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Simulating multi-scale optimization and variable selection in species distribution modeling

Ecological Informatics 2024年 83卷

作者： Cushman, Samuel A. Kaszta, Zaneta M. Burns, Patrick Hakkenberg, Christopher R. Jantz, Patrick Macdonald, David W. Brodie, Jedediah F. Deith, Mairin C.M. Goetz, Scott Wildlife Conservation Research Unit The Recanati-Kaplan Centre Department of Biology University of Oxford United Kingdom GEODE Laboratory School of Informatics Computing & Cyber Systems Northern Arizona University United States Division of Biological Sciences and Wildlife Biology Program University of Montana Missoula 59812 MT United States Institute of Biodiversity and Environmental Conservation Universiti Malaysia Sarawak Kota Samarahan 94300 Malaysia Institute for the Oceans and Fisheries University of British Columbia 2202 Main Mall Vancouver V6T 1Z4 BC Canada Department of Biology Northern Arizona University United States

Species distribution modeling (SDM) is a fundamental tool in theoretical and applied ecology. However, relatively little is known about the performance of different approaches for scale optimization, model selection, and algorithmic prediction in the context of nonlinear, multiscale and interactive relationships between environmental variables and species occurrence. Modelers often struggle to optimize a tradeoff between ecological relevance, model robustness, complexity, and overfitting. In this paper, we investigated several methods designed to optimize spatial scale and variable selection in SDMs, in each case evaluating model fitness, parsimony and predictive performance. We used a simulation approach to produce a large pool of alternative underlying habitat relationships that reflect a broad range of realistic habitat associations. We also compared several different modeling algorithms, including logistic regression with a generalized linear model (GLM), Lasso and Elastic-Net Regularized GLMs (GLMNet), and random forest (RF), as well as alternative variable and scale selection methods. We found that GLM methods employing all-subsets dredge routines for variable selection were consistently the best predictors based on all criteria of our model performance assessment and across all attributes of the simulated underlying relationship, including nonlinearity and interaction. We had expected machine learning approaches, such as random forest, to perform better in these more complex forms of species-environment relationships. GLM using dredge variable selection was also the method that included the fewest spurious covariates and included the most correct predictors as a proportion of all predictors. We found that univariate scaling was the most robust method of variable and scale selection, along with Minimal Redundancy Maximal Relevancy (MRMR) which performed equivalently. The simulation experiment presented here provides a robust assessment of simulated multi-speci

关键词： Methods comparison Scale optimization Simulation experiment Species distribution modeling Variable selection

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Assessing Engraftment Following Fecal Microbiota Transplant

arXiv

引用

arXiv 2024年

作者： Herman, Chloe Barker, Bridget M. Bartelli, Thais F. Chandra, Vidhi Krajmalnik-Brown, Rosa Jewell, Mary Li, Le Liao, Chen McAllister, Florencia Nirmalkar, Khemlal Xavier, Joao B. Caporaso, J. Gregory Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Department of Clinical Cancer Prevention University of Texas MD Anderson Cancer Center HoustonTX United States Biodesign Center for Health Through Microbiomes Arizona State University TempeAZ United States School of Sustainable Engineering and the Built Environment Arizona State University TempeAZ United States Independent author Salt Lake CityUT United States Program for Computational and Systems Biology Memorial Sloan-Kettering Cancer Center New YorkNY United States Department of Gastrointestinal Medical Oncology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Immunology The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Biological Sciences Northern Arizona University FlagstaffAZ United States

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn’t respond because the engrafted microbiome didn’t produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn’t engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment using methods such as alpha diversity comparisons, beta diversity comparisons, and microbiome source tracking. 2) Donated Microbiome Indicator Features tracks donated microbiome features (e.g., amplicon sequence variants or species of interest) as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified (e.g., from FMT or disease-relevant literature). 3) Temporal Stability examines how resistant post-FMT recipient’s microbiomes are to reverting back to their baseline microbiome. Individually, these criteria each highlight a critical aspect of microbiome engraftment;investigated together, however, they provide a clearer assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria,

关键词： Diseases

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Predicting User Preferences with XGBoost Learning to Rank Method

Predicting User Preferences with XGBoost Learning to Rank Me...

引用

International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Nunung Nurul Qomariyah Dimitar Kazakov Ahmad Nurul Fajar Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia University of York York United Kingdom BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728184067

ISBN: (纸本)9781728184074

Learning user preferences become very important as the personalization systems grow rapidly in this current era. Offering special and personal services can be an added value for the companies to maintain their customer loyalty. Building a personalized recommendation requires a good machine learning model to understand the individual preferences. Every user can be presented with a list of items sorted by its score learned from the individual preferences. So the first couple items shown will be the most liked items by the user. We can borrow the Learning to Rank algorithm from Information Retrieval to solve this problem. In this paper, we present the implementation of user preferences learning by using XGBoost Learning to Rank method in movie domain. We show the evaluation of three different approaches in Learning to Rank according to their Normalized Discounted Cumulative Gain (NDCG) score. We can conclude that in our case study, the pairwise approach appears to be the best solution to produce a personalized list of recommendation.

关键词： Predictive models Measurement Motion pictures Task analysis Linear programming Boosting Web search

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User Preference Modelling from Movie Database

User Preference Modelling from Movie Database

引用

International Conference on ICT For Smart Society (ICISS)

ISBN: (数字)9781665404228

ISBN: (纸本)9781665404235

Film industry becomes one of the largest amongst the other economic sectors worldwide nowadays. This industry also has a significant impact in the global economic market. Many movies have been produced each year. However, some movies still fail to reach break-even points every year. Players in this industry need to think carefully when spending most of their money to make a great and successful movie in the market. Several attempts also need to be made to estimate the success performance before a movie can be released. On the audience side, there is an indicator that we can use to decide that a movie is good by looking to its ratings. The industry can use this rating systems as a factor to be considered when evaluating the past movie performances to make better movie for the next production. This study aims to predict the movie ratings and find the most important features which correlate to the ratings. We performed an experiment on IMDb dataset which contains 5,043 movie metadata on four models of machine learning, Linear Regression (LR), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN). From the experiment, Random Forest achieves the best performance amongst the four models.

关键词： Motion pictures Industries Prediction algorithms Machine learning algorithms Predictive models Social networking (online) Random forests

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A microservice-based approach for increasing software reusability in health applications 16

A microservice-based approach for increasing software reusab...

引用

16th ACS/IEEE International Conference on Computer systems and Applications, AICCSA 2019

作者： Da Silva, Marcio Alexandre Pereira Times, Valeria Cesario De Araujo, Andre Magno Costa Da Silva, Paulo Caetano Center for Informatics Federal University of Pernambuco Recife/PE Brazil Department of Information Systems Federal University of Alagoas Penedo/AL Brazil Master Program in Computing Salvador University Salvador/BA Brazil

ISBN: (纸本)9781728150529

Traditionally, legacy Health applications use software architecture models that make it difficult to reuse components. Reusability is an essential attribute in the software lifecycle, as it improves the quality of applications and reduces maintenance and development costs. This paper proposes the cloud tool Microservice4EHR, which dynamically generates reusable components from existing software artifacts (e.g., graphical interfaces), while conforming to the standards used in the healthcare domain. A software architecture based on Connectors and Microservice components is specified and made tangible by means of three algorithms. The use of both components is applied to a real-world scenario (a Brazilian blood donation center) and serves as an example. As a result, it is possible to notice that Health applications achieve greater reusability when they employ the microservice architecture. Thus, Microservice4EHR enables the use of reusable components in Health application architectures (for both new and legacy systems), increasing software reusability in this context. © 2019 IEEE.

关键词： Health

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Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning

arXiv

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arXiv 2024年

作者： Gao, Fengyi Zhang, Xingyu Sivarajkumar, Sonish Denny, Parker E. Aldhahwani, Bayan M. Visweswaran, Shyam Shi, Ryan Hogan, William Bove, Allyn Wang, Yanshan Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Communication Science and Disorders University of Pittsburgh PittsburghPA United States Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Physical Therapy University of Pittsburgh PittsburghPA United States Department of Medical Rehabilitation Sciences Umm Al-Qura University Makkah Saudi Arabia Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Department of Computer Science University of Pittsburgh Medical Center PittsburghPA United States Data Science Institute Medical College of Wisconsin MilwaukeeWI United States Hillman Cancer Center University of Pittsburgh Medical Center PittsburghPA United States

Objective In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients’ rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. Through this study, our ultimate goal is to pinpoint the specific rehabilitation exercises that can effectively aid post-stroke patients in improving their functional outcomes in basic mobility (BM) and applied cognitive (AC) domains. Data sources We collected data for 265 stroke patients from the University of Pittsburgh Medical Center, accessed through the Rehabilitation Datamart With Informatics iNfrastructure for Research (ReDWINE). Methods We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. AM-PAC scores were collected at the initial rehabilitation visit and followed up at one and two months—key recovery periods. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models—logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)—to predict outcomes in functional ability. Results Statistical analyses revealed significant associations between functional improvements and specific exercises. In the AC domain, the BALANCE exercise showed substant

关键词： Statistical tests

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DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection

DeepGuard: Deep Generative User-behavior Analytics for Ranso...

引用

IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Gaddisa Olani Ganfure Chun-Feng Wu Yuan-Hao Chang Wei-Kuan Shih Social Networks and Human Centered Computing Program Taiwan International Graduate Program Institute of Information Science Nangang Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan National Tsing Hua University Hsinchu City Taiwan

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

关键词： Training Training data Organizations Tools Ransomware Security Informatics

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Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification

arXiv

引用

arXiv 2021年

作者： Luo, Jun Kitamura, Gene Arefan, Dooman Doganay, Emine Panigrahy, Ashok Wu, Shandong Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Radiology School of Medicine University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh PittsburghPA United States Department of Biomedical Informatics Department of Bioengineering University of Pittsburgh PA United States

Elbow fracture diagnosis often requires patients to take both frontal and lateral views of elbow X-ray radiographs. In this paper, we propose a multiview deep learning method for an elbow fracture subtype classification task. Our strategy leverages transfer learning by first training two single-view models, one for frontal view and the other for lateral view, and then transferring the weights to the corresponding layers in the proposed multiview network architecture. Meanwhile, quantitative medical knowledge was integrated into the training process through a curriculum learning framework, which enables the model to first learn from "easier" samples and then transition to "harder" samples to reach better performance. In addition, our multiview network can work both in a dual-view setting and with a single view as input. We evaluate our method through extensive experiments on a classification task of elbow fracture with a dataset of 1,964 images. Results show that our method outperforms two related methods on bone fracture study in multiple settings, and our technique is able to boost the performance of the compared methods. The code is available at https://***/ljaiverson/multiview-curriculum. Copyright © 2021, The Authors. All rights reserved.

关键词： Curricula

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FaSTrack:A Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

FaSTrack:A Modular Framework for Real-Time Motion Planning a...

引用

作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime Fernandez Bansal, Somil Han, Soojean Tomlin, Claire J. School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada Department of Mechanical and Aerospace Engineering University of California San Diego San DiegoCA92122 United States Department of Electrical Engineering Princeton University PrincetonNJ08536 United States Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94704 United States Control and Dynamical Systems Program California Institute of Technology Los AngelesCA90019 United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. © 1963-2012 IEEE.

关键词： Real time systems

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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