Search results “Data mining using resources”
Enhanced Resource Allocation: Business Use of Predictive Analytics and Data Mining
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: 538 TDWI
Data Mining (Introduction for Business Students)
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: 6524 tutor2u
Data resources
Views: 310 E-business
Google Analytics Data Mining with R (includes 3 Real Applications)
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: 30890 Tatvic Analytics
Data Mining with Scala at Identified
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: 3551 InfoQ
Paul Lucey - The impact of analytics and data on the mining industry
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/
Data Resource Management in business
data resource management adalah | data resource management chapter 5 | data resource management definition | data resource management inc | data resource management methods | data resource management methods of today | data resource management processes | database trends affecting data resource management in business | electronic business systems | telecommunications and networks | what is data resource management
Views: 2245 Jsmart Kosal
Twitter API - Data Mining #3
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: 18079 Roshan
Logistic Regression: Part 1 ("Data Mining for Business Intelligence")
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: 8591 Galit Shmueli
Big Data for Human Resources - 2016
Discover the benefits of Big Data for HR management!
Views: 2071 Sopra HR Software
Decision Resources Group: Finding Actionable Insights in Healthcare Big Data
See what's new in our latest version - https://www.talend.com/products/?utm_medium=socialpost&utm_source=youtube
Views: 28 Talend
Data Science for Business: Data Mining Process and CRISP DM
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: 16765 Cognitir
Mining the Next Natural Resource:  Big Data
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: 1596 IBM for CIO
V2 - Deep Space Data Center: Enabling Asteroid Mining - R. Ramadorai & M. Allen, Planetary Resources
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: 1100 The Linux Foundation
Targeting crimes and criminals through data, Dr Rick Adderley
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.
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
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: 2253 TEDx Talks
Mkango CEO says new data confirms Malawi as serious mining destination
Will Dawes, chief executive officer at Mkango Resources Ltd (LON:MKA) says he is very excited about the Rare Earth sector going forward. Just two weeks since listing on AIM, the group is to get its hands on new airborne data fund by the World Bank that will confirm it as a serious mining investment destination in the future. The data will cover the group’s licenses in Malawi and confirm existing anomalies and compliment ongoing discovery programmes. “We are one of the very few advanced stage Rare Earth projects and we’re the only focused Rare Earth company listed on AIM,” says Dawes. He told Proactive Investors that he was confident of the fundamentals of Rare Earth going forward. Dawes says that the outlook is “very good” over the next few years. Touching on the feasibility study back in November at the Songwe Hill mine in Malawi, Dawes said it had revealed a lot of resource upside in what is a “major significant rare earth deposit”. “We have identified areas where we can reduce costs, and that is a major focus for us at the moment.”
Big Data in Extraction and Natural Resource Industries: Mining, Water, Timber, Oil and Gas 2014 - 19
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
Learn Data Science in 3 Months
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: 330579 Siraj Raval
Country Analyst#1  (Resources): Finding Resource data
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: 688 mjmfoodie
Introduction to Event Log Mining with R
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/H0hC4gG0 Watch the latest video tutorials here: https://hubs.ly/H0hC5sv0 See what our past attendees are saying here: https://hubs.ly/H0hC4jb0 -- 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: 7348 Data Science Dojo
Airborne Electromagnetic data - mapping mineral and groundwater resources
A movie highlighting the world’s largest airborne electromagnetic survey in the Northern Territory and Queensland. The new geoscience data has been collected by Geoscience Australia’s Exploring for the Future program, in partnership with the Northern Territory and Queensland geological surveys. A total of 60,000 line kilometres of data were acquired over 2017 and 2018. Providing new insights into mineral-rich areas in Northern Australia that have not been extensively explored. This new research will enable government, the exploration industry and the research community to better understand the mineral potential of northern Australia. ga.gov.au/eftf
Views: 1118 GeoscienceAustralia
Using Data Mining to Predict Hospital Admissions From the Emergency Department
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
Social Media Data Mining With Raspberry Pi (Part 3: Operating Systems)
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: 1395 James Cook
Marketing Data Mining
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: 999 Cheap Domains
Human resources, CRM, data mining and social media concept - officer looking for employee represente
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
Data Mining // Tecnologia em 3 Minutos #01
Aprenda em 3 MINUTOS o que é: DATA MINING (Mineração de Dados). Este é o primeiro vídeo da série Tecnologia em 3 Minutos, onde explicamos termos e novas tecnologias em apenas 3 minutos. Nos sigam nas redes sociais: 👨🏼‍💻 GUSTAVO CAETANO: ▪ https://instagram.com/gustac 👩🏼‍💻 MAITÊ MARQUES: ▪ https://instagram.com/magrimarques
Views: 1655 Tecnologia em Vídeo
Wind Resource Assessment Data Analysis Using MATLAB
See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r In this webinar, you will learn how to use MATLAB for data analysis from data access through visualization and modeling. Using measured wind data for wind farm siting, MathWorks engineers will demonstrate the use of MATLAB and data analysis products for the entire data analysis and modeling process. Webinar highlights include: • Importing measured data recorded from a data logger • Performing data quality assurance tests for erroneous and missing data • Exploratory data analysis and visualization, including wind rose plots and velocity histograms • Turbine performance estimation • Automating repetitive data analysis and reporting tasks This webinar is for scientists and engineers in industry and academia needing to accelerate their data analysis and modeling tasks.
Views: 6164 MATLAB
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Views: 1 ganesh pg
Train, Test, & Validation Sets explained
In this video, we explain the concept of the different data sets used for training and testing an artificial neural network, including the training set, testing set, and validation set. We also show how to create and specify these data sets in code with Keras. 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👉 Check out the blog post and other resources for this video here: 🔗 https://deeplizard.com/learn/video/Zi-0rlM4RDs 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available as a perk to the members of the deeplizard hivemind: 🔗 https://www.patreon.com/posts/27743395 🧠 Support collective intelligence, and join the deeplizard hivemind: 🔗 https://deeplizard.com/hivemind 🤜 Or support collective intelligence, and create a quiz question for this video: 🔗 https://deeplizard.com/create-quiz-question 🚀 Boost collective intelligence by sharing this video on social media! ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: Peder B. Helland 👀 Follow deeplizard: Twitter: https://twitter.com/deeplizard Facebook: https://www.facebook.com/Deeplizard-145413762948316 Patreon: https://www.patreon.com/deeplizard YouTube: https://www.youtube.com/deeplizard Instagram: https://www.instagram.com/deeplizard/ 🎓 Other deeplizard courses: Reinforcement Learning - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xoWNVdDudn51XM8lOuZ_Njv NN Programming - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG DL Fundamentals - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU Keras - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL TensorFlow.js - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xr83l8w44N_g3pygvajLrJ- Data Science - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrth-Cqs_R9- Trading - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xr17PqeytCKiCD-TJj89rII 🛒 Check out products deeplizard recommends on Amazon: 🔗 https://www.amazon.com/shop/deeplizard 📕 Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard’s link: 🔗 https://amzn.to/2yoqWRn 🎵 deeplizard uses music by Kevin MacLeod 🔗 https://www.youtube.com/channel/UCSZXFhRIx6b0dFX3xS8L1yQ 🔗 http://incompetech.com/
Views: 38482 deeplizard
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
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: 320169 CS Dojo
Advantages of Data mining in Data science
In this article, we will learn the profits of the data. As was in our original blog covering all mining issues. So blog understands the importance of information about computer use by getting a variety of software for mining. https://www.besanttechnologies.com/training-courses/data-warehousing-training/datascience-training-institute-in-chennai https://www.besanttechnologies.com/training-courses/data-science-training-in-bangalore https://www.besanttechnologies.com/data-science-training-in-kalyan-nagar http://www.besanttechnologies.in/data-science-training-in-kalyan-nagar.html https://www.gangboard.com/big-data-training/data-science-training http://www.trainingpune.in/data-science-training-in-pune.html
Views: 217 Nila shri
Tech Talk: How Route Data Mining Helps You Make Better Decisions
In this Tech Talk you will learn about the features of the SUPERLOAD Route Data Miner (RDM) tool and see the significant upgrades to the capabilities and interface available with the latest SUPERLOAD CONNECT Edition. Data collected from permitted trips can hold significant value for a transportation agency including: Planning the best places to spend limited resources Forecasting demand Identifying the optimal way to leverage enforcement resources For more, visit https://www.bentley.com/en/products/brands/superload
Prediction of effective rainfall and crop water needs using data mining techniques
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 NETWORKING 1. A Non-Monetary Mechanism for Optimal Rate Control Through Efficient Cost Allocation 2. A Probabilistic Framework for Structural Analysis and Community Detection in Directed Networks 3. A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems 4. Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements 5. Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach 6. Achieving High Scalability Through Hybrid Switching in Software-Defined Networking 7. Adaptive Caching Networks With Optimality Guarantees 8. Analysis of Millimeter-Wave Multi-Hop Networks With Full-Duplex Buffered Relays 9. Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic 10. Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors 11. Asynchronously Coordinated Multi-Timescale Beamforming Architecture for Multi-Cell Networks 12. Attack Vulnerability of Power Systems Under an Equal Load Redistribution Model 13. Congestion Avoidance and Load Balancing in Content Placement and Request Redirection for Mobile CDN 14. Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network Architecture 15. Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market 16. Distributed Packet Forwarding and Caching Based on Stochastic NetworkUtility Maximization 17. Dynamic, Fine-Grained Data Plane Monitoring With Monocle 18. Dynamically Updatable Ternary Segmented Aging Bloom Filter for OpenFlow-Compliant Low-Power Packet Processing 19. Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints 20. Efficient Embedding of Scale-Free Graphs in the Hyperbolic Plane 21. Encoding Short Ranges in TCAM Without Expansion: Efficient Algorithm and Applications 22. Enhancing Fault Tolerance and Resource Utilization in Unidirectional Quorum-Based Cycle Routing 23. Enhancing Localization Scalability and Accuracy via Opportunistic Sensing 24. Every Timestamp Counts: Accurate Tracking of Network Latencies Using Reconcilable Difference Aggregator 25. Fast Rerouting Against Multi-Link Failures Without Topology Constraint 26. FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks 27. Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services 28. Greenput: A Power-Saving Algorithm That Achieves Maximum Throughput in Wireless Networks 29. ICE Buckets: Improved Counter Estimation for Network Measurement 30. Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach 31. Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks 32. Joint Resource Allocation for Software-Defined Networking, Caching, and Computing 33. Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints 34. Memory-Efficient and Ultra-Fast Network Lookup and Forwarding Using Othello Hashing 35. Minimizing Controller Response Time Through Flow Redirecting in SDNs 36. MobiT: Distributed and Congestion-Resilient Trajectory-Based Routing for Vehicular Delay Tolerant Networks
Bioinformatics part 2 Databases (protein and nucleotide)
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: 104597 Shomu's Biology
Getting Started with SAS Enterprise Miner: Setting Up an Enterprise Miner Project
http://support.sas.com/software/products/miner/index.html Chip Robie of SAS presents the first in a series of six "Getting Started with SAS Enterprise Miner 13.2" videos. This first video demonstrates how to set up an Enterprise Miner project. For more information regarding SAS Enterprise Miner, please visit http://support.sas.com/software/products/miner/index.html SAS ENTERPRISE MINER SAS Enterprise Miner streamlines the data mining process so you can create accurate predictive and descriptive analytical models using vast amounts of data. Our customers use this software to detect fraud, minimize risk, anticipate resource demands, reduce asset downtime, increase response rates for marketing campaigns and curb customer attrition. LEARN MORE ABOUT SAS ENTERPRISE MINER http://www.sas.com/en_us/software/analytics/enterprise-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 88678 SAS Software
evaluation of predictive data mining algorithms in soil data classification for optimized crop
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
DataLearner - Data Mining and Knowledge Discovery Software for Android
DataLearner is an easy-to-use tool for data mining and knowledge discovery from your own compatible training datasets. It’s fully self-contained, requires no external storage or network connectivity – it builds machine-learning models directly on your phone or tablet. DataLearner features classification, association and clustering algorithms from the open-source Weka (Waikato Environment for Knowledge Analysis) package, plus new algorithms developed by the Data Science Research Unit (DSRU) at Charles Sturt University. Combined, the app provides over 30 machine-learning/data-mining algorithms, including RandomForest, C4.5 (J48) and NaiveBayes. DataLearner collects no information – it requires access to your device storage simply to load your datasets and build your requested models. Get the resources: GPL3-licensed source code on Github: https://github.com/darrenyatesau/DataLearner App on Google Play store: https://play.google.com/store/apps/details?id=au.com.darrenyates.datalearner Research paper on arXiv: https://arxiv.org/abs/1906.03773 Algorithms include: • Bayes – BayesNet, NaiveBayes • Functions – Logistic, SimpleLogistic • Lazy – IBk (K Nearest Neighbours), KStar • Meta – AdaBoostM1, Bagging, LogitBoost, MultiBoostAB, Random Committee, RotationForest • Rules – Conjunctive Rule, Decision Table, DTNB, JRip, OneR, PART, Ridor, ZeroR • Trees – ADTree, BFTree, DecisionStump, ForestPA, J48 (C4.5), LADTree, Random Forest, RandomTree, REPTree, SimpleCART, SPAARC, SysFor. • Clusterers – DBSCAN, Expectation Maximisation (EM), Farthest-First, FilteredClusterer, SimpleKMeans • Associations – Apriori, FilteredAssociator, FPGrowth Training datasets must conform to either the Weka’ ARFF format or CSV (must include header row, class attribute in last column, class attribute is forced to nominal/categorical).
Views: 53 DataLearner
Sunil Gupta Talks About Data Mining, Encryption & Cyber Security || GCC Conclave 2019
"Cyber Security Is Not The End Solution," Sunil Gupta, Co-founder & CEO, QuNu Labs In Conversation With Govindraj Ethiraj, BOOM. Tune in to watch updates from all delegates and speakers and find out whats new at the Global Capability Centres (GCC) Conclave, 2019. Don't miss the interviews, sessions, and most importantly the excitement this year. Follow this link to our channel and Subscribe: https://www.youtube.com/channel/UCcPmIaxi4V01iLrQrE68qlA
Views: 9185 Nasscom Events
Big Data Pipelines and Use Cases at StumbleUpon - SF Data Mining Meetup Talk
WANT TO EXPERIENCE A TALK LIKE THIS LIVE? Barcelona: https://www.datacouncil.ai/barcelona New York City: https://www.datacouncil.ai/new-york-city San Francisco: https://www.datacouncil.ai/san-francisco Singapore: https://www.datacouncil.ai/singapore You can read the full post for more information: http://www.hakkalabs.co/articles/big-data-pipelines-use-cases-stumbleupon This talk is from the SF Data Mining Meetup hosted by Trulia. StumbleUpon indexes over 100 million web pages for serendipitous retrieval for over 25 million registered users. Debora Donato (Principal Data Scientist, StumbleUpon) walks through StumbleUpon's big data architecture, mobile optimization efforts, and data mining projects. FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai Facebook: https://www.facebook.com/datacouncilai
Views: 896 Data Council
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Dancho, Business Science
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: 5548 H2O.ai
How to become a Data Analyst in India - Course and career
This video discuss How to become a data analyst in India. For more videos on Jobs &Careers :https://www.youtube.com/channel/UCEFTTJFLp4GipA7BLZNTXvA?view_as=subscriber For aptitude classes :https://www.youtube.com/watch?v=lxm6ez2cx6Y&list=PLjLhUHPsqNYnM1DmZhIbtd9wNhPO1HGPT Every business collects data such as sales figures, market research, logistics, or transportation costs. A data analyst's job is to take that data and analyse it to help companies make better business decisions. Some examples of a data analyst basic job functions include: 1) estimating market shares; 2) establishing a price of new materials for the market; 3) reducing transportation costs; 4) timing of sales and 5) figuring out when to hire or reduce the workforce.Data analysts are responsible for collecting, manipulating, and analyzing data. How To Get There? By obtaining a university degree, learning important analytical skills, and gaining valuable work experience, you can become a successful data analyst. A bachelor's degree is needed for most entry-level jobs, and a master's degree will be needed for many upper-level jobs. To become an initial level data analyst, you’ll have to earn a degree in a subject such as mathematics, statistics, economics, marketing, finance, or computer science. Higher level data analyst jobs may require a master’s or doctoral degree, and they usually guarantee higher pay. Individuals looking for data analyst jobs must be knowledgeable in computer programs such as Microsoft Excel, Microsoft Access, SharePoint, and SQL databases. Data analysts also must have good communication skills, as they must have an open line of communication with the companies with which they work. Lets see some of the Best courses on Analytics offered in India. 1. Advanced Analytics for Management – IIM This program enables practitioners, managers, and decision-makers to use advanced analytics for better decision-making 2. Analytics Essentials – IIIT, Bangalore “Analytics Essentials”is a 3 months week-end program by International Institute of Information Technology Bangalore (IIITB)providing a foundational certification course in Business Analytics 3. Business Analytics and Intelligence (BAI) – IIM Bangalore This course provides in-depth knowledge of handling data and Business Analytics’ tools that can be used for fact-based decision-making. The participants will be able to analyse and solve problems from different industries such as manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace etc. 4. Certificate Program in Business Analytics – ISB, Hyderabad A combination of classroom and Technology aided learning platform, .Participants will typically be on campus for a 5 day schedule of classroom learning every alternate month for a span of 12 months, which would ideally be planned to include a weekend. 5. Data Analysis Online courses – SRM University SRM University offers part time online courses in data analysis in collaboration with Coursera, edX, Udacity. 6. Executive Program in Business Analytics – IIM Calcutta This executive 1 year long distance program is designed to expose participants to the tools and techniques of analytics. The program covers topics such as Data Mining, Soft Computing, Design of Experiments, Survey Sampling, Statistical Inference, Investment Management, Financial Modelling, Advanced marketing Research etc. 7. Executive Program in Business Analytics and Business Intelligence – IIM Ranchi Course duration is 3 months. Classes will be conducted by eminent professors and industry experts in the weekends in Mumbai/ Kolkata /Delhi /Bengaluru and in addition to these, there will be one-week learning in IIM Ranchi. 8. Jigsaw Academy courses Jigsaw Academy provides some online analytics courses.Their courses include; Foundation Course in Analytics Data Science Certification Human Resources (HR) Analytics Course Big Data Analytics Using Hadoop and R Advanced Certification in Retail Analytics Advanced Course in Financial Analytics Analytics with R Great Lakes PG Course in Business Analytics 9. M. Tech. Computer Science and Engineering with Specialization in Big Data Analytics – VIT VIT offers full time course in Big Data analysis to promote an academic career for further research in theoretical as well as applied aspects of Big Data Analytics 10. M.Tech (Database Systems) – SRM University SRM University offers a two year full time course in database systems where the students are exposed to theoretical concepts complemented by related practical experiments. 11. M.Tech Computer Engineering and Predictive Analytics – Crescent Engineering College Salary The Salary of Data analysts depends on job responsibilities. An entry-level data analyst with basic technical tools might be looking at anything from Rs. 5 lakhs to 12 lakhs per year. A senior data analyst with the skills of a data scientist can command a high price. #dataanalyst #careeroptions #datascience
T- Cloud Computing Student Project- Data Mining joined with Cloud
Cloud Computing project using Data Mining Techniques and Cloud Resources
Using Data Mining to Predict Hospital Admissions from the Emergency Department
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.
Data Science for Business: The 9 Most Common Data Mining Tasks
This video highlights the 9 most common data mining methods used in practice. For a related video, watch "Supervised vs. Unsupervised Methods": https://www.youtube.com/watch?v=i3itDGwhLq4 This video was created by Cognitir. 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: 2827 Cognitir
Novel Data Mining Methods for Virtual Screening - PhD Defense
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: 489 Othman Soufan