machine learning 2017 IEEE PAPER
Machine learning and prediction in medicine beyond the peak of inflated expectations
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Big data, we have all heard, promise to transform health care with the widespread capture of electronic health records and high-volume data streams from sources ranging from insurance claims and registries to personal genomics and biosensors. 1 Artificial-
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
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Abstract Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction
Using Machine Learning to Understand Top-Down Effects in an Ecosys-tem: Opportunities, Challenges, and Lessons Learned
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Abstract The soil decomposer community is a primary driver of carbon cycling in forest ecosystems. Understanding the processes that regulate this community is critical to our understanding of the global carbon cycle and fungal mediated impact on climate change.
Statistical Learning in the Age ofBig Dataand Machine Learning
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The courseStatistical Learning in the Age ofBig Dataand Machine Learningaddresses master students of Business Administration, Economics, Internationale Wirtschaft und Governance, and PhilosophyEconomics. Advanced interested bachelor students may
Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning
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Abstract imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be
Does Machine Learning Automate Moral Hazard and Error
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Patients in the emergency department (ED) can be difficult to diagnose. Subtle symptoms can often overlap between diseases of differing severity: nausea could reflect heart attack, or acid reflux. Take the case of patients who are either having a stroke, or are at high risk of
Component-Based Machine Learning Modelling Approach For Design Stage Building Energy Prediction: Weather Conditions And Size
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Abstract Building energy predictions are playing an important role in steering the design towards the required sustainability regulations. Time-consuming nature of detailed Building Energy Modelling (BEM) has introduced simplified BEM and metamodels within the design ABSTRACT We present a novel approach for monitoring beverage intake. Our system is composed of an ultrasonic sensor, an RGB color sensor, and machine learning algorithms. The system not only measures beverage volume but also detects beverage types. The
JSAT: Java Statistical Analysis Tool, a Library for Machine Learning
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Abstract Java Statistical Analysis Tool (JSAT) is a Machine Learning library written in pure Java. It works to fill a void in the Java ecosystem for a general purpose library that is relatively high performance and flexible, which is not adequately fulfilled by Weka (Hall et
Glycosylation Site Prediction Using Machine Learning Approaches
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Glycosylation is one of the most complex post-translational modifications (PTMs) which occurs in many proteins in eukaryotic cells. In general, the result of glycosylation influences protein folding (some proteins cannot fold properly unless they have been glycosylated),
Statistical and Machine-Learning Data Mining
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Page 1. Statistical and Machine-Learning Data Mining Techniques for Better Predictive Modeling and Analysis of Big Data Second Edition Bruce Ratner CRC Press TaylorFrancis Croup Boca Raton London New York CRC Press is an imprint of the Taylor St Francis Croup, an Informs
Tux2: Distributed Graph Computation for Machine Learning.
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Abstract TUX2 is a new distributed graph engine that bridges graph computation and distributed machine learning. TUX2 inherits the benefits of an elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs; we
Machine learning and electronic health records: a paradigm shift
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In this issue of the Journal, Barack-Corren et al.(1) use machine learning methods to build a highly predictive model of suicidal behavior using longitudinal electronic health records (EHRs). They do so using a well-established probabilitybased machine learning algorithm,
Machine Learning wearable device information in Parkinson unwellness Health Watching.
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AbstractFor the treatment and observance of Parkinson unwellness (PD) to be scientific, a key demand is that measurements of unwellness stages and severity area unit quantitative, reliable and repeatable. The last fifty years in Pd analysis are dominated by qualitative,
Predicting long-term mortality with first week post-operative data after Coronary Artery Bypass Grafting using Machine Learning models
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Abstract Coronary Artery Bypass Graft (CABG) surgery is the most common cardiac operation and its complications are associated with increased long-term mortality rates. Although many factors are known to be linked to this, much remains to be understood about
Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds.
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Abstract Machine learning (ML) is widely used to derive useful information from large-scale data (such as user activities, pictures, and videos) generated at increasingly rapid rates, all over the world. Unfortunately, it is infeasible to move all this globally-generated data to a
Optimization methods for large-scale machine learning
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Abstract This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how
Data visualisation and machine learning web application with potential use in sports data analytics
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Abstract This project deals with the design and implementation of a machine learning and statistical analysis web application used to model user data. The application takes the user dataset as an input and can graphically display the data and generate statistical models to
A Phishing Email Detection Approach Using Machine Learning Techniques
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Abstract According to APWG reports of 2014 and 2015, the number of unique Phishing e- mail reports received from consumers has increased tremendously from 68270 e-mails in October 2014 to 106421 e-mails in September 2015. This significant increase is a proof of
A comparison of machine learning classifiers for leak detection and isolation in urban networks
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ABSTRACT Leak detection and isolation (LDI) is a problem of interest for water management companies and their technical staff. Main reasons for this are that early detection of leakages can reduce dramatically (1) water losses in urban networks and (2) the
Machine Learning for predicting in a big data world
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Reductionist attitude: ML is a modern buzzword which equates to statistics plus marketing Positive attitude: ML paved the way to the treatment of challenging problems, sometimes overlooked by statisticians (nonlinearity, classification, pattern recognition, missing
COP: Planning Conflicts for Faster Parallel Transactional Machine Learning.
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ABSTRACT Machine learning techniques are essential to extracting knowledge from data. The volume of data encourages the use of parallelization techniques to extract knowledge faster. However, schemes to parallelize machine learning tasks face the trade-off between
Using supervised machine learning algorithms to detect suspicious URLs in online social networks
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Abstract The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious
SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning.
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Page 1. 2017 IBM Corporation SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning Tarek Elgamal2, Shangyu Luo3, Matthias Boehm1, Alexandre V. Evfimievski1, Shirish Tatikonda4,Page 2. 2017 IBM Corporation MotivationDeclarative
A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot
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Abstract We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning
BIDViz: Real-time Monitoring and Debugging of Machine Learning Training Processes
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Executive Summary Artificial Intelligence is a thriving field with many applications, whether in automating routinary human labors or support basic research such as diagnosing diseases (Goodfellow et. al., 2016). Deep learning, or machine learning with deep neural
Prediction of mRNA expression in cows milk using mRNA secondary structures and Machine Learning classifiers
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Abstract: The mRNA molecules expressed in cows milk are important molecular biomarkers for different physiological and pathological conditions in cattle. The prediction of the quantity that a specific mRNA type could be expressed in cows milk is a challenging theoretical task.
Trusted Machine Learning: Model Repair and Data Repair for Probabilistic Models
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Abstract When machine learning algorithms are used in life-critical or mission-critical applications (eg, self driving cars, cyber security, surgical robotics), it is important to ensure that they provide some high-level correctness guarantees. We introduce a paradigm called
Project topics for the course Special Course in Bioinformatics II: Machine Learning in Bioinformatics
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Background: In untargeted metabolomics studies complex biological sample with possibly thousands of molecules are encountered. Tandem mass spectrometry (MS/MS) is a widely used technique to extract patterns from biological samples to identify the molecules in it.
MACHINE LEARNING TECHNIQUES FOR THE EVALUATION OF EFFICIENCY OF THE SOFTWARE RELIABILITY GROWTH MODELS
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ABSTRACT Efficiency is a vital factor in the domain of software. Several different approaches had been used for this purpose, but no one completely assessed the efficiency and parameters of the software reliability. In this paper, a genetic alogorithm based
Automatic Classification of Z-ring Formation Stages at the Single Cell Level in Escherichia Coli by Machine Learning.
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Abstract: In E. coli, Z-ring formation precedes the assembly of the membrane that partitions a cell into two daughter cells. Initially, as FtsZ proteins are expressed, they preferentially locate at the poles. After, they form a ring at midcell, in between the nucleoids,markingwhere a
Decision Making with Machine Learning Techniques in Consumer Performance: Empathy, Personality, Emotional Intelligence as Mediators
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Abstract Despite the importance of emotion in decision making (eg, ohm and Clore 2002; Luce 1998; Pham 1998; Ruth 2001), research has yet to fully understand how consumers use emotional information to make effective decisions. A growing body of research
A comparison of Machine Learning approaches for classifying Multiple Sclerosis courses using MRSI and brain segmentations
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Abstract. The objective of this paper is to classify Multiple Sclerosis courses using features extracted from Magnetic Resonance Spectroscopic Imaging (MRSI) combined with brain tissue segmentations of gray matter, white matter, and lesions. To this purpose we trained
A Statistical and Machine Learning Model to Detect Money Laundering: an Application
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Page 1. A Statistical and Machine Learning Model to Detect Money Laundering: an Application Dr. Miguel Agustín Villalobos Dr. Eliud Silva Actuarial Sciences Department Anahuac University June 15th, 2017 1 Director Advanced Analytics Institute Professor
Diagnosing Machine Learning Pipelines with Fine-grained Lineage
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ABSTRACT We present the Hippo system to enable the diagnosis of distributed machine learning (ML) pipelines by leveraging fine-grained data lineage. Hippo exposes a concise yet powerful API, derived from primitive lineage types, to capture fine-grained data lineage
Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning.
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ABSTRACT Design rule check (DRC) violations after detailed routing prevent a design from being taped out. To solve this problem, state-of-the-art commercial EDA tools global-route the design to produce a global-route congestion map; this map is used by the placer to
PREDICTING PROTEIN PROTEIN INTERACTIONS USING SEQUENCE HOMOLOGY AND MACHINE-LEARNING METHODS
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ABSTRACT: Protein protein interactions (PPIs) play an essential role in various biological processes. A range of computational methods have been proposed to predict PPIs from protein sequences. Among these, homology-based methods and machine-learning methods
Machine Learning Meets iOS Malware: Identifying Malicious Applications on Apple Environment.
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Abstract: The huge diffusion of the so-called smartphone devices is boosting the malware writer community to write more and more aggressive software targeting the mobile platforms. While scientific community has largely studied malware on Android platform, few attention is
Air quality monitoring using mobile microscopy and machine learning
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Abstract Rapid, accurate, and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of≤ 2.5m have been classified as carcinogenic by the World Health
A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot
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Abstract We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, in the form of a Unit Abstract. The fight against social exclusion is at the heart of the Europe 2020 strategy: 120 million people are at risk of suffering this condition in the EU. Risk prediction models are widely used in insurance companies and health services. However, the use of these models
Real Estate Investment Advising Using Machine Learning
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Abstract-The project makes a comparative study of various Machine Learning algorithms namely Linear Regression using gradient descent, K nearest neighbor regression and Random forest regression for prediction of real estate price trends. The aim of this paper is to
Dynamic unstructured bargaining with private information: theory, experiment, and outcome prediction via machine learning
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Abstract We study dynamic unstructured bargaining with deadlines and one-sided private information about the amount available to share (thepie size ). Using mechanism design theory, we show that given the players incentives, the equilibrium incidence of bargaining
Dyna: toward a self-optimizing declarative language for machine learning applications
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Abstract Declarative programming is a paradigm that allows programmers to specify what they want to compute, leaving how to compute it to a solver. Our declarative programming language, Dyna, is designed to compactly specify computations like those that are frequently
A Machine Learning Framework for Intraoperative Segmentation and Quality Assessment of Pedicle Screw X-Rays
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Abstract Pedicle screw fixation is a technically demanding procedure with potential difficulties and reoperation rates are currently on the order of 11%. The most common intraoperative practice for position assessment of pedicle screws is biplanar fluoroscopic
SLAQ: Quality-Driven Scheduling for Distributed Machine Learning
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Abstract Training machine learning (ML) models with large datasets can incur significant resource contention on shared clusters. This training typically involves many iterations that continually improve the quality of the model. Yet in exploratory settings, better models can
Machine Learning Theory and Applications for Healthcare
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The explosive growth of health-related data presented unprecedented opportunities for improving health of a patient. Machine learning plays an essential role in healthcare field and is being increasingly applied to healthcare, including medical image segmentation,
MAChINe LeARNING TeChNIQUeS FOR ANALYSIS OF PhYSICAL PROPeRTIeS PROFILeS OF E. COLI PROMOTeRS
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Motivation and Aim: An astonishing amount of DNA primary structure data provided by recent sequencing techniques gives opportunity to study multiple genomes and metagenomes at a time. however there are obstacles such as low accuracy of regulatory
Extending Loom: A Fast, Non-parametric Bayesian Machine Learning Algorithm
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Abstract Loom is a scalable machine learning algorithm that finds meaning in large, high dimensional datasets. Loom infers a latent structure over tabular datasets ranging from tens of rows to millions of rows. At its core, the Loom algorithm is built on Bayesian inference in a
Differentiation of Malignant and Benign Breast Lesions Using Machine Learning Algorithms
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AbstractMedical diagnosis is a process which requires critical decisions to be made by a medical professional. These decisions can be made using a Clinical Decision Support System (CDSS) to speedup or assist the decision making process. Many Machine Learning
Predicting Authoritarian Selections: Theoretical and Machine Learning Predictions of Politburo Promotions for the 19th Party Congress of the Chinese
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The starting argument for this paper is that elite selection and popular elections are both selection of leaders by a selectorate. Although the selectorates are small and their preference is largely hidden from public view, the Leninist institutions and established norms
Credit Scoring using Machine Learning Techniques
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ABSTRACT Lenders such as banks and credit card companies while reviewing a client s request for loan use credit scores. Credit scores help measure the creditworthiness of the client using a numerical score. Now it has been found out that the problem can be optimized
Spoken language understanding and interaction: machine learning for human-like conversational systems
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Abstract In recent years, the interest in research in speech understanding and spoken interaction has soared due to the emergence of virtual personal assistants. However, whilst the ability of these agents to recognise conversational speech is maturing rapidly, their
Stratified, computational interaction via machine learning
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AbstractWe present a control loop framework which enables humans to flexibly adapt their level of engagement in human computer interaction loops by delegating varying elements of sensing, actuation and control to computational algorithms. We give examples of the use
Machine Learning in Multimodal Medical Imaging
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Machine learning techniques have been increasingly applied in the medical imaging field for developing computer-aided diagnosis and prognosis models. Multimodal medical imaging can provide us with separate yet complementary structure and function information
Machine learning versus human understanding
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Just a couple of days after AlphaGo beat the best human player and ended its Go career due to the lack of worthy opponents, it is very easy to predict that machine learning will be successful in all domains of human activity. A natural extension of this thought is the
Demonstrating Interactive Machine Learning Tools for Rapid Prototyping of Gestural Instruments in the Browser
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ABSTRACT These demonstrations will allow visitors to prototype gestural, interactive musical instruments in the browser. Different browser based synthesisers can be controlled by either a Leap Motion sensor or a Myo armband. The visitor will be able to use an
Machine Learning Technology Applied to Production Lines: Image Recognition System
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The recent trend toward mass customization has increased the demand for multiproduct/ multivolume production and driven a need for autonomous production systems that can respond quickly to changes on production lines. Production facilities using cameras and
FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA.
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ABSTRACT: Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based
Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects
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Abstract Decision trees and related ensemble methods like random forest are state-of-theart tools in the field of machine learning for predictive regression and classification. However, they lack of interpretability and can be less relevant in credit scoring applications, where About this Series The seriesStudies in Computational Intelligence (SCI) publishes new develop- ments and advances in the various areas of computational intelligencequickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational
A MACHINE LEARNING FRAMEWORK FOR THE CATEGORIZATION OF ELEMENTS IN IMAGES OF MUSICAL DOCUMENTS
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ABSTRACT Musical documents may contain heterogeneous information such as music symbols, text, staff lines, ornaments, annotations, and editorial data. Before any attempt at automatically recognizing the information on scores, it is usually necessary to detect and
Quantitative feature extraction for machine learning analysis of resting-state fMRI data
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Altered regional synchronization in epileptic patients with generalized tonic-clonic seizures. Epilepsy Res. 2011 Nov;97(1-2):83-91.)The relative magnitude of low frequency fluctuations in brainALFF: Total power within the low-frequency
A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot
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Abstract We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, in the form of a Unit
Deep Neural Network Architectures for Component-Based Machine Learning Model in Building Energy Predictions
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Abstract. Artificial Neural Networks (ANN) are a universal approximator for any non-linear function. However, ANN approximation strongly depends on the architecture, ie the structure of the neurons and the training methods. This paper evaluates ANN architectures to model
Brief Announcement: Byzantine-Tolerant Machine Learning
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ABSTRACT We report on Krum, the rst provably Byzantine-tolerant aggregation rule for distributed Stochastic Gradient Descent (SGD). Krum guarantees the convergence of SGD even in a distributed setting where (asymptotically) up to half of the workers can be
MACHINE LEARNING TECHNIQUE FOR PRODUCT CLASSIFICATION IN E-COMMERCE DATA USING MICROSOFT AZURE CLOUD
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AbstractRapid growth of online shopping and marketing increase the field of e-commerce, which got boosted rapidly with the application of machine learning. Machine Learning (ML) has empowered businesses by finding useful patterns from customers search patterns and
Swayam: Distributed Autoscaling to Meet SLAs of Machine Learning Inference Services with Resource E ciency
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Abstract Machine Learning (ML) service platforms allow clients to train and deploy ML models as web services, which can be used for inference (prediction). A key challenge for providers is to guarantee response time Service Level Agreements (SLAs) for inference
8 Ways Machine Learning Is Improving Companies Work Processes
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Todays leading organizations are using machine learning based tools to automate decision processes, and theyre starting to experiment with more-advanced uses of artificial intelligence (AI) for digital transformation. Corporate investment in artificial intelligence is
Modelling Spatial Behaviour in Music Festivals Using Mobile Generated Data and Machine Learning
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Summary This study explores the utility of location data collected from a mobile phone app as a means of modelling spatial behaviour for consumer analysis, focusing on data from a music festival. Our aim was to harvest geo-temporal variables from the app data to model
A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models
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Abstract. Peatlands store large amounts of soil carbon and constitute an important component of the global carbon cycle. Accurate information on the global extent and distribution of peatlands is presently lacking but it important for earth system models (ESMs)
Rules of Machine Learning: Best Practices for ML Engineering
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This document is intended to help those with a basic knowledge of machine learning get the benefit of best practices in machine learning from around Google. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to
Application of machine learning on searching for Be Star Candidates within the OGLE-IV Gaia South Ecliptic Pole Field
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Long time surveys yield a large number of high quality light curves among their subproducts, useful to identify new variable stars. Since the number of observed objects is too large, machine learning techniques ought to be used to obtain results in reasonable periods of
MutSs Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning
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MutS is a key component in the mismatch repair (MMR) pathway. This protein is responsible for initiating the signaling pathways for DNA repair or cell death. Herein we investigate this heterodimers post-recognition, post-binding response to three types of DNA AbstractBridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements
The Rise of the Machines, Analytics, and the Digital Oilfield: Artificial Intelligence in the Age of Machine Learning and Cognitive Analytics
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Abstract In the next ten years, instantaneous value from digital oilfield systems will dramatically alter the oil and gas landscape as cost and operational efficiencies are attained through the reliance on artificial intelligence. The digital oilfield will radically change how
Machine Learning, Text Data, and Supreme Court Forecasting
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Abstract What predicts the behavior of Justices on the US Supreme Court Previous attempts to develop predictive models of Supreme Court behavior have found success using either (1) text data taken from oral argument proceedings, or (2) quantitative legal data. In
A survey on predictive analysis of cancer survivability rate using machine learning algorithm
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ABSTRACT Cancer has been described as a heterogeneous disease comprising of various subtypes. The early determination and visualization of a tumor has turned into a need in cancer research, as it can encourage the consequent clinical administration of patients. The
Machine Learning, Renormalization Group and Phase Transition
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Abstract In this essay, we review recent research on the interaction between machine learning, renormalization group and phase transition. There are two important questions to be addressed. The first one is how to understand and improve machine learning algorithms
Machine Learning Algorithms: Trends, Perspectives and Prospects
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Abstract: Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of todays most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial
A Survey on Machine Learning: Concept, Algorithms and Applications
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ABSTRACT: Over the past few decades, Machine Learning (ML) has evolved from the endeavour of few computer enthusiasts exploiting the possibility of computers learning to play games, and a part of Mathematics (Statistics) that seldom considered computational
Practical Secure Aggregation for Privacy Preserving Machine Learning.
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AbstractWe design a novel, communication-efficient, failurerobust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, userheld data vectors from mobile devices in a secure manner (ie without learning
Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning
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Abstract BACKGROUND: Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds
CYBER ATTACK DETECTION AND CLASSIFICATION USING MACHINE LEARNING TECHNIQUE USING MICROSOFT AZURE CLOUD
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AbstractInformation security is always a very important issue for modern computer world. Intrusion Detection System (IDS) as the security technique and is widely used against intrusions. Researchers use Data Mining and Machine learning techniques in intrusion
Crop Selection Method Based on Various Environmental Factors Using Machine Learning
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Abstract-These India is an agriculture based economy whose most of the GDP comes from farming. In an economy where most of the produced food is from agriculture, selection of crop (s) plays a very important role. In light of the decreasing crop produce and shortage of
Can machine learning on learner analytics produce a predictive model on student performance
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The process of learning how to program is wildly regarded as a difficult task icult task [1]. The increasing demand for computing graduates has resulted in increasing class sizes in universities. A positive student learning experience is being diminished due to negative
Machine Learning Applications in Medical Image Analysis
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Multiple todays medical imaging modalities, for example, X-ray CT, MRI/fMRI, and PET scanners, supply computeraided diagnostics (CAD) with a host of complex and highly informative images. The resulting big volumes of raw visual information are extremely
GITCoP: A Machine Learning Based Approach to Predicting Merge Conflicts from Repository Metadata
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Abstract Version Control Systems are a crucial tool in modern software development processes. They offer a number of features supporting the collaboration of multiple developers on a common code base. An important feature is branching which offers the
Machine Learning Approaches to Sentiment Analytics
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ABSTRACT One key aspect of sentiment analytics is emotion classification. This research studies the use of machine learning approaches to classify human emotion. Two different machine learning approaches were compared in an experimental study. In one approach,
Instance Spaces for Machine Learning Classification
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Abstract This paper tackles the issue of objective performance evaluation of machine learning classifiers, and the impact of the choice of test instances. Given that statistical properties or features of a dataset affect the difficulty of an instance for particular
Dynamic Access Control Policy based on Blockchain and Machine Learning for the Internet of Things
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AbstractThe Internet of Things (IoT) is now destroying the barriers between the real and digital worlds. However, one of the huge problems that can slow down the development of this global wave, or even stop it, concerns security and privacy requirements. The criticality
A Review of Home Automation System with Speech Recognition and Machine Learning
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Abstract: Many of the technologies currently used in smart homes can be adapted to meet a number of needs. By emerging these technologies in homes can eliminate the need of care and grant independence to those who might struggle with day to day activities or in
Handbook of Research on Machine Learning Innovations and Trends
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ABSTRACT The clustering techniques suffer from cluster centers initialization and local optima problems. In this chapter, the new metaheuristic algorithm, Sine Cosine Algorithm (SCA), is used as a search method to solve these problems. The SCA explores the search
Near-Data Processing for Differentiable Machine Learning Models
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Machine Learnings Success Big data Powerful parallel processorsSophisticated models Hyeokjun Choe et al.Page 7. a full-fledged ISP-supporting SSD platform Easy to implement machine learning
Data Analytics for YRBS (Youth Risk Behavior Survey) Data using Machine Learning and Data Mining Techniques
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Behaviors that contribute to unintentional injuries and violence Sexual behaviors related to unintended pregnancy and sexually transmitted infections, including HIV infection Alcohol and other drug use Tobacco use Unhealthy dietary behaviors Inadequate
SES-RREF: The Machine Learning Approach to Credible Metrics of Scholastic Evidence via Recursive Referencing
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Abstract Citation network analysis of scholarly articles and journals has already been explored in-depth and the subtlety of the differences between citations and references has also been recognized. Despite this recognition, citation network is mainly used for judging
Application of Probabilistic Modeling and Machine Learning to the Diagnosis of FTTH GPON Networks
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Exploratory Machine Learning Analysis of Real Network Log Data
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Abstract Intrusion detection systems often rely on hard checks of incoming requests to identify whether traffic is safe or malicious. Various machine learning approaches have been developed to mine large-scale network logs and help to identify anomalous traffic patterns.
Share Market Analysis and Prediction System using Machine Learning
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Abstract: Share Market investment is often believed to be fraught with risk and uncertainty. It is a dynamic system where even small news can have drastic effect on the share price. It is a place where a right investment can make you millionaire and at the same time a wrong one
AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE.
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ABSTRACT: Classification rule set is important for Land Cover classification, which refers to features and decision rules. The selection of features and decision are based on an iterative trial-and-error approach that is often utilized in GEOBIA, however, it is time-consuming and CSE PROJECTS