machine learning 2019-2

Measuring Aggregate Housing Wealth: New Insights from Machine Learning
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We construct a new measure of aggregate housing wealth for the US based on (1) homevalue estimates derived from machine learning algorithms applied to detailed information on property characteristics and recent transaction prices, and (2) Census

Machine Learning for Medical Imaging
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Machine learning contains a set of methods, which allow a machine to learn meaningful patterns from data directly with minimal human interaction. e strength of a machinelearning technique is, in part, dependent on human knowledge. Such knowledge can help a machine

Make Some ROOM for the Zeros: Data Sparsity in Secure Distributed Machine Learning .
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Exploiting data sparsity is crucial for the scalability of many data analysis tasks. However, while there is an increasing interest in efficient secure computation protocols for distributed machine learning , data sparsity has so far not been considered in a principled way in that

First Steps towards Machine – Learning supported Material Parameter Identification
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Machine learning is becoming more and more part of our world. Even though most people have so far only passively used the possibilities of this technology, eg for search queries or product recommendations, many have surely already thought about how these new

A Proposal for Determining the Evidence Types of Biomedical Documents Using a Drug-drug Interaction Ontology and Machine Learning .
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Expand the existing classifiers to other evidence types in the DIDEO ontology. Run the classifiers on new articles (eg from PubMed Central) to get predictions of PDDI evidence types as well as to identify potentially new evidence types. Run the model with different

Bridging the Scales: A Machine Learning Directed Macro to Micro Scale Simulation to Model RAS Initiation of Cancer
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Molecular dynamics (MD) simulations can reveal the atomistic scale mechanisms of biological systems in great detail. However, MD calculations are computationally intensive, and many important biological phenomena are inaccessible because of large time and

Machine learning models to predict the precise progression of Tay-Sachs and Related Disease
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Background Tay Sachs is a very rare neurodegenerative disorder and the second most common lipid storage disorder. The testimony of TSD in infants is marked by the gradual loss in vision, hearing impairment, increased muscle stiffness and cherry red retinal spot

Targeting Prospective Customers: Robustness of Machine Learning Methods to Typical Data Challenges
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We investigate how firms can use the results of field experiments to optimize the targeting of promotions when prospecting for new customers. We evaluate seven widely used machine learning methods using a series of two large-scale field experiments. The first field

Machine learning analysis of metabolomics and neuro-imaging data for Parkinsons disease
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Page 1. Enrico Glaab Luxembourg Centre for Systems Biomedicine Machine learning analysis of metabolomics and neuro- imaging data for Parkinsons disease Page 2. 1 Outline of analyses 1) Cohort overview 2) Neuroimaging data analysis (FDG and F-DOPA PET) 3) Metabolomics The limitless number of possible ways to configure database management systems (DBMSs) has rightfully earned them the reputation of being difficult to manage and tune. Optimizing a DBMS to meet the needs of an application has surpassed the abilities of

Machine Learning Techniques for Evolving Threats
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Malicious threats pose a serious problem for everyday activities. The number of attacks are always increasing and automatic means of analysis have been employed to deal with this growth. In this scenario, it is difficult to trust the automatic decision making-criteria because

Machine Learning Applied to Cervical Cancer Data
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Cervical Cancer is one of the main reason of deaths in countries having a low capita income. It becomes quite complicated while examining a patient on basis of the result obtained from various doctors preferred test for any automated system to determine if the Advances in hardware are a challenge but also a new opportunity. In particular, devices like FPGAs and GPUs are a chance to extend and customize relational engines with new operations that would be difficult to support otherwise. Doing so would offer database users

Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence
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Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study

Use of Machine Learning to Predict Diagnosis Codes for Nonalcoholic Steatohepatitis in Administrative Healthcare Data
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♦ Background The natural history of nonalcoholic steatohepatitis (NASH) is poorly understood Analysis of US administrative claims data to characterize the long-term consequences of NASH has been hampered by the grouping of NASH with other

Editorial overview: Machine learning , big data, and neuroscience
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Editorial introduction to a special issue of Current Opinion in Neurobiology on Machine learning , big data, and neuroscience . The articles in this issue explore the growing use and uses of machine – learning methods in neuroscience. The field of machine learning is a

Trends in Alzheimers Disease Research Based upon Machine Learning Analysis of PubMed Abstracts
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About 29.8 million people worldwide had been diagnosed with Alzheimers disease (AD) in 2015, and the number is projected to triple by 2050. In 2018, AD was the fifth leading cause of death in Americans with 65 years of age or older, but the progress of AD drug research is

γBOriS: Identification of Origins of Replication in Gammaproteobacteria using Motif-based Machine Learning Supplementary Information
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The biology of bacterial cells is, in general, grounded on the information encoded on circular chromosomes. Consequently, maintenance of these through regulation of chromosome replication is an essential process which mostly takes place at a single genomic site, called

Non-Convex Optimization for Signal Processing and Machine Learning
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Motivation Optimization is now widely reckoned as an indispensable tool in signal processing and machine learning . Although convex optimization remains a powerful, and is by far the most extensively used, paradigm for tackling signal processing and machine

Machine Learning for Instance Selection in SMT Solving
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SMT solvers are among the most suited tools for quantifier-free first-order problems, and their support for quantified formulas has been improving in recent years. To instantiate quantifiers, they rely on heuristic techniques that generate thousands of instances, most of

COMMENT computer-science


machine learning 2019-2 IEEE PAPER
  • IEEE PAPERS ON CSE-COMPUTER SCIENCE ENGINEERING iot-internet-of-things robotics hadoop-technology machine-learning cloud-computing big-data image-processing information-retrieval-ieee-paper information-retrieval-2019 face-recognition-2019 face-recognition-ieee-papers machine-learning-2019-2
  • CSE-IEEE-PAPERS-2019 iot-internet-of-things robotics hadoop-technology machine-learning cloud-computing big-data image-processing information-retrieval-ieee-paper information-retrieval-2019 face-recognition-2019 face-recognition-ieee-papers
  • face recognition 2019 Custom silicone Face Masks: Vulnerability of Commercial Face Recognition ...
  • information retrieval 2019 Information Retrieval from Electronic Health Records for Patient ...