotiadis Ioannis Georgiou Marika Syrrou Unit of Medical Technology and Intelligent Information Systems Department of Computer Science University of Ioannina (UoI) Ioannina Greece Genetics Unit Department of Obstetrics and Gynecology Medical School Ioannina Greece Genetics Unit Laboratory of General Biology Medical School Ioannina Greece

M-FISH (multicolor fluorescence in situ hybridization) is a recently developed cytogenetic technique for cancer diagnosis and research on genetic disorders which uses 5 fluors to label uniquely each chromosome and a fluorescent DNA stain. In this paper, an automated method for chromosome classification in M-FISH images is presented. The chromosome image is initially decomposed into a set of primitive homogeneous regions through the morphological watershed transform applied to the image intensity gradient magnitude. Each segmented area is then classified using a Bayes classifier. We have evaluated our methodology on a commercial available M-FISH database. The classifier was trained and tested on non-overlapping chromosome images and an overall accuracy of 89% is achieved. By introducing feature averaging on watershed basins, the proposed technique achieves substantially better results than previous methods at a lower computational cost

关键词： Image segmentation Biological cells Image classification Fluorescence Cancer Genetics DNA Image databases Spatial databases Testing

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 38

Binding in hippocampal-entorhinal circuits enables compositi...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Kymn, Christopher J. Mazelet, Sonia Thomas, Anthony Kleyko, Denis Frady, E. Paxon Sommer, Friedrich T. Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley United States Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Department of Electrical and Computer Engineering UC Davis Davis United States Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara United States Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley United States

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. © 2024 Neural information processing systems foundation. All rights reserved.

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

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Procedia computer science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud Computing Diagnosis Digital Health Edge Computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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