• Statistical inference: sampling distributions, estimation, and hypothesis testing. (durham.ac.uk)
  • Identifiability is a prerequisite for statistical inference, such as parameter estimation and hypothesis testing. (columbia.edu)
  • Some of the topics covered include bootstrapping, ensemble methods such as boosting and random forests, unsupervised machine learning methods such as principal components analysis and clustering algorithms as well as applications of machine learning methods to problems that are relevant for business and economics, such as causal inference and text analysis. (lu.se)
  • Exploratory statistics: descriptive statistics, data types and data collection. (durham.ac.uk)
  • Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance. (belnet.be)
  • Perform exploratory data analyses, generate and test working hypotheses, prepare and analyze historical data and identify patterns. (shine.com)
  • It provides a variety of statistical techniques, such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification and clustering. (gnu.org)
  • R and libraries written in it provide numerous graphical and statistical techniques like classical statistical tests, linear and nonlinear modeling, time-series analysis , classification, clustering, and etc. (kdnuggets.com)
  • R offers a wide variety of statistical and graphical techniques including time series analysis, linear and nonlinear modelling, classical statistical tests, classification, clustering, and more). (linuxlinks.com)
  • Most data analysis methods are unable to handle such large amounts of data. (mdpi.com)
  • You'll also build your knowledge of applying statistics and data science methods to real-world problems and increasingly complex data. (reading.ac.uk)
  • METHODS We conducted a retrospective longitudinal analysis of Vermont all-payer claims (2009-2016) for children aged 0 to 21 years. (annfammed.org)
  • Especially computational methods for analysing clinical datasets that consist of multivariate time series data can benefit from High-Performance Computing when applying computing-intensive Recurrent Neural Networks. (researchgate.net)
  • Considering that this enables a better understanding of what Deep Learning models can be useful to apply to specific medical datasets, our case study leverages the three data science methods Gated Recurrent Units, one-dimensional convolutional layers, and their combination. (researchgate.net)
  • The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. (researchgate.net)
  • With its open service-oriented development platform, SAS enables rapid and efficient development of generic building blocks of the statistical production system and their integration with other development platforms at SURS," adds Ružić, and continues that SAS is a central platform for data preparation (which includes cleaning, arrangement, and transformation) and the implementation of statistical methods and analyses. (sas.com)
  • Developing advanced computational methods for automated image analysis and downstream data analytics, The Computer Vision Group employs methods that are based on solid mathematical and statistical modelling, as well as data-driven artificial intelligence using machine and deep learning. (edu.au)
  • Classification and clustering methods. (durham.ac.uk)
  • Understanding the use of statistical methods as an underpinning of classification methods. (durham.ac.uk)
  • Sufficient mastery of statistical concepts to enable engagement with data science methods. (durham.ac.uk)
  • h) statistical methods for genetic data. (unibo.it)
  • Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. (springer.com)
  • For time-to-event data, we propose a weighted Kaplan-Meier estimator based on the method of inverse-probability weighting and compare its properties to that of the standard Kaplan-Meier estimator, and two other existing methods such as marginal mean model based estimator and weighted risk set estimator. (econbiz.de)
  • We propose three methods incorporating inverse probability weighting, mixed models, multiple imputations, and pattern mixture models to assess the effect of treatment regimes on the longitudinal HRSD24 scores. (econbiz.de)
  • Proposed statistical methods provide useful tools for unbiased estimation of the effects of treatment regimes from sequentially randomized designs. (econbiz.de)
  • This course teaches the basics of machine learning and it does so by focusing on those methods that build in one way or another on standard regression analysis. (lu.se)
  • Some of the topics covered are classification based on logistic regression, model selection using information criteria and cross-validation, shrinkage methods such as lasso, ridge regression and elastic nets, dimension reduction methods such as principal components regression and partial least squares, and neural networks. (lu.se)
  • The course introduces legal thinking, and it provides an overview as well as a practical application of legal concepts and methods used to analyse the relevant legal rules and principles related to data analytics. (lu.se)
  • the deployment of PM sensors in occupational settings aimed at developing methods for the collection and interpretation of sensor data for occupational exposures. (cdc.gov)
  • Our analysis has been the amino acid sequences in proteins differ from what is carried out using two different methods, which differ substantially expected from random sequences in a statistically significant from what is used in ref. 3, although the starting point is similar. (lu.se)
  • Three of the methods were for clustering and three for network community analysis. (lu.se)
  • To obtain the most reliable and robust grouping and a consistent and robust view of the disease grouping patterns, a consensus classification based on the co-occurrence of the diseases in four, five or six methods was generated. (lu.se)
  • Standard analysis and linear algebra, Numerical analysis of ordinary differential equations (including the corresponding programming skills), Basic probability theory, fundamentals of the concepts of SDEs and how to develop and analyse numerical methods for their simulation. (lu.se)
  • Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. (hindawi.com)
  • This course offers an introduction to supervised machine learning theories and algorithms, and their application to classification and regression tasks. (rit.edu)
  • This course introduces students to machine learning algorithms as used in real-world applications, and to the experimental methodology necessary to perform statistical analysis of large-scale data from unpredictable processes. (cam.ac.uk)
  • Evaluation and Modeling- The transformed data must then be structured into a predictive model using algorithms that perform deep statistical analysis to uncover repetitions, patterns, and other connections. (datamation.com)
  • An increasing number of data science approaches that take advantage of deep learning in computational medicine and biomedical engineering require parallel and scalable algorithms using High-Performance Computing systems. (researchgate.net)
  • This paper proposes a dynamic data science platform consisting of modular High-Performance Computing systems using accelerators for innovative Deep Learning algorithms to speed-up medical applications that take advantage of large biomedical scientific databases. (researchgate.net)
  • Be able to apply data mining algorithms using R. (southampton.ac.uk)
  • Have a good understanding of algorithms that can be used for classification, assortment, clustering and text analytics. (southampton.ac.uk)
  • New cached-sufficient statistics algorithms for quickly answering statistical questions. (sigmod.org)
  • Algorithms for discovering bucket orders from data. (sigmod.org)
  • This means that the platform will leverage an enterprise's existing data sets to train a classification or sentiment analysis model via machine learning -based algorithms. (attivio.com)
  • Predictive Maintenance is based on Condition Monitoring, anomaly detection and classification algorithms, and integrates predictive models which can estimate the remaining machine runtime left, according to detected anomalies. (st.com)
  • They gather and log pre-processed, secure data to be displayed in visualization tools and used in other processing algorithms. (st.com)
  • Processing at the Edge can also use Machine Learning and Artificial Intelligence (AI) algorithms to enhance smart sensor node and gateway mission profiles and to broaden the scope of anomaly detection and classification. (st.com)
  • Statistical significance was determined by multilevel mixed-effects generalized linear models and mixed-effects regression models. (lww.com)
  • Peter Wessels (TNO) and Abbas Virji (NIOSH) transitioned the workshop towards the discussion on the advanced mathematical and statistical models needed for transforming the data into information. (cdc.gov)
  • We used the χ2 test for bivariate analysis and binary logistic regression model for multivariate analysis for all 3 countries. (who.int)
  • These associations remained after logistic regression model. (bvsalud.org)
  • P2P platform relies on cloud computing, social networking, and other channels to collect, organize, and record data, which greatly enhances the ability of financial risk prevention and control based on data mining technology. (hindawi.com)
  • In this case, the platform needs to rely on big data, combined with data mining technology, to build a scientific and reasonable credit risk assessment model to provide necessary basis for platform risk supervision and user investment. (hindawi.com)
  • Data mining is a technological means of pulling valuable information from raw data by looking for patterns and correlations. (datamation.com)
  • Data mining is the cornerstone for predictive analysis and informed business decision-making-done right, it can turn massive volumes of data into actionable intelligence. (datamation.com)
  • This article looks at six of the most common data mining techniques and how they are driving business strategies in a digitized world. (datamation.com)
  • What is Data Mining? (datamation.com)
  • The primary objective of data mining is to separate the signal from the noise in raw data sets by looking for patterns and correlations and retrieving useful information. (datamation.com)
  • Data mining is done using tools with powerful statistical and analytical capabilities . (datamation.com)
  • Preparation- Reformatting the data into the desired format or structure can help align with data mining goals and make it easier to identify patterns and relationships. (datamation.com)
  • There are different approaches to data mining, and which one is used will depend upon the specific requirements of each project. (datamation.com)
  • This approach to data mining is aimed at discovering interesting relationships within data sets. (datamation.com)
  • By identifying network usage patterns, the association rules approach to data mining can search through consumer call behavior and social media to identify trends, groups, and segments, and to detect customer communication preferences. (datamation.com)
  • In the data mining process, data is sorted and classified based on different attributes. (datamation.com)
  • The module provides an introduction to data analytics and data mining. (southampton.ac.uk)
  • It will combine practical work using R and SQL with an introduction to some of the theory behind standard data mining techniques. (southampton.ac.uk)
  • Gain practice in working as part of a technical team on a data mining project. (southampton.ac.uk)
  • Be able to carry out a practical data mining project. (southampton.ac.uk)
  • Develop a good understanding of the underpinning statistical ideas behind data mining. (southampton.ac.uk)
  • Underlying statistical ideas needed for data mining, including maximum likelihood estimation, linear & logistic regression, principal components analysis and measures of similarity/dissimilarity. (southampton.ac.uk)
  • The aim of this workshop is to bring together researchers and experts in machine learning, data mining, pattern analysis and statistics and create a platform for sharing research challenges, as well as advancing the research on temporal data analysis. (wikicfp.com)
  • Assessing data mining results via swap randomization. (sigmod.org)
  • A new efficient probabilistic model for mining labeled ordered trees. (sigmod.org)
  • On privacy preservation against adversarial data mining. (sigmod.org)
  • Mining relational data through correlation-based multiple view validation. (sigmod.org)
  • Bayesian networks, Markov models), and reinforcement learning. (rit.edu)
  • Bayesian models) and modern approaches such as deep learning and recurrent neural networks will be presented. (lu.se)
  • Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. (projecteuclid.org)
  • Applications of our current greatest interest are to detect genomic variants in next-generation sequenced genomes and the prediction and classification of disease (sub-)types by integrated analysis of most recent genomic and transcriptomic data. (cwi.nl)
  • They presented a case of dynamic noise mapping and the application of classification, unsupervised clustering, and then prediction. (cdc.gov)
  • Directed/Supervised approach - Attivio takes a directed/supervised approach of machine learning to achieve text analytics techniques such as classification and sentiment analysis . (attivio.com)
  • Justify modelling approaches as well as draw appropriate conclusions and make appropriate recommendations. (durham.ac.uk)
  • Translate business objectives into analytic approaches and identify data sources to support analysis. (shine.com)
  • In the aspect of loan success rate, the relationship between personal information and loan success rate has been studied, and then the borrowing strategies are comprehensively discussed with the help of quantitative analysis tools. (hindawi.com)
  • Join our BSc Mathematics and Statistics with Data Science with Placement Year degree and develop the skills you'll need to become a quantitative scientist. (reading.ac.uk)
  • Classification and Regression Tree (CART) analysis is a statistical modeling approach that uses quantitative data to predict future outcomes by generating decision trees. (ed.gov)
  • This study demonstrated the ability of statistical features to provide a quantitative, individualized measurement of glioblastoma patient and assess the phenotype progression. (hindawi.com)
  • Deriving quantitative models for correlation clusters. (sigmod.org)
  • Creation of appropriate statistical models, with emphasis on formatting, presentation, and interpretation of data. (durham.ac.uk)
  • Interpretation of statistical models including diagnostics and validation. (durham.ac.uk)
  • He has authored a book titled 'Statistics Made Simple' using Excel and SPSS, apart from delivering lectures on software based analysis and interpretation. (routledge.com)
  • Analysis and learning from temporal data covers a wide scope of tasks including learning metrics, learning representations, unsupervised feature extraction, clustering, classification and interpretation. (wikicfp.com)
  • Many useful tools/packages (e.g. scikit-learn) have been developed to make the various elements of data handling, processing, modeling, and interpretation accessible. (springer.com)
  • Such a non-empirical approach may lead to misspecification of the Q-matrix and substantial lack of model fit, resulting in erroneous interpretation of testing results. (columbia.edu)
  • The course provides an introduction to theoretical and practical aspects of data visualisation. (lu.se)
  • The course covers scientific programming in Python, including writing numerical codes with NumPy, data handling, visualisation with Matplotlib and ParaViews, writing user interfaces with Qt, and creating Python environments for scientific applications. (lu.se)
  • Convolutional Neural Networks, Recurrent Neural Networks), probabilistic graphical models (e.g. (rit.edu)
  • The aim of this course is to introduce students to common deep learnings architectues such as multi-layer perceptrons, convolutional neural networks and recurrent models such as the LSTM. (lu.se)
  • Students could develop the reasoned argumentation by using problem analysis process, data gathering and reasoning, facts and opinions classification through argument, brainstorming of various choices and summary, which are higher than pre-test at the statistical significance level of 0.05. (ed.gov)
  • Statistical significance associations were found between stress and scholar difficulties, socioeconomic class C2 and D (p = 0,013) and asthma symptoms in a period less than or equal to 7 years (p = 0,003). (bvsalud.org)
  • There was no statistical significance association between asthma gravity and stress. (bvsalud.org)
  • SIGNIFICANCE: The trajectory analysis showed different patterns of urinary OPE metabolite concentrations, suggesting the need to collect multiple samples to adequately reflect OPE exposure. (cdc.gov)
  • Theoretical properties of the test statistic are studied. (columbia.edu)
  • While genomic and clinical data, as provided by most recent techological advances, carry a wealth of information, their analysis pose major, both theoretical and practical challenges. (cwi.nl)
  • Therefore a particular expertise of ours is on statistical modeling and learning as theoretical frameworks. (cwi.nl)
  • The preliminary data reported highlight the potentialities of the proposed methodology that, once validated in larger cohorts and with multi-centered studies, could represent an innovative minimally invasive and accurate procedure to determine the PD onset, progression and to monitor therapies and rehabilitation efficacy. (frontiersin.org)
  • It is not subject to the sampling and incomplete response bias present in the National Ambulatory Medical Care Survey methodology or the self-report bias of the American Board of Family Medicine recertification surveys, and it offers patient-level longitudinal data of utilization. (annfammed.org)
  • Generalized estimating equations modeled the outcome of child attribution to a FP practice annually, with covariates for calendar year, child age, sex, insurance, and child Rural Urban Commuting Area (RUCA) category. (annfammed.org)
  • The LAeq8hr index provided the best fit to the data for all outcome variables, with the Pfander and Smoorenburg indices second and third except in the case of the continuous outcome for permanent threshold shift. (cdc.gov)
  • For time-to-event data, the best outcome is the longest survival time, and for longitudinal data, the best outcome is greatest reduction (or increase) in some scores such as reduction 24-item Hamilton Rating Scale of Depression (HRSD24) score. (econbiz.de)
  • For longitudinal data, outcome such as HRSD24 scores are collected repeatedly to monitor the progress of the subject. (econbiz.de)
  • Assessing the effect of treatment regimes on longitudinally observed outcome data is important in Public Health since clinicians will be able to identify effective treatment regimes for treating chronic diseases. (econbiz.de)
  • This health effects evaluation involves a balanced review and integration of site-related environmental data, site-specific exposure factors, and toxicological, radiological, epidemiologic, medical, and health outcome data to help determine whether exposure to contaminant levels might result in harmful effects. (cdc.gov)
  • Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics, edited by Bhushan Patil and Manisha Vohra, IGI Global, 2021, pp. 1-18. (igi-global.com)
  • The Data Science Core in the Waisman Center provides bioinformatics and biostatistics services, computational resources, and consultations to investigators within our center as well as to other investigators at the University of Wisconsin - Madison. (wisc.edu)
  • Statistical tests for classification tasks. (cam.ac.uk)
  • Practical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. (amazon.science)
  • Recent works towards mitigating this problem have focused on transfer learning using embeddings pre-trained on often unrelated tasks, for instance, language modeling. (amazon.science)
  • Data scientists use this language for diverse yet specific tasks. (kdnuggets.com)
  • Jobs, tasks, production activity, and operational data are other examples of contextual data to consider. (cdc.gov)
  • Representation- The extracted insights are rendered accessible using visualization tools and reports to draw conclusions and make the data actionable. (datamation.com)
  • A rigorous analysis of functional principal components analysis was done in the 1970s by Kleffe, Dauxois and Pousse including results about the asymptotic distribution of the eigenvalues. (wikipedia.org)
  • Cleaning- Because erroneous or inconsistent data can introduce inaccuracies and complexities to subsequent analysis, a rigorous data cleaning process will ensure there are no anomalies. (datamation.com)
  • However, it is not trivial for most investigators to assemble these elements into a rigorous, replicatable, unbiased, and effective data analysis pipeline. (springer.com)
  • Here, we introduce STREAMLINE, a simple, transparent, end-to-end AutoML pipeline designed as a framework to easily conduct rigorous ML modeling and analysis (limited initially to binary classification). (springer.com)
  • These surrogates benefit greatly from the high accuracy of the mechanistic models they emulate, while avoiding the computation overhead associated with equilibrating multiple complex differential equations. (researchgate.net)
  • Stochastic Differential Equations (SDEs) have become a standard tool to model differential equation systems subject to noise. (lu.se)
  • Analysis of social networks, including detection of cliques and central nodes (5 sessions). (cam.ac.uk)
  • One of the characteristics of big data sources its non-structured nature, which prevents their classification and subsequently enables the detection of the characteristics of this data," says the General Director. (sas.com)
  • Event detection from evolution of click-through data. (sigmod.org)
  • Simultaneous record detection and attribute labeling in web data extraction. (sigmod.org)
  • Classification features for attack detection in collaborative recommender systems. (sigmod.org)
  • Smart sensor nodes are key enablers of predictive analysis. (st.com)
  • Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). (projecteuclid.org)
  • integration often employs specialized tools designed for efficient data consolidation. (datamation.com)
  • Efficient anonymity-preserving data collection. (sigmod.org)
  • Moreover, the extracted data were significantly correlated with clinical data used nowadays for the PD diagnosis and monitoring. (frontiersin.org)
  • The clinical and laboratory data were collected for statistical analysis. (nature.com)
  • The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia. (nature.com)
  • Experimental and in particular clinical data is often noisy and comes with clear statistical biases. (cwi.nl)
  • The analysis was performed using cross-sectional survey data from 2019, 2017-2018 and 2018 Multiple Indicator Cluster Surveys from Bangladesh, Ghana and Costa Rica, respectively. (who.int)
  • Functional data analysis (FDA) is a branch of statistics that analyses data providing information about curves, surfaces or anything else varying over a continuum. (wikipedia.org)
  • Functional data analysis has roots going back to work by Grenander and Karhunen in the 1940s and 1950s. (wikipedia.org)
  • The term "Functional Data Analysis" was coined by James O. Ramsay. (wikipedia.org)
  • We make use of a conditional generator for data augmentation that is trained directly using the meta-learning objective and simultaneously with prototypical networks, hence ensuring that data augmentation is customized to the task. (amazon.science)
  • Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), neural models (e.g. (rit.edu)
  • We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields. (kdnuggets.com)
  • Data scientists have their own weapons -- machine learning (ML) software. (kdnuggets.com)
  • One of the use cases of Python machine learning is model development and particularly prototyping. (kdnuggets.com)
  • Data science competence leader at AltexSoft Alexander Konduforov says he uses it primarily as a language for building machine learning models. (kdnuggets.com)
  • Automated machine learning (AutoML) seeks to address these issues by simplifying the process of ML analysis for all. (springer.com)
  • Discovery/Unsupervised approach - Attivio also uses a discovery/unsupervised approach to machine learning when performing activities such as statistical entity extraction . (attivio.com)
  • This approach uses a wide range of tools, such as statistical analyses and Machine Learning to predict the state of the equipment. (st.com)
  • The high intrinsic dimensionality of these data brings challenges for theory as well as computation, where these challenges vary with how the functional data were sampled. (wikipedia.org)
  • However, the high or infinite dimensional structure of the data is a rich source of information and there are many interesting challenges for research and data analysis. (wikipedia.org)
  • Data capture, storage and analysis have major challenges. (mdpi.com)
  • The Statistical Office of the Republic of Slovenia (SURS) has developed general software solutions for statistical data processing by means of the SAS solution in order to overcome the challenges of integrating business processes. (sas.com)
  • Solutions offered by SAS enable it to overcome the challenges of business process integration and introduce service-oriented architecture (SOA) in the statistical production system. (sas.com)
  • Raman analysis was applied to collect the global signal from the saliva of 23 PD patients and related pathological and healthy controls. (frontiersin.org)
  • Gateways are either implemented to collect and process data from several smart sensor nodes or to act as a connectivity bridge to enable secure connection to the cloud using ethernet, Wi-Fi, cellular or LPWAN technologies. (st.com)
  • The Stress Childhood Scale, the Criterion of Economic Classification Brazil and a questionnaire were used to collect data. (bvsalud.org)
  • Reduction- To narrow the data set and eliminate obviously irrelevant information, techniques such as dimensionality and numerosity reduction are used to pare it down and ensure a focus on pertinent information while preserving its fundamental integrity. (datamation.com)
  • We hypothesize that radiomics with the availability techniques in image processing applied on the raw data derived from MRI can make radiological examinations more effective. (hindawi.com)
  • We welcome contributions that address aspects including, but not limited to: novel techniques, innovative use and applications, techniques for the use of hybrid models. (wikicfp.com)
  • For a passing grade, the student shall · be able to apply techniques to solve various textual data analysis problems, · independently be able to identify and formulate an issue related to textual data and be able to present a solution to this, and · in writing be able to report clearly and discuss his/her findings of various textual data analysis problems. (lu.se)
  • The course also treats how to extract data from a database using techniques such as SQL (Structured Query Language) and how to analyse such data. (lu.se)
  • Smart sensor nodes can also process data and detect anomalies by reducing computational latency. (st.com)
  • For a passing grade, the student shall · be able to make assessments of model choices based on the issue and available data and computational capacity. (lu.se)
  • The systematic classification method applies advanced computational tools for clustering and network analysis. (lu.se)
  • Even data sets from different sources may have correlations and co-occurrences, and when identified, these patterns can help shine a light on market trends, explain customer behavior, or expose fraudulent activities. (datamation.com)
  • The association rules technique can identify fraudulent activities and unusual purchase patterns by analyzing transactional data to detect any irregular spending behavior. (datamation.com)
  • The classification technique serves this purpose and segments data into different classes based on similarities, making it easier to extract meaningful insights and identify patterns. (datamation.com)
  • Generating semantic annotations for frequent patterns with context analysis. (sigmod.org)
  • Python was always a language of data analysis, and, over the time, became a de-facto language for deep learning with all modern libraries built for it. (kdnuggets.com)
  • Processing text data, analysing word frequency (tf-idf), bag of words, with option to cover topic modelling (LDA - Latent Dirichlet Allocation). (southampton.ac.uk)
  • Condition monitoring tools can for instance map equipment degradation when a vibration analysis shows a change in the harmonic frequency of rotating equipment components. (st.com)
  • Frequency analyses can be based both on vibrometer and microphone data. (st.com)
  • ST also offers high-performance, cost-competitive sensors and Inertial Measurement Units (IMUs) with a 10-year supply guarantee (longevity program), including accelerometers and ultra-sound analog microphones enabling vibration analysis from simple Pass/Fail monitoring to high-accuracy, frequency-based data analysis. (st.com)
  • In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. (hindawi.com)
  • The Raman database was used to create a classification model able to discriminate each spectrum to the correct belonging group, with accuracy, specificity, and sensitivity of more than 97% for the single spectra attribution. (frontiersin.org)
  • Different sets of network topologies have different results, and the best network model is selected. (mdpi.com)
  • Understand how to present results from a complex data analysis to a non-expert. (southampton.ac.uk)
  • The results and analysis strategies are general in the sense that they can be further extended to other diagnostic models. (columbia.edu)
  • STM32 Arm® Cortex® M4/M33/M7-based microcontrollers and STM32 Arm® Cortex®-A7® microprocessor series with floating points capabilities can process sensor data at the edge. (st.com)
  • Location information can be used to complement primary sensor data, (i.e. bring context to exposure data), or alternatively be used on their own as primary data, (e.g. as a proxy for risk of personal exposure to hazards present in the workplace). (cdc.gov)
  • An ideal system should accept sensors data and contextual non-sensor data simultaneously. (cdc.gov)