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Updating a Sliding Window for Flow Control
 
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Answer to a quiz question about the state parameters in a sliding window flow control protocol. Part of ITS323 Introduction to Data Communications. Course material, including lecture notes and quiz, via: http://sandilands.info/sgordon/teaching
Views: 13791 Steven Gordon
Pocket Data Mining
 
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http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-02710-4 Pocket Data Mining PDM is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. Related publications: Stahl F., Gaber M. M., Bramer M., and Yu P. S, Distributed Hoeffding Trees for Pocket Data Mining, Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), Special Session on High Performance Parallel and Distributed Data Mining (HPPD-DM 2011), July 4 -- 8, 2011, Istanbul, Turkey, IEEE press. http://eprints.port.ac.uk//3523 Stahl F., Gaber M. M., Bramer M., Liu H., and Yu P. S., Distributed Classification for Pocket Data Mining, Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011), Warsaw, Poland, 28-30 June, 2011, Lecture Notes in Artificial Intelligence LNAI, Springer Verlag. http://eprints.port.ac.uk/3524/ Stahl F., Gaber M. M., Bramer M., and Yu P. S., Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments, Proceedings of the IEEE 22nd International Conference on Tools with Artificial Intelligence (ICTAI 2010), Arras, France, 27-29 October, 2010. http://eprints.port.ac.uk/3248/
Views: 2951 Mohamed Medhat Gaber
Meta data  in 5 mins hindi
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 85332 Last moment tuitions
DBSCAN ( Density Based Spatial  Clustering of Application with Noise )  in Hindi | DWM | Data Mining
 
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Sample Notes : https://drive.google.com/file/d/19xmu... for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho : https://goo.gl/85HQGm for full notes please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made notes of data warehouse and data mining its only 200rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 8833 Last moment tuitions
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 764589 Dr Nic's Maths and Stats
22 Types of Digital Communication
 
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Download the Show Notes: [URL] [Description] Visit the Learn Xtra Website: http://learn.mindset.co.za View the Learn Xtra Live Schedule: http://learn.mindset.co.za/xtra/live Join us on Facebook: http://www.facebook.com/learnxtra Follow us on Twitter: http://twitter.com/learnxtra
Views: 464 Mindset Learn
Lec-33 Dimensionality reduction Using PCA
 
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Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 28374 nptelhrd
Lec-19 Back Propagation Algorithm
 
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Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 65997 nptelhrd
Gaurang Panchal - Data Mining/Machine Learning Project
 
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Dataset: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing# Overview: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. This dataset consists of client information of a bank; 41188 records with 20 inputs, ordered by date (from May 2008 to November 2010). Aim: The classification goal is to predict if the client will subscribe (yes/no) a term deposit. The data includes information about the clients and marketing calls. Together with this data there is a record of whether the clients are currently enrolled for a term deposit. All of the variables should be considered and modeled to produce classification to accurately predict an entry for a client. Attribute Information: Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') # related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. # other attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') # social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')
Views: 166 Gaurang Panchal
Perry Samson - Mining My Students Notes to Create Study Guides | Lectures On-Demand
 
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Professor Perry Samson, Atm, Oceanic & Space Sci. - CoE, University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 273572 Quantitative Specialists
2017 Grafstein Lecture in Communications - Tim Wu, "The Attention Merchants"
 
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On March 23, 2017, Prof. Tim Wu (Columbia) gave the annual Grafstein Lecture in Communications at the University of Toronto Faculty of Law. Prof. Wu spoke about his recent book, "The Attention Merchants", which chronicles the long rise of industries that "feed on human attention".
Views: 796 UTorontoLaw
Mod-01 Lec-05 Bayes Decision Theory
 
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Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
Views: 27795 nptelhrd
Detect malicious android applications with data mining techniques
 
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A diploma thesis of one of my undergraduate students. Mr. Konstantinos Ousantzopoulos. ABSTRACT The Android operating system gives access to applications based on model of permissions. In this work we use the permissions of safe and malicious applications as a data structure to excavate knowledge so that we can predict if an application from Google Play is safe or malicious using Rapidminer various data mining techniques and algorithms to get the best possible result. We will show the way data was collected and their analysis to arrive at a desired result which we will apply with an android application and a Java server. The user through a simple android application will be able to type the name of the application on Google Play which wants to check. Then the application will communicate locally with the server where the analysis and prediction through Rapidminer take place . Finally it returns to the screen of the user the prediction whether the application he searched is malicious or not.
Big Data Analytics Lectures | Euclidean Distance  with Solved Example in Hindi
 
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Video credit : Atharva hello friends, In this video we will be learning the one of the most famous of technique for calculating the distance between 2 points in space. AND also please have a look at the distance measures video before watching this ALL the Best and Have a nice day. visit our website for full course www.lastmomenttuitions.com NOTES: https://lastmomenttuitions.com/how-to-buy-notes/ bda notes form : https://goo.gl/Ti9CQj introduction to Hadoop : https://goo.gl/LCHC7Q Introduction to Hadoop part 2 : https://goo.gl/jSSxu2 Distance Measures : https://goo.gl/1NL3qF Euclidean Distance : https://goo.gl/6C16RJ Jaccard distance : https://goo.gl/C6vmWR Cosine Distance : https://goo.gl/Sm48Ny Edit Distance : https://goo.gl/dG3jAP Hamming Distance : https://goo.gl/KNw95L FM Flajolit martin Algorithm : https://goo.gl/ybjX9V Random Sampling Algorithm : https://goo.gl/YW1AWh PCY ( park chen yu) algorithm : https://goo.gl/HVWs21 Collaborative Filtering : https://goo.gl/GBQ7JW Bloom Filter Basic concept : https://goo.gl/uHjX5B Naive Bayes Classifier : https://goo.gl/dbRYYh Naive Bayes Classifier part2 : https://goo.gl/LWstNv Decision Tree : https://goo.gl/5m8JhA Apriori Algorithm :https://goo.gl/mmpxL6 FP TREE Algorithm : https://goo.gl/S29yV8 Agglomerative clustering algorithmn : https://goo.gl/L9nGu8 Hubs and Authority and Hits Algorithm : https://goo.gl/D2EdFG Betweenness Centrality : https://goo.gl/czZZJR
Views: 4431 Last moment tuitions
Sampling & its 8 Types: Research Methodology
 
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Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm
Views: 250051 Examrace
Strategic Intelligence, Dr. Michael Maccoby, University of the District of Columbia
 
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Strategic Intelligence Dr. Michael Maccoby Speaker, Distinguished Lecture Series School of Business and Public Administration University of the District of Columbia Washington, DC, USA www.udc.edu/sbpa www.udc.edu November 5, 2014 Lecture Notes: https://drive.google.com/file/d/0B0apITffZbNKbmdXNHRaN0NJZVU/view?usp=sharing Learn more about Dr. Maccoby: http://en.wikipedia.org/wiki/Michael_Maccoby http://hbswk.hbs.edu/archive/1565.html http://maccoby.com/ Author of many internationally known books: http://www.amazon.com/Michael-Maccoby/e/B001ITTM8S/ref=sr_tc_2_0?qid=1352238267&sr=1-2-ent scholar-contributor to The Washington Post: http://www.washingtonpost.com/michael-maccoby/2011/02/28/ABt7EqN_page.html and frequent lecturer and speaker at Harvard, Oxford, Google
Views: 3106 sergeygwu
Management information system part 1 easy way
 
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Management information system in simple way Very easy way to understand management information system
Views: 60219 Sanket pandit
Data- What is the Importance of DATA in Tamil?
 
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Data is collection of information . Data store and Data process Play List : https://www.youtube.com/playlist?list=PLLa_h7BriLH2U05m3eN43779AnrmieYHz YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Views: 13940 atoz knowledge
Mobile Computing Lecture - -  Bluetooth Architecture easy explanation (Eng-Hindi)
 
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Bluetooth Architecture explanation in short method -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 43979 Well Academy
OSI Model (OSI Reference Model) : The 7 Layers Explained
 
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Enroll to Full Course: https://goo.gl/liK0Oq The "OSI Model" also known as "OSI Reference Model" is discussed here in 2 parts: a) Understanding OSI Reference Model b) OSI Model layers a) Understanding OSI Model (00:22): http://youtu.be/p7UR7Nipqcs?t=22s The OSI reference model is one such communication model. OSI stands for "Open Systems Interconnection" which means that every system participating in this model is open for communication with other systems. This model was first defined by an organization called as ISO. The OSI model divides the communication into 7 layers. b) OSI Model layers (2:15) : http://youtu.be/p7UR7Nipqcs?t=2m15s Quick Look of the 7 layers of the OSI reference Model: 7) Application Layer is where the users interact with applications to provide data 6) Presentation Layer is concerned with the format of data exchanged between the end systems 5) Session Layer allows users on different machines to create sessions between them 4) Transport Layer is concerned with end to end communication of messages 3) Network Layer is concerned with routing of packets to correct destination 2) Data Link Layer is concerned with transmission of error free data in the form of Frames 1) Physical Layer is concerned about transmission of raw bits over the communication link Search Terms: OSI Model, OSI layers, OSI Model Layers, OSI 7 Layers, Network Layer, 7 Layers of OSI model, OSI network Model, what is osi model, OSI system, OSI Reference Model, ISO OSI Model, OSI Model layers, video URL : https://www.youtube.com/watch?v=p7UR7Nipqcs Watch ALL CN VIDEOS: https://www.youtube.com/playlist?list=PL9OIoIp8YySF4mkIihOb_j2HZIRIlYuEx For more, visit http://www.udemy.com/u/EngineeringMentor Facebook: https://www.facebook.com/SkillGurukul Twitter : https://twitter.com/Engi_Mentor
Views: 580315 Skill Gurukul
Day2 JustAlhaji x264
 
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Reda Alhajj Department of Computer Science, the University of Calgary CANADA Title Facilitating Big Data Analysis Using Limited Computing Resources Abstract The rapid development in technology and social media has gradually shifted the focus in research, industry and community from traditional into dynamic environments where creativity and innovation dominate various aspects of the daily life. This facilitated the automated collection and storage of huge amount of data which is necessary for effective decision making. The value of data is increasingly realized and there is a tremendous need for effective techniques to maintain and handle the collected data starting from storage to processing and analysis leading to knowledge discovery. This talk will focus on techniques and structures which could maximize the benefit from data beyond what is traditionally supported. We emphasize on data intensive domains which require developing and utilizing advance computational techniques for informative discoveries. We describe some of our accomplishments, ongoing research and future research plans. The notion of big data will be addressed to show how it is possible to process incrementally available big data using limited computing resources. The benefit of various data mining and network modeling mechanisms for data analysis and prediction will be addressed with emphasize on some practical applications ranging from forums and reviews to social media as effective means for communication, sharing and discussion leading to collaborative decision making and shaping of future plans. Biography Reda Alhajj is a professor in the Department of Computer Science at the University of Calgary. He published over 500 papers in refereed international journals, conferences and edited books. He served on the program committee of several international conferences. He is founding editor in chief of the Springer premier journal “Social Networks Analysis and Mining”, founding editor-inchief of Springer Series “Lecture Notes on Social Networks”, founding editor-in-chief of Springer journal “Network Modeling Analysis in Health Informatics and Bioinformatics”, founding coeditor-in-chief of Springer “Encyclopedia on Social Networks Analysis and Mining”, founding steering chair of the flagship conference “IEEE/ACM International Conference on Advances in Social Network Analysis and Mining”, and three accompanying symposiums FAB, FOSINT-SI and HI-BI-BI. He is member of the editorial board of the Journal of Information Assurance and Security, Journal of Data Mining and Bioinformatics, Journal of Data Mining, Modeling and Management; he has been guest editor of a number of special issues and edited a number of conference proceedings. Dr. Alhajj's primary work and research interests focus on various aspects of data science and big data with emphasis on areas like: (1) scalable techniques and structures for data management and mining, (2) social network analysis with applications in computational biology and bioinformatics, homeland security, etc., (3) sequence analysis with emphasis on domains like financial, weather, traffic, energy, etc., (4) XML, schema integration and reengineering. He currently leads a large research group of PhD and MSc candidates. He received best graduate supervision award and community service award at the University of Calgary. He recently mentored a number of successful teams, including SANO who ranked first in the Microsoft Imagine Cup Competition in Canada and received KFC Innovation Award in the World Finals held in Russia, TRAK who ranked in the top 15 teams in the open data analysis competition in Canada, Go2There who ranked first in the Imagine Camp competition organized by Microsoft Canada, Funiverse who ranked first in Microsoft Imagine Cup Competition in Canada. http://www.tophpc.com
Views: 8 TopHPC Office
Pipelining concept in Hindi
 
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Sample Notes :https://goo.gl/fkHZZ1 PDS NOTES FORM :https://goo.gl/AmzHVA For full notes of PDS its only 100 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id thanx you so much last moment tuitions ko itna support karne ko even you can join us on whatsapp for getting latest update of videos + some motivation speeeches everyday have a nice day connect us on whatsapp for latest update of videos + motivation and inspiration speech whatsapp :7038604912 CHECK OUT ALL THE VIDEOS OF PARALLEL COMPUTING AND DISTRIBUTED SYSTEM INTRODUCTION:-https://goo.gl/j4Mzn6 FLYNN's Classification:-https://goo.gl/2Jx5QX FENG's Classifications:-https://goo.gl/3625Tb Amdahl's Law:-https://goo.gl/T8rMnL Pipeline concept:-https://goo.gl/qeie9W Fixed point Floating point:-https://goo.gl/sAjZaw Digit product multiplication:-https://goo.gl/DZd4Uy Sychronization:-https://goo.gl/Df9eQJ Cristian Algo:-https://goo.gl/Lqp86v Berkeley's Algo:-https://goo.gl/Utaspb NTP Algo:-https://goo.gl/HyKUp6 Logical clock:-https://goo.gl/chrxv9 Lamport Logical clock:-https://goo.gl/vZAC6U Vector Logical clock:-https://goo.gl/GFq2ac Lamport non token based:-https://goo.gl/2U1wwB rickart aggarwala Algo:-https://goo.gl/hf6HV3 raymonds Algo:-https://goo.gl/f67UEQ Suzuki kasami Algo:-https://goo.gl/BnqtgE Election Algo:-https://goo.gl/ycdkuJ RMI:-https://goo.gl/vwYqjs RPC:-https://goo.gl/3BEYUn subscribe karke rakho channel for more video updates
Views: 220835 Last moment tuitions
Ulrich Korwitz - Conference Opening and Welcome Notes
 
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“From Big Data to Smart Knowledge – Text and Data Mining in Science and Economy”, Conference in Cologne February 23 to 24 2015 www.textminingconference.de
Lecture -20 Discrete Wavelet Transforms
 
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Lecture Series on Digital Voice and Picture Communication by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engg ,IIT Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 79859 nptelhrd
Mod-01 Lec-29 Support Vector Machine
 
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Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
Views: 48997 nptelhrd
[Hindi] Cloud Computing Explained in Detail
 
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Namaskaar Dosto, is video mein maine aapko CLOUD Computing ke baare mein bataya hai, Cloud computing kya hai, iska naam clouc computing kyu hai, aap is se kya kya kaam kar sakte hai? Aur cloud Computing ka future kya hai? yeh sabhi baatein maine aapko is video mein batayi hai. Mujhe Umeed hai ki Cloud Computing ki yeh video aapko acchi lagegi. Cloud computing ek bahut hi acchi technology hai, aur aane wale dino mein aur bhi jyada popular hone wali hai, hum sabhi aaj bhi kisi na kisi tareeke se cloud computing ka use karte hai, magar hume pata nahi hota ki hum cloud computing technology ko use kar rahe hai. Enter MEGA Giveaway here: http://bit.ly/1PPOvbc Share, Support, Subscribe!!! Subscribe: http://bit.ly/1Wfsvt4 Youtube: http://www.youtube.com/c/TechnicalGuruji Twitter: http://www.twitter.com/technicalguruji Facebook: http://www.facebook.com/technicalguruji Instagram: http://instagram.com/technicalguruji Google Plus: https://plus.google.com/+TechnicalGuruji About : Technical Guruji is a YouTube Channel, where you will find technological videos in Hindi, New Video is Posted Everyday :)
Views: 392855 Technical Guruji
Arun Kumar Notes
 
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Views: 1156 Crunch
Lecture - 24 Graphs
 
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Lecture Series on Data Structures and Algorithms by Dr. Naveen Garg, Department of Computer Science and Engineering ,IIT Delhi. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 101064 nptelhrd
Multimedia in Hindi
 
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Multimedia
Views: 64820 All in one
What is E-Commerce in Hindi  (Basic Information for Beginners)
 
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Types of E-Commerce : https://youtu.be/m7x6zYEBYEM What is EDI in eCommerce ?: https://youtu.be/zN237-EpFQI ------------------------------------------------------------------------- What is E-Commerce in Hindi what is ecommerce meaning in hindi ecommerce explained e commerce means in hindi ecommerce means introduction to ecommerce in hindi ecommerce theory -------------------------------------------------------------- This is my Blog: http://mystudymafia.blogspot.in/2018/02/e-commerce-stands-for-electronic.html
Views: 121609 STUDY Mafia
Algorithms Lecture 1 -- Introduction to asymptotic notations
 
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In this video big-oh, big-omega and theta are discussed
Research Methodology Meaning Types Objectives [Hindi]
 
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Methodology is the systematic, theoretical analysis of the methods applied to a field of study. A research method is a systematic plan for conducting research. Sociologists draw on a variety of both qualitative and quantitative research methods, including experiments, survey research, participant observation, and secondary data.
Views: 110382 Manager Sahab
Social Network Analysis
 
02:06:01
An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 3117 Microsoft Research
Next in (Data) Science | Part 1 | Radcliffe Institute
 
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The Next in Science Series provides an opportunity for early-career scientists whose innovative, cross-disciplinary research is thematically linked to introduce their work to one another, to fellow scientists, and to nonspecialists from Harvard and the greater Boston area. This year’s program focuses on innovative applications of data science to a wide range of disciplines. The speakers’ talks demonstrate how data science approaches have become critical to a variety of fields, including social media, the movie industry, public health, and the study of the origins of our universe. Welcome and Introduction Alyssa A. Goodman, faculty codirector of the science program, Radcliffe Institute for Advanced Study, and Robert Wheeler Willson Professor of Applied Astronomy, Faculty of Arts and Sciences, Harvard University (5:55) “Uncovering Online Censorship and Propaganda in China” Jennifer Pan, assistant professor of communication and, by courtesy, of political science and sociology, Stanford University (31:16) “Hollywood Data Science: The Role of Inference and Prediction” Nathan Sanders, vice president of quantitative analytics, Legendary Entertainment
Views: 4498 Harvard University
Regression Analysis| Part 1 of 4 by Vijay Adarsh || Stay Learning (HINDI) | (हिंदी)
 
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Benefits of StayLearning One to One Tutoring • LIVE recorded video lectures. (Full Syllabus Covered) • Step by Step Solution • Conceptual Clarity • Learn at your Own Speed - Play | Pause | Re-watch | Skip • Learn at your Own Time • Complete classroom recordings of our actual classroom program at Delhi. • We are most trusted and tested in the field of coaching. • 45,000 + Hours of Experience in Training Students We also offer blended learning program Customized to your need. We Provide Complete Video Lectures for Class XI, XII, B.Com (Prog), B. Com (Hons), M. Com, MBA. About StayLearning: StayLearning is an online educational Organisation which caters to the need of quality education. Our highly experienced faculty provides proper guidance and ensures that learning is easy and fun. We provide tutorials accompanied by visual representation. The visual literacy improves the child's ability to comprehend and understand the topic with much more efficacy. It leaves a deeper and a more long lasting impact. We lay out a host of teaching and learning solutions that include rich-media digital learning materials which make it easier for the students to grasp the concept. Practice assignments and self-evaluation tests are provided to make self-study more efficient and interesting. The students can also replay the previous classes thereby helping in brushing up the earlier learned concepts. Our content repository consists of several such topics that students usually find hard to decipher but are made easy with the help of visual literacy. Video Lectures for Accountancy by Vijay Adarsh evolved as utility services for our own students. We had thought that recorded lecture would be an excellent reinforcement tool for the students and it proved to be exactly that. We have video lectures for Class 11th, 12th, B.Com (H/P), M.Com, MBA examination. These are our classroom lectures which form a very good source of study material. Now we also have special set of video lectures which are specially prepared to suit the need for the board students. The Lectures Covers in full depth, the description of all the involved concepts. Studying through lectures largely reduces the need of individual tuition. Lectures can be use at a pace which suits us. Students can pause and rewind the lectures according to their need. Complete practice tests and solutions of every topic would also be provided. About Vijay Adarsh: Vijay Adarsh is a Successful Teacher and Famous Motivational Coach. He is the most enthusiastic, dynamic, informative and result oriented coach. He is a commerce graduate from Delhi University. After completing B.com (Hons), he completed his post-graduation and now pursuing PhD. He started teaching students of and motivating people at the age of 17 and possesses a vast experience of teaching more than 45,000 hrs. He has simplified subjects and made it very interesting, Learning with Fun and Easy for the students. His easy class notes, beautiful animated & graphic presentations are popular among the students. He is popular among the student community for possessing the excellent ability to communicate the concepts in analytical and graphical way. He has conducted many seminars & workshops on various topics for Students, Teachers, Schools, Businessman, Housewife, Income Tax Offices, Doctors, CA's and Corporate Houses. He is also the author of several Books, e-Books, Motivational Articles & Stories Books and Launched many Audio & Video Programs. BUY NOW complete LIVE Video Lectures Contact: +91 9212373738 +91 9268373738 E-mail: [email protected] [email protected] Website: www.vijayadarsh.com www.staylearning.com Join us on Facebook: https://www.facebook.com/VijayAdarshIndia
Views: 62985 StayLearning
Big Data Analytics Lectures : Hamming Distance  with solved Example in Hindi
 
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In this videos we have explain hamming distance with solved example. Hamming code is used for secured bit transfer and it is of great use in big data analytics . This is used to measure the distance between strings made of bits(0 and 1) AND also please have a look at the distance measures video before watching this in Big data analysis series in hindi videos credit goes to : Atharva bda notes form : https://goo.gl/Ti9CQj introduction to Hadoop : https://goo.gl/LCHC7Q Introduction to Hadoop part 2 : https://goo.gl/jSSxu2 Distance Measures : https://goo.gl/1NL3qF Euclidean Distance : https://goo.gl/6C16RJ Jaccard distance : https://goo.gl/C6vmWR Cosine Distance : https://goo.gl/Sm48Ny Edit Distance : https://goo.gl/dG3jAP Hamming Distance : https://goo.gl/KNw95L FM Flajolit martin Algorithm : https://goo.gl/ybjX9V Random Sampling Algorithm : https://goo.gl/YW1AWh PCY ( park chen yu) algorithm : https://goo.gl/HVWs21 Collaborative Filtering : https://goo.gl/GBQ7JW Bloom Filter Basic concept : https://goo.gl/uHjX5B Naive Bayes Classifier : https://goo.gl/dbRYYh Naive Bayes Classifier part2 : https://goo.gl/LWstNv Decision Tree : https://goo.gl/5m8JhA Apriori Algorithm :https://goo.gl/mmpxL6 FP TREE Algorithm : https://goo.gl/S29yV8 Agglomerative clustering algorithmn : https://goo.gl/L9nGu8 Hubs and Authority and Hits Algorithm : https://goo.gl/D2EdFG Betweenness Centrality : https://goo.gl/czZZJR
Views: 2386 Last moment tuitions
Learn Predictive Modeling Techniques Without Programming and Do Data Mining With IBM SPSS Modeler
 
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http://bit.ly/LearnIBMSPSSModeler Learn Predictive Modeling Techniques Without Programming and How To Do Data Mining With IBM SPSS Modeler. IBM SPSS Modeler is a data mining workbench that helps you build predictive models quickly and intuitively, without programming. Analysts typically use SPSS Modeler to analyze data by doing data mining and then deploying models. Overview: This course introduces students to data mining and to the functionality available within IBM SPSS Modeler. The series of stand-alone videos, are designed to introduce students to specific nodes or data mining topics. Each video consists of detailed instructions explaining why we are using a technique, in what situations it is used, how to set it up, and how to interpret the results. This course is broken up into phases. The Introduction to Data Mining Phase is designed to get you up to speed on the idea of data mining. You will also learn about the CRISP-DM methodology which will serve as a guide throughout the course and you will also learn how to navigate within Modeler. The Data Understanding Phase addresses the need to understand what your data resources are and the characteristics of those resources. We will discuss how to read data into Modeler. We will also focus on describing, exploring, and assessing data quality. The Data Preparation Phase discusses how to integrate and construct data. While the Modeling Phase will focus on building a predictive model. The Evaluation Phase focuses how to take your data mining results so that you can achieve your business objectives. And finally the Deployment Phase allows you to do something with your findings. What are the requirements? This course is for anyone that would like to learn how to use IBM SPSS Modeler. This course is for anyone that would like to learn how to do Data Mining. No statistical or data mining background is necessary. What are you going to get from this course? Over 22 lectures and 4 hours of content! Data Mining and Advanced Analytics Defined Modeling Methods in Modeler CRISP-DM Overview General Modeler Orientation Reading Data Assessing Data Quality Integrating Data Constructing Data Modeling Evaluation Deployment What is the target audience? This course is for anyone that would like to learn how to use IBM SPSS Modeler. This course is for anyone that would like to learn how to do Data Mining. Enroll "IBM SPSS Modeler: Getting Started" Course Here: http://bit.ly/LearnIBMSPSSModeler For More Video Uploads In The Future Please Subscribe To This Youtube Channel by Clicking Subscribe Button Below! and Don't Forget To Giving Your "LIKE" For This Video! Please Click LIKE Button Below! Thanks For Watching The Video! See You Next Time! Note: All The Links In The Video Description Are Affiliate Links, So I Can Make Money If Visitor Purchase The Products!
CAREERS IN CSE –COMPUTER SCIENCE ENGINEERING,GATE,Software Jobs,MBA,MTech
 
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CSE-COMPUTER SCIENCE ENGINEERING CAREERS. Go through the career opportunities of CSE, Govt jobs and Employment News channel from Freshersworld.com – The No.1 job portal for freshers in India. Visit http://www.freshersworld.com for detailed job information, campus recruitment GATE notification, GATE pattern, higher education details of CSE- COMPUTER SCIENCE ENGINEERING. Computer Science engineering deals with design, implementation, management of information system of both software and hardware processes. Computer Science engineers are involved in developing the software applications, testing and debugging the code , deployment of software and designing and modification of the component. They use different software to store and manage data in a secured manner. Every computer science engineer will have a basic knowledge about the programming languages like c,c++,java, python etc.. ,they will have problem solving skills, logical skills, data mining, knowledge in artificial intelligence, different algorithms and current technologies which are existing and used by the enterprises. Apart from this there are various domain specifications as well, like ruby on rails, agile, php, etc. Thus Computer science engineers after completion of the degree, has got engrossing and challenging oppurtunities available when compared to the other fields of engineering. One can work in database management, IT, embedded systems, Telecommunication, computer hardware & software implementation & maintenance, multimedia, web designing, gaming, and almost all other industries in this sector. It is worthwhile to note that the computer industry has witnessed such phenomenal growth in recent years that IT majors like Infosys & TCS have been the major recruiters across all other branches throughout the engineering colleges in the country. • When it comes to job, we have competitive exams like GATE exam which provide a CTC upto 8.00+lacs and also other firms like TCS • Infosys • Wipro • HCL • Accenture • Cognizant • Microsoft • IBM • Adobe • Google • Accenture • Cisco • Oracle • Sun Microsystems • Yahoo • Tech Mahindra • Mahindra Satyam • Toshiba, • Amazon, etc,.recruits a huge number of employees every year with eye-catching pacakages. (masters in computer science ) Those who are interested in pursuing higher ducation after bachelor’s have vast choices like Msc in data mining, artificial intelligence, distribution system, computer design, human-computer interaction, visual computing, software development, robot designing etc,. The degree will be worth for those who have a desire towards programming and other cutting edge technologies. Visit Preparation and placement tips for IT jobs at: http://placement.freshersworld.com?src=Youtube For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
Ugc net computer science December 2018 syllabus
 
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How to prepare for ugc net computer science December 2018 aper II Syllabus 1. Discrete Structures Sets, Relations, Functions. Pigeonhole Principle, Inclusion-Exclusion Principle, Equivalence and Partial Orderings, Elementary Counting Techniques, Probability. Measure (s) for information and Mutual information. Computability: Models of computation-Finite Automata, Pushdown Automata, Non – determinism and NFA, DPDA and PDAs and Languages accepted by these structures. Grammars, Languages, Non – computability and Examples of non – computable problems. Graph : Definition, walks, paths, trails, connected graphs, regular and bipartite graphs, cycles and circuits. Tree and rooted tree. Spanning trees. Eccentricity of a vertex radius and diameter of a graph. Central Graphs. Centres of a tree. Hamiltonian and Eulerian graphs, Planar graphs. Groups : Finite fields and Error correcting / detecting codes. 2. Computer Arithmetic Propositional (Boolean) Logic, Predicate Logic, Well – formed – formulae (WFF), Satisfiability and Tautology. Logic Families: TTL, ECL and C – MOS gates. Boolean algebra and Minimization of Boolean functions. Flip-flops – types, race condition and comparison. Design of combinational and sequential circuits. Representation of Integers : Octal, Hex, Decimal, and Binary. 2′s complement and 1′s complement arithmetic. Floating point representation. 3. Programming in C and C++ Programming in C: Elements of C – Tokens, identifiers, data types in C. Control structures in C. Sequence, selection and iteration(s). Structured data types in C-arrays, struct, union, string, and pointers. O – O Programming Concepts: Class, object, instantiation. Inheritance, polymorphism and overloading. C++ Programming: Elements of C++ – Tokens, identifiers. Variables and constants, Datatypes, Operators, Control statements. Functions parameter passing. Class and objects. Constructors and destructors. Overloading, Inheritance, Templates, Exception handling. 4. Relational Database Design and SQL E-R diagrams and their transformation to relational design, normalization – INF, 2NF, 3NF, BCNF and 4NF. Limitations of 4NF and BCNF. SQL: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL) commands. Database objects like-Views, indexes, sequences, synonyms, data dictionary. 5. Data and File structures Data, Information, Definition of data structure. Arrays, stacks, queues, linked lists, trees, graphs, priority queues and heaps. File Structures: Fields, records and files. Sequential, direct, index-sequential and relative files. Hashing, inverted lists and multi – lists. B trees and B+ trees. 6. Computer Networks Network fundamentals : Local Area Networks (LAN), Metropolitan Area Networks (MAN), Wide Area Networks (WAN), Wireless Networks, Inter Networks. Reference Models: The OSI model, TCP / IP model. Data Communication: Channel capacity. Transmission media-twisted pair, coaxial cables, fibre – optic cables, wireless transmission-radio, microwave, infrared and millimeter waves. Lightwave transmission. Thelephones – local loop, trunks, multiplexing, switching, narrowband ISDN, broadband ISDN, ATM, High speed LANS. Cellular Radio. Communication satellites-geosynchronous and low-orbit. Internetworking: Switch / Hub, Bridge, Router, Gateways, Concatenated virtual circuits, Tunnelling, Fragmentation, Firewalls. Routing: Virtual circuits and datagrams. Routing algorithms. Conjestion control. Network Security: Cryptography-public key, secret key. Domain Name System ( DNS ) – Electronic Mail and Worldwide Web ( WWW ). The DNS, Resource Records, Name servers. E-mail-architecture and Serves. 7. System Software and Compilers Assembly language fundamentals ( 8085 based assembly language programming ). Assemblers-2-pass and single-pass. Macros and macroprocessors. Loading, linking, relocation, program relocatability. Linkage editing. Text editors. Programming Environments. Debuggers and program generators. Compilation and Interpretation. Bootstrap compilers. Phases of compilation process. Lexical analysis. Lex package on Unix system. Context free grammars. Parsing and parse trees. Representation of parse ( derivation ) trees as rightmost and leftmost derivations. Bottom up parsers-shift-reduce, Main concepts in Geographical Information System (GIS), E – cash, E – Business, ERP packages. Data Warehousing: Data Warehouse environment, architecture of a data warehouse methodology, analysis, design, construction and administration. Data Mining: Windows Programming: Introduction to Windows programming – Win32, Microsoft Foundation Classes (MFC), Documents and views, Resources, Message handling in windows. Simple Applications (in windows) : Scrolling, splitting views, docking toolbars, status bars, common dialogs. Advanced Windows Programming:
Views: 5827 Nisha Mittal
DataScience@NIH: Current State, Future Directions
 
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* Note: Video may display green background for about 1 minute and disappears for the remainder of the video. We apologize for any inconvenience. Dr. Patricia Flatley Brennan, National Library of Medicine Director and Interim Associate NIH Director for Data Science presents webinar, "[email protected]: Current State, Future Directions." Webinar Description NIH has a strong commitment to data science and supporting discovery through data. In January 2017, as Phil Bourne left to assume an academic post, Patti Brennan was appointed interim Associate Director for Data Science at NIH. The BD2K program direction moved to DPCPSI and the remainder of the ADDS office work, including inter-governmental collaborations, communications, policy, and training moved to the Data Science Coordinating Unit at the National Library of Medicine. In this lecture Brennan will provide an update of data science efforts at NIH, a summary of infrastructure investments designed to ensure that data are FAIR and discovery from data accelerate, and a vision for how the NLM will become the hub of data science at NIH. For more information, visit: https://datascience.nih.gov/blog View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JURHQ5VlBva0daRGs About Our speaker Patricia Flatley Brennan, RN, PhD, is the Director of the National Library of Medicine (NLM). The NLM is the world’s largest biomedical library and the producer of digital information services used by scientists, health professionals and members of the public worldwide. She assumed the directorship in August 2016. In January 2017, with the transition of the trans-NIH data science initiatives to NLM, as recommended by the NLM Working Group Report to the NIH Director, Dr. Brennan also assumed the role of NIH Interim Associate Director for Data Science (ADDS). Dr. Brennan came to NIH from the University of Wisconsin-Madison, where she was the Lillian L. Moehlman Bascom Professor at the School of Nursing and College of Engineering. She also led the Living Environments Laboratory at the Wisconsin Institutes for Discovery, which develops new ways for effective visualization of high dimensional data. Dr. Brennan is a pioneer in the development of information systems for patients. She developed ComputerLink, an electronic network designed to reduce isolation and improve self-care among home care patients. She directed HeartCare, a web-based information and communication service that helps home-dwelling cardiac patients recover faster, and with fewer symptoms. She also directed Project HealthDesign, an initiative designed to stimulate the next generation of personal health records. Dr. Brennan has also conducted external evaluations of health information technology architectures and worked to repurpose engineering methods for health care. She received a master of science in nursing from the University of Pennsylvania and a PhD in industrial engineering from the University of Wisconsin-Madison. Following seven years of clinical practice in critical care nursing and psychiatric nursing, Dr. Brennan held several academic positions at Marquette University, Milwaukee; Case Western Reserve University, Cleveland; and the University of Wisconsin-Madison. A past president of the American Medical Informatics Association, Dr. Brennan was elected to the Institute of Medicine of the National Academy of Sciences (now the National Academy of Medicine) in 2001. She is a fellow of the American Academy of Nursing, the American College of Medical Informatics, and the New York Academy of Medicine. Visit our website to view archived videos covering various topics in data science: bigdatau.org/data-science-seminars
Philip Evans: How data will transform business
 
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What does the future of business look like? In an informative talk, Philip Evans gives a quick primer on two long-standing theories in strategy — and explains why he thinks they are essentially invalid. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
Views: 222520 TED
Lecture 1: Introduction to Information Theory
 
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Lecture 1 of the Course on Information Theory, Pattern Recognition, and Neural Networks. Produced by: David MacKay (University of Cambridge) Author: David MacKay, University of Cambridge A series of sixteen lectures covering the core of the book "Information Theory, Inference, and Learning Algorithms" (Cambridge University Press, 2003, http://www.inference.eng.cam.ac.uk/mackay/itila/) which can be bought at Amazon (http://www.amazon.co.uk/exec/obidos/ASIN/0521642981/davidmackay0f-21), and is available free online (http://www.inference.eng.cam.ac.uk/mackay/itila/). A subset of these lectures used to constitute a Part III Physics course at the University of Cambridge. The high-resolution videos and all other course material can be downloaded from the Cambridge course website (http://www.inference.eng.cam.ac.uk/mackay/itprnn/). Snapshots of the lecture can be found here: http://www.inference.eng.cam.ac.uk/itprnn_lectures/ These lectures are also available at http://videolectures.net/course_information_theory_pattern_recognition/ (synchronized with snapshots and slides)
Views: 130066 Jakob Foerster
Proteus modem - Secure and Resilient technology for military, government and business users
 
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Supporting efficient delivery of resilient, secure services and is inherently flexible to meet today’s and tomorrow’s needs. Designed for fixed, land mobile, airborne and maritime platforms, Proteus delivers maximum bandwidth efficiency and data throughput while offering robust protection against threats and interception.
What is Assembler and Assembly Language (in Hindi)
 
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please fill the form for notes :https://goo.gl/kKb4gK to buy the full hand made notes email us at email : [email protected] Full course : https://goo.gl/S9FYDQ Topicwise: Compiler Design Introduction Lecture : https://goo.gl/QWUHLE Assembler and Assembly Language : https://goo.gl/MGrJZc Assembly language statement : https://bit.ly/2G6y9MC https://bit.ly/2G6y9MC : https://bit.ly/2ujoQDt Flow chart of two pass assembler : https://goo.gl/TWLNP8 Macros and Macroprocessors : https://goo.gl/8v39jo Macro vs Subroutine : https://goo.gl/iVhwuw Macros pass 1 and pass 2 flowchart : https://goo.gl/vDAhUw Phases of compiler : https://goo.gl/H4VR9y Eliminate left recursion and left factoring : https://goo.gl/q4HNPE How to Find First and Follow Basics : https://goo.gl/2GKYXT First and Follow solved example : https://goo.gl/cFJm72 Predictive Parser : https://goo.gl/THRXME Predictive Parser part 2 : https://goo.gl/GNM4uG Recursive Descent parser : https://goo.gl/CNCvQ2 Operator Precedence Parser : https://goo.gl/7pSj2Z Operator Precedence Parser part 2 : https://goo.gl/UkGFDn LR Parsing | LR (0) item : https://goo.gl/Uc8RFn SLR (1) parsing : https://goo.gl/2Xk5es Examples of LR(0) or SLR(1) : https://goo.gl/nUjH4R DAG(direct acyclic graph) : https://goo.gl/GVw8Co Editor Basics with Architecture : https://goo.gl/E2ovsA LEX tool full basic concept : https://goo.gl/MKQiP4 Yacc (Yet another compiler compiler ) : https://goo.gl/aX8JPi VIVA: spcc basic concept:https://goo.gl/6nuhJx Forward reference problem and compiler:https://goo.gl/p7o4ts first and follow:https://goo.gl/vSdWkf More videos coming soon so Subscribe kark rakho
Views: 105093 Last moment tuitions
[Re]Form: New Investigations in Urban Form, Panel 1
 
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Panel 1: Reconstructing the Agency of Form Panelists: Pier Vittorio Aureli, Neyran Turan Moderator: Charles Waldheim Introduction by Mohsen Mostafavi Re]FORM: New Investigations in Urban Form aims to reposition the discourse of urban form within contemporary urban theory. Inspired by Henri Lefebvre’s seminal text Urban Form (1970), this conference aspires to provoke new investigations and debates on urban form and its relevance in contemporary urbanizations. Specifically, by acknowledging the fluctuating conditions of political, economic, and technological contexts and practices, this conference interrogates the meanings of “form” and its role in contemporary urbanization through the agency of the design disciplines, and gauges such discourses against the debates on urban theory situated within contemporary urban conditions and transitions. ​ Almost half a century has passed since the publication of Urban Form by Henri Lefebvre, the urban, as he avers, is — “a place of encounter, assembly, simultaneity” — which constitutes a logical form and embodies a kind of knowledge, has been vastly defined through geographical, political and economic substances and practices ever since. Particularly in the past three decades, emerging technologies have drastically challenged the idea of urban and its form ranging in scale from the planetary to the individual. The unprecedented urban complex generally prioritizes the practices over its agencies and thus obscures the logics of the urban upon which it operates. The process of contemporary urbanization produces radical phenomena and urban objects, yet prioritizing urban form as a key instrument for contemporary urbanization remains an elusive notion. Recovering the epistemological agency of urban form is a pressing matter.
Views: 769 Harvard GSD