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: 104680
Well Academy
Search based optimization technique.Based on natural selection and natural genetics.
Views: 13611
Smita Tiwari
#askfaizan | #syedfaizanahmad
PlayList : Artificial Intelligence : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH
Genetic Algorithm is - Optimization Algorithm
- Based on natural phenomenon
- Nature inspired approach based on Darwin’s law of Survival of the fittest and bio-inspired operators such as Pairing Crossover and Mutation.
- frequently used to find optimal or near-optimal solutions to difficult problems
Optimization- is the process of making something better
Terminology - Population
- Chromosomes
- Gene
Operators are - Selection
- Crossover
- Mutation
for Complete Artificial Intelligence Videos click on the link :
https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH
Thank you for watching
share with your friends
Follow on :
Facebook page : https://www.facebook.com/askfaizan1/
Instagram page : https://www.instagram.com/ask_faizan/
Twitter : https://twitter.com/ask_faizan/
Views: 11871
Ask Faizan
Website + download source code @ http://www.zaneacademy.com
Views: 2149
zaneacademy
Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 153131
nptelhrd
A brief introduction to genetic algorithms with examples.
Views: 128942
chriskam1250
Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases
Steps of Classification:
1. Model construction: Describing a set of predetermined classes
Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute.
The set of tuples used for model construction is training set.
The model is represented as classification rules, decision trees, or mathematical formulae.
2. Model usage: For classifying future or unknown objects
Estimate accuracy of the model
If the accuracy is acceptable, use the model to classify new data
MLP- NN Classification Algorithm
The MLP-NN algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer.
Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of “neuronlike” units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used.
The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples.
Algorithm of MLP-NN is as follows:
Step 1: Initialize input of all weights with small random numbers.
Step 2: Calculate the weight sum of the inputs.
Step 3: Calculate activation function of all hidden layer.
Step 4: Output of all layers
For more information and query visit our website:
Website : http://www.e2matrix.com
Blog : http://www.e2matrix.com/blog/
WordPress : https://teche2matrix.wordpress.com/
Blogger : https://teche2matrix.blogspot.in/
Contact Us : +91 9041262727
Follow Us on Social Media
Facebook : https://www.facebook.com/etwomatrix.researchlab
Twitter : https://twitter.com/E2MATRIX1
LinkedIn : https://www.linkedin.com/in/e2matrix-training-research
Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar
Pinterest : https://in.pinterest.com/e2matrixresearchlab/
Tumblr : https://www.tumblr.com/blog/e2matrix24
Views: 560
E2MATRIX RESEARCH LAB
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-521298714/m-534408629
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 9849
Udacity
This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 47894
StudyKorner
Genetic Algorithm me selection , crossover and mutation ke operators
Views: 6626
Red Apple Tutorials
Take the Full Course of Artificial Intelligence
What we Provide
1) 28 Videos (Index is given down)
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in Artificial Intelligence
Sample Notes : https://goo.gl/aZtqjh
To buy the course click
https://goo.gl/H5QdDU
if you have any query related to buying the course feel free to email us : [email protected]
Other free Courses Available :
Python : https://goo.gl/2gftZ3
SQL : https://goo.gl/VXR5GX
Arduino : https://goo.gl/fG5eqk
Raspberry pie : https://goo.gl/1XMPxt
Artificial Intelligence Index
1)Agent and Peas Description
2)Types of agent
3)Learning Agent
4)Breadth first search
5)Depth first search
6)Iterative depth first search
7)Hill climbing
8)Min max
9)Alpha beta pruning
10)A* sums
11)Genetic Algorithm
12)Genetic Algorithm MAXONE Example
13)Propsotional Logic
14)PL to CNF basics
15) First order logic solved Example
16)Resolution tree sum part 1
17)Resolution tree Sum part 2
18)Decision tree( ID3)
19)Expert system
20) WUMPUS World
21)Natural Language Processing
22) Bayesian belief Network toothache and Cavity sum
23) Supervised and Unsupervised Learning
24) Hill Climbing Algorithm
26) Heuristic Function (Block world + 8 puzzle )
27) Partial Order Planing
28) GBFS Solved Example
Views: 208517
Last moment tuitions
Welcome Guys, we will see How to find Genetic Algorithm Maximize f(x)= x^2. Show one Crossover? In soft computing in Hindi.
Genetic Algorithm problem with a solution.
If you like my video plz LIKE, SHARE & SUBSCRIBE my THAPATECHNICAL channel :)
Views: 19496
Thapa Technical
Website + download source code @ http://www.zaneacademy.com
Views: 20729
zaneacademy
Hello Friends Welcome to well academy
For Notes of Artificial Intelligence click on the link :
In this video i have discussed Genetic Algorithm Example in Artificial Intelligence with detail explanation of MAXONE example. as earlier i have explained Genetic Algorithm in Artificial Intelligence. So this video will be properly of Example if you want to see the explanation of Genetic Algorithm in Artificial Intelligence explanation click on below link.
Genetic Algorithm in artificial intelligence
https://www.youtube.com/watch?v=FwPgHgbncPk&t=2s
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
Views: 52803
Well Academy
Contact : 9606359471
Views: 118
vihaan sudhan
Views: 502
IMSUC FLIP
For More Explanation And Techniques
Contact:K.Manjunath,9535866270,
http://www.tmksinfotech.com
Bangalore,Karnataka.
Views: 990
manju nath
#ArtificialNeuralNetwork | Beginners guide to how artificial neural network model works. Learn how neural network approaches the problem, why and how the process works in ANN, various ways errors can be used in creating machine learning models and ways to optimise the learning process.
- Watch our new free Python for Data Science Beginners tutorial: https://greatlearningforlife.com/python
- Visit https://greatlearningforlife.com our learning portal for 100s of hours of similar free high-quality tutorial videos on Python, R, Machine Learning, AI and other similar topics
Know More about Great Lakes Analytics Programs:
PG Program in Business Analytics (PGP-BABI): http://bit.ly/2f4ptdi
PG Program in Big Data Analytics (PGP-BDA): http://bit.ly/2eT1Hgo
Business Analytics Certificate Program: http://bit.ly/2wX42PD
#ANN #MachineLearning #DataMining #NeuralNetwork
About Great Learning:
- Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more.
- Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM
- What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U
- Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670
- Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU
- For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1
- Learn More at: https://www.greatlearning.in/
For more updates on courses and tips follow us on:
- Google Plus: https://plus.google.com/u/0/108438615307549697541
- Facebook: https://www.facebook.com/GreatLearningOfficial/
- LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 67684
Great Learning
Title: Automatic Time Table Generation Using Genetic Algorithm
Domain: Data Mining
Key Features:
1. Generation of time table using genetic algorithm.
2. Time table generation separately for teacher and students.
3. Downloadable in .xls file
4. Facility of curd model for teacher and students, etc.
For more details contact:
E-Mail: [email protected]
Buy Whole Project Kit for Rs 5000%.
Project Kit:
• 1 Review PPT
• 2nd Review PPT
• Full Coding with described algorithm
• Video File
• Full Document
Note:
*For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us.
*Contact for Real Time Projects, Web Development and Web Hosting services.
*Comment and share on this video and win exciting developed projects for free of cost.
Search Terms:
1. 2017 ieee projects
2. latest ieee projects in java
3. latest ieee projects in data mining
4. 2016 – 2017 data mining projects
5. 2016 – 2017 best project center in Chennai
6. best guided ieee project center in Chennai
7. 2016 – 2017 ieee titles
8. 2016 – 2017 base paper
9. 2016 – 2017 java projects in Chennai, Coimbatore, Bangalore, and Mysore
10. time table generation projects
11. instruction detection projects in data mining, network security
12. 2016 – 2017 data mining weka projects
13. 2016 – 2017 b.e projects
14. 2016 – 2017 m.e projects
15. 2016 – 2017 final year projects
16. affordable final year projects
17. latest final year projects
18. best project center in Chennai, Coimbatore, Bangalore, and Mysore
19. 2017 Best ieee project titles
20. best projects in java domain
21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore
22. 2016 – 2017 ieee base paper free download
23. 2016 – 2017 ieee titles free download
24. best ieee projects in affordable cost
25. ieee projects free download
26. 2017 data mining projects
27. 2017 ieee projects on data mining
28. 2017 final year data mining projects
29. 2017 data mining projects for b.e
30. 2017 data mining projects for m.e
31. 2017 latest data mining projects
32. latest data mining projects
33. latest data mining projects in java
34. data mining projects in weka tool
35. data mining in intrusion detection system
36. intrusion detection system using data mining
37. intrusion detection system using data mining ppt
38. intrusion detection system using data mining technique
39. data mining approaches for intrusion detection
40. data mining in ranking system using weka tool
41. data mining projects using weka
42. data mining in bioinformatics using weka
43. data mining using weka tool
44. data mining tool weka tutorial
45. data mining abstract
46. data mining base paper
47. data mining research papers 2016 - 2017
48. 2016 - 2017 data mining research papers
49. 2017 data mining research papers
50. data mining IEEE Projects
52. data mining and text mining ieee projects
53. 2017 text mining ieee projects
54. text mining ieee projects
55. ieee projects in web mining
56. 2017 web mining projects
57. 2017 web mining ieee projects
58. 2017 data mining projects with source code
59. 2017 data mining projects for final year students
60. 2017 data mining projects in java
61. 2017 data mining projects for students
Views: 13963
InnovationAdsOfIndia
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://goo.gl/to1yMH
or Fill the form we will contact you
https://goo.gl/forms/2SO5NAhqFnjOiWvi2
if you have any query email us at
[email protected]
or
[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: 333198
Last moment tuitions
Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms.
Support this channel on Patreon: https://patreon.com/codingtrain
Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics
Contact: https://twitter.com/shiffman
Links discussed in this video:
The Nature of Code: http://natureofcode.com/
BoxCar2D: http://boxcar2d.com/
Source Code for the Video Lessons: https://github.com/CodingTrain/Rainbow-Code
p5.js: https://p5js.org/
Processing: https://processing.org
For More Genetic Algorithm videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV
For More Nature of Code videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aFlwukCmDf0-1-uSR7mklK
Help us caption & translate this video!
http://amara.org/v/Sld6/
Views: 203265
The Coding Train
Views: 34488
Machine Learning- Sudeshna Sarkar
ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to various Data mining unsupervised techniques namely Clustering, Dimension Reduction, Association Rules, Recommender System or Collaborative filtering, Network Analytics.
Things you will learn in this video
1)What is DataMining
2)DataMining in Nutshell
3)Types of methods
4)DataMining process
5)Approaches
6)Types of Clustering Algorithms
To buy eLearning course on DataScience click here https://goo.gl/oMiQMw
To enroll for the virtual online course click here https://goo.gl/m4MYd8
To register for classroom training click here https://goo.gl/UyU2ve
SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx
For Introduction to Clustering Analysis clicks here https://goo.gl/wuXN48
For Introduction to K-mean clustering click here https://goo.gl/PYqXRJ
#ExcelRSolutions #DataMining#clusteringTechniques
#datascience #datasciencetutorial #datascienceforbeginners #datasciencecourse
-----
For More Information:
Toll Free (IND) : 1800 212 2120
| +91 80080 09706
Malaysia: 60 11 3799 1378
USA: 001-844-392-3571
UK: 0044 203 514 6638
AUS: 006 128 520-3240
Email: [email protected]
Web: www.excelr.com
Connect with us:
Facebook: https://www.facebook.com/ExcelR/
LinkedIn: https://www.linkedin.com/company/exce...
Twitter: https://twitter.com/ExcelrS
G+: https://plus.google.com/+ExcelRSolutions
For downloadable versions of these lectures, please go to the following link:
http://www.slideshare.net/DerekKane/presentations
https://github.com/DerekKane/YouTube-Tutorials
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Views: 22492
Derek Kane
#askfaizan | #syedfaizanahmad | #decisiontree
PlayList : Artificial Intelligence : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH
Bayesian Network in Artificial Intelligence | Bayesian Belief Network | https://youtu.be/0U5xH4b7nPc
Decision Tree Learning using ID3 Algorithm | Artificial intelligence https://youtu.be/pvTejBgiF3I
Supervised Learning and Unsupervised Learning | Learning in Artificial Intelligence https://youtu.be/Wn2JgBfAsSM
Genetic Algorithm | Artificial Intelligence Tutorial in Hindi Urdu https://youtu.be/frB2zIpOOBk
Comparison of Search Algorithm https://youtu.be/QMz7jwXDvwg
Resolution in Artificial Intelligence | Resolution Rules in AI https://youtu.be/oQmqJPLqHZA
Inference rules in Predicate logic https://youtu.be/Y8KCh4VRRwM
Predicate logic in AI | First order logic in Artificial Intelligence https://youtu.be/sFINpc5KA3E
Wumpus World Proving | Propositional logic Example https://youtu.be/bDu9iNJ8h58
PROPOSITIONAL LOGIC | Artificial Intelligence https://youtu.be/oUR11UUIDvA
Knowledge based Agents | Logical agents https://youtu.be/Y7CS-1BfA6o
Alpha Beta Pruning | Problem #2 https://youtu.be/QL-g1FDls74
A Decision tree represents a function that takes as input a vector of attribute values and returns a “decision”—a single output value.
The input and output values can be discrete or continuous.
A decision tree reaches its decision by performing a sequence of tests.
There are many specific decision-tree algorithms. Notable ones include:
ID3 (Iterative Dichotomiser 3)
C4.5 (successor of ID3)
CART (Classification And Regression Tree)
CHAID (Chi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees.
MARS: extends decision trees to handle numerical data better.
ID3 is one of the most common decision tree algorithm
Dichotomisation means dividing into two completely opposite things.
Algorithm iteratively divides attributes into two groups which are the most dominant attribute and others to construct a tree.
Then, it calculates the Entropy and Information Gains of each attribute. In this way, the most dominant attribute can be founded.
After then, the most dominant one is put on the tree as decision node.
Entropy and Gain scores would be calculated again among the other attributes.
Procedure continues until reaching a decision for that branch.
algorithm steps:
Calculate the entropy of every attribute using the data set S
Entropy(S) = ∑ – p(I) . log2p(I)
Split the set S into subsets using the attribute for which the resulting entropy (after splitting) is minimum (or, equivalently, information gain is maximum)
Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ]
Make a decision tree node containing that attribute
Recurse on subsets using remaining attributes.
for Complete Artificial Intelligence Videos click on the link :
https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH
Thank you for watching
share with your friends
Follow on :
Facebook page : https://www.facebook.com/askfaizan1/
Instagram page : https://www.instagram.com/ask_faizan/
Twitter : https://twitter.com/ask_faizan/
Views: 27869
Ask Faizan
Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool.
Code for this video:
https://github.com/llSourcell/Predicting_Winning_Teams
Please Subscribe! And like. And comment.
More learning resources:
https://arxiv.org/pdf/1511.05837.pdf
https://doctorspin.me/digital-strategy/machine-learning/
https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/
http://data-informed.com/predict-winners-big-games-machine-learning/
https://github.com/ihaque/fantasy
https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/
Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Views: 88853
Siraj Raval
In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting.
Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform.
This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl
For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz
Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 30228
InfoQ
This tutorial shows how to select features from a set of features that performs best with a classification algorithm using filter method.
Views: 66839
Rushdi Shams
Get the project at http://nevonprojects.com/credit-card-fraud-detection-project/
The credit card fraud detection features uses user behavior and location scanning to check for unusual patterns.
Views: 21468
Nevon Projects
Credit Card Fraud Detection System using Genetic Algorithm
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: http://www.jpinfotech.org,
Blog: http://www.jpinfotech.blogspot.com
Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.
Views: 1257
JPINFOTECH PROJECTS
In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 401212
Thales Sehn Körting
A tutorial about classification and prediction in Data Mining .
Views: 29676
Red Apple Tutorials
Classification in Data Mining with classification algorithms. Explanation on classification algorithm the decision tree technique with Example.
Views: 37448
IT Miner - Tutorials,GK & Facts
fuzzy logic in artificial intelligence in hindi | fuzzy logic example | #28
Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.
The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO.
The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as −
CERTAINLY YES
POSSIBLY YES
CANNOT SAY
POSSIBLY NO
CERTAINLY NO
well,academy,Fuzzy logic in hindi,fuzzy logic in artificial intelligence in hindi,artificial intelligence fuzzy logic,fuzzy logic example,fuzzy logic in artificial intelligence,fuzzy logic with example,fuzzy logic in artificial intelligence in hindi with exapmle,fuzzy logic,what is fuzzy logic in hindi,what is fuzzy logic with example,introduction to fuzzy logic
Views: 121334
Well Academy
Welcome to TECHNICAL SUPPORT BY RAHUL SAHANI
=====================================================
All about technical concepts, technical subject , and mobile application.
-------------------------------------------------------------------------------------------------
For more videos please
| LIKE , SUBSCRIBE, SHARE, COMMENT |
Views: 1860
TECHNICAL SUPPORT by rahul sahani
MIT 6.034 Artificial Intelligence, Fall 2010
View the complete course: http://ocw.mit.edu/6-034F10
Instructor: Patrick Winston
This lecture explores genetic algorithms at a conceptual level. We consider three approaches to how a population evolves towards desirable traits, ending with ranks of both fitness and diversity. We briefly discuss how this space is rich with solutions.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Views: 329285
MIT OpenCourseWare
By Dorit Simona Hochbaum.
The dominant algorithms for machine learning tasks fall most often in the realm of AI or continuous optimization of intractable problems. This tutorial presents combinatorial algorithms for machine learning, data mining, and image segmentation that, unlike the majority of existing machine learning methods, utilize pairwise similarities. These algorithms are efficient and reduce the classification problem to a network flow problem on a graph. One of these algorithms addresses the problem of finding a cluster that is as dissimilar as possible from the complement, while having as much similarity as possible within the cluster. These two objectives are combined either as a ratio or with linear weights. This problem is a variant of normalized cut, which is intractable. The problem and the polynomial-time algorithm solving it are called HNC. It is demonstrated here, via an extensive empirical study, that incorporating the use of pairwise similarities improves accuracy of classification and clustering. However, a drawback of the use of similarities is the quadratic rate of growth in the size of the data. A methodology called “sparse computation” has been devised to address and eliminate this quadratic growth. It is demonstrated that the technique of “sparse computation” enables the scalability of similarity-based algorithms to very large-scale data sets while maintaining high levels of accuracy. We demonstrate several applications of variants of HNC for data mining, medical imaging, and image segmentation tasks, including a recent one in which HNC is among the top performing methods in a benchmark for cell identification in calcium imaging movies for neuroscience brain research.
Views: 83
INFORMS
Title: Managing Privacy Of Sensitive Attributes Using MFSARNN Clustering With Optimization Technique
Domain: Data Mining
Key Features:
1. The slicing algorithm partitions the data into vertical and horizontal columns. The attributes and tuples in the slicing algorithm is clustered based on their similarity. In vertical partitioning, the attributes are grouped by Modified Fully Self Adaptive Resonance Neural Networks (MFSARNN).
2. The cluster formation has been improved by Genetic Algorithm based feature selection. In the horizontal partition, the tuples are grouped by Meta heuristic Fireflies Algorithm with Minkowsi Distance Measure (MFAMD). In this way the proposed system overcomes the privacy threats such as Identity disclosure, Attribute disclosure and Membership disclosure.
3. Slicing is one of the Anonymization techniques which is used to improve the privacy in data publishing. The slicing algorithm has been working with three different phases like, attribute partitioning, column generalization and tuple partitioning. The slicing algorithm partition the data set into vertical partition and horizontal partition.
4. Cluster analysis is the process of grouping the related information together, which is used for further processing and classification. Clustering techniques work with the high dimensionality of the data and produces the best result without losing the data and it also utilizes the data for entire processing. For this reason, slicing algorithm is working with clustering techniques. Clustering of attributes in the vertical partition is done by using Modified Fully Self Adaptive Resonance Neural Network (MFSARNN).
5. The working of MFSARNN as follows. Initially the sensitive features are selected by using a feature selection method. So, in the proposed system Genetic Algorithm is used for feature selection with three different steps like selection, crossover and mutation. In GA fitness function is one of the most important parameter which is used to select the sensitive features from the data.
For more details contact:
E-Mail: [email protected]
Buy Whole Project Kit for Rs 5000%.
Project Kit:
• 1 Review PPT
• 2nd Review PPT
• Full Coding with described algorithm
• Video File
• Full Document
Note:
*For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us.
*Contact for Real Time Projects, Web Development and Web Hosting services.
*Comment and share on this video and win exciting developed projects for free of cost.
Search Terms:
1. 2017 ieee projects
2. latest ieee projects in java
3. latest ieee projects in data mining
4. 2016 – 2017 data mining projects
5. 2016 – 2017 best project center in Chennai
6. best guided ieee project center in Chennai
7. 2016 – 2017 ieee titles
8. 2016 – 2017 base paper
9. 2016 – 2017 java projects in Chennai, Coimbatore, Bangalore, and Mysore
10. time table generation projects
11. instruction detection projects in data mining, network security
12. 2016 – 2017 data mining weka projects
13. 2016 – 2017 b.e projects
14. 2016 – 2017 m.e projects
15. 2016 – 2017 final year projects
16. affordable final year projects
17. latest final year projects
18. best project center in Chennai, Coimbatore, Bangalore, and Mysore
19. 2017 Best ieee project titles
20. best projects in java domain
21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore
22. 2016 – 2017 ieee base paper free download
23. 2016 – 2017 ieee titles free download
24. best ieee projects in affordable cost
25. ieee projects free download
26. 2017 data mining projects
27. 2017 ieee projects on data mining
28. 2017 final year data mining projects
29. 2017 data mining projects for b.e
30. 2017 data mining projects for m.e
31. 2017 latest data mining projects
32. latest data mining projects
33. latest data mining projects in java
34. data mining projects in weka tool
35. data mining in intrusion detection system
36. intrusion detection system using data mining
37. intrusion detection system using data mining ppt
38. intrusion detection system using data mining technique
39. data mining approaches for intrusion detection
40. data mining in ranking system using weka tool
41. data mining projects using weka
42. data mining in bioinformatics using weka
43. data mining using weka tool
44. data mining tool weka tutorial
45. data mining abstract
46. data mining base paper
47. data mining research papers 2016 - 2017
48. 2016 - 2017 data mining research papers
49. 2017 data mining research papers
50. data mining IEEE Projects
Views: 99
InnovationAdsOfIndia
I built an electron app that also uses react. I wanted to try experimenting with machine learning, so I started learning about genetic algorithms. A genetic algorithm is one in which possible solutions are randomly generated, and then form a 'population'. Solutions can then 'mate' and form offspring. Additionally, solutions can 'mutate', or change slightly. The algorithm must be given a cost function in order to determine how good the solutions are (this is important, because good solutions will be more likely to 'mate' and produce more good solutions). I chose two tasks where developing the cost function would be easy: evaluating a function (finding its zeroes) and evaluating a string against a given input (e.g. given 'I like coding.', the algorithm would eventually produce a solution of 'I like coding.'). Since the cost functions were simple, both performed well, and the 'evolution' of the solution was clear in the console.There were two things that I enjoyed about building this project. The first was seeing how the algorithm followed the natural process of evolution to develop a solution. The second was seeing how the mating and mutation functions, as well as population size, could be adjusted to change the way that the solution developed. Mutations could occur frequently or infrequently, and they could cause small or large changes. Mating could be more or less selective (i.e. how 'good' of a solution can mate), and the number of offspring could possibly vary. A population could be larger or smaller. There could even chances of 'natural disasters' in which the bottom portion of solutions die.
----
Fullstack Academy was recently ranked the #1 coding bootcamp in the U.S. Learn more at https://www.fullstackacademy.com
Views: 114
Fullstack Academy
What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean? EVOLUTIONARY DATA MINING meaning - EVOLUTIONARY DATA MINING definition - EVOLUTIONARY DATA MINING explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ
Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes." For instance, a banking institution might want to predict whether a customer's credit would be "good" or "bad" based on their age, income and current savings. Evolutionary algorithms for data mining work by creating a series of random rules to be checked against a training dataset. The rules which most closely fit the data are selected and are mutated. The process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm.
Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned, which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results.
If data comes from more than one database, they can be integrated, or combined, at this point. When dealing with large datasets, it might be beneficial to also reduce the amount of data being handled. One common method of data reduction works by getting a normalized sample of data from the database, resulting in much faster, yet statistically equivalent results.
At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset. The training dataset will be used to let rules evolve which match it closely. The test dataset will then either confirm or deny these rules.
Evolutionary algorithms work by trying to emulate natural evolution. First, a random series of "rules" are set on the training dataset, which try to generalize the data into formulas. The rules are checked, and the ones that fit the data best are kept, the rules that do not fit the data are discarded. The rules that were kept are then mutated, and multiplied to create new rules.
This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible. When this rule is obtained, it is then checked against the test dataset. If the rule still matches the data, then the rule is valid and is kept. If it does not match the data, then it is discarded and the process begins by selecting random rules again.
Views: 160
The Audiopedia
Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.
Views: 12220
Ben Rodick
kNN, k Nearest Neighbors Machine Learning Algorithm tutorial.
Follow this link for an entire Intro course on Machine Learning using R, did I mention it's FREE:
https://www.youtube.com/playlist?list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn
Also, be sure to check out my channel for over 300 tutorials on Excel, R, Statistics, basic Math, and more.
Views: 64306
Jalayer Academy
When was the last time you wrote an algorithm to plan your diet? As programmers we do amazing things in our everyday job, but rarely do we use our knowledge at home. In this talk I will introduce genetic algorithms and share how I coded a genetic algorithm from scratch and used it to generate my weekly schedule and to create a smart diet planer. We will go through the different stages of the algorithm and understand how they affect the algorithm’s solutions. Let me show you a different side of genetic algorithms and you will discover a new way to solve your everyday problems.
https://summit2018.reversim.com/session/5b0d4dfc4b330d00147e3374
Views: 697
Reversim