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Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 211550 Last moment tuitions
Data Mining (Introduction for Business Students)
 
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This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 3829 tutor2u
Enhanced Resource Allocation: Business Use of Predictive Analytics and Data Mining
 
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Visit http://tdwi.org for more information on business intelligence and data warehousing training and education. TDWI Boston 2014 Keynote: Enhanced Resource Allocation: Business Use of Predictive Analytics and Data Mining Tony Rathburn Senior Consultant & Training Director The Modeling Agency StarSoft Solutions, Inc. Advanced technology has been a cultural obsession over the past few decades as business and government have invested heavily in pursuit of competitive advantage. The exponential growth in data repositories combined with advances in analytic techniques have left many organizations searching for the opportunities that justify these investments. Predictive analytics expert and author Tony Rathburn explores a business-driven perspective on using analytics that offers measurable organizational benefits, rapid implementation potential, minimal new investments, and lowrisk implementation strategies that can have near-immediate impact on virtually all organizations.
Views: 536 TDWI
Big Data in Extraction and Natural Resource Industries: Mining, Water, Timber, Oil and Gas 2014 - 19
 
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Market Research Reports, Inc. has announced the addition of "Big Data in Extraction and Natural Resource Industries: Mining, Water, Timber, Oil and Gas 2014 - 2019" research report to their offering. See more at- http://mrr.cm/ZsY
Country Analyst#1  (Resources): Finding Resource data
 
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This video is for those students who are starting to work on the "Country Analyst: Resources" project, and need some help! I walk you through where to find the resource data, and what to do with it once you have it.
Views: 677 mjmfoodie
Google Analytics Data Mining with R (includes 3 Real Applications)
 
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R is already a Swiss army knife for data analysis largely due its 6000 libraries but until now it lacked an interface to the Google Analytics API. The release of RGoogleAnalytics library solves this problem. What this means is that digital analysts can now fully use the analytical capabilities of R to fully explore their Google Analytics Data. In this webinar, Andy Granowitz, ‎Developer Advocate (Google Analytics) & Kushan Shah, Contributor & maintainer of RGoogleAnalytics Library will show you how to use R for Google Analytics data mining & generate some great insights. Useful Resources:http://bit.ly/r-googleanalytics-resources
Views: 29811 Tatvic Analytics
Data Science for Business: Data Mining Process and CRISP DM
 
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This lesson provides an introduction to the data mining process with a focus on CRISP-DM. This video was created by Cognitir (formerly Import Classes). Cognitir is a global company that provides live training courses to business & finance professionals globally to help them acquire in-demand tech skills. For additional free resources and information about training courses, please visit: www.cognitir.com
Views: 14709 Cognitir
Introduction to Event Log Mining with R
 
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Event logs are everywhere and represent a prime source of Big Data. Event log sources run the gamut from e-commerce web servers to devices participating in globally distributed Internet of Things (IoT) architectures. Even Enterprise Resource Planning (ERP) systems produce event logs! Given the rich and varied data contained in event logs, mining these assets is a critical skill needed by every Data Scientist, Business/Data Analyst, and Program/Product Manager. At this meetup, presenter Dave Langer, will show how easy it is to get started mining your event logs using the OSS tools of R and ProM. Dave will cover the following during the presentation: • The scenarios and benefits of event log mining • The minimum data required for event log mining • Ingesting and analyzing event log data using R • Process Mining with ProM • Event log mining techniques to create features suitable for Machine Learning models • Where you can learn more about this very handy set of tools and techniques *R source code will be made available via GitHub here: https://github.com/EasyD/IntroToEventLogMiningMeetup Find out more about David here: https://www.meetup.com/data-science-dojo/events/235913034/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8y2K0 See what our past attendees are saying here: https://hubs.ly/H0f8xNz0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 6602 Data Science Dojo
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 230989 CS Dojo
Big Data vs. Data Mining
 
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This short video discusses what big data is, and compares it with data mining. While these two terms are related, they are not the same thing. Some articles are reporting that big data and data mining are the same thing, so this is my response to help you interpret these better.
Views: 7534 Rich H
Targeting crimes and criminals through data, Dr Rick Adderley
 
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The Society of Data Miners, in association with the Alan Turing Institute, is delighted to announce the second in a series of practitioner seminars. This talk will discuss the challenges of mining Police data to provide operational intelligence. Rick will introduce the data and systems involved in day-to-day reporting, resource tasking and arresting offenders, including the issues of linking data across systems and the challenges of extracting useful information from free text. Digging into more advanced analytics, Rick will discuss criminal network analysis or CNA, an important tool in crime prevention and detection, and the differences between analysing overt networks (SNA) and covert networks (CNA). Rick will describe how supervised and unsupervised learning methods have been used in the identification of prolific and priority offenders, and how the results are used to solve crimes and target offenders, and to use resources effectively. Finally Rick will describe the EU-funded FP7 project Valcri (www.valcri.org), and its task to provide a Police data set that is suitable for release into the research community. Rick Adderley Bio: Rick is a retired Police Officer having served for 32 years in an operational capacity. His legacy to the Service is an intelligence product which was developed for the West Midlands region and is now used by all UK Police Forces; he specialises in profiling criminal activity. Rick retired in 2003 and started his data mining company, A E Solutions, focusing within the UK Emergency Services arena. Rick is also a director of the Society of Data Miners.
Novel Data Mining Methods for Virtual Screening - PhD Defense
 
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The Defense of PhD degree in Computer Science in King Abdullah University of Science and Technology (KAUST). Abstract: Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a confident conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by the big expansion in data and its dimensionality are inevitable. In this thesis, I address the main challenges that come when data mining techniques are used for virtual screening. In order to achieve an efficient virtual screening using data mining, I start by addressing the problem of feature selection and provide analysis of best ways to describe a chemical compound for an enhanced screening performance. High-throughput screening (HTS) assays data used for virtual screening are characterized by a great class imbalance. To handle this problem of class imbalance, I suggest using a novel algorithm called DRAMOTE to narrow down promising candidate chemicals aimed at interaction with specific molecular targets before they are experimentally evaluated. Existing works are mostly proposed for small-scale virtual screening based on making use of few thousands of interactions. Thus, I propose enabling large-scale (or big) virtual screening through learning millions of interaction while exploiting any relevant dependency for a better accuracy. A novel solution called DRABAL that incorporates structure learning of a Bayesian Network as a step to model dependency between the HTS assays, is showed to achieve significant improvements over existing state-of-the-art approaches.
Views: 464 Othman Soufan
The Agile Future of HR and Talent Acquisition - Prof. Dr. Armin Trost
 
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Presentation by Prof. Dr. Armin Trost, Author, Consultant and Professor at Furtwangen University, held at Textkernel's conference "Intelligent Machines and the Future of Recruitment" on 2 June 2016 in Amsterdam. Human resource management in the 21st century will have little to do with what has been promoted in recent years or decades and written in the text-books. Instead of finding “the right people, at the right time and at the right place” we will make the employees and their individual preferences, talents, life plans, and ambitions the focus of attention. We will say goodbye to mechanistic, technocratic, and often bureaucratic approaches. They fit in a past that was stable and predictable. If you regard your employees as your most valuable asset, you will give them freedom, trust, and responsibility. Moreover you will appreciate individuality and individual life-plans. Human resources management will therefore deal less with hierarchical processes, systems, responsibilities, KPIs, etc., in the future. Rather, it will be about how to empower teams to think on their own responsibility, communicate, collaborate, learn, and develop their talent in the long term. HR-Technology will be there to make the life of managers and employees easier instead of supporting the HR-function only. For instance, in the area of recruiting all this will lead to a more intense usage of social networks, artificial intelligence, big data, data mining etc.
Views: 14759 Textkernel
Learn Data Science in 3 Months
 
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I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP, ether through a full-time position or contract work. You’ll be learning a host of tools like SQL, Python, Hadoop, and even data storytelling, all of which make up the complete data science pipeline. Curriculum for this video: https://github.com/llSourcell/Learn_Data_Science_in_3_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Week 1 - Learn Python - EdX https://www.edx.org/course/introduction-python-data-science-2 - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Week 2 - Statistics & Probability - KhanAcademy https://www.khanacademy.org/math/statistics-probability Week 3 - Data Pre-processing, Data Vis, Exploratory Data Analysis - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis Week 4 - Kaggle Project #1 Week 5-6 - Algorithms & Machine Learning - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/ Week 7 - Deep Learning - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 8 - Kaggle Project #2 Week 9 - Databases (SQL + NoSQL) - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2 Week 10 - Hadoop & Map Reduce + Spark - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf Week 11 - Data Storytelling - Edx https://www.edx.org/course/analytics-storytelling-impact-1 Week 12- Kaggle Project #3 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/ Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 236314 Siraj Raval
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
 
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 2115 TEDx Talks
Using Data Mining to Predict Hospital Admissions From the Emergency Department
 
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Using Data Mining to Predict Hospital Admissions From the Emergency Department -- The World Health Organization estimates that by 2030 there will be approximately 350 million young people (below 30 to 40 years) with various diseases associated with renal complications, stroke and peripheral vascular disease. Our aim is to analyze the risk factors and system conditions to detect disease early with prediction strategies. By using the effective methods to identify and extract key information that describes aspects of developing a prediction model, sample size and number of events, risk predictor selection. Crowding within emergency departments (EDs) can have significant negative consequences for patients. EDs therefore need to explore the use of innovative methods to improve patient flow and prevent overcrowding. One potential method is the use of data mining using machine learning techniques to predict ED admissions. This system highlights the potential utility of three common machine learning algorithms in predicting patient admissions. In this proposed approach, we considered a heart disease as a main concern and we start prediction over that disease. Because in India a strategic survey on 2015-6016 resulting that every year half-a million of people suffer from various heart diseases. Practical implementation of the models developed in this paper in decision support tools would provide a snapshot of predicted admissions from the ED at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic regression models, although GBM's will be useful where accuracy is paramount. Using the strategic algorithm such as Logistic Regression, Decision Trees and Gradient Boosted Machine, we can easily identify the disease with various attributes and risk factor specifications. Based on these parameters, the analysis of high risk factors of developing disease is identified using mining principles. Use of data mining algorithms will result in quick prediction of disease with high accuracy. Data mining, emergency department, hospitals, machine learning, predictive models -- For More Details, Contact Us -- Arihant Techno Solutions www.arihants.com E-Mail-ID: [email protected] Mobile: +91-75984 92789
Introducing Poker Datamining
 
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To read the full article, click here: http://www.458casino.com/822/introducing-poker-datamining/ 458Casino.com lists the best resources on Casinos and Poker including Top Casino Sites, Casino News Updates, Casino Tips, Online Casinos, and Casino Reviews. Get all the latest poker information by clicking here: http://www.458casino.com/
Views: 72 458Casino
productronica 2017 - Process Optimizing through Data Mining and Machine Learning
 
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Florian Schwarz: "A warm welcome to productronica 2017. The special shows here are a big highlight – because that's where you can experience electronics manufacturing live!" "Founded in April 2017: the research fab Microelectronics Germany. This is where research capacities all over the country are bundled together and connected, to give the fab more weight internationally as a centre for microelectronics."   "Ah, Dr. Olowinsky. Hello!"    "Laser microwelding. What exactly are we looking at here?"   Dr. Alexander Olowinsky: "Laser microwelding is an established method in electronics and precision engineering for creating electrical and mechanical connections.Here you can see a laser beam melting material – and that's what creates the connection. In this particular version, the laser head contains the beam guidance, beam forming and mechanical pressing combined, for a flexible manufacturing process."   Florian Schwarz: "And what are the areas of application?"   Dr. Alexander Olowinsky: "What you see here: classic battery technology, production of battery modules and of battery packs, production of electrical connections,all the way to printed circuit board technology, because we need to create connections there too."   Florian Schwarz: "Dr. Olowinsky, thanks a lot!" Florian Schwarz: "From microelectronics to the special show devoted to hardware data mining.With me now is Ulf Oestermann, business developer at Fraunhofer IZM.Good morning!"   FlorianSchwarz: "Mr. Oestermann, what's the connection between microelectronics and hardware data mining?"   Ulf Oestermann: "The research fab Microelectronics Germany supposed to develop technologies and processes for the future. And they then have to be ported into mass production and scaled, so that they're ready to use there. That's exactly what hardware data mining is all about – showing what data records accumulate at what location in the individual process steps, and how robust they have to be in order to be used."   Florian Schwarz: "So we're talking about 'digging' data? Can we take a closer look?"   Ulf Oestermann: "Sure. No problem."   Ulf Oestermann: "Based on the data matrix code, you can immediately establish when this subassembly was manufactured, at what temperature, and in what humidity, and then conclusions can be drawn about possible errors."   Florian Schwarz: "I guess it helps save on resources – only having to replace individual components?"   Ulf Oestermann: "It's showing how thick wire is bonded. A very, very large number of wires are needed to get a high current density in the contact."   Florian Schwarz: "Mr. Oestermann, thanks very much for the tour. Hardware data mining. I'm going to the VDMA now to see what's being done with the data. And you? Back to work?"   Ulf Oestermann: "That's right!"   Florian Schwarz: "Ok - thanks. Ciao! We've just mined and collected the data. The data has to go somewhere, it has to be processed. And that brings me to the special show of the VDMA: "Smart-Data-Future Manufacturing."   "With me now is Mr. Müller from the VDMA. I've just taken a look round your stand. There's a lot of data being generated here. What's going to be done with it?"    Daniel Müller: "In the next stage, it's simply stored in various cloud systems, to make the long-term data actually usable. For models, for instance – like predictive maintenance."   Florian Schwarz: "Smart Data. How do you see the future of that?"   Daniel Müller: "A very exciting future topic is machinelearning - where companies try to make machines learn. So they can avoid errors, or correct them, all by themselves."   Florian Schwarz: "Wow. Thank you very much, Mr. Müller! Smart Data Future Manufacturing – it's a topic we're going to keep a close eye on. Well, that's all from productronica 2017. I'm already looking forward to 2019! Goodbye!"
Views: 324 productronica
Marketing Data Mining
 
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http://liquidQuantum.com Market Data Mining Before I show you how to properly begin to research your specific markets, I want to give you access to specific sites that give you some information about the possible markets that you're about to go into. We will be looking at specific market data mining sites and resources.
Views: 944 Cheap Domains
Using Data Mining Technique to Improve Billing System Performance  In Semiconductor Industry
 
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TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM ,EMAIL:[email protected] NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Views: 13 NEXGEN TECHNOLOGY
IEEE 2017-2018 DATA MINING PROJECTS RESOURCE ALLOCATION IN
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2017 ieee projects, 2017 ieee java projects, 2017 ieee dotnet projects, 2017 ieee android projects, 2017 ieee matlab projects, 2017 ieee embedded projects, 2017 ieee robotics projects, 2017 IEEE EEE PROJECTS, 2017 IEEE POWER ELECTRONICS PROJECTS, ieee 2017 android projects, ieee 2017 java projects, ieee 2017 dotnet projects, 2017 ieee mtech projects, 2017 ieee btech projects, 2017 ieee be projects, ieee 2017 projects for cse, 2017 ieee cse projects, 2017 ieee it projects, 2017 ieee ece projects, 2017 ieee mca projects, 2017 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2017 mtech projects, 2017 mphil projects, 2017 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2017 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2017 ieee omnet++ projects, ieee 2017 oment++ project, innovative ieee projects, latest ieee projects, 2017 latest ieee projects, ieee cloud computing projects, 2017 ieee cloud computing projects, 2017 ieee networking projects, ieee networking projects, 2017 ieee data mining projects, ieee data mining projects, 2017 ieee network security projects, ieee network security projects, 2017 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2017 wireless networking projects ieee, 2017 ieee web service projects, 2017 ieee soa projects, ieee 2017 vlsi projects, NS2 PROJECTS,NS3 PROJECTS.
Views: 1 ganesh pg
Uncharted Lecture Series: "A Framework for Data Mining in Wind Power Time Series"
 
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On Thursday, March 19, 2015, Oliver Kramer, a juniorprofessor for computational intelligence at the University of Oldenburg in Germany and an ICSI alumnus, gave a talk about his work on data mining and green energy. Dr. Kramer's full abstract and bio are available at https://www.icsi.berkeley.edu/icsi/events/2015/03/kramer-data-mining-framework Abstract: Wind energy is playing an increasingly important part for ecologically friendly power supply. The fast growing infrastructure of wind turbines can be seen as a large sensor system that screens the wind energy at a high temporal and spatial resolution. The resulting databases consist of huge amounts of wind energy time series data that can be used for prediction, controlling, and planning purposes. In this talk, I describe WindML, a Python-based framework for wind energy related machine learning approaches. Read the full abstract at https://www.icsi.berkeley.edu/icsi/events/2015/03/kramer-data-mining-framework
Views: 625 ICSIatBerkeley
Twitter API - Data Mining #3
 
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Using Tweepy to search for tweets and process them. We also cover Cursor object for iteration which helps in retrieving large amount of data from Twitter. GitHub/NBViewer Link: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/3.%20Twitter%20API.ipynb
Views: 17731 Roshan
Data Mining with Scala at Identified
 
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Scala is an excellent tool for big data cleaning, gleaning, and modeling. Jan Prach visited SF Scala and discussed how his company, Identified, uses Scala to gain insights into people ranking and search. He overviews the overall data flow, focuses on several points of interest, and explains how Scala makes it all happen. Jan Prach is a Scala Developer, at Identified, using Machine Learning and Data Mining. **Check out more Scala resources: http://marakana.com/s/tags/scala
Views: 3541 InfoQ
Logistic Regression: Part 1 ("Data Mining for Business Intelligence")
 
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Part of the MBA-level course "Data Mining for Business Intelligence" offered by Prof Galit Shmueli @ University of Maryland's Smith School of Business and @ Indian School of Business. Logistic regression is introduced in the context of predictive analytics. Related resources: Textbook "Data Mining for Business Intelligence" by Shmueli, Bruce & Patel (http://dataminingbook.com)
Views: 8537 Galit Shmueli
Human resources, CRM, data mining and social media concept - officer looking for employee represente
 
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To download this template in PowerPoint format (.pptx) please go to link below: http://www.smiletemplates.com/powerpoint-templates/human-resources-crm-data-mining-and-social-media-concept-officer-looking-for-employee-represente/09570/ If you want see some related templates on these theme, please go to: http://www.smiletemplates.com/search/powerpoint-templates/professional/0.html
Mining the Next Natural Resource:  Big Data
 
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IBM's Big Data platform is leading clients into a new era of computing by making data exploration and analysis simple, fast and economical. The newest crop of innovations, which represent the work of thousands of IBMers in labs around the world, are designed to help companies harness all data and turn it into valuable and actionable information, from better healthcare to improved traffic management to more personalized marketing. To learn more about how IBM can help unlock the value of Big Data at the Speed of Business visit http://www-01.ibm.com/software/data/bigdata/big-data-management/
Views: 1589 IBM for CIO
1/2: Karianne Bergen: Big data for small earthquakes
 
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Part 1 of 2: Dr. Karianne Bergen, Harvard Data Science Initiative Fellow at Harvard U., presents "Big data for small earthquakes: a data mining approach to large-scale earthquake detection" at the MIT Earth Resources Laboratory on September 28, 2018. "Earthquake detection, the problem of extracting weak earthquake signals from continuous waveform data recorded by sensors in a seismic network, is a critical and challenging task in seismology. New algorithmic advances in “big data” and artificial intelligence have created opportunities to advance the state-of-the-art in earthquake detection algorithms. In this talk, I will present Fingerprint and Similarity Thresholding (FAST; Yoon et al, 2015), a data mining approach to large-scale earthquake detection, inspired by technology for rapid audio identification. FAST leverages locality sensitive hashing (LSH), a technique for efficiently identifying similar items in large data sets, to detect new candidate earthquakes without template waveforms ("training data"). I will present recent algorithmic extensions to FAST that enable detection over a seismic network and limit false detections due to local correlated noise (Bergen & Beroza, 2018). Using the foreshock sequence prior to the 2014 Mw 8.2 Iquique earthquake as a test case, we demonstrate that our approach is sensitive and maintains a low false detections rate, identifying five times as many events as the local seismicity catalog with a false discovery rate of less than 1%. We show that our new optimized FAST software is capable of discovering new events with unknown sources in 10 years of continuous data (Rong et al, 2018). I will end the talk with recommendations, based on our experience developing the FAST detector, for how the solid Earth geoscience community can leverage machine learning and data mining to enable data-driven discovery. "
Social Media Data Mining With Raspberry Pi (Part 3: Operating Systems)
 
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This video is third in a series that walks through all the steps necessary to mine and analyze social media data using the inexpensive computer called a Raspberry Pi. Part 3 describes the two operating system environments of the Raspberry Pi: the Windows-like graphic user interface and the Linux text-based terminal environment.
Views: 1342 James Cook
Applied and efficient modeling in natural resources  case studies of mining and oil and gas
 
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Commercial interests in natural resources present particular challenges to the modeler, such as price volatility, weather-dependent demand cycles, changing regulations, technological developments, and many other factors. The complexity of modeling these challenges can be daunting, and in fact, undue attention to getting the model “right” can overwhelm the usefulness of the final product. Good model design is instrumental to building simple models of complex systems that can deliver immediate and valuable insight to decision makers. Good model design allows translating the essential needs of the decision-maker into an efficient and effective tool. The design of the model will determine the time required building it and running it, but also how data-intensive the model will be. Model design also has implications for risk management and decision making. In this talk, we discuss several case studies from our client work where we had to strike a balance between model realism and structural simplicity. We will emphasize moments when we were able to exploit realistic assumptions or fundamental statistical theory to simplify the model and still obtain robust results. We will also discuss commonly-seen mistakes that can result in systematically misleading models. Kurt Rinehart, MS, PhD Risk and Statistical Consultant EpiX Analytics www.epixanalytics.com Francisco J. Zagmutt, DVM, MPVM, PhD Managing Partner EpiX Analytics www.epixanalytics.com
Views: 563 Palisade
evaluation of predictive data mining algorithms in soil data classification for optimized crop
 
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evaluation of predictive data mining algorithms in soil data classification for optimized crop recom - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS 1. RRPhish Anti-Phishing via Mining Brand Resources Request 2. Confidence-interval Fuzzy Model-based Indoor Localization COMPUTER-BASED MEDICAL SYSTEMS (CBMS) 1. Population Health Management exploiting Machine Learning Algorithms to identify High-Risk Patients (23 July 2018) PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1. Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition ( April 1 2018 ) 2. Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection 3. Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search SOFTWARE ENGINEERING,COMPUTER GRAPHICS 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events 3. Performance Specification and Evaluation with Unified Stochastic Probes and Fluid Analysis 4. Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links 5. Amorphous Slicing of Extended Finite State Machines 6. Test Case-Aware Combinatorial Interaction Testing 7. Using Timed Automata for Modeling Distributed Systems with Clocks: Challenges and Solutions 8. EDZL Schedulability Analysis in Real-Time Multicore Scheduling 9. Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler 10. Locating Need-to-Externalize Constant Strings for Software Internationalization with Generalized String-Taint Analysis 11. Systematic Elaboration of Scalability Requirements through Goal-Obstacle Analysis 12. Centroidal Voronoi Tessellations- A New Approach to Random Testing 13. Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm 14. Pair Programming and Software Defects--A Large, Industrial Case Study 15. Automated Behavioral Testing of Refactoring Engines 16. An Empirical Evaluation of Mutation Testing for Improving the Test Quality of Safety-Critical Software 17. Self-Management of Adaptable Component-Based Applications 18. Elaborating Requirements Using Model Checking and Inductive Learning 19. Resource Management for Complex, Dynamic Environments 20. Identifying and Summarizing Systematic Code Changes via Rule Inference 21. Generating Domain-Specific Visual Language Tools from Abstract Visual Specifications 22. Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers 23. On Fault Representativeness of Software Fault Injection 24. A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud 25. Coverage Estimation in Model Checking with Bitstate Hashing 26. Synthesizing Modal Transition Systems from Triggered Scenarios 27. Using Dependency Structures for Prioritization of Functional Test Suites
Views: 21 MICANS VIDEOS
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Dancho, Business Science
 
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This presentation was recorded at #H2OWorld 2017 in Mountain View, CA. Enjoy the slides: https://www.slideshare.net/0xdata/hr-analytics-using-machine-learning-to-predict-employee-turnover. Learn more about H2O.ai: https://www.h2o.ai/. Follow @h2oai: https://twitter.com/h2oai. - - - In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O. Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
Views: 4588 H2O.ai
Statistics and Data Mining at Linköping University
 
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Shaheer Mansoor is a master´s student from Pakistan. He is studying Statistics and Datamining at Linköping University in Sweden. There is a rapidly increasing demand for specialists who are able to exploit the new wealth of information in large and complex datasets to improve analysis, prediction and decision making. The programme focuses on modern developments in the intersection of statistics, artificial intelligence and database management, providing the participants with unique competence in the labour market. Read more: http://www.liu.se/statistics-data-mining
Views: 5850 LinkopingUniversity
Data Science Bangla Tutorial for beginners
 
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https://datajobs.com/what-is-data-science https://www.kaggle.com/wiki/Tutorials http://blog.datacamp.com/wp-content/uploads/2014/08/How-to-become-a-data-scientist.jpg https://www.quora.com/How-can-I-become-a-data-scientist-1 http://www.kdnuggets.com/2015/09/free-data-science-books.html http://www.learndatasci.com/best-data-science-online-courses/ https://www.simplilearn.com/resources-to-learn-data-science-online-article http://www.forbes.com/sites/drewhansen/2016/10/21/become-data-scientist/#6e201e6a5b1b https://www.datacamp.com/community/tutorials/how-to-become-a-data-scientist#gs.FLqYd58 http://www.kdnuggets.com/2016/08/become-data-scientist-part-1.html http://www.itcareerfinder.com/it-careers/big-data-scientist.html http://www.kdnuggets.com/2014/11/9-must-have-skills-data-scientist.html http://www.mastersindatascience.org/careers/data-scientist/ https://www.udacity.com/course/intro-to-data-science--ud359 https://www.datacamp.com/subscribe?coupon_code=NY-2017-PROMO https://blog.modeanalytics.com/data-science-career/ https://www.simplilearn.com/data-science-interview-questions-article https://www.quora.com/What-is-a-data-scientists-career-path-1 http://blog.udacity.com/2014/11/data-science-job-skills.html http://101.datascience.community/2016/11/28/data-scientists-data-engineers-software-engineers-the-difference-according-to-linkedin/ https://www.learnpython.org/ https://www.r-bloggers.com/how-to-learn-r-2/ http://www.hadoop360.com/blog/comprehensive-list-of-data-science-resources http://datasciencereport.com/2016/12/21/best-of-2016-data-science-central-most-popular-articles-this-year/#.WHJ7HVV97ct https://datascienceplus.com/learn-r-from-scratch-part-1/ http://noeticforce.com/best-free-tutorials-to-learn-python-pdfs-ebooks-online-interactive https://blog.modeanalytics.com/data-science-career/ https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/ https://www.analyticsvidhya.com/blog/2016/02/complete-tutorial-learn-data-science-scratch/ https://www.import.io/post/38-great-resources-for-learning-data-mining-concepts-and-techniques/
Views: 5956 Farhana Sharmin
genetic algorithm in artificial intelligence | genetic algorithm in hindi | Artificial intelligence
 
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Hey friends welcome to well academy here is the topic genetic algorithm in artificial intelligence in hindi DBMS Gate Lectures Full Course FREE Playlist : https://goo.gl/Z7AAyV Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy genetic algorithm in artificial intelligence, genetic algorithm in artificial intelligence in hindi, genetic algorithm in artificial intelligence example, genetic algorithm in artificial intelligence tutorial, genetic algorithm in artificial intelligence in urdu, genetic algorithm in artificial intelligence hindi, genetic algorithm in hindi, genetic algorithm in ai, genetic algorithm artificial intelligence, genetic algorithm, genetic algorithm ai, genetic algorithm well academy, genetic algorithm crossover genetic algorithm tutorial genetic algorithm example genetic algorithm genetic algorithm fitness function genetic algorithm artificial intelligence artificial intelligence well academy well academy artificial intelligence artificial intelligence tutorial artificial intelligence in hindi artificial intelligence lecture artificial intelligence lecture in hindi
Views: 113120 Well Academy
Prediction of effective rainfall and crop water needs using data mining techniques
 
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Prediction of effective rainfall and crop water needs using data mining techniques- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project PARALLEL AND DISTRIBUTED SYSTEMS 1. Enhancing Collusion Resilience in Reputation Systems 2. A Crowdsourcing Worker Quality Evaluation Algorithm on MapReduce for Big Data Applications 3. Evaluating Replication for Parallel Jobs: An Efficient Approach 4. Conditions and Patterns for Achieving Convergence in OT-Based Co-Editors 5. Prefetching on Storage Servers through Mining Access Patterns on Blocks 6. SPA: A Secure and Private Auction Framework for Decentralized Online Social Networks 7. Predicting Cross-Core Performance Interference on Multicore Processors with Regression Analysis 8. Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds 9. RFHOC: A Random-Forest Approach to Auto-Tuning Hadoop's Configuration 10. Deadline Guaranteed Service for Multi-Tenant Cloud Storage 11. Carbon-Aware Online Control of Geo-Distributed Cloud Services 12. Online Resource Scheduling Under Concave Pricing for Cloud Computing 13. Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds 14. Performance Evaluation of Cloud Computing Centers with General Arrivals and Service 15. TMACS: A Robust and Verifiable Threshold Multi-Authority Access Control System in Public Cloud Storage 16. Heads-Join: Efficient Earth Mover's Distance Similarity Joins on Hadoop 17. Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement 18. Quantum-Inspired Hyper-Heuristics for Energy-Aware Scheduling on Heterogeneous Computing Systems 19. A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data 20. A High Performance Parallel and Heterogeneous Approach to Narrowband Beamforming 21. EcoUp: Towards Economical Datacenter Upgrading 22. Hadoop Performance Modeling for Job Estimation and Resource Provisioning 23. Optimization of the Processing of Data Streams on Roughly Characterized Distributed Resources 24. A Secure Anti-Collusion Data Sharing Scheme for Dynamic Groups in the Cloud 25. Exploring Heterogeneity within a Core for Improved Power Efficiency 26. Efficient File Search in Delay Tolerant Networks with Social Content and Contact Awareness 27. Exploiting Workload Characteristics and Service Diversity to Improve the Availability of Cloud Storage Systems 28. PerfCompass: Online Performance Anomaly Fault Localization and Inference in Infrastructure-as-a-Service Clouds 29. Energy and Makespan Tradeoffs in Heterogeneous Computing Systems using Efficient Linear Programming Techniques 30. An Efficient Privacy-Preserving Ranked Keyword Search Method 31. GrapH: Traffic-Aware Graph Processing (June 1 2018) 32. Automatic construction of vertical search tools for the Deep Web (Feb. 2018) 33. Towards Long-View Computing Load Balancing in Cluster Storage Systems 34. ATOM: Efficient Tracking, Monitoring, and Orchestration of Cloud Resources 35. Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds 36. Repair Tree: Fast Repair for Single Failure in Erasure-coded Distributed Storage Systems 37. A Load Balancing and Multi-tenancy Oriented Data Center Virtualization Framework 38. Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds
REIS Episode 257: Paul Del Pozo: Data Mining for Leads
 
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Paul Del Pozo Investment Group, LLC. is a South Florida based Real Estate Investment Company. He specializes in Buying, Selling, Rehabbing, and Flipping Wholesale properties. In his free time he lives in a fitness world building a health and bodybuilding background. What you’ll learn about in this episode: • How Paul went from being a bodybuilder and personal trainer to becoming a successful real estate investor • Paul’s process of obtaining and working leads, networking, and learning the fundamentals of wholesaling • Why Paul believes it’s important to develop your skills in finding leads before you do anything else • How Paul integrates technology into the operation of his real estate business • How Paul uses a MLS data program called Propstream to find lists and comps, and how it has changed his business • How Propstream allows you to filter lists based on different categories like equity, property characteristics and more • How Paul has found financial independence in the 3 1/2 years that he’s been working in real estate • Why getting into real estate has been a catalyst of self-improvement even in other areas of Paul’s life • Which markets Paul is working in now, and why he has been moving his focus more into cash flow • How the slogan “Flex and Flip” has become a cornerstone of Paul’s business philosophy and his professional calling card Resources: • http://REInvestorSummit.com/Data • http://REInvestorSummit.com/Machine • http://REInvestorSummit.com/Everywhere • http://REInvestorSummit.com/aof • http://REInvestorSummit.com/coaching
Views: 133 Mitch Stephen
V2 - Deep Space Data Center: Enabling Asteroid Mining - R. Ramadorai & M. Allen, Planetary Resources
 
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LinuxCon and CloudOpen New Orleans, 2013: When talking about spacecraft technology, "state of the art" and "disruptive" are not descriptors that immediately come to mind. Deep space vehicles built via traditional methods are extremely conservative, due to the requirement that they cannot fail. Planetary Resources, the asteroid mining company, has a different strategy: develop low cost spacecraft that are engineered to create highly reliable systems from less expensive, commercial components. Once the spacecraft has become a commodity, it can rapidly evolve and keep pace with modern technology, and failure of any one spacecraft is not catastrophic. This is crucial to commercial space exploration and development, and is a key part of the Planetary Resources strategy. In order to make this a reality, we are adopting philosophies that are common to modern data centers and leveraging many technologies that are ubiquitous in cloud computing. In this talk we will describe how we use embedded and desktop Linux, virtualization, software redundancy, and web technologies to build spacecraft in new ways. We will also illustrate interesting parallels to conventional applications, and talk about the challenges of using open source and commodity hardware in the space environment
Views: 1098 The Linux Foundation
Paul Lucey - The impact of analytics and data on the mining industry
 
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We got up close and personal with Paul Lucey, CEO of Mine Vision Systems at the 2016 International Mining and Resources Conference. In this video, Paul explains how using data can speed up the decision making process and what he thinks is coming next in mining technology. IMARC returns to the Melbourne Convention & Exhibition Centre 30 October - 2 November 2017. For more information please visit http://imarcmelbourne.com/
X4 Foundations - Getting Started Mining Tutorial Guide
 
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X4: Foundations features a number of mining opportunities. In this X4 Foundations tutorial we look at how to mine.
Views: 25314 ObsidianAnt
Extremely Fast Decision Tree Mining for Evolving Data Streams
 
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Extremely Fast Decision Tree Mining for Evolving Data Streams Albert Bifet (Telecom ParisTech) Jiajin Zhang (Noah's Ark Lab, Huawei) Wei Fan (Huawei Noah’s Ark Lab) Cheng He (Noah's Ark Lab, Huawei) Jianfeng Zhang (Noah's Ark Lab, Huawei) Jianfeng Qian (Huawei Noah's Ark Lab) Geoffrey Holmes (University of Waikato) Bernhard Pfahringer (University of Waikato) Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning. In this paper, we present a new system called streamDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. streamDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision tree. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources. More on http://www.kdd.org/kdd2017/
Views: 568 KDD2017 video
Develop a Data Science Project | Solving a Data Science Problem | Data Science Tutorial | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) Watch sample class recording: http://www.edureka.co/data-science?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. Data Science is one of the most-sought after professions today. Universities across the world are offering courses in this discipline which stands testimony to this emerging profession. There are a very few professionals with the required skill and the demand for data scientists is racing ahead. The tutorial wil give a brief understanding about Data Science. The topics covered in the video: 1.Problem Statement 2.Variable Desriptions 3.Data EXploration 4.Data Cleaning and Preparation 5.Reading from Other Sources 6.Titanic Data Sets 7.Decision Trees and Random Forests 8.Build a Decision Tree 9.Build a Random Forest 10.Linear Regression 11.Logistic Regression 12.Machine Learning 13.Data Mining 14.Machine Learning and Data Mining Resources 15.Solving a Data Science Problem using R, Hadoop, Mahout Related Posts: http://www.edureka.co/blog/who-can-take-up-a-data-science-tutorial/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/enroll-for-a-data-science-course/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/types-of-data-scientists/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/core-data-scientist-skills/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. ‘Develop a Data Science Project’ have been widely covered in our course ‘Data Science’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 31176 edureka!
Was ist eigentlich Data Mining?
 
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Dieses kurze Video erklärt, was Data Mining eigentlich bedeutet. Anhand eines Beispiels einer Kampagnenoptimierung im Marketing wird deutlich gemacht, wie wertvoll Data Mining auch für Ihr Unternehmen sein kann. Marketinginvest reduzieren und dennoch die Responsequoten erhöhen? Mit Data Mining und IBM SPSS ist es möglich.
Views: 21453 SIEVERSSNC
Data Mining with Big Data | IEEE 2014 project
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Data Mining with Big Data | IEEE 2014 project in HADOOP Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
Views: 3850 jpinfotechprojects
Bioinformatics part 2 Databases (protein and nucleotide)
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.[9] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 95692 Shomu's Biology
@RBShow420 #474 Drug War Data Mining - The Resources at Ballotpedia org
 
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A look at Ballotpedia, a wiki of all the state and local ballot measures and candidates for office, past and present.
Views: 32 Russ Belville
Machine Learning #73 BIRCH Algorithm | Clustering
 
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Machine Learning #73 BIRCH Algorithm | Clustering In this lecture of machine learning we are going to see BIRCH algorithm for clustering with example. BIRCH algorithm (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm which is used to perform hierarchical clustering over particularly large data-sets.The advantage of using BIRCH algorithm is its ability to incrementally & dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). single scan of the database is needed by BIRCH algorithm in most of the cases. Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 8846 Xoviabcs
Introduction to Text and Data Mining
 
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Heard about Text and Data Mining (TDM) and wondering if it might be a good fit for your research? Find out what text and data mining is and how it can usefully be applied in a research context. Also learn about data sources for text and data mining projects and support, tools, and resources for learning more.
Views: 78 UniSydneyLibrary