machine learning application


Machine learning application to the Korean freshwater ecosystems
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This paper considers the advantage of Machine Learning (ML) implemented to freshwater ecosystem research. Currently, many studies have been carried out to find the patterns of environmental impact on dynamics of communities in aquatic ecosystems. Ecological

Designing a machine learning Based framework for enhancing performance of livestock mobile application system
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a real intelligent one. Figure 10. Typical learning. 4.3.3. Steps in Developing Machine Learning Application Below are steps that will be used in developing machine learning application . 1) Collect data. Data will be collected

On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
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Page 1. On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning

Machine Learning Application for Stock Market Prices Prediction.
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The development of a vibrant application for analyzing and predicting stock market prices is a basic tool aimed at increasing the rate of investors interest in stock markets. This paper explains the development and implementation of a stock price prediction application using

Extremal Entropy: Information Geometry, Numerical Entropy Mapping, and Machine Learning Application of Associated Conditional Independences
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Entropy and conditional mutual information are the key quantities information theory provides to measure uncertainty of and independence relations between random variables. While these measures are key to diverse areas such as physics, communication, signal

Machine Learning Application to Improve COCOMO Model using Neural Networks
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Millions of companies expend billions of dollars on trillions of software for the development and maintenance. Still many projects result in failure causing heavy financial loss. Major reason is the inefficient effort estimation techniques which are not so suitable for the current

Machine Learning Methods Application to Search for Regularities in Chemical Data
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The success of approach that was put forward in IMET has given an impetus to many investigations which were connected with machine learning application to inorganic chemistry and materials science and carried-out in various countries

MACHINE LEARNING APPLICATION IN THE FINANCIAL MARKETS INDUSTRY
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Machine Learning is increasingly prevalent in Stock Market trading. The goal of this paper is to investigate whether the machine learning technique is able to retrieve information from past prices and predict price movement and future trends. We explore using trend trading

Machine learning application in optimization of flexible circuit configuration
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One solution is to place semi-rigid transistors on a flexible polymer substrate. Next, we can optimize the transistor placement on the substrate using particle swarm optimization to minimize some objective (area, factor of safety, etc.). This has been successfully performed

MACHINE LEARNING APPLICATION IN LOAN DEFAULT PREDICTION
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In Todays world, most of world population has access to banking services. Consumers has increased many fold in last few years. For the banks, risks related to bank loans has increased especially after The Great Recession (2007 2012) and job threats due to

Machine Learning Application On Detecting Nudity In Images
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We built a mobile app that help people get opinions and recommendations from their social network. The app enables people to create picture poll and have friends to vote on it. Since it contains user-generated content, it is essential to censor the content to ensure that there is

MOBILE MIND: A FULLY MOBILE PLATFORM BASED MACHINE LEARNING APPLICATION
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In recent years, mobile devices have developed significantly in terms of technical capabilities, computing power, storage capacity and ability of sensing different activities via intelligent built-in sensors. In this perspective, capabilities of ultimate mobile phone

A Machine Learning Application for Latency Prediction in Operational 4G Networks
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Measuring performance on Internet is always challenging. When it comes to the mobile networks, the variety of technology characteristics coupled with the opaque network configuration make the performance evaluation even a more difficult task. Latency is one of

Selection of indicators by machine learning : Application to estimate permanent grassland plant richness
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Indicator based on key species or other data from field observation are very useful to manage permanent grasslands or to control result-oriented agri-environment schemes. These indicators must generally fulfil the following features: purpose relevance (ie optimize

ILP for Cosmetic Product Selection-Use of Smart Phone for Real-World Machine Learning Application
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In this study, we design a real-world machine learning system using a smart phone. This system can acquire images taken with the camera of a smart phone using learners (ILP and SVM) and automatically diagnose the new image. To develop this system, we implement an

Analysis of Machine Learning Research and Application
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similarity category. This algorithm is simple and computational speed, particularly the faster speed of classification, when compared with other algorithms. C. Machine Learning application in Data Mining Machine learning methods

Building real time object detection iOS application using machine learning
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(NPL) with a limited number of APIs and is only available for iOS 11 and above . Figure 6. General machine learning application structure. Copied from Apple documentation forward usage. In order to start integrating machine learning application , one just needs

Data visualisation and machine learning web application with potential use in sports data analytics
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data 3. Can a machine learning application , such as the one implemented in this project, be used in sports science to make predictions about athletic performance using biomarker data 2 Page 11. 1.3.2 Research Objectives

Increasing Portable Machine Learning Performance by Application of Rewrite Rules on Google Tensorflow Data Flow Graphs
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3 1.1 Contributions The goal of this project is to be able to execute as much of an existing Google Ten- sorflow machine learning application within the data parallel programming language Lift. This approach allows the Lift programming language to find a method in order to



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