The document provides an introduction to big data and data mining. It defines big data as massive volumes of structured and unstructured data that are difficult to process using traditional techniques. Data mining is described as finding new and useful information within large amounts of data. The document then discusses characteristics of big data like volume, variety and velocity. It also outlines challenges of big data like privacy and hardware resources. Finally, it presents tools for big data mining and analysis like Hadoop, Apache S4 and Mahout.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Deep learning in healthcare: Oppotunities and challenges with Electronic Medi...Thien Q. Tran
Interested in deep learning for healthcare has grown strongly recent years besides with the successes in other domains such as Computer Vision, Natural Language Processing, Speech Recognition and so forth. This talk will try to give a brief look into the recent effort of research in deep learning for healthcare. Especially, this talk focuses on the opportunities and challenges in using electronic health records (EHR) data, which is one of the most important data sources in healthcare domain.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
What Are The Latest Trends in Data Science?Bernard Marr
The benefits of a data-driven approach to business are well established but not set in stone. The relentless march of technological progress means the boundaries of what is possible are constantly being redrawn, spawning new behaviors, trends and buzzwords.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
This PPT contain detail information about data cleansing that is done in R language. PPT contains information about four stages that are performed for data cleansing and visualization of variables on chart. In presentation codes of data cleansing are given which are supported by through explanation. Through charts and boxes codes are explained. presentation also contain audio format so that listener can understand codes in better way. With logic are codes are discussed in detail in PPT. Thus, one who wants to enhance knowledge about data cleansing can learn a lot from relevant presentation.
Deep learning in healthcare: Oppotunities and challenges with Electronic Medi...Thien Q. Tran
Interested in deep learning for healthcare has grown strongly recent years besides with the successes in other domains such as Computer Vision, Natural Language Processing, Speech Recognition and so forth. This talk will try to give a brief look into the recent effort of research in deep learning for healthcare. Especially, this talk focuses on the opportunities and challenges in using electronic health records (EHR) data, which is one of the most important data sources in healthcare domain.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
What Are The Latest Trends in Data Science?Bernard Marr
The benefits of a data-driven approach to business are well established but not set in stone. The relentless march of technological progress means the boundaries of what is possible are constantly being redrawn, spawning new behaviors, trends and buzzwords.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
This PPT contain detail information about data cleansing that is done in R language. PPT contains information about four stages that are performed for data cleansing and visualization of variables on chart. In presentation codes of data cleansing are given which are supported by through explanation. Through charts and boxes codes are explained. presentation also contain audio format so that listener can understand codes in better way. With logic are codes are discussed in detail in PPT. Thus, one who wants to enhance knowledge about data cleansing can learn a lot from relevant presentation.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
TOP 10 B TECH COLLEGES IN JAIPUR 2024.pptxnikitacareer3
Looking for the best engineering colleges in Jaipur for 2024?
Check out our list of the top 10 B.Tech colleges to help you make the right choice for your future career!
1) MNIT
2) MANIPAL UNIV
3) LNMIIT
4) NIMS UNIV
5) JECRC
6) VIVEKANANDA GLOBAL UNIV
7) BIT JAIPUR
8) APEX UNIV
9) AMITY UNIV.
10) JNU
TO KNOW MORE ABOUT COLLEGES, FEES AND PLACEMENT, WATCH THE FULL VIDEO GIVEN BELOW ON "TOP 10 B TECH COLLEGES IN JAIPUR"
https://www.youtube.com/watch?v=vSNje0MBh7g
VISIT CAREER MANTRA PORTAL TO KNOW MORE ABOUT COLLEGES/UNIVERSITITES in Jaipur:
https://careermantra.net/colleges/3378/Jaipur/b-tech
Get all the information you need to plan your next steps in your medical career with Career Mantra!
https://careermantra.net/
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
2. Introduction
Datamining and bigdata analytics is
the process of examining data to
uncover hidden patterns, unknown
correlations and other useful
information that can be used to make
better decisions.
3. Definitions:
Big Data is a phrase used to
mean a massive volume of
both structured and
unstructured data that is so
large it is difficult to process
using traditional database
and software techniques.
Data mining is about
finding new information in a
lot of data. The information
obtained from data
mining is hopefully both
new and useful. In many
cases, data is stored so it
can be used later.
4. Interesting Facts
The volume of business data worldwide, across all companies, doubles
every 1.2 years (was 1.5 years)
Daily 2500 quadrillion of data are produced and more than 90 percentage
of data are produced within past two years.
A regular person is processing daily more data than a 16th century
individual in his entire life
In the last years cost of storage and processing power dropped significantly
Bad data or poor data quality costs US businesses $600 billion annually
By 2015, 4.4 million IT jobs globally will be created to support big data
(Gartner)
Facebook processes 10 TB of data every day / Twitter 7 TB
Google has over 3 million servers processing over 2 trillion searches per
year in 2012 (only 22 million in 2000)
5. Characteristics of Big Data
Volume - The quantity of data
Variety - categorizing the data
Velocity - speed of generation of data or the speed of processing the data
Variability - Inconsistency
Complexity - Managing the data
6. Big Data Mining Algorithm
Big data applications have so many sources to gather information.
If we want to mine data, we need to gather all distributed data to the
centralized site. But it is prohibited because of high data transmission
cost and privacy concerns.
Most of the mining levels order to achieve the pattern of correlations,
or patterns can be discovered from combined variety of sources.
The global data mining is done through two steps process.
Model level
Knowledge level.
Each and every local sites use local data to calculate the data statistics
and it share this information in order to achieve global data distribution
in their data level.
7. In model level it will produce local pattern. This pattern will be
produced after mined local data.
By sharing these local patterns with other local sites, we can produce a
single global pattern.
At the knowledge level, model correlation analysis investigates the
relevance between models generated from various data sources to
determine how related the data sources are correlated to each other,
and how to form accurate decisions based on models built from
autonomous sources
8. Applications of Big Data
Healthcare organizations can achieve better insight into disease trends
and patient treatments.
Public sector agencies can catch fraud and other threats in real-time.
Applications of Multimedia data
To find travelling pattern of travelers
CC TV camera footage
Photos and Videos from social network
Recommender system
Integration and mining of Bio data from various sources in Biological
network by NSF (National Science Foundation).
Classifying the Big data stream in run time, by Australian Research
council.
9. Applications of Data Mining
It uses data and analytics to identify best practices that improve care and
reduce costs.
Market basket analysis is a modelling technique based upon a theory that if
you buy a certain group of items you are more likely to buy another group of
items. This technique may allow the retailer to understand the purchase
behaviour of a buyer.
There is a new emerging field, called Educational Data Mining, concerns with
developing methods that discover knowledge from data originating from
educational Environments.
There is a new emerging field, called Educational Data Mining, concerns with
developing methods that discover knowledge from data originating from
educational Environments.
10. DATA MINING CHALLENGES WITH BIG DATA
Main challenge for an intelligent database is handling Big data. The
important thing is scaling the large amount of data and provide
solution for these problem by HACE theorem
11. Challenges
Location of Big Data sources- Commonly Big Data are stored in different locations
Volume of the Big Data- size of the Big Data grows continuously.
Hardware resources- RAM capacity
Privacy- Medical reports, bank transactions
Having domain knowledge
Getting meaningful information
12. Solutions
Parallel computing programming
An efficient platform for computing will not have centralized data storage instead
of that platform will be distributed in big scale storage.
Restricting access to the data
13. BIG Data Mining Tools
Hadoop
Apache S4
Strom
Apache Mahout
MOA
14. Hadoop
It is developed by Apache Software Foundation project and open
source software platform for scalable, distributed computing.
Apache Hadoop software library is a framework that allows for the
distributed processing of large data sets across clusters of computers
using simple programming models.
Hadoop provides fast and reliable analysis of both Structured and un
structured data.
It is designed to scale up from single servers to thousands of machines,
each offering local computation and storage.
Hadoop uses MapReduce programming model to mine data.
This MapReduce program is used to separate datasets which are sent as
input into independent subsets. Those are process parallel map task.
Map() procedure that performs filtering and sorting
Reduce() procedure that performs a summary operation
15. Data Mining Software
•Weka - an open-source software for data mining
•RapidMiner - an open-source system for data and text mining
•KNIME - an open-source data integration, processing, analysis, and exploration
platform
•The Mahout machine learning library - mining large data sets. It supports
recommendation mining, clustering, classification and frequent itemset mining.
•Rattle - a GUI for data mining using R
16. From the dawn of civilization until
2003, humankind generated five
exabytes of data. Now we produce
five exabytes every two days…and
the pace is accelerating.
Eric Schmidt,
Executive Chairman, Google
Editor's Notes
Sourcessssssssss
Social network
Satellite data
Geographical data
Live streaming data