MACHINE LEARNING-2020


Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves

Development of reduced structural theories for composite plates and shells via machine learning
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This paper presents a new approach for the development of structural models via three well- established frameworks, namely, the Carrera Unified Formulation (CUF) , the Axiomatic/Asymptotic Method (AAM) , and Artificial Neural Networks (NN) . CUF and

Staff Working Paper No. 848 Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach
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We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870 2016. Machine learning models mostly outperform logistic regression in out‑of‑sample predictions and forecasting

An empirical study on the perceived fairness of realistic, imperfect machine learning models
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There are many competing definitions of what statistical properties make a machine learning model fair. Unfortunately, research has shown that some key properties are mutually exclusive. Realistic models are thus necessarily imperfect, choosing one side of a tradeoff or

Performance analysis of binary and multiclass models using azure machine learning .
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Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms

Prediction of Cardiovascular Disease Risk for Newly Arrived Adult Canadian Immigrants Using Large Data and Machine Learning
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Background Immigrants are a fundamental pillar of the Canadian population and policy framework (1). Approximately 22 000 immigrants are accepted yearly (2). The health of immigrants at the time of arrival is significantly better than the health of the nativeborn

Machine learning for speaker recognition
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In the last 10 years, many methods have been developed and deployed for real-world biometric applications and multimedia information systems. Machine learning has been playing a crucial role in these applications where the model parameters could be learned

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method
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Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data Space technology has become an integral part of critical infrastructures and key elements for great power. Space telemetry data contain a wealth of information about the system behavior of artificial satellites. Recent developments in data mining techniques for anomaly

Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance
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A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and With the rise of smart devices and the internet of things (Yang et al.), computers continue to advance into everyday life. The common personal computer that many people use at home or at work is only one of many devices that offer computational power and its

Machine Learning and Artificial Intelligence
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by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms Data mining and machine learning are two very active areas of research due to their importance in both theory and practical applications. New methods have been developed and traditional techniques have been enhanced in the last two decades. Accompanying

Combining machine learning models and scores using combo library
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GETTING STARTED 1 combo Documentation, Release 0.0. 8 combo is featured for: Unified APIs, detailed documentation, and interactive examples across various algorithms. Advanced and latest models, such as Stacking/DCS/DES/EAC/LSCP. Comprehensive Machine learning is a core, transformative way by which were rethinking everything were doing. Were thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. Were in the early days, but youll see us in a systematic way think about how we can

Basics of Machine Learning
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2.1 Probability Theory and Random Variables . 11 2.2 Defining distributions 13 2.2.1 Probability mass functions . . 13 2.2.2 Probability density functions . 16 2.3 Multivariate random variables

MACHINE LEARNING FOR SURVEY DATA
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This course will provide survey and social science researchers with a broad overview of big data and opportunities it can provide for study design and analysis. We will also spend considerable time focusing on how to apply machine learning methods to analyze and

Out of the Clinic, into the Home: The in-Home Use of Phantom Motor Execution Aided by Machine Learning and Augmented Reality for the Treatment of
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Purpose: Phantom motor execution (PME) facilitated by augmented/virtual reality (AR/VR) and serious gaming (SG) has been proposed as a treatment for phantom limb pain (PLP). Evidence of the efficacy of this approach was obtained through a clinical trial involving

Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery
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In this article, we propose a methodology to leverage Machine Learning (ML) for the detection of web application vulnerabilities. Web applications are particularly challenging to analyse, due to their diversity and the widespread adoption of custom programming