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Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 43203 Well Academy
Last Minute Tutorials | Data mining | Introduction | Examples
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 54796 Last Minute Tutorials
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn
 
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This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes video are as follows: 1. What is Naive Bayes? ( 01:06 ) 2. Naive Bayes and Machine Learning ( 05:45 ) 3. Why do we need Naive Bayes? ( 05:46 ) 4. Understanding Naive Bayes Classifier ( 06:30 ) 5. Advantages of Naive Bayes Classifier ( 20:17 ) 6. Demo - Text Classification using Naive Bayes ( 22:36 ) To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/Cw9wqy #NaiveBayes #MachineLearningAlgorithms #DataScienceCourse #DataScience #SimplilearnMachineLearning - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. 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. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course 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. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Naive-Bayes-Classifier-l3dZ6ZNFjo0&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 45768 Simplilearn
Soil Classification Using Data Mining Techniques: A Comparative Study | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 200 Clickmyproject
Sampling: Simple Random, Convenience, systematic, cluster, stratified - Statistics Help
 
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This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 790536 Dr Nic's Maths and Stats
Microsoft Excel Data Mining: Classification
 
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Microsoft Excel Data Mining: Classification. For more visit here: www.dataminingtools.net
Testing and Training of Data Set Using Weka
 
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how to train and test data in weka data mining using csv file
Views: 17477 Tutorial Spot
database recovery management | DBMS
 
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3 states of recovery - pre condition - condition - post condition
Views: 40437 Education 4u
parallel computing and types of architecture in hindi
 
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#Pds #pdc #parallelcomputing #distributedsystem #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)23 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice (sample Notes : https://goo.gl/fkHZZ1) To buy the course click here: https://goo.gl/E9NxXR if you have any query email us at [email protected] Index 1.Introduction to Parallel Computing and Types of Architecture 2.flynn’s classification or taxonomy in parallel computing 3.feng’s classification in parallel computing 4.Amdahl’s law in parallel computing 5.Pipelining Concept in Distributed System 6.Fixed point and Floating Point addition in Pipelining 7.Digit Product and Fixed Point Multiplication 8.Synchronization in process distribution system 9.Cristian algorithm 10.berkeley algorithm in process distribution system 11.Network time protocol in process distribution system 12.Logical clock in distributed system 13.Lamport’s logical clock algorithm in distributed system 14.Vector logical clock algorithm in distributed system 15.Lamports non token based algorithm in mutual execution 16.Ricart agarwala algorithm 17.Suzuki kasami algorithm with example 18.Raymonds algorithms 19.Bully and Ring Election algorithm in Distributed System 20.RMI remote method invocation 21.RPC(remote procedure call) in distributed system 22.Resources management in Distributed System 23.Load Balancing Algorithm and Design issues
Views: 200976 Last moment tuitions
HEART ATTACK PREDICTION SYSTEM USING FUYZZY C-MEANS CLASSIFIER
 
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In this project I have used a Fuzzy C Means Classifier Algorithm for extracting the patients health related data-sets in the database(UCI), comparing and analyzing the data-sets to determine whether there is chance of heart attack in the patient to prevent affected from heart attack.
Views: 1635 Praveen Natarajan
ssc: An R Package for Semi-Supervised Classification
 
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Semi-supervised classification has become a popular area of machine learning, where both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. We present the R package ssc (https://cran.r-project.org/package=ssc) that implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented in the ssc package can be applied to classification problems in several domains by the specification of a suitable learning scheme. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.
Views: 576 R Consortium
More Data Mining with Weka (2.4: Document classification)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Document classification http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8066 WekaMOOC
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 471463 Brandon Weinberg
Weka Tutorial 32: Document classification 2 (Application)
 
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The tutorial demonstrates how you can classify documents using Weka's String to Word vector attribute filter. The tutorial has a preceding tutorial that demonstrates the used of the filter in detail and can be found at http://www.youtube.com/watch?v=jSZ9jQy1sfE
Views: 19000 Rushdi Shams
Critical study on Data Mining with IDSS using RAPID technique for Diabetes
 
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Critical study on Data Mining with IDSS using RAPID technique for Diabetes To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: http://www.jpinfotech.org In data mining, knowledge is extracted using key elements and concepts after identifying relevant and reliable data. But in the field of health care, researchers are finding it difficult to convert the bio-medical database into knowledge at a rapid pace. The medical data is huge, complex and heterogeneous in nature. Data Mining principles& tools are used in conjunction with health care expert systems to extract inherent relationships among data elements as knowledge. By integrating different data mining concepts with expert systems, a new system called “Integrated Decision Support System” (IDSS) is proposed, which can provide better results compared to existing ones. It converts knowledge into useful format and uses different tools for construction of its architecture. To reduce possible solutions for diabetic diagnosis, Case Based Reasoning (CBR), Rule Based Reasoning (RBR) and Web Based Portal Joint Asia Diabetes Evaluation( JADE) programs are integrated with Reliable Access and Probabilistic Inference based on clinical Data (RAPID) in the developed IDSS system to enhance existing systems for fast extraction of knowledge.
Views: 33 jpinfotechprojects
More Data Mining with Weka (4.6: Cost-sensitive classification vs. cost-sensitive learning)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Cost-sensitive classification vs. cost-sensitive learning http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8621 WekaMOOC
Data Mining with Weka (4.3: Classification by regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 3: Classification by regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 29251 WekaMOOC
Different Data Mining Approaches for Forecasting Use of Bike Sharing System
 
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R Codes are available on below link: https://github.com/mayurkmane/ADM-Project-A12-Group Document related to this data mining study is available on below link: https://www.dropbox.com/s/r5qw4mofej23gbg/Group-A12%20ADM%20Project.pdf?dl=0 https://ie.linkedin.com/in/mayurkmane
Views: 118 Mayur Mane
Exploiting Data Mining for Authenticity Assessment...
 
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Title: Exploiting Data Mining for Authenticity Assessment and Protection of High-Quality Italian Wines from Piedmont Authors: Marco Arlorio, Jean Daniel Coisson, Giorgio Leonardi, Monica Locatelli, Luigi Portinale Abstract: This paper discusses the data mining approach followed in a project called TRAQUASwine, aimed at the definition of methods for data analytical assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont region in Italy. This is a big issue in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros. The objective is twofold: to show that the problem can be addressed without expensive and hyper-specialized wine analyses, and to demonstrate the actual usefulness of classification algorithms for data mining on the resulting chemical profiles. Following Wagstaff's proposal for practical exploitation of machine learning (and data mining) approaches, we describe how data have been collected and prepared for the production of different datasets, how suitable classification models have been identified and how the interpretation of the results suggests the emergence of an active role of classification techniques, based on standard chemical profiling, for the assesment of the authenticity of the wines target of the study. ACM DL: http://dl.acm.org/citation.cfm?id=2788596 DOI: http://dx.doi.org/10.1145/2783258.2788596
mod01lec01
 
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Views: 41809 Data Mining - IITKGP
Quantifying the Visual Impact of Classification Boundaries in Choropleth Maps
 
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One critical visual task when using choropleth maps is to identify spatial clusters in the data. If spatial units have the same color and are in the same neighborhood, this region can be visually identified as a spatial cluster. However, the choice of classification method used to create the choropleth map determines the visual output. The critical map elements in the classification scheme are those which lie near the classification boundary as those elements could potentially belong to different classes with a slight adjustment of the classification boundary. Thus, these elements have the most potential to impact the visual features (i.e., spatial clusters) that occur in the choropleth map. This paper identifies the critical boundary cases that can impact the overall visual presentation of the choropleth map with respect to spatial autocorrelation. A classification metric is used for identifying map elements that are near class boundaries, and a metric for quantifying the visual impact of classification edge effects in choropleth maps is presented.
Views: 210 VADER ASU
Advanced Data Mining with Weka (4.3: Using Naive Bayes and JRip)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 3: Using Naive Bayes and JRip http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4419 WekaMOOC
Combinatorial optimization and sparse computation for large scale data mining; Dorit Hochbaum
 
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We present here a novel model of data mining and machine learning that is based on combinatorial optimization, solving the optimization problem of “normalized cut prime” (NC’). NC’ is closely related to the NP-hard problem of normalized cut, yet is polynomial time solvable. NC’ is shown to be effective in image segmentation and in approximating the objective function of Normalized Cut as compared to the spectral technique. Its adaptation as a supervised classification data mining technique is called Supervised Normalized Cut (SNC). In a comparative study with the most commonly used data mining and machine learning methods, including Support Vector Machines (SVM), neural networks, PCA, logistic regression, SNC was shown to deliver highly accurate results within competitive run times. In scaling SNC to large scale data sets, its use of pairwise similarities poses a challenge since the rate of growth of the matrix of pairwise comparisons is quadratic in the size of the dataset. We describe a new approach called sparse computation that generates only the significant weights without ever generating the entire matrix of pairwise comparisons. The sparse computation approach runs in linear time in the number of non-zeros in the output and in that it contrasts with known “sparsification” approaches that require to generate, in advance, the full set of pairwise comparisons and thus take at least quadratic time. Sparse computation is applicable in any set-up where pairwise similarities are employed, and can be used to scale the spectral method and the k-nearest neighbors as well. The efficacy of sparse computation for SNC is manifested by its retention of accuracy, compared to the use of the fully dense matrix, while achieving a dramatic reduction in matrix density and thus run times. Parts of the research presented are joint with: P. Baumann, E. Bertelli, C. Lyu and Y. Yang.
Views: 388 MMDS Foundation
KDD2016 paper 679
 
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Title: Structural Neighborhood Based Classification of Nodes in a Network Authors: Sharad Nandanwar*, Indian Institute of Science Musti Narasimha Murty, Indian Institute of Science Abstract: Classification of entities based on the underlying network structure is an important problem. Networks encountered in practice are sparse and have many missing and noisy links. Even though statistical learning techniques have been used for intra-network classification based on local neighborhood, they perform poorly as they exploit only local information. In this paper, we propose a novel structural neighborhood based learning using a random walk. For classifying a node we take a random walk from the corresponding node, and make a decision based on how nodes in the respective k th - level neighborhood are getting classified. We observe that random walks of short length are helpful in classification. Emphasizing role of longer random walk may cause under- lying markov-chain to converge towards stationary distribu- tion. Considering this, we take a lazy random walk based ap- proach with variable termination probability for each node, based on its structural properties including degree. Our ex- perimental study on real world datasets demonstrates the superiority of the proposed approach over the existing state- of-the-art approaches. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 2515 KDD2016 video
KNN theory
 
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Download the dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP What is kNN theory? In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression. In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors. k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms. Both for classification and regression, a useful technique can be to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. What is Artificial Intelligence? (AI) Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". the next part: https://www.youtube.com/watch?v=cIsjZnwDkY8&list=PLA-CsqNypl-RtrpjMeWHDyIDKm1TAQf4t&index=2 the full Playlist: https://www.youtube.com/playlist?list=PLA-CsqNypl-RtrpjMeWHDyIDKm1TAQf4t 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g/videos?sub_confirmation=1 Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] Phone Number: +8801727659044, +8801728697998 #machinelearning #bigdata #ML #DataScience #DeepLearning # #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #BLE #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificationModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #KNNThoery #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #machinelearningcrashcourse #imageprocessing #SpyderIDE
Views: 1207 Planeter
K Nearest Neighbour (KNN) Example
 
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Download the dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP What is kNN theory? In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression. In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors. k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms. Both for classification and regression, a useful technique can be to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. What is Artificial Intelligence? (AI) Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". the next part: https://www.youtube.com/watch?v=DIRDA5-lY2k&list=PLA-CsqNypl-RtrpjMeWHDyIDKm1TAQf4t&index=3 the full Playlist: https://www.youtube.com/playlist?list=PLA-CsqNypl-RtrpjMeWHDyIDKm1TAQf4t the previous part: https://www.youtube.com/watch?v=h99yt5Y2r4M&index=2&t=29s&list=PLA-CsqNypl-RtrpjMeWHDyIDKm1TAQf4t 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g/videos?sub_confirmation=1 Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] Phone Number: +8801727659044, +8801728697998 #machinelearning #bigdata #ML #DataScience #DeepLearning # #robotics #রোবোটিক্স #প্ল্যানেটার #Planter #Pleneter #প্লেনেটার #Planeter #ieeeprotocols #BLE #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificationModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #KNNThoery #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #Planter #Pleneter #প্লেনেটার #machinelearningcrashcourse #imageprocessing #SpyderIDE
Views: 398 Planeter
Mod-01 Lec-02 Data Mining, Data assimilation and prediction
 
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Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 1938 nptelhrd
Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
 
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Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases 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 Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a range of applications. In this paper, we focus on privacy preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent itemsets from a collective dataset, and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient homomorphic encryption scheme and a secure comparison scheme. We then propose a cloud-aided frequent itemset mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. In comparison to the only known solution achieving a similar privacy level as our proposed solutions, the performance of our proposed solutions is 3 to 5 orders of magnitude higher. Based on our experiment findings using different parameters and datasets, we demonstrate that the run time in each of our solutions is only one order higher than that in the best non-privacy-preserving data mining algorithms. Since both data and computing work are outsourced to the cloud servers, the resource consumption at the data owner end is very low.
Views: 365 jpinfotechprojects
More Data Mining with Weka (1.3: Comparing classifiers)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Comparing classifiers http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 17424 WekaMOOC
Thesaurus based Text Mining - PoolParty Tutorial #18
 
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This video demonstrates how Thesaurus based Text Mining with PoolParty Extractor provides high-precision text analytics (See: http://www.poolparty.biz). It shows how any kind of content can be transformed into SKOS/RDF and how it later can be used for semantic mashups. Entity extraction based on knowledge graphs in contrast to simple term extraction offers an approach for information integration based on semantic knowledge models.
STATISTICAL APPROACH TO PERFORMANCE COMPARISON OF PREDICTIVE ALGORITHMS
 
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Resistance Spot Welding (RSW) is the dominant process to fabricate body closures and structural components in automotive industry. RSW is a complex process with inconsistent data and highly non-linear relation between process parameters. Several machine learning algorithms have been used to construct predictive models to assess weldability condition of RSW joints. However, to the best of our knowledge, a comprehensive analysis to compare performance of RSW weldability predictive models is lacking. In this investigation, a statistical framework is developed to assess performance superiority (high-accuracy and low-variability) of several machine learning algorithm(s) in RSW applications. First, machine learning algorithms popular in RSW literature are selected and pooled. As our contribution to this pool, a state-of-the-art Deep Neural Net (DNN) algorithm is added. Second, using a ten-fold cross-validation scheme, predictive models are constructed using Advanced High Strength Steel (AHSS) welding data from a major automotive original equipment manufacturer. Third, using Monte Carlo statistical simulation analysis, original and bootstrapped test sets are applied to the pool of constructed models to generate sampling distribution of the estimates, i.e. accuracy measure for each algorithm. Finally, statistical comparative experiments are used to determine the superior predictive algorithm(s) with results that indicate that the DNN model outperforms other models. Our study shows that the DNN model improves accuracy and variability by approximately 19% and 7% on average, respectively. As an improvement for the research for the case of big data scenarios, DATAVIEW a big data infrastructure is used instead of traditional data analytics framework that is developed based on R. DATAVIEW and R scientifically compared by developing full-factorial statistical experiments. Our research indicate that DATAVIEW significantly outperforms R in terms of computational costs and performance efficiency.
Views: 106 Saeed Z.Gavidel
R tutorial: Cross-validation
 
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Learn more about machine learning with R: https://www.datacamp.com/courses/machine-learning-toolbox In the last video, we manually split our data into a single test set, and evaluated out-of-sample error once. However, this process is a little fragile: the presence or absence of a single outlier can vastly change our out-of-sample RMSE. A better approach than a simple train/test split is using multiple test sets and averaging out-of-sample error, which gives us a more precise estimate of true out-of-sample error. One of the most common approaches for multiple test sets is known as "cross-validation", in which we split our data into ten "folds" or train/test splits. We create these folds in such a way that each point in our dataset occurs in exactly one test set. This gives us 10 test sets, and better yet, means that every single point in our dataset occurs exactly once. In other words, we get a test set that is the same size as our training set, but is composed of out-of-sample predictions! We assign each row to its single test set randomly, to avoid any kind of systemic biases in our data. This is one of the best ways to estimate out-of-sample error for predictive models. One important note: after doing cross-validation, you throw all resampled models away and start over! Cross-validation is only used to estimate the out-of-sample error for your model. Once you know this, you re-fit your model on the full training dataset, so as to fully exploit the information in that dataset. This, by definition, makes cross-validation very expensive: it inherently takes 11 times as long as fitting a single model (10 cross-validation models plus the final model). The train function in caret does a different kind of re-sampling known as bootsrap validation, but is also capable of doing cross-validation, and the two methods in practice yield similar results. Lets fit a cross-validated model to the mtcars dataset. First, we set the random seed, since cross-validation randomly assigns rows to each fold and we want to be able to reproduce our model exactly. The train function has a formula interface, which is identical to the formula interface for the lm function in base R. However, it supports fitting hundreds of different models, which are easily specified with the "method" argument. In this case, we fit a linear regression model, but we could just as easily specify method = 'rf' and fit a random forest model, without changing any of our code. This is the second most useful feature of the caret package, behind cross-validation of models: it provides a common interface to hundreds of different predictive models. The trControl argument controls the parameters caret uses for cross-validation. In this course, we will mostly use 10-fold cross-validation, but this flexible function supports many other cross-validation schemes. Additionally, we provide the verboseIter = TRUE argument, which gives us a progress log as the model is being fit and lets us know if we have time to get coffee while the models run. Let's practice cross-validating some models.
Views: 48254 DataCamp
Modeling Data Streams Using Sparse Distributed Representations
 
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In this screencast, Jeff Hawkins narrates the presentation he gave at a workshop called "From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications." The workshop was held May 7-11, 2012 at the University of California, Berkeley. Slides: http://www.numenta.com/htm-overview/05-08-2012-Berkeley.pdf Abstract: Sparse distributed representations appear to be the means by which brains encode information. They have several advantageous properties including the ability to encode semantic meaning. We have created a distributed memory system for learning sequences of sparse distribute representations. In addition we have created a means of encoding structured and unstructured data into sparse distributed representations. The resulting memory system learns in an on-line fashion making it suitable for high velocity data streams. We are currently applying it to commercially valuable data streams for prediction, classification, and anomaly detection In this talk I will describe this distributed memory system and illustrate how it can be used to build models and make predictions from data streams. Live video recording of this presentation: http://www.youtube.com/watch?v=nfUT3UbYhjM General information can be found at https://www.numenta.com, and technical details can be found in the CLA white paper at https://www.numenta.com/faq.html#cla_paper.
Views: 20661 Numenta
Data Mining with Weka (3.6: Nearest neighbor)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Nearest neighbor http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/YjZnrh https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 47783 WekaMOOC
Data Mining with Weka (4.4: Logistic regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Logistic regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 33880 WekaMOOC
Security Evaluation of Pattern Classifiers under Attack
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Security Evaluation of Pattern Classifiers under Attack Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be purposely manipulated by humans to undermine their operation. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation may severely affect their performance, and consequently limit their practical utility. Extending pattern classification theory and design methods to adversarial settings is thus a novel and very relevant research direction, which has not yet been pursued in a systematic way. In this paper, we address one of the main open issues: evaluating at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. We propose a framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and give examples of its use in three real applications. Reported results show that security evaluation can provide a more complete understanding of the classifier’s behavior in adversarial environments, and lead to better design choices
Views: 1376 jpinfotechprojects
Classification Interface - Rubine, Image Based and Adaptive Boosting
 
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SHREY PAREEK MS Mechanical Engineering University at Buffalo, NY www.shreypareek.com [email protected] The video describes a classification interface developed by me, using some very famous classification schemes. The interface can be used for hand-written numbers between 0 and 9. It employs the use of three classification algorithms (refer references). Disclaimer - I do not own any of these algorithms and have simply implemented them using MATLAB. References [1] J. J. LaViola and R. C. Zeleznik, "A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer," Pattern Anal. Mach. Intell. IEEE Trans. On, vol. 29, no. 11, pp. 1917--1926, 2007. [2] L. B. Kara and T. F. Stahovich, "An image-based, trainable symbol recognizer for hand-drawn sketches," Comput. Graph., vol. 29, no. 4, pp. 501--517, 2005. [3] D. Rubine, Specifying gestures by example, vol. 25. ACM, 1991.
Views: 153 Shrey Pareek
More Data Mining with Weka (4.2: The Attribute Selected Classifier)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: The Attribute Selected Classifier http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 12078 WekaMOOC
Predicting the Share Values by using Clustering Algorithm in BigData
 
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In recent days the share marketing values cannot be predicted correctly .This leads to heavy loss for the client. As one important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. It is difficult for PCM to produce a good result for clustering big data, especially for heterogeneous data, since it is initially designed for only small structured dataset. To tackle this problem, we propose a high-order PCM algorithm (HOPCM) for big data clustering. This project is used to find the profit and loss for the customers share based on clustering analysis technique for the particular tickers. And, it clusters total no. of customers for those particular tickers and the shares invested by them and also the minimum and maximum share for the particular tickers in High-order Possibility by using C-Means Algorithm. Furthermore, cloud servers are employed to improve the efficiency for big data clustering by designing a distributed HOPCM scheme depending on MapReduce.
Views: 36 Spy Selvaperumal
More Data Mining with Weka (1.2: Exploring the Experimenter)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 2: Exploring the Experimenter http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 18818 WekaMOOC
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining
 
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Along with privacy, discrimination is a very important issue when considering the legal and ethical aspects of data mining. It is more than obvious that most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those attributes are used for making decisions about them like giving them a job, loan, insurance, etc. Discovering such potential biases and eliminating them from the training data without harming their decision-making utility is therefore highly desirable.
Publishing SKOS Concept Schemes with SKOSMOS
 
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With more and more thesauri, classifications and other knowledge organization systems being published as Linked Data using SKOS, the question arises how best to make them available on the web. While just publishing the Linked Data triples is possible using a number of RDF publishing tools, those tools are not very well suited for SKOS data, because they cannot support term-based searching and lookup. This webinar presents Skosmos, an open source web­-based SKOS vocabulary browser that uses a SPARQL endpoint as its back­end. It can be used by e.g. libraries and archives as a publishing platform for controlled vocabularies such as thesauri, lightweight ontologies, classifications and authority files. The Finnish national thesaurus and ontology service Finto, operated by the National Library of Finland, is built using Skosmos. Skosmos provides a multilingual user interface for browsing and searching the data and for visualizing concept hierarchies. The user interface has been developed by analyzing the results of repeated usability tests. All of the SKOS data is made available as Linked Data. A developer­-friendly REST API is also available providing access for using vocabularies in other applications such as annotation systems. We will describe what kind of infrastructure is necessary for Skosmos and how to set it up for your own SKOS data. We will also present examples where Skosmos is being used around the world.
Views: 523 AIMS CIARD
Data Mining with Weka (4.2: Linear regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 43973 WekaMOOC
Immutable Authentication and Integrity Schemes for Outsourced Databases
 
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Immutable Authentication and Integrity Schemes for Outsourced Databases
Views: 118 Chennai Sunday
Lecture - 35 Rule Induction and Decision Trees - II
 
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Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 15196 nptelhrd
Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
 
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Towards Real-Time, Country-Level Location Classification of Worldwide Tweets 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, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org The increase of interest in using social media as a source for research has motivated tackling the challenge of automatically geolocating tweets, given the lack of explicit location information in the majority of tweets. In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets at the country level, which is so far unexplored in a real-time scenario. We analyse the extent to which a tweet’s country of origin can be determined by making use of eight tweet-inherent features for classification. Furthermore, we use two datasets, collected a year apart from each other, to analyse the extent to which a model trained from historical tweets can still be leveraged for classification of new tweets. With classification experiments on all 217 countries in our datasets, as well as on the top 25 countries, we offer some insights into the best use of tweet-inherent features for an accurate country-level classification of tweets. We find that the use of a single feature, such as the use of tweet content alone – the most widely used feature in previous work – leaves much to be desired. Choosing an appropriate combination of both tweet content and metadata can actually lead to substantial improvements of between 20% and 50%. We observe that tweet content, the user’s self-reported location and the user’s real name, all of which are inherent in a tweet and available in a real-time scenario, are particularly useful to determine the country of origin. We also experiment on the applicability of a model trained on historical tweets to classify new tweets, finding that the choice of a particular combination of features whose utility does not fade over time can actually lead to comparable performance, avoiding the need to retrain. However, the difficulty of achieving accurate classification increases slightly for countries with multiple commonalities, especially for English and Spanish speaking countries.
Views: 468 jpinfotechprojects
THREE PHASE INDUCTION MOTOR FAULT CLASSIFIER USING CASCADE NEURAL NETWORK
 
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Induction motors are subject to different faults which, if undetected, may lead to serious machine failures. The objective of this design is to develop an alternative NN based fault-detection scheme that overcomes the limitations of the present schemes. The present schemes are costly and applicable for large motors furthermore, many design parameters are requested. Concerning long-time operating machines, these parameters cannot be available easily. In some existing schemes, either a detail mathematical model is required, or many features must be extracted, for which costly instrumentation is needed. In this scheme, only stator current is captured, and simple statistical parameters of the current waveform are used as inputs to detect the four conditions of the motor. Comparing with existing methods i.e NN-based fault-detection, a single network is used, the proposed method is simple, accurate, reliable, economical and uses a cascade connection of RBF–multilayer-perceptron (MLP) (RBF-MLP) network is developed to achieve a classification better accuracy. In the design, the first layer of cascade NN, which is the RBF with conscience full competitive rule and Boxcar metric with nearly 36 cluster centers. For the second layer of the network, Momentum learning rule and Tanh transfer function give the optimal results. For generalization, the network is trained and tested rigorously. It has been found that network is able to detect the faults in induction motor with average classification accuracies above 90% when tested on testing data and CV data, respectively. Other performance measures are shown in the graphs. The training time required per epoch per exemplar is fast enough since the proposed classifier is to be used in real time, where measurement noise is anticipated, the robustness of the classifier to the noise is verified. For a demonstration of cascade NN-based fault classifier, experimental results are used instead of simulation to make the classifier more practical. For design, the Matlab 2018a version is used. Reference Paper: Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor Author’s Name: Vilas N. Ghate and Sanjay V. Dudul Source: IEEE-IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS Year: 2011 Send student identity card to get the design file, contact us +91 7904568456 by whatsapp we will validate the details and send the code.
Udyog Aadhaar - Details - Registration - Live Demo -  Explained - In Hindi
 
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Udyog Aadhaar registration process has been created to encourage small entrepreneurs to online file or register your business in India which is also known as MSME(Micro, Small and Medium Enterprises). Previously, the UA registration was known under the names of SSI (Small Scale Industries) Certificate or MSME Registration. The SSI Certificate was renamed to Udyog Aadhaar with an easier one-page registration process instead of long time-consuming EM (Entrepreneurs Memorandum) I/II forms. Information and Documents Required for Online Aadhaar Registration 1. Aadhar Number 2. Name of Business Owner as per Aadhaar Card 3. Social Category Gen\SC\ST\OBC. 4. Name of Enterprise or your business 5. Types of organisation you need to select from the below options: Proprietorship Partnership Firm Hindu Undivided Family Private Limited Company Co-Operative Public Limited Company Self Help Group Others(LLP-Limited Liability Partnership) 6. Postal Address 7. Date of Commencement or Date of Business started 8. Previous Registration Details if any 9. Bank Details 10. Area of Activity (Manufacturing or Service) 11. NIC Code (As per business) check the below link for the codes: http://udyogaadhaar.gov.in/ua/Document/nic_2008_17apr09.pdf 12. Persons employed in the business 13. Investment in Plant & Machinery/Equipment 14. DIC - Details of the District Industry Center nearest to the business Main Website http://udyogaadhaar.gov.in/UA/UAM_Registration.aspx Udyog Aadhaar Booklet http://msme.gov.in/sites/default/files/Udyog_Aadhar_Booklet.pdf Register your complaint http://udyogaadhaar.gov.in/UA/Complaint_regtister.aspx Login to update your details later on http://udyogaadhaar.gov.in/UA/UA_EntrepreneurLogin.aspx Mobile App https://play.google.com/store/apps/details?id=msme.mymsme MSME Industries are divided as per below classification on basis of investment Manufacturing 1. Micro enterprise in Manufacturing Sector (Rs 25 Lakhs ) 2. Small enterprise in Manufacturing Sector (Rs 500 Lakhs) 3. Medium enterprise in Manufacturing Sector (Rs 1000 lakhs) Service 1. Micro enterprise in Service Sector (Rs 10 lakhs) 2. Small enterprise in Service Sector (Rs 200 Lakhs) 3. Medium enterprise in Service Sector (Rs 500 lakhs) De-Registration A Micro, small and medium Enterprises can violate the regulations in the following ways which will make it liable for de-registration: 1. It crosses the investment limits. 2. It starts manufacturing any new item or items that require an industrial license or other kind of statutory license. 3. It does not satisfy the condition of being owned, controlled or being a subsidiary of any other industrial undertaking. Disclaimer: Above data and pictures in this video has been taken from government website for informative and education purposes. Please watch the complete video for proper understanding. My eBook Real Ways to Make Money Online - E-book (Lifetime Free updates) (Rs.149) : https://goo.gl/oB95Pt Donate us to Keep Motivated paypal.me/techbulu My Amazon eStore https://www.amazon.in/shop/techbulu Products I use Samson Go Mic: https://amzn.to/2LoefhP Pop filter: https://amzn.to/2uyZRJR Microsoft Office 365: https://amzn.to/2JBVP8y My phone: https://amzn.to/2uMuwCV Desktop : https://amzn.to/2JCe5yF Digital Pen: https://amzn.to/2LpvCin Share, Support, Subscribe!!! Youtube: https://www.youtube.com/c/TECHBULU Twitter: https://twitter.com/techbulu Facebook: https://www.facebook.com/techbulu/ Pinterest: https://www.pinterest.com/techbulu/ Linkedin: https://www.linkedin.com/in/tech-bulu-15834b140/ BlogSite: http://www.techbulu.com/ About : TECHBULU is a YouTube Channel, where you will find technical, travel and lifestyle videos, New Video is Posted Everyday
Views: 233589 TECH BULU
privacy preserving data mining methods metrics and applications
 
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privacy preserving data mining methods metrics and applications -IEEE PROJECTS 2017-2018 MICANS INFOTECH PVT LTD, CHENNAI ,PONDICHERRY http://www.micansinfotech.com http://www.finalyearprojectsonline.com http://www.softwareprojectscode.com +91 90036 28940 ; +91 94435 11725 ; [email protected] Download [email protected] http://www.micansinfotech.com/VIDEOS.html Abstract: The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the most relevant PPDM techniques from the literature and the metrics used to evaluate such techniques and presents typical applications of PPDM methods in relevant fields. Furthermore, the current challenges and open issues in PPDM are discussed.