machine learning ieee papers 2019


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

Adaptive Protection Combined with Machine Learning for Microgrids
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This paper presents a rule-based adaptive protection scheme using machine learning methodology for microgrids in extensive distribution automation. The uncertain elements in a microgrid are first analyzed quantitatively by Pearson correlation coefficients from data

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

Interactive Visualizer to Facilitate Game Designers in Understanding Machine Learning
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Machine Learning (ML) is a useful tool for modern game designers but often requires a technical background to understand. This gap of knowledge can intimidate less technical game designers from employing ML techniques to evaluate designs or incorporate ML into

Improving RF-based Partial Discharge Localization via Machine Learning Ensemble Method
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Partial discharge (PD) is regarded as a precursor to plant failure and therefore an effective indication of plant condition. Locating the source of PD before failure is key to efficient maintenance and improving reliability of power systems. This paper presents a low cost

Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models
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Without good models and the right tools to interpret them, data scientists risk making decisions based on hidden biases, spurious correlations, and false generalizations. This has led to a rallying cry for model interpretability. Yet the concept of interpretability remains

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

MACHINE LEARNING DATA ANALYTICS
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The potential of machine learning techniques enables new possibilities for the analysis of psychological data. Machine learning has many advantages compared to the traditional statistical methods. In the field of health psychology, there is a huge interest in exploring the

A framework for investigating illegal wildlife trade on social media with machine learning
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Unsustainable harvesting is one of the major threats driving the global extinction crisis . Wildlife trade is a multibillion-dollar industry, in which thousands of animals, plants, and associated products are traded globally as food, pets, medicines

Data Validation for Machine Learning
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Machine learning is a powerful tool for gleaning knowledge from massive amounts of data. While a great deal of machine learning research has focused on improving the accuracy and efficiency of training and inference algorithms, there is less attention in the equally important

A Machine Learning Approach to Reveal the Neuro-Phenotypes of Autisms
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Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample

Machine Learning and Super-Resolution Microscopy Reveal Detailed Hierarchy of Ryanodine Receptor Distribution in Cardiac Pacemaker Cells
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Local calcium releases crucially contribute to sinoatrial-node-cell (SANC) pacemaker function. They are calcium wavelets and their timely, synchronous occurrence during diastole regulates automaticity via positive-feedback interactions with electrogenic Na/Ca

The Role of Network Topology for Distributed Machine Learning
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Many learning problems are formulated as minimization of some loss function on a training set of examples. Distributed gradient methods on a cluster are often used for this purpose. In this paper, we study how the variability of task execution times at cluster nodes affects the

Performance Evaluation in Machine Learning : The Good, The Bad, The Ugly and The Way Forward
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This paper gives an overview of some ways in which our understanding of performance evaluation measures for machine -learned classifiers has improved over the last twenty years. I also highlight a range of areas where this understanding is still lacking, leading to ill

Mash: software tools for developing interactive and transparent machine learning systems
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We present Mash, a library that provides software tools for developing interactive and transparent machine learning systems. Mash provides tools for (1) back end: specifying machine learning models, and (2) front end: generating User Interfaces (UIs) that allow non

Labor Division with Movable Walls: Composing Executable Specifications with Machine Learning and Search
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Artificial intelligence (AI) techniques, including, eg, machine learning , multi-agent collaboration, planning, and heuristic search, are emerging as ever-stronger tools for solving hard problems in real-world applications. Executable specification techniques (ES)

Learning Everywhere: Pervasive machine learning for effective High-Performance computation: Application background
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This paper describes opportunities at the interface between large-scale simulations, experiment design and control, machine learning (ML including deep learning DL) and High- Performance Computing. We describe both the current status and possible research issues

Mathematics for Machine Learning
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These lecture notes support the course Mathematics for Machine Learning in the Department of Computing at Imperial College London. The aim of the course is to provide students the basic mathematical background and skills necessary to understand, design and

A framework for benchmarking discrimination-aware models in machine learning
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Discrimination-aware models in machine learning are a recent topic of study that aim at minimizing the adverse impact of machine learning decisions for certain groups of people due to ethical and legal implications. We propose a benchmark framework for assessing

Redefining the Protein Kinase Conformational Space with Machine Learning
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We have shown by a nuclear magnetic resonance measurement that the ureadenatured protein apoazurin becomes elongated when the synthetic crowder, dextran 20, is present, which opposes a theoretical prediction based only on the consideration of volume exclusion





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