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CS229 Final Project Information One of CS229s main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well- Format: Your milestone should be at most 3 pa Grading: The milestone is mostly intended to get Contributions: Please include a section that de 8 Fun Machine Learning Projects for Beginners
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conducting original practical research by applying machine learning methods to a public or private dataset (see project ideas below). You are welcome and Images for machine learning projects

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MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the IEEE-PROJECTS-ON-MACHINE-LEARNING Engpaper
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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-

The Accuracy of Machine Learning (ML) Forecasting Methods versus Statistical Ones: Extending the Results of the M3-Competition
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Abstract Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for forecasting. Yet, scant evidence is available about their performance in terms of accuracy and computational requirements. The purpose of this

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 Data and Machine Learning addresses master students of Business Administration, Economics, Internationale Wirtschaft und Governance, and PhilosophyEconomics. Advanced interested bachelor students may

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives
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Abstract The concept of big datahas been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly

Machine learning annotation of human branch points
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Abstract Motivation: The branch point element is required for the first lariat-forming reaction in splicing. However current catalogues of human branch points remain incomplete due to the difficulty in experimentally identifying these splicing elements. To address this limitation, we

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

Machine Learning Summer 2017 Exercise Sheet 7
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Exercise 5-1 Programming Assignment: Building an MLP with Theano In this exercise we aim at classifying digits using the famous MNIST digits dataset. The dataset consists of 60000 training images and 10000 test images of handwritten digits. Each image has size

Unsupervised Machine Learning Analysis of Urinary Transcriptome Reveals Distinct Genotypic Clustering in Surgical Sepsis
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CONCLUSIONS: To our knowledge, this is the first study to show statistically significant differences in TEG parameters due to the addition of clotting cascade activators in trauma patients at high risk for VTE. The main advantage of rTEG is speed, but the disadvantage is

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within
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Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, structural imaging biomarkers 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

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

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),

Towards A Unified Graph Model for Supporting Data Management and Usable Machine Learning
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Abstract Data management and machine learning are two important tasks in data science. However, they have been independently studied so far. We argue that they should be complementary to each other. On the one hand, machine learning requires data

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

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

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 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,

DEVELOPMENT OF ALGORITHM FOR CREATING ATOM-ATOMIC MAPPING USINGNAIVEBAYES MACHINE LEARNING METHOD
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The chemical reaction is the conversion one or more substrate into products that differs from them in the chemical composition or structure. Knowledge of the reaction mechanism allows us to describe in detail the changes that occur at each elementary stage or for several

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

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

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

Machine Learning Based Method for Alzheimers Disease Detection
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ABSTRACT Alzheimers disease (AD), the most common form of dementia, is a degenerative disorder of the brain that leads to memory loss. Anatomical changes observed in samples of Alzheimers are dramatic shrinkage of the cerebral cortex, fatty deposits in blood vessels,

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

Data Mining, Soft Computing, Machine Learning and Bio-Inspired Computing for Heart Disease Classification/Prediction A Review
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Abstract: Data mining is the most common research area in the field of computer science and allied areas. Decision making in clinical data mining plays a significant role in patients life. In this survey research article we aim to portray various data mining algorithms, soft

The Impact of Machine Learning on Economics
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Abstract This paper provides an assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions. It begins by briefly overviewing some themes from the literature on machine learning, and then draws some

Machine Learning for Bioelectromagnetics: Prediction Model using Data of Weak Radiofrequency Radiation Effect on Plants
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AbstractPlant sensitivity and its bio-effects on non-thermal weak radio-frequency electromagnetic fields (RF-EMF) identifying key parameters that affect plant sensitivity that can change/unchange by using big data analytics and machine learning concepts are quite Abstract. Increasing accessibility to virtual environments has resulted in higher incidences of Cyberbullying attacks and the consequences of these attacks can affect the victims life for a long time or even permanently. For this reason, it is extremely important to develop tools to

Cloud Filtering and Novelty Detection using Onboard Machine Learning for the EO-1 Spacecraft
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Abstract We deployed three new data analysis algorithms onboard the Earth Observing 1 (EO-1) spacecraft and evaluated their performance over a five-month period. The algorithms include two cloud detectors and an unsupervised novelty detector. Together they provide the

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 AbstractThis research proposes a novel approach with vertical design space exploration (DSE) of several levels of configurable architecture design using Beyond Moore devices. ferrimagnets, Multistate Electrostatically Formed Nanowire transistors (MSET), and

ECNet: Large scale machine learning projects for fuel property prediction
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Summary ECNet is an open source Python package for creating large scale machine learning projects with a focus on fuel property prediction. ECNet can predict a variety of fuel properties including cetane number, octane number and yield sooting index using

Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance, Atmos
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Abstract. Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where

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 LearningIBM 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 . Deep learning, or machine learning with deep neural

orchid: a novel management, annotation, and machine learning framework for analyzing cancer mutations
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Abstract Motivation: As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of

Second-Order Stochastic Optimization for Machine Learning in Linear Time
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Abstract First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity. Second-order methods, while able to provide faster convergence, have been much less explored due to the high

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

Propensity scores and causal inference using machine learning methods
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OverviewMachine learning methods dominant for classification/prediction problems.Prediction is useful for causal inference if one is trying to predict propensity scores (probability ofcausal inference: greater bias or mean squared error

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

Emerging Trends in Machine Learning for Signal Processing
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Recently, there is an increasing interest in developingsmartdevices and systems able to interact with their environment, for example, Internet of Things and Human-Machine Interfaces. The termsmartis used to describe a set of advanced functionalities

USING MACHINE LEARNING TO DEFINE THE ASSOCIATION BETWEEN CARDIORESPIRATORY FITNESS AND ALL-CAUSE MORTALITY: THE FIT (HENRY
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ABSTRACT Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined

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

Develop machine learning methods for early diagnosis of Alzheimers
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Abstract The early analysis of Alzheimers through machine learning techniques is increasingly a very frequent topic in the field of neuro imaging. The use of a large amount of data requires to find some good fast techniques and refined to be able to carry out an

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

Swayam: Distributed Autoscaling to Meet SLAs of Machine Learning Inference Services with Resource E iciency
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ABSTRACT Developers use Machine Learning (ML) platforms to train ML models and then deploy these ML models as web services for inference (prediction). A key challenge for platform providers is to guarantee response-time Service Level Agreements (SLAs) for

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

Supervised and unsupervised machine-learning methods for pain research
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Along with the increasing molecular and clinical knowledge pathomechanisms of disease, the data acquired during biomedical research become increasingly complex. This poses challenges on the bioinformatical analytics that are increasingly accommodated by current

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

How to Collaboratively Learn to Taste BeerOnline Algorithms for Decentralized and Personalized Machine Learning on Graphs
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Machine Learning, Distributed Algorithms, Graph-based

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

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

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

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 have been classified as carcinogenic by the World Health

A poly-omics machine-learning method to predict metabolite production in CHO cells
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Abstract: The success of biopharmaceuticals as highly effective clinical drugs has 12 recently led industrial biotechnology towards their large-scale production. The 13 ovary cells of the Chinese hamster (CHO cells) are one of the most common 14 production cell line.

Enhancing the Security of Web Sites and Patients Portals by Detecting Malicious Web Robots Using Machine Learning Techniques
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There is increasing demand for access to medical information via web sites and patients portals, but one of the challenges towards widespread utilization of such service is maintaining the security of those web sites and portals. Recent reports show an alarming

Black-box Solar Performance Modeling: Comparing Physical, Machine Learning, and Hybrid Approaches
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ABSTRACT The increasing penetration of solar power in the grid has motivated a strong interest in developing real-time performance models that estimate solar output based on a deployments unique location, physical characteristics, and weather conditions. Solar

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

Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes
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Abstract: We perform an extensive comparison between 11 stochastic to 9 machine learning methods regarding their multi-step ahead forecasting properties by conducting 12 large- scale computational experiments. Each of these experiments uses 2 000 time series

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,

Who Do Sovereign Investors Say They Are Using Machine Learning Techniques to Build A Taxonomy of Sovereign Investors
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ABSTRACT Sovereign investors (sovereign wealth funds and pension funds) are often referred to as long-term investors. But it is still unclear that these investors pertain to the same category, as they vary on many dimensions. Previous research proposed typologies of

Corrigendum toPlant MicroRNA Prediction by Supervised Machine Learning Using C5. 0 Decision Trees
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In the article titledPlant MicroRNA Prediction by Supervised Machine Learning Using C. Decision Trees [] the name of the second author was given incorrectly as Rod Eyles. e authors name should have been written as Rodney P. Eyles. e revised authors list is shown

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

Machine Learning Based Simulation of Particle Physics Detectors
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Abstract A key part of experimental particle physics is the simulation of a detectors response to an event. Current simulators are polarised between those that are fast and approximate and those that are accurate and slow. Generative Adversarial Networks (GANs) are a class

Complexity vs. Performance: Empirical analysis of machine learning as a service
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ABSTRACT Machine learning classifiers are basic research tools used in numerous types of network analysis and modeling. To reduce the need for domain expertise and costs of running local ML classifiers, network researchers can instead rely on centralized Machine

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

Wire me through machine learning
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Page 1. WIRE ME THROUGH MACHINE LEARNING Ankit Singh Threat Analyst Engineer, Security Response Lead, Target profile prediction Ground Truth Anti Spam telemetry

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

Machine Learning-Based Topical Web Crawler: An Ensemble Approach Incorporating Meta-Features
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Abstract: A topical web crawler is to collect web pages that describe some pre-specified topics. The web pages collected by the topical crawler share the same or similar words and however among them not a few pages can be irrelevant to the given topics. In particular, the

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 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

Performance Evaluation of Machine Learning Algorithms in the Classification of Parkinson Disease Using Voice Attributes
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Abstract Nerve cells, the building blocks of the nervous system in the brain dont reproduce when damaged. On damage, the dopamine produced by these nerve cells are not produced which hinders motor skills and speech. Voice undergoes changes at an earlier stage before

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

FPGA accelerated dense linear machine learning: A precision-convergence trade-off
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Abstract Stochastic gradient descent (SGD) is a commonly used algorithm for training linear machine learning models. Based on vector algebra, it benefits from the inherent parallelism available in an FPGA. In this paper, we first present a singleprecision floating-

Machine Learning and Correlative Microscopy: How Big DataTechniques Can Benefit Thin Film Solar Cell Characterization
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Abstract Correlative microscopy techniques have improved tremendously in the last 5 years and enabled simultaneous high spatial resolution mapping of a variety of material parameters. As acquisition speeds and resolution increase giving us a greater density of

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

Litz: An Elastic Framework for High-Performance Distributed Machine Learning
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Abstract Machine Learning (ML) is becoming an increasingly popular application in the cloud and data-centers, inspiring a growing number of distributed frameworks optimized for it. These frameworks leverage the specific properties of ML algorithms to achieve orders of

Classifying Single-Cell Types from Mouse Brain RNA-Seq Data using Machine Learning Algorithms
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Abstract The cerebral cortex carries the cognitive and sensory functions of the mammalian body as well as any social behavior. Normal brain function depends on a variety of differentiated cell types, such as neurons, glia, and vasculature. Definitive identification of

Predicting Sweet Spots in Shale Plays by DNA Fingerprinting and Machine Learning
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Summary This paper presents a method to generate a> 70% accurate predictive map of sweet spots in shale plays prior to drilling. It indicates where to drill, and where not. The approach uses DNA analysis of surface soil samples, to derive information on the mix of 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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The relative magnitude of low frequency fluctuations in brainALFF: Total power within the low-frequency

machine learning in smart city




Machine Learning based traffic congestion prediction in a IoT based Smart City
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Abstract In a smart city roads would be equipped with the sensors for analyzing the traffic flow. Hence, free flowing of road traffic is important for faster connectivity and transportation systems. Few traffic flow prediction methods use Neural Networks and other prediction


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