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Correction to: Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rulebased models

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International Journal of Educational Technology in Higher Education 2022年第1期19卷 1-1页

作者： Albreiki, Balqis Habuza, Tetiana Zaki, Nazar Department of Computer Science and Software Engineering College of Information Technology UAE University Al Ain UAE Office of Institutional Effectiveness UAE University Al Ain UAE Big Data Analytics Center (BIDAC) UAE University Al Ain UAE

Machine learning applications for therapeutic tasks with genomics data

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arXiv 2021年

作者： Huang, Kexin Xiao, Cao Glass, Lucas M. Critchlow, Cathy W. Gibson, Greg Sun, Jimeng Health Data Science Program Harvard University Boston02120 United States Analytics Center of Excellence IQVIA CambridgeMA02139 United States Center for Observational Research Amgen Thousand OaksCA91320 United States Center for Integrative Genomics Georgia Institute of Technology AtlantaGA30332 United States Department of Computer Science Carle's Illinois College of Medicine University of Illinois at Urbana-Champaign Urbana61820 United States

The genome contains instructions for building the function and structure, and guiding the evolution of molecules and organisms. Recent high-throughput techniques allow the generation of a vast amount of genomics data. However, the path of transforming genomics data into tangible therapeutics is filled with obstacles. We observe that genomics data alone are insufficient but rather require investigation of its interplay with data such as compounds, proteins, electronic health records, images, texts, etc. To make sense of these complex data, machine learning techniques are often utilized for identifying patterns and drawing insights from data. In this review, we study twenty-two genomics applications of machine learning that can enable faster and more efficacious therapeutic development, from discovering novel targets, personalized medicine, developing gene-editing tools all the way to clinical trials and post-market studies. Challenges remain, including technical issues such as learning under different contexts given specific low resource constraints, and practical issues such as mistrust of models, privacy, and fairness. Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology

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arXiv 2023年

作者： El Nahhas, Omar S.M. van Treeck, Marko Wölflein, Georg Unger, Michaela Ligero, Marta Lenz, Tim Wagner, Sophia J. Hewitt, Katherine J. Khader, Firas Foersch, Sebastian Truhn, Daniel Kather, Jakob Nikolas Else Kroener Fresenius Center for Digital Health Medical Faculty Carl Gustav Carus Technical University Dresden Dresden Germany School of Computer Science University of St Andrews St Andrews United Kingdom Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Institute of Pathology University Medical Center Mainz Mainz Germany Pathology & Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom University Hospital Heidelberg Heidelberg Germany Department of Medicine 1 University Hospital Faculty of Medicine Carl Gustav Carus Technische Universität Dresden Dresden Germany

Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic- and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages which have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of MSI-high tumors. Moreover, we provide an open-source codebase which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge. © 2023, CC BY.

关键词： Pathology

Volatile Memory Extraction-Based Approach for Level 0-1 CPS Forensics

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Volatile Memory Extraction-Based Approach for Level 0-1 CPS ...

2019 IEEE International Symposium on Technologies for Homeland Security, HST 2019

作者： Awad, Rima Asmar Lopez, Juan Rogers, Mike National Security Sciences Direct. Oak Ridge National Laboratory Cyber and Data Analytics Division Oak RidgeTN United States Collage of Engineering Tennessee Technological University Computer Science Department CookevilleTN United States

ISBN: (数字)9781728150925

ISBN: (纸本)9781728150925

Most security analyzers operate on system state that is far removed from end-point components in cyber-physical systems (CPS) identified as level 0-1 in the Purdue Architecture Reference Architecture (PERA) [1]. For example, many operate on system logs and other data dumps to disks. Tremendous value that can be gained in cyber security forensics if low level details such as dynamic changes to volatile memory can be extracted and provided to more sophisticated analysis tools. However, obtaining detailed and dynamic system state at the level of volatile memory is extremely challenging [2]. Here, we attempt to apply IT memory forensic mechanisms to CPS end-point devices and statistically evaluate them. Our focus is to extract volatile and dynamically changing internal information form CPS 0-1 level devices, and design preliminary schemes to exploit that extracted information. This new capability of generating a sequence of volatile memory snapshots for offline, detailed and sophisticated analysis opens a new class of cyber security schemes for CPS forensic analysis. As a case study for our ongoing research, we apply the proposed methodology to Modicon PLC using Modbus protocol. We extract the memory layout and subject the device to read operations at the most critical regions of memory. Similarly, write operations are initiated to carefully determine memory locations (for example, bytes that hold the firmware version number). This capability of generating a sequence of volatile memory snapshots for offline, detailed and sophisticated analysis opens a new class of cyber security schemes for CPS forensic analysis. Also, the ability to dynamically make controlled modifications to specific memory locations opens the potential for new mechanisms such as taint analysis and watermarking. © 2019 IEEE.

关键词： Dynamics

Inferring hierarchical mixture structures: A Bayesian nonparametric approach

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arXiv 2019年

作者： Huang, Weipeng Laitonjam, Nishma Piao, Guangyuan Hurley, Neil J. Insight Centre for Data Analytics University College Dublin Ireland Department of Computer Science Maynooth University Ireland

We present a Bayesian Nonparametric model for Hierarchical Clustering (HC). Such a model has two main components. The first component is the random walk process from parent to child in the hierarchy and we apply nested Chinese Restaurant Process (nCRP). Then, the second part is the diffusion process from parent to child where we employ Hierarchical Dirichlet Process Mixture Model (HDPMM). This is different from the common choice which is Gaussian-to-Gaussian. We demonstrate the properties of the model and propose a Markov Chain Monte Carlo procedure with elegantly analytical updating steps for inferring the model variables. Experiments on the real-world datasets show that our method obtains reasonable hierarchies and remarkable empirical results according to some well known metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Mixtures

Hyperspectral image classification using Uniform Manifold Approximation and Projection with fusion deep learning network

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Applied Soft Computing 2025年 180卷

作者： Chen Lou Mohammed A.A. Al-qaness Sike Ni Dalal Al-Alimi Robertas Damaševičius Saeed Hamood Alsamhi College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua 321004 China Zhejiang Institute of Photoelectronics Jinhua 321004 China Faculty of Engineering Sana’a University Sana’a 12544 Yemen Centre of Real Time Computer Systems Kaunas University of Technology Kaunas 51368 Lithuania Insight Centre for Data Analytics University of Galway Ireland School of ICT Bahrain Polytechnic Isa Town Bahrain Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 Republic of Korea

Hyperspectral images (HSI) are crucial for remote sensing applications as they provide detailed spectral information that accurately identifies and analyzes various materials and land features. HSI classification faces challenges due to large data volumes and high dimensionality. Dimensionality reduction techniques help address these problems by simplifying model computation, reducing redundancy, and improving feature selection. However, traditional methods struggle to capture nonlinear structures and local–global relationships in hyperspectral data. We propose a new multi-feature fusion classification model called the Uniform Manifold Approximation and Projection (UMAP) and Simple Attention Module (SimAM) mechanism fusion network (UMAPSAMFN). The main workflow of the model consists of several steps. The network uses UMAP to map high-dimensional HSI data into a low-dimensional space while maintaining a local and global structure. The feature data are sent to the convolutional neural network (CNN) and graph convolutional network (GCN) modules to capture pixel-level features and contextual information. These data are separately processed through multi-head attention modules to enhance the ability to represent feature information. Finally, the processed data are jointly fed into a fusion module with an attention mechanism to boost feature information and achieve deep modeling results. The experimental results on three benchmark HSI datasets show that UMAPSAMFN consistently exhibits the highest classification accuracy, with overall classification accuracies of 93.28%, 96.13%, and 97.73% on the Indian Pines, Pavia University, and Salinas datasets, respectively.

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Modelling excess zeros in count data: A new perspective on modelling approaches

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arXiv 2020年

作者： Haslett, John Parnell, Andrew C. Hinde, John de Andrade Moral, Rafael School of Computer Science and Statistics Trinity College Dublin Ireland Hamilton Institute Insight Centre for Data Analytics Maynooth University Ireland School of Mathematics Statistics and Applied Mathematics NUI Galway Ireland Department of Mathematics and Statistics Maynooth University Ireland

We consider the analysis of count data in which the observed frequency of zero counts is unusually large, typically with respect to the Poisson distribution. We focus on two alternative modelling approaches: Over-Dispersion (OD) models, and Zero-Inflation (ZI) models, both of which can be seen as generalisations of the Poisson distribution;we refer to these as Implicit and Explicit ZI models, respectively. Although sometimes seen as competing approaches, they can be complementary;OD is a consequence of ZI modelling, and ZI is a by-product of OD modelling. The central objective in such analyses is often concerned with inference on the effect of covariates on the mean, in light of the apparent excess of zeros in the counts. Typically the modelling of the excess zeros per se is a secondary objective and there are choices to be made between, and within, the OD and ZI approaches. The contribution of this paper is primarily conceptual. We contrast, descriptively, the impact on zeros of the two approaches. We further offer a novel descriptive characterisation of alternative ZI models, including the classic hurdle and mixture models, by providing a unifying theoretical framework for their comparison. This in turn leads to a novel and technically simpler ZI model. We develop the underlying theory for univariate counts and touch on its implication for multivariate count data. Copyright © 2020, The Authors. All rights reserved.

关键词： Poisson distribution

Street Network Generation with Adjustable Complexity Using k-Means Clustering

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Street Network Generation with Adjustable Complexity Using k...

IEEE Southeastcon

作者： Quentin Goss Mustafa İlhan Akbaş Luis G. Jaimes R. Sanchez-Arias Department of Computer Science Florida Polytechnic University Lakeland FL Department of Data Science and Business Analytics Florida Polytechnic University Lakeland FL

ISBN: (数字)9781728101378

ISBN: (纸本)9781728101385

The transportation system is an important part of the daily lives of a major portion of the world's population. Therefore, innovative applications are designed to improve the experience of drivers and pedestrians in this system as technological advances allow. These applications analyze street networks to come up with suggestions such as fastest route or optimal ride-sharing route. Graph theory has been used frequently for these analyses, where each junction is represented by a vertex and each road segment is represented by an edge. However, applications also require grouping of these vertices so that they can run optimization methods in a larger *** this paper, we propose a method for creating street networks with adjustable complexity. By using k-Means clustering, our mechanism allows for the increase or decrease of the gradient of a street network. The implementation results demonstrate the proposed method's efficiency and flexibility for providing street network graphs with adjustable complexity.

关键词： Roads Tools Graph theory XML Complexity theory Urban planning

Partial (neighbourhood) singleton arc consistency for constraint satisfaction problems 31

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Partial (neighbourhood) singleton arc consistency for constr...

31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018

作者： Wallace, Richard J. Insight Centre for Data Analytics Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9781577357964

Algorithms based on singleton arc consistency (SAC) show considerable promise for improving backtrack search algorithms for constraint satisfaction problems (CSPs). The drawback is that even the most efficient of them is still comparatively expensive. Even when limited to preprocessing, they give overall improvement only when problems are quite difficult to solve with more typical procedures such as maintained arc consistency (MAC). The present work examines a form of partial SAC and neighbourhood SAC (NSAC) in which a subset of the variables in a CSP are chosen to be made SAC-consistent or neighbourhood-SAC-consistent. These consistencies are well-characterized in that algorithms have unique fixpoints and there are well-defined dominance relations. Heuristic strategies for choosing an effective subset of variables are described and tested, in particular a strategy of choosing by constraint weight after random probing. Experimental results justify the claim that these methods can be nearly as effective as full (N)SAC in terms of values discarded while significantly reducing the effort required. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Constraint satisfaction problems