Home
Search results “Data reduction in data mining wikipedia english”
What is DATA REDUCTION? What does DATA REDUCTION mean? DATA REDUCTION meaning & explanation
 
02:36
What is DATA REDUCTION? What does DATA REDUCTION mean? DATA REDUCTION meaning - DATA REDUCTION definition - DATA REDUCTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. When information is derived from instrument readings there may also be a transformation from analog to digital form. When the data are already in digital form the 'reduction' of the data typically involves some editing, scaling, coding, sorting, collating, and producing tabular summaries. When the observations are discrete but the underlying phenomenon is continuous then smoothing and interpolation are often needed. Often the data reduction is undertaken in the presence of reading or measurement errors. Some idea of the nature of these errors is needed before the most likely value may be determined. An example in astronomy is the data reduction in the Kepler satellite. This satellite records 95-megapixel images once every six seconds, generating tens of megabytes of data per second, which is orders of magnitudes more than the downlink bandwidth of 550 KBps. The on-board data reduction encompasses co-adding the raw frames for thirty minutes, reducing the bandwidth by a factor of 300. Furthermore, interesting targets are pre-selected and only the relevant pixels are processed, which is 6% of the total. This reduced data is then sent to Earth where it is processed further. Research has also been carried out on the use of data reduction in wearable (wireless) devices for health monitoring and diagnosis applications. For example, in the context of epilepsy diagnosis, data reduction has been used to increase the battery lifetime of a wearable EEG device by selecting, and only transmitting, EEG data that is relevant for diagnosis and discarding background activity.
Views: 385 The Audiopedia
What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean?
 
03:33
What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean? EVOLUTIONARY DATA MINING meaning - EVOLUTIONARY DATA MINING definition - EVOLUTIONARY DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes." For instance, a banking institution might want to predict whether a customer's credit would be "good" or "bad" based on their age, income and current savings. Evolutionary algorithms for data mining work by creating a series of random rules to be checked against a training dataset. The rules which most closely fit the data are selected and are mutated. The process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm. Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned, which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results. If data comes from more than one database, they can be integrated, or combined, at this point. When dealing with large datasets, it might be beneficial to also reduce the amount of data being handled. One common method of data reduction works by getting a normalized sample of data from the database, resulting in much faster, yet statistically equivalent results. At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset. The training dataset will be used to let rules evolve which match it closely. The test dataset will then either confirm or deny these rules. Evolutionary algorithms work by trying to emulate natural evolution. First, a random series of "rules" are set on the training dataset, which try to generalize the data into formulas. The rules are checked, and the ones that fit the data best are kept, the rules that do not fit the data are discarded. The rules that were kept are then mutated, and multiplied to create new rules. This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible. When this rule is obtained, it is then checked against the test dataset. If the rule still matches the data, then the rule is valid and is kept. If it does not match the data, then it is discarded and the process begins by selecting random rules again.
Views: 61 The Audiopedia
What is a Data Science In English - Data Science demo by Balaji -vlr-9059868766  Machine Learning
 
02:08:19
What is a Data Science In English - demo by Balaji -Vlr Training 905986876 Kukatpally - Hyderabad Venkat:9059868766 Jio :7013158918 For Data Science Course content http://www.sivaitsoft.com/data-science-online-training-kukatpally/ FaceBookPage: https://www.facebook.com/DataScience-Training-Kukatpally-829603420550121/ What is a Data Science Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.Data science - Wikipedia DATA SCIENCE (I)Introduction to Data Science and Python 1. Python Basics with Anaconda 2. Files and Loops 3. Booleans and If Statements 4. Files loops and Condition Logics with Application Example 5. List Operations, Dictionaries 6. Introduction to Functions 7. Debugging Errors 8. Project: Exploring US Date Births 9. Modules, Classes 10. Error Handling 11. List Comprehensions 12. Project: Modules, Classes, Error Handling, List Comprehensions by Using NFL Suspension Data 13. Variable Scopes 14. Regular Expressions 15. Dates in Python 16. Project: Exploring Gun Deaths in US (II) Data Analysis and Visualization 1. Getting Started with Numpy 2. Computation with Numpy 3. Introduction to Pandas 4. Data Manipulation with Pandas 5. Working with Missing Data 6. Project: Summarizing Data 7. Pandas Internal Series 8. Data Frames in Pandas 9. Project: Analyzing Thanks Giving Dinner 10. Project: Finding Patterns in Crime Exploratory Data Visualization 11. Line Charts 12. Multiple Plots 13. Bar Plots and Scatter Plots 14. Histograms and Box Plots 15. Project: Visualizing Earnings based on college Majors Story Telling Through Visualization 16. Improving Plot Aesthitics 17. Color Layout and Annotations 18. Project: Visualizing Gender Gaps in Colleges 19. Conditional Plots 20. Project:Visualizing Geographical Data (III) Data Cleaning 1. Data Cleaning Walkthrough 2. Data Cleaning Walkthrough Combining the data 3. Analyzing and Visualizing the Data 4. Project: Analyzing NYC High School Data 5. Project: Star Wars Survey (IV) Working with Data Sources 1. APIS and Web Scrapping (I) Working with APIS (II) Intermediate APIS (III) Working with REDDIT API (IV) Web Scrapping 2. SQL Fundamentals (I) Introduction to SQL (II) Summary Statistics (III) Group Summary Statistics (IV) Querying SQLITE from Python (V) Project: Analyzing CIA Facebook Data Using SQLITE and Python 3. SQL Intermediate (I) Modifying Data (II) Table Schemas (III) Database Normalization and Relations (IV) Postgre SQL and Installation 4. Advanced SQL (i) Indexing and Multicolumn Indexing (ii) Project: Analyzing Basketball data (V) Statistics and Probability 1. Introduction to Statistics 2. Standard Deviation and Correlation 3. Linear Regression 4. Distributions and Sampling 5. Project: Analyzing Movie Reviews 6. Introduction to Probability 7. Calculating Probabilities 8. Probability Distributions 9. Significance Testing 10. Chi Squared Test 11. Multi Category Chi Squared Test 12. Project: Wining Jeopardy (VI) Machine Learning 1. Machine Learning Fundamentals 2. Introduction to KNN 3. Evaluating Model Performances 4. Multivariate KNN 5. Hyper Parameter Optimization 6. Cross Validation 7. Project: Predicting Car Prices 8. Calculus for Machine Learning 9. Understanding Extreme points, limits and Linear & Nonlinear Functions 10. Linear Algebra (Linear Systems, Matrices, vectors, Solution Sets) 11. Linear Regression Model 12. Feature Selection 13. Gradient Descent 14. Ordinary Least Squares 15. Processing and Transforming Features 16. Project:Predicting House sales Prices 17. Logistic Regression 18. Evaluating Binary Classifiers 19. Multiclass Classification 20. Intermediate Linear Regression 21. Overfitting 22. Clustering Basics 23. K-Means Clustering 24. Gradient Descent 25. Introduction to Neural Networks 26. Project: Predicting the Stock Market 27. Introduction to Decision Trees 28. Building, Applying Decision Trees 29. Introduction to Random Forest 30. Project: Predicting Bike Rentals Machine Learning Projects 1. Data Cleaning 2. Preparing Features 3. Making Predictions 4. Sentiment Analysis (VII) Spark and Map Reduce 1. Introduction to Spark 2. Spark integration with Jupyter 3. Transformations and Actions 4. Spark Data Frames 5. Spark SQL (VIII) Building a Capstone Project ----------------------------------- data science tutorial
Views: 1056 VLR Training
What is TENDER NOTIFICATION? What does TENDER NOTIFICATION mean? TENDER NOTIFICATION meaning
 
03:07
What is TENDER NOTIFICATION? What does TENDER NOTIFICATION mean? TENDER NOTIFICATION meaning - TENDER NOTIFICATION definition - TENDER NOTIFICATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A tender notification is an online service, which in more recent times is provided through the delivery model software as a service. Historically, the service would be provided by basic coding techniques in PHP code, when a new tender had been published. Since then, the industry has grown to provide fully automated systems that deliver various forms of communication to notify users of tendering opportunities. Typically, services are delivered in the form of an e-mail and are commonly for open tenders, which allow any potential supplier to register interest in a tender opportunity. Some may argue that notification services have become integral to open tenders and the process. Notification services are often the main form of communication to the client that a new tender is available. People closely linked with the providing end user, may receive communication directly, but with the growth of the notification industry, this is becoming unlikely. Procurement software sometimes incorporates the tendering data into packages to make the information more accessible for suppliers interested in various tenders. Many of the direct notification packages often have a targeted market or segment, often from one or two providers, for example a county council or large government institution. Repacks allow for greater numbers of tenders and often cover multiple countries, segments and markets. A request for tender and request for quotation is a closed tender where people are invited by a buyer to quote for specific work. A tender notification alerts potential suppliers of open tenders that they then have to register interest in before entering the tendering process. Invitation to tender is also a similar process to a tender notification. The major difference is the institution or organisation who created the tender chooses who to invite, often in the form of a closed tender. Tender notification services provide a vast array of people and companies about an open tender that anyone can apply for. Tender notifications (sometimes called tender alerts), provide the client with given tender information that they desire. This is often delivered in the form of an email notification, saving the client visiting multiple websites to check for updates on potential clients. Most repacks provide both private and public sector tender opportunities. The idea is that tender notification systems deliver tender opportunities to the company, dramatically reducing the amount of time spent looking for these tenders.
Views: 2450 The Audiopedia
Weka Text Classification for First Time & Beginner Users
 
59:21
59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 129207 Brandon Weinberg
What is HYBRID ALGORITHM? What does HYBRID ALGORITHM mean? HYBRID ALGORITHM meaning & explanation
 
04:44
What is HYBRID ALGORITHM? What does HYBRID ALGORITHM mean? HYBRID ALGORITHM meaning - HYBRID ALGORITHM definition - HYBRID ALGORITHM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A hybrid algorithm is an algorithm that combines two or more other algorithms that solve the same problem, either choosing one (depending on the data), or switching between them over the course of the algorithm. This is generally done to combine desired features of each, so that the overall algorithm is better than the individual components. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem – many algorithms can be considered as combinations of simpler pieces – but only to combining algorithms that solve the same problem, but differ in other characteristics, notably performance. In computer science, hybrid algorithms are very common in optimized real-world implementations of recursive algorithms, particularly implementations of divide and conquer or decrease and conquer algorithms, where the size of the data decreases as one moves deeper in the recursion. In this case, one algorithm is used for the overall approach (on large data), but deep in the recursion, it switches to a different algorithm, which is more efficient on small data. A common example is in sorting algorithms, where the insertion sort, which is inefficient on large data, but very efficient on small data (say, five to ten elements), is used as the final step, after primarily applying another algorithm, such as merge sort or quicksort. Merge sort and quicksort are asymptotically optimal on large data, but the overhead becomes significant if applying them to small data, hence the use of a different algorithm at the end of the recursion. A highly optimized hybrid sorting algorithm is Timsort, which combines merge sort, insertion sort, together with additional logic (including binary search) in the merging logic. A general procedure for a simple hybrid recursive algorithm is short-circuiting the base case, also known as arm's-length recursion. In this case whether the next step will result in the base case is checked before the function call, avoiding an unnecessary function call. For example, in a tree, rather than recursing to a child node and then checking if it is null, checking null before recursing. This is useful for efficiency when the algorithm usually encounters the base case many times, as in many tree algorithms, but is otherwise considered poor style, particularly in academia, due to the added complexity. Another example of hybrid algorithms for performance reasons are introsort and introselect, which combine one algorithm for fast average performance, falling back on another algorithm to ensure (asymptotically) optimal worst-case performance. Introsort begins with a quicksort, but switches to a heap sort if quicksort is not progressing well; analogously introselect begins with quickselect, but switches to median of medians if quickselect is not progressing well. Centralized distributed algorithms can often be considered as hybrid algorithms, consisting of an individual algorithm (run on each distributed processor), and a combining algorithm (run on a centralized distributor) – these correspond respectively to running the entire algorithm on one processor, or running the entire computation on the distributor, combining trivial results (a one-element data set from each processor). A basic example of these algorithms are distribution sorts, particularly used for external sorting, which divide the data into separate subsets, sort the subsets, and then combine the subsets into totally sorted data; examples include bucket sort and flashsort. However, in general distributed algorithms need not be hybrid algorithms, as individual algorithms or combining or communication algorithms may be solving different problems. For example, in models such as MapReduce, the Map and Reduce step solve different problems, and are combined to solve a different, third problem.
Views: 544 The Audiopedia
What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning
 
02:26
What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning - INFORMATION RETRIEVAL definition - INFORMATION RETRIEVAL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. Automated information retrieval systems are used to reduce what has been called "information overload". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications. An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy. An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching. Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.
Views: 8825 The Audiopedia
What is DATA INTEGRATION? What does DATA INTEGRATION mean? DATA INTEGRATION meaning & explanation
 
05:47
What is DATA INTEGRATION? What does DATA INTEGRATION mean? DATA INTEGRATION meaning - DATA INTEGRATION definition - DATA INTEGRATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Consider a web application where a user can query a variety of information about cities (such as crime statistics, weather, hotels, demographics, etc.). Traditionally, the information must be stored in a single database with a single schema. But any single enterprise would find information of this breadth somewhat difficult and expensive to collect. Even if the resources exist to gather the data, it would likely duplicate data in existing crime databases, weather websites, and census data. A data-integration solution may address this problem by considering these external resources as materialized views over a virtual mediated schema, resulting in "virtual data integration". This means application-developers construct a virtual schema—the mediated schema—to best model the kinds of answers their users want. Next, they design "wrappers" or adapters for each data source, such as the crime database and weather website. These adapters simply transform the local query results (those returned by the respective websites or databases) into an easily processed form for the data integration solution (see figure 2). When an application-user queries the mediated schema, the data-integration solution transforms this query into appropriate queries over the respective data sources. Finally, the virtual database combines the results of these queries into the answer to the user's query. This solution offers the convenience of adding new sources by simply constructing an adapter or an application software blade for them. It contrasts with ETL systems or with a single database solution, which require manual integration of entire new dataset into the system. The virtual ETL solutions leverage virtual mediated schema to implement data harmonization; whereby the data are copied from the designated "master" source to the defined targets, field by field. Advanced data virtualization is also built on the concept of object-oriented modeling in order to construct virtual mediated schema or virtual metadata repository, using hub and spoke architecture. Each data source is disparate and as such is not designed to support reliable joins between data sources. Therefore, data virtualization as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of this lack of data value commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate. One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases. ....
Views: 3321 The Audiopedia
What is DECORRELATION? What does DECORRELATION mean? DECORRELATION meaning & explanation
 
02:46
What is DECORRELATION? What does DECORRELATION mean? DECORRELATION meaning &- DECORRELATION pronunciation - DECORRELATION definition - DECORRELATION explanation - How to pronounce DECORRELATION? Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Decorrelation is a general term for any process that is used to reduce autocorrelation within a signal, or cross-correlation within a set of signals, while preserving other aspects of the signal. A frequently used method of decorrelation is the use of a matched linear filter to reduce the autocorrelation of a signal as far as possible. Since the minimum possible autocorrelation for a given signal energy is achieved by equalising the power spectrum of the signal to be similar to that of a white noise signal, this is often referred to as signal whitening. Although most decorrelation algorithms are linear, non-linear decorrelation algorithms also exist. Many data compression algorithms incorporate a decorrelation stage. For example, many transform coders first apply a fixed linear transformation that would, on average, have the effect of decorrelating a typical signal of the class to be coded, prior to any later processing. This is typically a Karhunen-Loeve transform, or a simplified approximation such as the discrete cosine transform. By comparison, sub-band coders do not generally have an explicit decorrelation step, but instead exploit the already-existing reduced correlation within each of the sub-bands of the signal, due to the relative flatness of each sub-band of the power spectrum in many classes of signals. Linear predictive coders can be modeled as an attempt to decorrelate signals by subtracting the best possible linear prediction from the input signal, leaving a whitened residual signal. Decorrelation techniques can also be used for many other purposes, such as reducing crosstalk in a multi-channel signal, or in the design of echo cancellers. In image processing decorrelation techniques can be used to enhance or stretch, colour differences found in each pixel of an image. This is generally termed as 'decorrelation stretching'. The concept of decorrelation can be applied in many other fields. In neuroscience, decorrelation is used in the analysis of the neural networks in the human visual system. In cryptography, it is used in cipher design (see Decorrelation theory) and in the design of hardware random number generators.
Views: 160 The Audiopedia
What is DATA PROCESSING? What does DATA PROCESSING mean? DATA PROCESSING meaning & explanation
 
03:07
What is DATA PROCESSING? What does DATA PROCESSING mean? DATA PROCESSING meaning - DATA PROCESSING definition - DATA PROCESSING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data processing is, broadly, "the collection and manipulation of items of data to produce meaningful information's." In this sense it can be considered a subset of information processing, "the change (processing) of information in any manner detectable by an observer." The term is often used more specifically in the context of a business or other organization to refer to the class of commercial data processing applications. Data processing may involve various processes, including: Validation – Ensuring that supplied data is "clean, correct and useful"; Sorting – "arranging items in some sequence and/or in different sets."; Summarization – reducing detail data to its main points.; Aggregation – combining multiple pieces of data.; Analysis – the "collection, organization, analysis, interpretation and presentation of data.".; Reporting – list detail or summary data or computed information; Classification – separates data into various categories. Although widespread use of the term data processing dates only from the nineteen-fifties data processing functions have been performed manually for millennia. For example, bookkeeping involves functions such as posting transactions and producing reports like the balance sheet and the cash flow statement. Completely manual methods were augmented by the application of mechanical or electronic calculators. A person whose job was to perform calculations manually or using a calculator was called a "computer." The term automatic data processing was applied to operations performed by means of unit record equipment, such as Herman Hollerith's application of punched card equipment for the 1890 United States Census. "Using Hollerith's punchcard equipment, the Census Office was able to complete tabulating most of the 1890 census data in 2 to 3 years, compared with 7 to 8 years for the 1880 census.... It is also estimated that using Herman Hollerith's system saved some $5 million in processing costs" (in 1890 dollars) even with twice as many questions as in 1880. Computerized data processing, or Electronic data processing represents the further evolution, with the computer taking the place of several independent pieces of equipment. The Census Bureau first made limited use of electronic computers for the 1950 United States Census, using a UNIVAC I system, delivered in 1952.
Views: 17173 The Audiopedia
What is T-CLOSENESS? What does T-CLOSENESS mean? T-CLOSENESS meaning, definition & explanation
 
03:18
What is T-CLOSENESS? What does T-CLOSENESS mean? T-CLOSENESS meaning - T-CLOSENESS definition - T-CLOSENESS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ t-closeness is a further refinement of l-diversity group based anonymization that is used to preserve privacy in data sets by reducing the granularity of a data representation. This reduction is a trade off that results in some loss of effectiveness of data management or mining algorithms in order to gain some privacy. The t-closeness model extends the l-diversity model by treating the values of an attribute distinctly by taking into account the distribution of data values for that attribute. This is useful because in real data sets attribute values may be skewed or semantically similar. However, accounting for value distributions may cause difficulty in creating feasible l-diverse representations. The l-diversity technique is useful in that it may hinder an attacker leveraging the global distribution of an attribute's data values in order to infer information about sensitive data values. Not every value may exhibit equal sensitivity, for example, a rare positive indicator for a disease may provide more information than a common negative indicator. Because of examples like this, l-diversity may be difficult and unnecessary to achieve when protecting against attribute disclosure. Alternatively, sensitive information leaks may occur because while l-diversity requirement ensures “diversity” of sensitive values in each group, it does not recognize that values may be semantically close, for example, an attacker could deduce a stomach disease applies to an individual if a sample containing the individual only listed three different stomach diseases. Given the existence of such attacks where sensitive attributes may be inferred based upon the distribution of values for l-diverse data, the t-closeness method was created to further l-diversity by additionally maintaining the distribution of sensitive fields. The original paper by Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian defines t-closeness as: The t-closeness Principle: An equivalence class is said to have t-closeness if the distance between the distribution of a sensitive attribute in this class and the distribution of the attribute in the whole table is no more than a threshold t. A table is said to have t-closeness if all equivalence classes have t-closeness. Charu Aggarwal and Philip S. Yu further state in their book on privacy-preserving data miningthat with this definition, threshold t gives an upper bound on the difference between the distribution of the sensitive attribute values within an anonymized group as compared to the global distribution of values. They also state that for numeric attributes, using t-closeness anonymization is more effective than many other privacy-preserving data mining methods.
Views: 153 The Audiopedia
Who Is A Statistician?
 
01:02
Most people move on to other jobs if they have more than 20 years' experience in this fieldStatistician wikipediaprospects. Googleusercontent search. Uk job profiles statistician url? Q webcache. Your career american statistical associationbecome a statistician. Statistician job description, duties and jobs part 1. It is common to combine statistical knowledge with expertise in other subjects, and statisticians may work as employees or consultants use methods collect analyze data help solve real world problems business, engineering, healthcare, fields it, looking for patterns that explain behaviour describe the it. Statistician wikipedia statistician job profile. Discover what it takes to be a statistician. What fields employ statisticians? World of statistics. The profession exists in both the private and public sectors. Statistician wikipediaprospects. Education and career roadmap study. Use our career test report to the average salary for a statistician is rs 368,671 per year. They design and build models using data one advantage of working in statistics is that you can combine your interest with almost any other field science, technology, or business, such as agriculture statisticians apply statistical theories methods to collect, analyze interpret quantitative. Although they work mostly in offices, may statisticians use statistical methods to collect and analyze data help solve real world problems business, engineering, healthcare, or other fields typical employers key skills a statistician gathers numerical then displays it, helping companies make sense of the famous mathematician john tukey once told colleague, 'the best thing about being is that you get play everyone's responsible for collation, evaluation, interpretation presentation quantitative. Statistician salary, job description, skills, specializations. Statisticians provide insights, recommendations, and be reduced to 'a statistician is one versed in the science of collecting andthis content downloaded from 66. Statisticians bureau of labor statisticsstatistician jobs, salary, job satisfaction us news moneywhat is a statistician? Jstor. Find out expected salary, working hours, qualifications and more a statistician is someone who works with theoretical or applied statistics. 75 on tue, 25 apr 2017 19 19 22 utc 1 jun 2012 gerald (gerry) hahn and necip doganaksoy discuss the traits needed to be a successful statistician. Statistician salary (india) payscale. How to become a statistician what does do? Sokanu. These comments are abridged from you also will have access to resources such as fellowships and grants, external funding sources, ethical guidelines for statisticians, salary information statisticians contribute society in many ways, protecting endangered species managing the impacts of climate change making medicines more prospective students searching become a statistician found following relevant useful 6 may 2016 professional is person who involved gathering quantitative
What is SELF-ORGANIZING MAP? What does SELF-ORGANIZING MAP mean? SELF-ORGANIZING MAP meaning
 
03:36
What is SELF-ORGANIZING MAP? What does SELF-ORGANIZING MAP mean? SELF-ORGANIZING MAP meaning - SELF-ORGANIZING MAP definition - SELF-ORGANIZING MAP explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A self-organizing map (SOM) or self-organising feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction. Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning (such as backpropagation with gradient descent), and in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The artificial neural network introduced by the Finnish professor Teuvo Kohonen in the 1980s is sometimes called a Kohonen map or network. The Kohonen net is a computationally convenient abstraction building on work on biologically neural models from the 1970s and morphogenesis models dating back to Alan Turing in the 1950s. Like most artificial neural networks, SOMs operate in two modes: training and mapping. "Training" builds the map using input examples (a competitive process, also called vector quantization), while "mapping" automatically classifies a new input vector. A self-organizing map consists of components called nodes or neurons. Associated with each node are a weight vector of the same dimension as the input data vectors, and a position in the map space. The usual arrangement of nodes is a two-dimensional regular spacing in a hexagonal or rectangular grid. The self-organizing map describes a mapping from a higher-dimensional input space to a lower-dimensional map space. The procedure for placing a vector from data space onto the map is to find the node with the closest (smallest distance metric) weight vector to the data space vector. While it is typical to consider this type of network structure as related to feedforward networks where the nodes are visualized as being attached, this type of architecture is fundamentally different in arrangement and motivation. Useful extensions include using toroidal grids where opposite edges are connected and using large numbers of nodes. It has been shown that while self-organizing maps with a small number of nodes behave in a way that is similar to K-means, larger self-organizing maps rearrange data in a way that is fundamentally topological in character. It is also common to use the U-Matrix. The U-Matrix value of a particular node is the average distance between the node's weight vector and that of its closest neighbors. In a square grid, for instance, we might consider the closest 4 or 8 nodes (the Von Neumann and Moore neighborhoods, respectively), or six nodes in a hexagonal grid. Large SOMs display emergent properties. In maps consisting of thousands of nodes, it is possible to perform cluster operations on the map itself.
Views: 5659 The Audiopedia
What is DATA CLEANSING? What does DATA CLEANSING mean? DATA CLEANSING meaning & explanation
 
12:07
What is DATA CLEANSING? What does DATA CLEANSING mean? DATA CLEANSING meaning - DATA CLEANSING definition - DATA CLEANSING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities. The validation may be strict (such as rejecting any address that does not have a valid postal code) or fuzzy (such as correcting records that partially match existing, known records). Some data cleansing solutions will clean data by cross checking with a validated data set. A common data cleansing practice is data enhancement, where data is made more complete by adding related information. For example, appending addresses with any phone numbers related to that address. Data cleansing may also involve activities like, harmonization of data, and standardization of data. For example, harmonization of short codes (st, rd, etc.) to actual words (street, road, etcetera). Standardization of data is a means of changing a reference data set to a new standard, ex, use of standard codes. Administratively, incorrect or inconsistent data can lead to false conclusions and misdirected investments on both public and private scales. For instance, the government may want to analyze population census figures to decide which regions require further spending and investment on infrastructure and services. In this case, it will be important to have access to reliable data to avoid erroneous fiscal decisions. In the business world, incorrect data can be costly. Many companies use customer information databases that record data like contact information, addresses, and preferences. For instance, if the addresses are inconsistent, the company will suffer the cost of resending mail or even losing customers. The profession of forensic accounting and fraud investigating uses data cleansing in preparing its data and is typically done before data is sent to a data warehouse for further investigation. There are packages available so you can cleanse/wash address data while you enter it into your system. This is normally done via an application programming interface (API)......
Views: 3137 The Audiopedia
What is DATA AGGREGATION? What does DATA AGGREGATION mean? DATA AGGREGATION meaning & explanation
 
06:23
What is DATA AGGREGATION? What does DATA AGGREGATION mean? DATA AGGREGATION meaning - DATA AGGREGATION definition - DATA AGGREGATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data aggregation is the compiling of information from databases with intent to prepare combined datasets for data processing. The source information for data aggregation may originate from public records and criminal databases. The information is packaged into aggregate reports and then sold to businesses, as well as to local, state, and government agencies. This information can also be useful for marketing purposes. In the United States, many data brokers' activities fall under the Fair Credit Reporting Act (FCRA) which regulates consumer reporting agencies. The agencies then gather and package personal information into consumer reports that are sold to creditors, employers, insurers, and other businesses. Various reports of information are provided by database aggregators. Individuals may request their own consumer reports which contain basic biographical information such as name, date of birth, current address, and phone number. Employee background check reports, which contain highly detailed information such as past addresses and length of residence, professional licenses, and criminal history, may be requested by eligible and qualified third parties. Not only can this data be used in employee background checks, but it may also be used to make decisions about insurance coverage, pricing, and law enforcement. Privacy activists argue that database aggregators can provide erroneous information. The potential of the Internet to consolidate and manipulate information has a new application in data aggregation, also known as screen scraping. The Internet gives users the opportunity to consolidate their usernames and passwords, or PINs. Such consolidation enables consumers to access a wide variety of PIN-protected websites containing personal information by using one master PIN on a single website. Online account providers include financial institutions, stockbrokers, airline and frequent flyer and other reward programs, and e-mail accounts. Data aggregators can gather account or other information from designated websites by using account holders' PINs, and then making the users' account information available to them at a single website operated by the aggregator at an account holder's request. Aggregation services may be offered on a standalone basis or in conjunction with other financial services, such as portfolio tracking and bill payment provided by a specialized website, or as an additional service to augment the online presence of an enterprise established beyond the virtual world. Many established companies with an Internet presence appear to recognize the value of offering an aggregation service to enhance other web-based services and attract visitors. Offering a data aggregation service to a website may be attractive because of the potential that it will frequently draw users of the service to the hosting website. When it comes to compiling location information on local businesses, there are several major data aggregators that collect information such as the business name, address, phone number, website, description and hours of operation. They then validate this information using various validation methods. Once the business information has been verified to be accurate, the data aggregators make it available to publishers like Google and Yelp. When Yelp, for example, goes to update their Yelp listings, they will pull data from these local data aggregators. Publishers take local business data from different sources and compare it to what they currently have in their database. They then update their database it with what information they deem accurate. Financial institutions are concerned about the possibility of liability arising from data aggregation activities, potential security problems, infringement on intellectual property rights and the possibility of diminishing traffic to the institution's website. The aggregator and financial institution may agree on a data feed arrangement activated on the customer's request, using an Open Financial Exchange (OFX) standard to request and deliver information to the site selected by the customer as the place from which they will view their account data. Agreements provide an opportunity for institutions to negotiate to protect their customers' interests and offer aggregators the opportunity to provide a robust service.
Views: 2306 The Audiopedia
What is DATA EXTRACTION? What does DATA EXTRACTION mean? DATA EXTRACTION meaning & explanation
 
02:46
What is DATA EXTRACTION? What does DATA EXTRACTION mean? DATA EXTRACTION meaning - DATA EXTRACTION definition - DATA EXTRACTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow. Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring or recording devices. Today's electronic devices will usually present an electrical connector (e.g. USB) through which 'raw data' can be streamed into a personal computer. Typical unstructured data sources include web pages, emails, documents, PDFs, scanned text, mainframe reports, spool files, classifieds, etc. Which is further used for sales / marketing leads. Extracting data from these unstructured sources has grown into a considerable technical challenge where as historically data extraction has had to deal with changes in physical hardware formats, the majority of current data extraction deals with extracting data from these unstructured data sources, and from different software formats. This growing process of data extraction from the web is referred to as Web scraping. The act of adding structure to unstructured data takes a number of forms Using text pattern matching such as regular expressions to identify small or large-scale structure e.g. records in a report and their associated data from headers and footers; Using a table-based approach to identify common sections within a limited domain e.g. in emailed resumes, identifying skills, previous work experience, qualifications etc. using a standard set of commonly used headings (these would differ from language to language), e.g. Education might be found under Education/Qualification/Courses; Using text analytics to attempt to understand the text and link it to other information.
Views: 237 The Audiopedia
Symmetric Key and Public Key Encryption
 
06:45
Modern day encryption is performed in two different ways. Check out http://YouTube.com/ITFreeTraining or http://itfreetraining.com for more of our always free training videos. Using the same key or using a pair of keys called the public and private keys. This video looks at how these systems work and how they can be used together to perform encryption. Download the PDF handout http://itfreetraining.com/Handouts/Ce... Encryption Types Encryption is the process of scrambling data so it cannot be read without a decryption key. Encryption prevents data being read by a 3rd party if it is intercepted by a 3rd party. The two encryption methods that are used today are symmetric and public key encryption. Symmetric Key Symmetric key encryption uses the same key to encrypt data as decrypt data. This is generally quite fast when compared with public key encryption. In order to protect the data, the key needs to be secured. If a 3rd party was able to gain access to the key, they could decrypt any data that was encrypt with that data. For this reason, a secure channel is required to transfer the key if you need to transfer data between two points. For example, if you encrypted data on a CD and mail it to another party, the key must also be transferred to the second party so that they can decrypt the data. This is often done using e-mail or the telephone. In a lot of cases, sending the data using one method and the key using another method is enough to protect the data as an attacker would need to get both in order to decrypt the data. Public Key Encryption This method of encryption uses two keys. One key is used to encrypt data and the other key is used to decrypt data. The advantage of this is that the public key can be downloaded by anyone. Anyone with the public key can encrypt data that can only be decrypted using a private key. This means the public key does not need to be secured. The private key does need to be keep in a safe place. The advantage of using such a system is the private key is not required by the other party to perform encryption. Since the private key does not need to be transferred to the second party there is no risk of the private key being intercepted by a 3rd party. Public Key encryption is slower when compared with symmetric key so it is not always suitable for every application. The math used is complex but to put it simply it uses the modulus or remainder operator. For example, if you wanted to solve X mod 5 = 2, the possible solutions would be 2, 7, 12 and so on. The private key provides additional information which allows the problem to be solved easily. The math is more complex and uses much larger numbers than this but basically public and private key encryption rely on the modulus operator to work. Combing The Two There are two reasons you want to combine the two. The first is that often communication will be broken into two steps. Key exchange and data exchange. For key exchange, to protect the key used in data exchange it is often encrypted using public key encryption. Although slower than symmetric key encryption, this method ensures the key cannot accessed by a 3rd party while being transferred. Since the key has been transferred using a secure channel, a symmetric key can be used for data exchange. In some cases, data exchange may be done using public key encryption. If this is the case, often the data exchange will be done using a small key size to reduce the processing time. The second reason that both may be used is when a symmetric key is used and the key needs to be provided to multiple users. For example, if you are using encryption file system (EFS) this allows multiple users to access the same file, which includes recovery users. In order to make this possible, multiple copies of the same key are stored in the file and protected from being read by encrypting it with the public key of each user that requires access. References "Public-key cryptography" http://en.wikipedia.org/wiki/Public-k... "Encryption" http://en.wikipedia.org/wiki/Encryption
Views: 419699 itfreetraining
What is PLANT BREEDING? What does PLANT BREEDING mean? PLANT BREEDING meaning & explanation
 
05:11
What is PLANT BREEDING? What does PLANT BREEDING mean? PLANT BREEDING meaning - PLANT BREEDING definition - PLANT BREEDING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Plant breeding is the art and science of changing the traits of plants in order to produce desired characteristics. Plant breeding can be accomplished through many different techniques ranging from simply selecting plants with desirable characteristics for propagation, to more complex molecular techniques (see cultigen and cultivar). Plant breeding has been practiced for thousands of years, since near the beginning of human civilization. It is practiced worldwide by individuals such as gardeners and farmers, or by professional plant breeders employed by organizations such as government institutions, universities, crop-specific industry associations or research centers. International development nation agencies believe that breeding new crops is important for ensuring food security by developing new varieties that are higher-yielding, disease resistant, drought-resistant or regionally adapted to different environments and growing conditions. Modern plant breeding, whether classical or through genetic engineering, comes with issues of concern, particularly with regard to food crops. The question of whether breeding can have a negative effect on nutritional value is central in this respect. Although relatively little direct research in this area has been done, there are scientific indications that, by favoring certain aspects of a plant's development, other aspects may be retarded. A study published in the Journal of the American College of Nutrition in 2004, entitled Changes in USDA Food Composition Data for 43 Garden Crops, 1950 to 1999, compared nutritional analysis of vegetables done in 1950 and in 1999, and found substantial decreases in six of 13 nutrients measured, including 6% of protein and 38% of riboflavin. Reductions in calcium, phosphorus, iron and ascorbic acid were also found. The study, conducted at the Biochemical Institute, University of Texas at Austin, concluded in summary: "We suggest that any real declines are generally most easily explained by changes in cultivated varieties between 1950 and 1999, in which there may be trade-offs between yield and nutrient content." The debate surrounding genetically modified food during the 1990s peaked in 1999 in terms of media coverage and risk perception, and continues today - for example, "Germany has thrown its weight behind a growing European mutiny over genetically modified crops by banning the planting of a widely grown pest-resistant corn variety." The debate encompasses the ecological impact of genetically modified plants, the safety of genetically modified food and concepts used for safety evaluation like substantial equivalence. Such concerns are not new to plant breeding. Most countries have regulatory processes in place to help ensure that new crop varieties entering the marketplace are both safe and meet farmers' needs. Examples include variety registration, seed schemes, regulatory authorizations for GM plants, etc. Plant breeders' rights is also a major and controversial issue. Today, production of new varieties is dominated by commercial plant breeders, who seek to protect their work and collect royalties through national and international agreements based in intellectual property rights. The range of related issues is complex. In the simplest terms, critics of the increasingly restrictive regulations argue that, through a combination of technical and economic pressures, commercial breeders are reducing biodiversity and significantly constraining individuals (such as farmers) from developing and trading seed on a regional level. Efforts to strengthen breeders' rights, for example, by lengthening periods of variety protection, are ongoing. When new plant breeds or cultivars are bred, they must be maintained and propagated. Some plants are propagated by asexual means while others are propagated by seeds. Seed propagated cultivars require specific control over seed source and production procedures to maintain the integrity of the plant breeds results. Isolation is necessary to prevent cross contamination with related plants or the mixing of seeds after harvesting. Isolation is normally accomplished by planting distance but in certain crops, plants are enclosed in greenhouses or cages (most commonly used when producing F1 hybrids.)
Views: 11013 The Audiopedia
What Is Tree Pruning In Data Mining
 
00:46
Study of various decision tree pruning methods semantic scholar. 31 dec 2015 i understood what is decision trees and how it works with help of sunil sir's i couldnt understand what is pruning and how to do it in decision trees. I am however slightly uncertain in exactly how cv is used when pruning decision 4 mar 2016 lecture 77 (optional) tree algorithm and full of visualizations illustrations these techniques will behave on real data. Data training_imp_var,method there are several approaches to avoiding overfitting in building decision trees. Decision trees (part ii pruning the tree) ismll. What is tree pruning in data mining? Youtubepruning decision trees ibmwhat and how to do mining mapdata with. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections tree provide little power to classify instances data mining induction learn simple and easy steps pruning performed order remove anomalies training 8 jul 2017. We may get a decision tree that might perform attribute selection measures, tree, post pruning, pre pruningdata mining is the extraction of hidden predictive information matteo matteucci retrieval & data mining• Usually based on statistical significance test. Pre pruning that stop growing the tree earlier, before it perfectly classifies this thesis presents algorithms for decision trees and lists are based should prove useful in practical data mining applications response to problem of overfitting nearly all modern adopt a strategy some sort. Test data keywords decision tree, tree pruning, miningdecision is one of the classification technique used in support system and i think have understood concepts between cross validation pruning. Wikipedia wiki pruning_(decision_trees)&sa u&ved 0ahukewjmxtqoi fvahutr48khypnaloqfggjmae&usg afqjcnhlpev_pbfseaco7iybewg5c15a3w"pruning (decision trees) wikipedia. Pruning (decision trees) wikipedia. Rpart rpart(promotion_name. Decision trees pruning matteo matteuccioverfitting of decision tree and in data mining techniques ijltet. Googleusercontent search. Data mining pruning (a decision tree, rules) [gerardnico]. Data mining cross validations and decision tree pruning (optional) algorithm university of washington. Many algorithms use a technique 26 nov 2008 lack of data points in the lower half diagram makes it difficult to predict correctly class labels that region. Insufficient number of 13 oct 2013 a decision tree is pruned to get (perhaps) that generalize better independent test data. Wikipedia wiki pruning_(decision_trees)&sa u&ved 0ahukewjmxtqoi fvahutr48khypnaloqfggjmae&usg afqjcnhlpev_pbfseaco7iybewg5c15a3w"pruning (decision trees) wikipedia pruning wikipedia en. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections tree provide little power to classify instances. • Stop growing the tree when there is no data& regression• If a decision tree is decision tree pruning methodologies. D
Views: 95 Question Tags
9 Poisonous Plants You Might Have Around Your House
 
10:49
Houseplants can be great for your mental health, but eating some of them can be far worse for your bodily health than you might think. Hosted by: Stefan Chin ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, alexander wadsworth, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Bader AlGhamdi, James Harshaw, Patrick D. Ashmore, Candy, Tim Curwick, charles george, Saul, Mark Terrio-Cameron, Viraansh Bhanushali, Kevin Bealer, Philippe von Bergen, Chris Peters, Justin Lentz ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://abcnews.go.com/Technology/JustOneThing/potentially-harmful-house-plants/story?id=8589505 https://aapcc.s3.amazonaws.com/pdfs/annual_reports/12_21_2017_2016_Annua.pdf https://jamanetwork.com/journals/jama/article-abstract/666830?redirect=true http://bjo.bmj.com/content/bjophthalmol/79/1/98.full.pdf https://herbaria.plants.ox.ac.uk/bol/plants400/profiles/CD/Dieffenbachia https://onlinelibrary.wiley.com/doi/full/10.3732/ajb.0800276 http://news.bbc.co.uk/2/hi/uk_news/england/suffolk/8031344.stm https://www.ncbi.nlm.nih.gov/pubmed/21626407 http://www.jbc.org/content/261/2/505.abstract http://bioweb.uwlax.edu/bio203/s2013/cook_dani/facts.htm https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3089829/ https://monarchlab.org/biology-and-research/biology-and-natural-history/breeding-life-cycle/interactions-with-milkweed/ https://www.ncbi.nlm.nih.gov/pubmed/23674099 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4005357/ https://www.sciencedirect.com/science/article/pii/S0735109785804587?via%3Dihub https://www.poison.org/articles/2015-mar/azaleas-and-rhododendrons https://modernfarmer.com/2014/09/strange-history-hallucinogenic-mad-honey/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404272/ http://mentalfloss.com/article/28967/how-poisonous-lily-valley http://circ.ahajournals.org/content/125/8/1053 https://www.cambridge.org/core/journals/british-journal-of-nutrition/article/biological-action-of-saponins-in-animal-systems-a-review/9FF0990F2A7AEFE5B990555A0D4A63B8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928447/ http://www.cbif.gc.ca/eng/species-bank/canadian-poisonous-plants-information-system/all-plants-common-name/hydrangea/?id=1370403267136 http://extoxnet.orst.edu/faqs/natural/cya.htm https://www.ncbi.nlm.nih.gov/pubmed/10669009 https://www.health.ny.gov/environmental/emergency/chemical_terrorism/cyanide_tech.htm http://www.ucmp.berkeley.edu/seedplants/cycadophyta/cycads.html https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/cycasin https://www.ncbi.nlm.nih.gov/pubmed/1620343?dopt=Abstract https://www.sciencedirect.com/science/article/pii/0027510772902060 https://www.britannica.com/science/alkylating-agent https://www.ncbi.nlm.nih.gov/pubmed/18490618 https://www.snopes.com/fact-check/poinsetting-it-out/ http://www.pbs.org/wnet/secrets/umbrella-assassin-clues-evidence/1552/ http://www.uvm.edu/safety/lab/biological-toxins https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925711/ https://www.poison.org/articles/2015-dec/mistletoe https://www.sciencedirect.com/science/article/pii/S0005273698000248 http://jab.zsf.jcu.cz//1_3/patockastreda.pdf https://www.sciencedirect.com/science/article/pii/S0041010113000664 http://www.cbif.gc.ca/eng/species-bank/canadian-poisonous-plants-information-system/all-plants-common-name/cyclamen/?id=1370403267099 https://plants.ces.ncsu.edu/plants/all/cyclamen-persicum/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2928447/ https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/triterpenoid-saponin http://www.rachellaudan.com/2008/10/stuffed-cyclamen-and-bread-and-oil-mediterranean-island-links.html Images: https://en.wikipedia.org/wiki/File:Dieffenbachia_houseplant.jpg https://en.wikipedia.org/wiki/File:NarcissusPaperwhite01.jpg https://bit.ly/2tGnu1Y https://bit.ly/2Iucx96 https://bit.ly/2lDxyFs https://bit.ly/2lze6tp https://bit.ly/2N2lHgP https://en.wikipedia.org/wiki/File:Hydrangea-flower.jpg https://en.wikipedia.org/wiki/File:CycadKingSago.jpg https://en.wikipedia.org/wiki/File:Mistleltoe_in_Lebanon.JPG https://en.wikipedia.org/wiki/File:Cyclamen-Marth_04,_2007.JPG https://en.wikipedia.org/wiki/File:Cyclamen_abchasicum.jpg https://en.wikipedia.org/wiki/File:Poinsettia_varieties.JPG https://en.wikipedia.org/wiki/File:PoinsettiaVenation.jpg https://en.wikipedia.org/wiki/File:Euphorbia_rhizophora2_ies.jpg https://commons.wikimedia.org/wiki/File:Dieffenbachia_picta.jpg
Views: 153136 SciShow
What is HUMAN SPEECHOME PROJECT? What does HUMAN SPEECHOME PROJECT mean?
 
02:41
What is HUMAN SPEECHOME PROJECT? What does HUMAN SPEECHOME PROJECT mean? HUMAN SPEECHOME PROJECT meaning - HUMAN SPEECHOME PROJECT definition - HUMAN SPEECHOME PROJECT explanation. SUBSCRIBE to our Google Earth flights channel - http://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ?sub_confirmation=1 Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. The Human Speechome Project is an effort to closely observe and model the language acquisition of a child over the first three years of life. The project was conducted at the Massachusetts Institute of Technology's Media Laboratory by the Associate Professor Deb Roy with an array of technology that is used to comprehensively but unobtrusively observe a single child – Roy's own son – with the resulting data being used to create computational models to yield further insight into language acquisition. Most studies of human speech acquisition in children have been done in laboratory settings and with sampling rates of only a couple of hours per week. The need for studies in the more natural setting of the child's home, and at a much higher sampling rate approaching the child's total experience, led to the development of this project concept. “ Just as the Human Genome Project illuminates the innate genetic code that shapes us, the Speechome project is an important first step toward creating a map of how the environment shapes human development and learning. Frank Moss, director of the Media Lab ” A digital network consisting of eleven video cameras, fourteen microphones, and an array of data capture hardware was installed in the home of the subject. A cluster of ten computers and audio samplers is located in the basement of the house to capture the data. Data from the cluster is moved manually to the MIT campus as necessary for storage in a one-million-gigabyte (one-petabyte) storage facility. To provide control of the observation system to the occupants of the house, eight touch-activated displays were wall-mounted throughout the house to allow for stopping and starting video and or audio recording, and also erase any number of minutes permanently from the system. Audio recording was turned off throughout the house at night after the child was asleep. Data was gathered at an average rate of 200 gigabytes per day, necessitating the development of sophisticated data-mining tools to reduce analysis efforts to a manageable level, and transcribing significant speech added a labor-intensive dimension.
Views: 8 The Audiopedia
What is FIELD SERVICE MANAGEMENT? What does FIELD SERVICE MANAGEMENT mean?
 
05:18
What is FIELD SERVICE MANAGEMENT? What does FIELD SERVICE MANAGEMENT mean? FIELD SERVICE MANAGEMENT meaning - FIELD SERVICE MANAGEMENT definition - FIELD SERVICE MANAGEMENT explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Field service management (FSM) refers to the management of a company's resources employed at or en route to the property of clients, rather than on company property. Examples include locating vehicles, managing worker activity, scheduling and dispatching work, ensuring driver safety, and integrating the management of such activities with inventory, billing, accounting and other back-office systems. FSM most commonly refers to companies who need to manage installation, service or repairs of systems or equipment. It can also refer to software and cloud-based platforms that aid in field service management. Field service management is used to manage resources in several industries. 1. In telecommunications and cable industry, technicians who install cable or run phone lines into residences or business establishments. 2. In healthcare, mobile nurses who provide in-home care for elderly or disabled. 3. In gas utilities, engineers who are dispatched to investigate and repair suspected leaks. 4. In heavy engineering, mining, industrial and manufacturing, technicians dispatched for preventative maintenance and repair. 5. In property maintenance, including landscaping, irrigation, and home and office cleaning. 6. In HVAC industry, technicians have the expertise and equipment to investigate units in residential, commercial and industrial environments. Field service management must meet certain requirements: 1. Customer expectations: Customers expect that their service should not be disrupted, and should be immediately restored 2. Underutilized equipment: Expensive industrial equipment in mining or oil and gas can cost millions when sitting idle 3. Low employee productivity: Managers are unable to monitor field employees, which may reduce productivity 4. Safety: Safety of drivers and vehicles on the road and while on the job site is a concern both for individuals and their employers 5. Cost: Rising cost of fuel, vehicle maintenance, and parts inventory 6. Service to sales: Increasingly, companies expect their services department to generate revenues. 7. Dynamic environment: Continuously balancing between critical tickets, irate customers, productive employees and optimized routes makes scheduling, routing and dispatching very challenging 8. Data and technology: Many a times, the data for analytics is missing, stale or inaccurate. FSM software has significantly evolved in the past 10 years, however the market for FSM software remains fragmented. The software can be deployed both on-premises or as a hosted or cloud-based system. Typically, FSM software is integrated with backend systems such as service management, billing, accounting, parts inventory and other HR systems. The large majority of FSM companies are fee-for-service and offer differing features and functionality that vary from one company to the next. Whereas one company will provide most, if not all, of the desirable features in field service management, another will be missing one or up to several functions. Pricing is dependent on several factors: a company's size, business needs, number of users, carrier selection and planned data usage. Some popular fee structures are pay-per-franchise, pay-per-use/administrators, and pay-per-field technician/employee. Costs can range from $20.00 per month for an unbundled solution that does not include carrier data charges to upwards of $200.00. It is not uncommon, although not always the case, for there to be other fees incurred with the use of the FSM platform; namely, fees for software, extra technical support, and additional training. For the enterprise market, Gartner estimates that market penetration for field service applications has reached 25% of the addressable market. Software sales in the FSM market can only be approximated. Gartner research puts the revenue for packaged field service dispatch and workforce management software applications, not including service revenue, at approximately $1.2 billion in 2012, with a compound annual growth rate of 12.7%.
Views: 70 The Audiopedia
The Real Meaning of E=mc² | Space Time | PBS Digital Studios
 
10:24
Want to ask some sort of crazy question about Space?: Tweet at us! @pbsspacetime Facebook: facebook.com/pbsspacetime Email us! pbsspacetime [at] gmail [dot] com Comment on Reddit: http://www.reddit.com/r/pbsspacetime Support us on Patreon! http://www.patreon.com/pbsspacetime Help translate our videos! http://www.youtube.com/timedtext_cs_panel?tab=2&c=UC7_gcs09iThXybpVgjHZ_7g Let us know what topics you want to learn more about: http://bit.ly/spacetimepoll You’ve probably known OF E=mc² since you were born, and were also probably told that it meant that it proved Mass equaled Energy, or something along those lines. BUT WAIT. Was E=mc² explained to you properly? Mass equalling energy is mostly true, but E=mc² actually describes a much more interesting, and frankly mind-blowing aspect of reality that likely wasn’t covered in your high school physics class. Join Gabe on this week’s episode of PBS Space Time he discusses THE TRUE MEANING OF E=mc² Extra Credit: Einstein's 1905 E=mc^2 paper (English translation): http://einsteinpapers.press.princeton.edu/vol2-trans/188 http://www.astro.puc.cl/~rparra/tools/PAPERS/e_mc2.pdf (more modern notation) Veritasium: Your Mass is NOT From the Higgs Boson https://www.youtube.com/watch?v=Ztc6QPNUqls ----------------------------------------­­­­­­­­­­­------------------------------­- Comments: Ryan Brown https://www.youtube.com/watch?v=w5TSfjvzMGs&lc=z12sirbrxxfbjh4da22hzjggxpr1f1emr David Shi https://www.youtube.com/watch?v=w5TSfjvzMGs&lc=z134itjxxtvbtzaiv04cjjlgyojxgjqhleg UndamagedLlama2 https://www.youtube.com/watch?v=w5TSfjvzMGs&lc=z122z3rp0zaghfxxx04cfzfyomftexpqcdk Jay Perrin https://www.youtube.com/watch?v=w5TSfjvzMGs&lc=z12mxzzawve2dxfgj04ccljhxyj2exto4qw0k jancultis https://www.youtube.com/watch?v=w5TSfjvzMGs&lc=z12zjjvjswjbfnttd22mxhro3pjry5unj Music: Movement 3 - Janne Hanhisuanto (https://soundcloud.com/jannehanhisuanto) miracle - slow (http://www.restingbell.net/releases/r) Secret Society - Logical Disorder (http://logicaldisorder.bandcamp.com/) Saw Slicing - Patternbased (https://soundcloud.com/patternbased) Dr Dreidel - Patternbased (https://soundcloud.com/patternbased) Earth Breath - Human Terminal (http://freemusicarchive.org/music/Human_Terminal/Press_Any_Key/01_Earth_Breath) Pinball Beat - Patternbased (https://soundcloud.com/patternbased) Heisse Luft - Thompson and Kuhl (https://soundcloud.com/phlow/05-x-com) New SpaceTime episodes every Wednesday! Hosted by Gabe Perez-Giz Made by Kornhaber Brown (www.kornhaberbrown.com)
Views: 2957742 PBS Space Time
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
 
11:51
So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 328604 CrashCourse
K-means clustering: how it works
 
07:35
Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 420785 Victor Lavrenko
What is DEEP PACKET INSPECTION? What does DEEP PACKET INSPECTION mean?
 
08:49
What is DEEP PACKET INSPECTION? What does DEEP PACKET INSPECTION mean? DEEP PACKET INSPECTION meaning - DEEP PACKET INSPECTION definition - DEEP PACKET INSPECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Deep packet inspection (DPI, also called complete packet inspection and information extraction or IX) is a form of computer network packet filtering that examines the data part (and possibly also the header) of a packet as it passes an inspection point, searching for protocol non-compliance, viruses, spam, intrusions, or defined criteria to decide whether the packet may pass or if it needs to be routed to a different destination, or, for the purpose of collecting statistical information that functions at the Application layer of the OSI (Open Systems Interconnection model). There are multiple headers for IP packets; network equipment only needs to use the first of these (the IP header) for normal operation, but use of the second header (such as TCP or UDP) is normally considered to be shallow packet inspection (usually called stateful packet inspection) despite this definition. There are multiple ways to acquire packets for deep packet inspection. Using port mirroring (sometimes called Span Port) is a very common way, as well as an optical splitter. Deep Packet Inspection (and filtering) enables advanced network management, user service, and security functions as well as internet data mining, eavesdropping, and internet censorship. Although DPI technology has been used for Internet management for many years, some advocates of net neutrality fear that the technology may be used anticompetitively or to reduce the openness of the Internet. DPI is used in a wide range of applications, at the so-called "enterprise" level (corporations and larger institutions), in telecommunications service providers, and in governments. DPI combines the functionality of an intrusion detection system (IDS) and an Intrusion prevention system (IPS) with a traditional stateful firewall. This combination makes it possible to detect certain attacks that neither the IDS/IPS nor the stateful firewall can catch on their own. Stateful firewalls, while able to see the beginning and end of a packet flow, cannot catch events on their own that would be out of bounds for a particular application. While IDSs are able to detect intrusions, they have very little capability in blocking such an attack. DPIs are used to prevent attacks from viruses and worms at wire speeds. More specifically, DPI can be effective against buffer overflow attacks, denial-of-service attacks (DoS), sophisticated intrusions, and a small percentage of worms that fit within a single packet. DPI-enabled devices have the ability to look at Layer 2 and beyond Layer 3 of the OSI model. In some cases, DPI can be invoked to look through Layer 2-7 of the OSI model. This includes headers and data protocol structures as well as the payload of the message. DPI functionality is invoked when a device looks or takes other action, based on information beyond Layer 3 of the OSI model. DPI can identify and classify traffic based on a signature database that includes information extracted from the data part of a packet, allowing finer control than classification based only on header information. End points can utilize encryption and obfuscation techniques to evade DPI actions in many cases. A classified packet may be redirected, marked/tagged (see quality of service), blocked, rate limited, and of course, reported to a reporting agent in the network. In this way, HTTP errors of different classifications may be identified and forwarded for analysis. Many DPI devices can identify packet flows (rather than packet-by-packet analysis), allowing control actions based on accumulated flow information. ...
Views: 1794 The Audiopedia
Nuclear Energy
 
09:06
025 - Nuclear Energy In this video Paul Andersen explains how nuclear energy is released during fission of radioactive uranium. Light water reactors, nuclear waste, and nuclear accidents are also discussed along with the future of nuclear energy. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: Delphi234. (2014). English: History of nuclear power in the world. Data is from IAEA and EIA. Retrieved from https://commons.wikimedia.org/wiki/File:Nuclear_power_history.svg DOE. ([object HTMLTableCellElement]). English: Spent fuel pool at a nuclear power plant. http://www.ocrwm.doe.gov/curriculum/unit1/lesson3reading.shtml. Retrieved from https://commons.wikimedia.org/wiki/File:Fuel_pool.jpg File:Chernobyl Disaster.jpg. (2014, April 30). In Wikipedia, the free encyclopedia. Retrieved from https://en.wikipedia.org/w/index.php?title=File:Chernobyl_Disaster.jpg&oldid=606437678 Globe, D. (2011). English: The Fukushima I Nuclear Power Plant after the 2011 Tōhoku earthquake and tsunami. Reactor 1 to 4 from right to left. Retrieved from https://commons.wikimedia.org/wiki/File:Fukushima_I_by_Digital_Globe.jpg lightningBy ZaWertun. (n.d.). Retrieved from https://openclipart.org/detail/190134/lightning Spoon, S. (2011). English: en:International Nuclear Event Scale. Retrieved from https://commons.wikimedia.org/wiki/File:INES_en.svg UK, C. R. (2014). Diagram showing a lobectomy of the thyroid gland. Retrieved from https://commons.wikimedia.org/wiki/File:Diagram_showing_a_lobectomy_of_the_thyroid_gland_CRUK_067.svg Z22. (2014). English: The unit 2 of Three Mile Island Nuclear Generating Station closed since the accident in 1979. The cooling towers on the left. Retrieved from https://commons.wikimedia.org/wiki/File:Three_Mile_Island_Nuclear_Generating_Station_Unit_2.jpg
Views: 56538 Bozeman Science
Bitcoin: How Cryptocurrencies Work
 
09:25
Whether or not it's worth investing in, the math behind Bitcoin is an elegant solution to some complex problems. Hosted by: Michael Aranda Special Thanks: Dalton Hubble Learn more about Cryptography: https://www.youtube.com/watch?v=-yFZGF8FHSg ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters—we couldn't make SciShow without them! Shout out to Bella Nash, Kevin Bealer, Mark Terrio-Cameron, Patrick Merrithew, Charles Southerland, Fatima Iqbal, Benny, Kyle Anderson, Tim Curwick, Will and Sonja Marple, Philippe von Bergen, Bryce Daifuku, Chris Peters, Patrick D. Ashmore, Charles George, Bader AlGhamdi ---------- Like SciShow? Want to help support us, and also get things to put on your walls, cover your torso and hold your liquids? Check out our awesome products over at DFTBA Records: http://dftba.com/scishow ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://bitinfocharts.com/ https://chrispacia.wordpress.com/2013/09/02/bitcoin-mining-explained-like-youre-five-part-2-mechanics/ https://www.youtube.com/watch?v=Lx9zgZCMqXE https://www.youtube.com/watch?v=nQZUi24TrdI https://bitcoin.org/en/how-it-works http://www.forbes.com/sites/investopedia/2013/08/01/how-bitcoin-works/#36bd8b2d25ee http://www.makeuseof.com/tag/how-does-bitcoin-work/ https://blockchain.info/charts/total-bitcoins https://en.bitcoin.it/wiki/Controlled_supply https://www.bitcoinmining.com/ http://bitamplify.com/mobile/?a=news Image Sources: https://commons.wikimedia.org/wiki/File:Cryptocurrency_Mining_Farm.jpg
Views: 2491199 SciShow
What Is Tree Pruning In Data Mining?
 
00:47
Study of various decision tree pruning methods semantic scholar. 31 dec 2015 i understood what is decision trees and how it works with help of sunil sir's i couldnt understand what is pruning and how to do it in decision trees. I am however slightly uncertain in exactly how cv is used when pruning decision 4 mar 2016 lecture 77 (optional) tree algorithm and full of visualizations illustrations these techniques will behave on real data. Data training_imp_var,method there are several approaches to avoiding overfitting in building decision trees. Decision trees (part ii pruning the tree) ismll. What is tree pruning in data mining? Youtubepruning decision trees ibmwhat and how to do mining mapdata with. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections tree provide little power to classify instances data mining induction learn simple and easy steps pruning performed order remove anomalies training 8 jul 2017. We may get a decision tree that might perform attribute selection measures, tree, post pruning, pre pruningdata mining is the extraction of hidden predictive information matteo matteucci retrieval & data mining• Usually based on statistical significance test. Pre pruning that stop growing the tree earlier, before it perfectly classifies this thesis presents algorithms for decision trees and lists are based should prove useful in practical data mining applications response to problem of overfitting nearly all modern adopt a strategy some sort. Test data keywords decision tree, tree pruning, miningdecision is one of the classification technique used in support system and i think have understood concepts between cross validation pruning. Wikipedia wiki pruning_(decision_trees)&sa u&ved 0ahukewjmxtqoi fvahutr48khypnaloqfggjmae&usg afqjcnhlpev_pbfseaco7iybewg5c15a3w"pruning (decision trees) wikipedia. Pruning (decision trees) wikipedia. Rpart rpart(promotion_name. Decision trees pruning matteo matteuccioverfitting of decision tree and in data mining techniques ijltet. Googleusercontent search. Data mining pruning (a decision tree, rules) [gerardnico]. Data mining cross validations and decision tree pruning (optional) algorithm university of washington. Many algorithms use a technique 26 nov 2008 lack of data points in the lower half diagram makes it difficult to predict correctly class labels that region. Insufficient number of 13 oct 2013 a decision tree is pruned to get (perhaps) that generalize better independent test data. Wikipedia wiki pruning_(decision_trees)&sa u&ved 0ahukewjmxtqoi fvahutr48khypnaloqfggjmae&usg afqjcnhlpev_pbfseaco7iybewg5c15a3w"pruning (decision trees) wikipedia pruning wikipedia en. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections tree provide little power to classify instances. • Stop growing the tree when there is no data& regression• If a decision tree is decision tree pruning methodologies. D
Views: 184 Evelina Hornak Tipz
What is SEMANTIC MATCHING? What does SEMANTIC MATCHING mean? SEMANTIC MATCHING meaning
 
03:23
What is SEMANTIC MATCHING? What does SEMANTIC MATCHING mean? SEMANTIC MATCHING meaning - SEMANTIC MATCHING definition - SEMANTIC MATCHING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Semantic matching is a technique used in computer science to identify information which is semantically related. Given any two graph-like structures, e.g. classifications, taxonomies database or XML schemas and ontologies, matching is an operator which identifies those nodes in the two structures which semantically correspond to one another. For example, applied to file systems it can identify that a folder labeled “car” is semantically equivalent to another folder “automobile” because they are synonyms in English. This information can be taken from a linguistic resource like WordNet. In the recent years many of them have been offered. S-Match is an example of a semantic matching operator. It works on lightweight ontologies, namely graph structures where each node is labeled by a natural language sentence, for example in English. These sentences are translated into a formal logical formula (according to an artificial unambiguous language) codifying the meaning of the node taking into account its position in the graph. For example, in case the folder “car” is under another folder “red” we can say that the meaning of the folder “car” is “red car” in this case. This is translated into the logical formula “red AND car”. The output of S-Match is a set of semantic correspondences called mappings attached with one of the following semantic relations: disjointness (?), equivalence (?), more specific (?) and less specific (?). In our example the algorithm will return a mapping between ”car” and ”automobile” attached with an equivalence relation. Information semantically matched can also be used as a measure of relevance through a mapping of near-term relationships. Such use of S-Match technology is prevalent in the career space where it is used to gauge depth of skills through relational mapping of information found in applicant resumes. Semantic matching represents a fundamental technique in many applications in areas such as resource discovery, data integration, data migration, query translation, peer to peer networks, agent communication, schema and ontology merging. Its use is also being investigated in other areas such as event processing. In fact, it has been proposed as a valid solution to the semantic heterogeneity problem, namely managing the diversity in knowledge. Interoperability among people of different cultures and languages, having different viewpoints and using different terminology has always been a huge problem. Especially with the advent of the Web and the consequential information explosion, the problem seems to be emphasized. People face the concrete problem to retrieve, disambiguate and integrate information coming from a wide variety of sources.
Views: 330 The Audiopedia
Swami Vivekananda - Life Story
 
20:24
www.hssus.org/sv150 Documentary on Swami Vivekananda
Views: 900729 HSSUS
Mod-01 Lec-38 Genetic Algorithms
 
54:52
Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 142615 nptelhrd
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
10:23
This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 61538 deltaDNA
What is LATENT SEMANTIC MAPPING? What does LATENT SEMANTIC MAPPING mean?
 
01:41
What is LATENT SEMANTIC MAPPING? What does LATENT SEMANTIC MAPPING mean? LATENT SEMANTIC MAPPING meaning - LATENT SEMANTIC MAPPING definition - LATENT SEMANTIC MAPPING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Latent semantic mapping (LSM) is a data-driven framework to model globally meaningful relationships implicit in large volumes of (often textual) data. It is a generalization of latent semantic analysis. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. LSM was derived from earlier work on latent semantic analysis. There are 3 main characteristics of latent semantic analysis: Discrete entities, usually in the form of words and documents, are mapped onto continuous vectors, the mapping involves a form of global correlation pattern, and dimensionality reduction is an important aspect of the analysis process. These constitute generic properties, and have been identified as potentially useful in a variety of different contexts. This usefulness has encouraged great interest in LSM. The intended product of latent semantic mapping, is a data-driven framework for modeling relationships in large volumes of data. Mac OS X v10.5 and later includes a framework implementing latent semantic mapping.
Views: 174 The Audiopedia
What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning
 
07:40
What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning - GEOSPATIAL ANALYSIS definition - GEOSPATIAL ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Geospatial analysis, or just spatial analysis, is an approach to applying statistical analysis and other analytic techniques to data which has a geographical or spatial aspect. Such analysis would typically employ software capable of rendering maps processing spatial data, and applying analytical methods to terrestrial or geographic datasets, including the use of geographic information systems and geomatics. Geographic information systems (GIS), which is a large domain that provides a variety of capabilities designed to capture, store, manipulate, analyze, manage, and present all types of geographical data, and utilizes geospatial analysis in a variety of contexts, operations and applications. Geospatial analysis, using GIS, was developed for problems in the environmental and life sciences, in particular ecology, geology and epidemiology. It has extended to almost all industries including defense, intelligence, utilities, Natural Resources (i.e. Oil and Gas, Forestry ... etc.), social sciences, medicine and Public Safety (i.e. emergency management and criminology), disaster risk reduction and management (DRRM), and climate change adaptation (CCA). Spatial statistics typically result primarily from observation rather than experimentation. Vector-based GIS is typically related to operations such as map overlay (combining two or more maps or map layers according to predefined rules), simple buffering (identifying regions of a map within a specified distance of one or more features, such as towns, roads or rivers) and similar basic operations. This reflects (and is reflected in) the use of the term spatial analysis within the Open Geospatial Consortium (OGC) “simple feature specifications”. For raster-based GIS, widely used in the environmental sciences and remote sensing, this typically means a range of actions applied to the grid cells of one or more maps (or images) often involving filtering and/or algebraic operations (map algebra). These techniques involve processing one or more raster layers according to simple rules resulting in a new map layer, for example replacing each cell value with some combination of its neighbours’ values, or computing the sum or difference of specific attribute values for each grid cell in two matching raster datasets. Descriptive statistics, such as cell counts, means, variances, maxima, minima, cumulative values, frequencies and a number of other measures and distance computations are also often included in this generic term spatial analysis. Spatial analysis includes a large variety of statistical techniques (descriptive, exploratory, and explanatory statistics) that apply to data that vary spatially and which can vary over time. Some more advanced statistical techniques include Getis-ord Gi* or Anselin Local Moran's I which are used to determine clustering patterns of spatially referenced data. Geospatial analysis goes beyond 2D and 3D mapping operations and spatial statistics. It includes: Surface analysis —in particular analysing the properties of physical surfaces, such as gradient, aspect and visibility, and analysing surface-like data “fields”; Network analysis — examining the properties of natural and man-made networks in order to understand the behaviour of flows within and around such networks; and locational analysis. GIS-based network analysis may be used to address a wide range of practical problems such as route selection and facility location (core topics in the field of operations research, and problems involving flows such as those found in hydrology and transportation research. In many instances location problems relate to networks and as such are addressed with tools designed for this purpose, but in others existing networks may have little or no relevance or may be impractical to incorporate within the modeling process....
Views: 1010 The Audiopedia
What is INSTANCE-BASED LEARNING? What does INSTANCE-BASED LEARNING mean?
 
02:23
What is INSTANCE-BASED LEARNING? What does INSTANCE-BASED LEARNING mean? INSTANCE-BASED LEARNING meaning - INSTANCE-BASED LEARNING definition - INSTANCE-BASED LEARNING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory. It is called instance-based because it constructs hypotheses directly from the training instances themselves. This means that the hypothesis complexity can grow with the data: in the worst case, a hypothesis is a list of n training items and the computational complexity of classifying a single new instance is O(n). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Instance-based learners may simply store a new instance or throw an old instance away. Examples of instance-based learning algorithm are the k-nearest neighbor algorithm, kernel machines and RBF networks.:ch. 8 These store (a subset of) their training set; when predicting a value/class for a new instance, they compute distances or similarities between this instance and the training instances to make a decision. To battle the memory complexity of storing all training instances, as well as the risk of overfitting to noise in the training set, instance reduction algorithms have been proposed. Gagliardi applies this family of classifiers in medical field as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in databases. One of these classifiers (called Prototype exemplar learning classifier (PEL-C) is able to extract a mixture of abstracted prototypical cases (that are syndromes) and selected atypical clinical cases.
Views: 477 The Audiopedia
What is ECOLOGICAL FOOTPRINT? What does ECOLOGICAL FOOTPRINT mean? ECOLOGICAL FOOTPRINT meaning
 
05:49
What is ECOLOGICAL FOOTPRINT? What does ECOLOGICAL FOOTPRINT mean? ECOLOGICAL FOOTPRINT meaning - ECOLOGICAL FOOTPRINT definition - ECOLOGICAL FOOTPRINT explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. An ecological footprint is a measure of human impact on Earth's ecosystems. It's typically measured in area of wilderness or amount of natural capital consumed each year. A common way of estimating footprint is, the area of wilderness of both land and sea needed to supply resources to a human population; This includes the area of wilderness needed to assimilate human waste. At a global scale, it is used to estimate how rapidly we are depleting natural capital. The Global Footprint Network calculates the global ecological footprint from UN and other data. They estimate that as of 2007 our planet has been using natural capital 1.6 times as fast as nature can renew it. Ecological footprint analysis is widely used around the Earth as an indicator of environmental sustainability. It can be used to measure and manage the use of resources throughout the economy and explore the sustainability of individual lifestyles, goods and services, organizations, industry sectors, neighborhoods, cities, regions and nations. Since 2006, a first set of ecological footprint standards exist that detail both communication and calculation procedures. The first academic publication about ecological footprints was by William Rees in 1992. The ecological footprint concept and calculation method was developed as the PhD dissertation of Mathis Wackernagel, under Rees' supervision at the University of British Columbia in Vancouver, Canada, from 1990–1994. Originally, Wackernagel and Rees called the concept "appropriated carrying capacity". To make the idea more accessible, Rees came up with the term "ecological footprint", inspired by a computer technician who praised his new computer's "small footprint on the desk". In early 1996, Wackernagel and Rees published the book Our Ecological Footprint: Reducing Human Impact on the Earth with illustrations by Phil Testemale. Footprint values at the end of a survey are categorized for Carbon, Food, Housing, and Goods and Services as well as the total footprint number of Earths needed to sustain the world's population at that level of consumption. This approach can also be applied to an activity such as the manufacturing of a product or driving of a car. This resource accounting is similar to life-cycle analysis wherein the consumption of energy, biomass (food, fiber), building material, water and other resources are converted into a normalized measure of land area called global hectares (gha). Per capita ecological footprint (EF), or ecological footprint analysis (EFA), is a means of comparing consumption and lifestyles, and checking this against nature's ability to provide for this consumption. The tool can inform policy by examining to what extent a nation uses more (or less) than is available within its territory, or to what extent the nation's lifestyle would be replicable worldwide. The footprint can also be a useful tool to educate people about carrying capacity and overconsumption, with the aim of altering personal behavior. Ecological footprints may be used to argue that many current lifestyles are not sustainable. Such a global comparison also clearly shows the inequalities of resource use on this planet at the beginning of the twenty-first century. In 2007, the average biologically productive area per person worldwide was approximately 1.8 global hectares (gha) per capita. The U.S. footprint per capita was 9.0 gha, and that of Switzerland was 5.6 gha, while China's was 1.8 gha. The WWF claims that the human footprint has exceeded the biocapacity (the available supply of natural resources) of the planet by 20%. Wackernagel and Rees originally estimated that the available biological capacity for the 6 billion people on Earth at that time was about 1.3 hectares per person, which is smaller than the 1.8 global hectares published for 2006, because the initial studies neither used global hectares nor included bioproductive marine areas.
Views: 7654 The Audiopedia
What is data science In telugu  - డేటా సైన్స్ అంటే ఏమిటి -9059868766 Artificial intelligence AI Demo
 
27:55
data science training python videos, datacamp data science python, intro to python for data science course by datacamp, python data science course, python data science tutorial, python for data science book, python for data science pdf, python training videos, youtube python data science, What is data science In telugu - డేటా సైన్స్ అంటే ఏమిటి Download data science content Pdf https://goo.gl/JN6iGs http://www.sivaitsoft.com/data-science-online-training-kukatpally/ What is data science course? What is a data scientist? Who coined data science? What is big data analysis? Data Science course content vlrtraining 9059868766 Hyderabad https://goo.gl/JN6iGs DATA SCIENCE ONLINE TRAINING Data Science Online Training kukatpally Hyderabad provided by VLR Trainings. Data Science is that the study ofDATA SCIENCE Online training wherever data comes from, what it represents and the way it is became a valuable resource in the creation of business and IT ways. More info Wikipedia DATA SCIENTIST A data scientist is someone who is better at statistics than any software engineer and better at Software engineering than any statistician.” WHAT A DATA SCIENTIST DOES Most data scientists in the industry have advanced degrees and training in statistics, math, and computer science. Their experience is a vast horizon that also extends to data visualization, data mining, and information management. It is fairly common for them to have previous experience in infrastructure design, cloud computing, and data warehousing. SKILLS REQUIRED TO BECOME A DATA SCIENTIST Statistic and probability Algorithms Programming Languages (Java, Scala ,SQL, R, Phyton) Data mining Machine learning Who should go for this course? Fresher’s/Graduates Job Seekers Managers Data analysts Business analysts Operators End users Developers IT professionals Data science Course Duration and details Course Duration 90Days (3 months) Course Fees 27000Rs Only online training Note* Everyday session recordings are also available Venkat: 9059868766 Jio:7013158918 Email: [email protected] Address: Vlrtraining/Sivaitsoft PlotNo 126/b,2nd floor,Street Number 4, Addagutta Society, Jal Vayu Vihar,, Kukatpally, Hyderabad, Telangana 500085 Map Link https://goo.gl/maps/Nk9LziFjVXS2 Data science Course Content data science, data science and analytics, data science certification, data science course, data science degree, data science online, data science pdf,, data science skills, data science syllabus, data science tools, data scientist profile, data scientist skills, introduction to data science, learn data science, mathematics for data science, python data science, science data, scientific database, Download Pdf Data Science course content vlrtraining 9059868766 Hyderabad http://www.sivaitsoft.com/wp-content/uploads/2017/10/Data-Science-course-content-vlrtraining-9059868766-Hyderabad.pdf
Views: 16057 VLR Training
Geology
 
11:04
003 - Geology In this video Paul Andersen explains how rock is formed and changed on the planet. The video begins with a brief description of rocks, minerals, and the rock cycle. Plate tectonics is used to describe structure near plate boundaries. Hot spots and natural hazards (like volcanos, earthquake, and tsunamis) are included. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: Benbennick, David. English: This Is a Locator Map Showing Kalawao County in Hawaii. For More Information, See Commons:United States County Locator Maps., February 12, 2006. Own work: English: The maps use data from nationalatlas.gov, specifically countyp020.tar.gz on the Raw Data Download page. The maps also use state outline data from statesp020.tar.gz. The Florida maps use hydrogm020.tar.gz to display Lake Okeechobee. https://commons.wikimedia.org/wiki/File:Map_of_Hawaii_highlighting_Kalawao_County.svg. “Earth.” Wikipedia, the Free Encyclopedia, August 23, 2015. https://en.wikipedia.org/w/index.php?title=Earth&oldid=677456791. File:Hawaiien (volcano).svg, n.d. https://commons.wikimedia.org/wiki/File:Hawaiien_(volcano).svg. File:Structure Volcano Unlabeled.svg, n.d. https://commons.wikimedia.org/wiki/File:Structure_volcano_unlabeled.svg. Fir0002. A Diagram of the Rock Cycle That Is Modified off of Rockcycle.jpg by User:Woudloper. The Changes Made to This Photo Were Made according to the Conversation at Where the Original Is Being Nominated for Featured Picture Status. It Is Very Important That You Change the Chance of You Getting a Rock of Bandshoe Very Rare Rock Very Costly Too There Are Only 3 Every like It in the World and It Costs 3 Gold Mines and the Mountains Ontop of Them., February 10, 2008. Own work. https://commons.wikimedia.org/wiki/File:Rockcycle_edit.jpg. “Gneiss.” Wikipedia, the Free Encyclopedia, July 29, 2015. https://en.wikipedia.org/w/index.php?title=Gneiss&oldid=673627696. Gringer. English: SVG Version of File:Pacific_Ring_of_Fire.png, Recreated by Me Using WDB Vector Data Using Code Mentioned in File:Worldmap_wdb_combined.svg., February 11, 2009. vector data from [1]. https://commons.wikimedia.org/wiki/File:Pacific_Ring_of_Fire.svg. H.Stauffer, Brian F. Atwater, Marco Cisternas V. , Joanne Bourgeois, Walter C. Dudley, James W. Hendley II, and Peter. English: Vertical Slice Through a Subduction Zone, 1999. U.S. Geological Survey, Circular 1187 (http://pubs.usgs.gov/circ/c1187/illustrations/BlockDigrm_1.ai). https://commons.wikimedia.org/wiki/File:Eq-gen1.svg. Karta24. Français : Trois Différents Types de Faille, January 20, 2008. http://earthquake.usgs.gov/learn/glossary/?term=fault earthquake.usgs.gov. https://commons.wikimedia.org/wiki/File:Fault_types.svg. Khruner. English: commons.wikimedia.org/wiki/File:Rocks_-_Pink_granite_Baveno.JPG. “Landslide.” Wikipedia, the Free Encyclopedia, August 27, 2015. https://en.wikipedia.org/w/index.php?title=Landslide&oldid=678171434. “Landslide.” Wikipedia, the Free Encyclopedia, August 27, 2015. https://en.wikipedia.org/w/index.php?title=Landslide&oldid=678171434. “Mount St. Helens.” Wikipedia, the Free Encyclopedia, August 8, 2015. https://en.wikipedia.org/w/index.php?title=Mount_St._Helens&oldid=675148427. “Plate Tectonics.” Wikipedia, the Free Encyclopedia, August 17, 2015. https://en.wikipedia.org/w/index.php?title=Plate_tectonics&oldid=676450570. “Ring of Fire.” Wikipedia, the Free Encyclopedia, August 20, 2015. https://en.wikipedia.org/w/index.php?title=Ring_of_Fire&oldid=676950168. “Tsunami.” Wikipedia, the Free Encyclopedia, July 19, 2015. https://en.wikipedia.org/w/index.php?title=Tsunami&oldid=672137584. User:Moondigger. Inside Lower Antelope Canyon, Looking out with the Sky near the Top of the Frame. Characteristic Layering in the Sandstone Is Visible., April 16, 2005. Own work. https://commons.wikimedia.org/wiki/File:Lower_antelope_3_md.jpg. USGS, derivative work: AnasofiapaixaoEarth_internal_structure png: English: Cutaway Diagram of Earth’s Internal Structure (to Scale) with Inset Showing Detailed Breakdown of Structure (not to Scale), April 27, 2013. Earth_internal_structure.png. https://commons.wikimedia.org/wiki/File:Earth-cutaway-schematic-english.svg.Own work. https://commons.wikimedia.org/wiki/File:Halema%27uma%27u_Crater_in_Kilauea_volcano,_Hawaii..jpg.
Views: 210989 Bozeman Science
What Is The Concept Of Intelligence Led Policing?
 
00:47
What is intelligence led policing? does youtube. Intelligence led policing definition & examples reducing crime through intelligence. Intelligence led policing wikipediaintelligence national criminal justice reference intelligence a new paradigm in law enforcement. From its origins in the new public management ethos of 1990s concepts fusion centers, data fusion, and associated philosophy intelligence led policing are abstract terms often misinterpreted poorly sep 26, 2006 jersey state police practical guide to njsp has deeply valued intelligence, devoting mar 20, 2017. Practice of intelligence led policing, conducted at the department crimes. Osce guidebook intelligence led policing organization for why would police departments want to adopt an. Australian institute of criminology intelligence led policing. What in the world is it, and why would any department worth its salt want to intelligence led policing jerry h. The intelligence and definition of led policing uk essays. Wikipedia wiki intelligence led_policing url? Q webcache. Intelligence led policing or intelligence citeseerx. Calls for intelligence led policing originated in the 1990s, both britain and united states years. Chapter 4 defining intelligence led policingintelligence policing springer. Intelligence led policing (ilp) is a model built around the assessment and management of risk. Intelligence led policing wikipedia. Ratcliffe 2nd edition compstat, predictive policing, intelligence led criminal intelligence, operation thumbs down. Intelligence led policing wikipedia en. This paper does not criticize the concepts or processes defined by national wardlaw and boughton argue that 'the concept of intelligence led policing is now origins from this move towards clarifying its may 2, 2017 a lot has been discussed about (ilp), yet there no standard definition ilp (mcgarrell, freilich, chermak, 2007) 12, although universally accepted understanding what entails leading 'a strategic, jun 24, 2009 term in common usage within australian law enforcement. Intelligence officers serve as guides to operations, rather than operations guiding intelligence. To implement intelligence led policing, police meaning and uses of intelligence, provides examples the term policing originated in for example, strategy emphasizes both horizontal vertical information sharing among agencies so that executive decision makers can establish objective crime reduction policies, but this approach is being implemented into a environment has traditionally rewarded individual ( combines data with top down business like operational model used to address specific issues ledthis project was supported by 2008 dd bx k675, applying these concepts pervasive crimes violence, bureau justice fighting guided effective gathering analysis it potential be most important law community gained from citizens helps define integration enforcement (ilp) now embedded lexicon around world. Fbi intelligence led policing in a fusion center. Intell
Views: 294 Question Bag
What is SNOWFLAKE SCHEMA? What does SNOWFLAKE SCHEMA mean? SNOWFLAKE SCHEMA meaning & explanation
 
05:30
What is SNOWFLAKE SCHEMA? What does SNOWFLAKE SCHEMA mean? SNOWFLAKE SCHEMA meaning - SNOWFLAKE SCHEMA definition - SNOWFLAKE SCHEMA explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computing, a snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape. The snowflake schema is represented by centralized fact tables which are connected to multiple dimensions.. "Snowflaking" is a method of normalising the dimension tables in a star schema. When it is completely normalised along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. The principle behind snowflaking is normalisation of the dimension tables by removing low cardinality attributes and forming separate tables. The snowflake schema is similar to the star schema. However, in the snowflake schema, dimensions are normalized into multiple related tables, whereas the star schema's dimensions are denormalized with each dimension represented by a single table. A complex snowflake shape emerges when the dimensions of a snowflake schema are elaborate, having multiple levels of relationships, and the child tables have multiple parent tables ("forks in the road"). Star and snowflake schemas are most commonly found in dimensional data warehouses and data marts where speed of data retrieval is more important than the efficiency of data manipulations. As such, the tables in these schemas are not normalized much, and are frequently designed at a level of normalization short of third normal form. Normalization splits up data to avoid redundancy (duplication) by moving commonly repeating groups of data into new tables. Normalization therefore tends to increase the number of tables that need to be joined in order to perform a given query, but reduces the space required to hold the data and the number of places where it needs to be updated if the data changes. From a space storage point of view, dimensional tables are typically small compared to fact tables. This often negates the potential storage-space benefits of the star schema as compared to the snowflake schema. Example: One million sales transactions in 200 shops in 220 countries would result in 1,000,200 records in a star schema (1,000,000 records in the fact table and 200 records in the dimensional table where each country would be listed explicitly for each shop in that country). A more normalized snowflake schema with country keys referring to a country table would consist of the same 1,000,000 record fact table, a 200 record shop table with references to a country table with 220 records. In this case, the star schema, although further denormalized, would only reduce the number or records by a (negligible) factor of 0.9997800923612083 (= divided by ) Some database developers compromise by creating an underlying snowflake schema with views built on top of it that perform many of the necessary joins to simulate a star schema. This provides the storage benefits achieved through the normalization of dimensions with the ease of querying that the star schema provides. The tradeoff is that requiring the server to perform the underlying joins automatically can result in a performance hit when querying as well as extra joins to tables that may not be necessary to fulfill certain queries. The snowflake schema is in the same family as the star schema logical model. In fact, the star schema is considered a special case of the snowflake schema. The snowflake schema provides some advantages over the star schema in certain situations, including: Some OLAP multidimensional database modeling tools are optimized for snowflake schemas. Normalizing attributes results in storage savings, the tradeoff being additional complexity in source query joins.....
Views: 443 The Audiopedia
What is DEFINITIVE DIAGNOSTIC DATA? What does DEFINITIVE DIAGNOSTIC DATA mean?
 
01:15
What is DEFINITIVE DIAGNOSTIC DATA? What does DEFINITIVE DIAGNOSTIC DATA mean? DEFINITIVE DIAGNOSTIC DATA meaning - DEFINITIVE DIAGNOSTIC DATA definition - DEFINITIVE DIAGNOSTIC DATA explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Definitive diagnostic data are a specific type of data used in the investigation and diagnosis of IT system problems; transaction performance, fault/error or incorrect output. To qualify as Definitive Diagnostic Data it must be possible to correlate the data with a user's experience of a problem instance, and for that reason they will typically be time stamped event information. Log and trace records are common sources of Definitive Diagnostic Data. Generally, statistical data can't be used as it lacks the granularity necessary to directly associate it with a user's experience of a problem instance. However, it can be adapted by reducing the sample interval to a value approaching the response time of the system transaction being performed.
Views: 56 The Audiopedia
6 Times Scientists Radically Misunderstood the World
 
12:16
Science has come a long way in understanding how our universe works and that road has been full of wrong turns and dead ends. Here are 6 scientific explanations that turned out to be way off track. Hosted by: Michael Aranda Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.wired.com/2014/06/fantastically-wrong-how-to-grow-a-mouse-out-of-wheat-and-sweaty-shirts/ https://www.britannica.com/biography/Louis-Pasteur/Spontaneous-generation https://www.britannica.com/science/biology#ref498783 https://ebooks.adelaide.edu.au/a/aristotle/history/book5.html https://ebooks.adelaide.edu.au/a/aristotle/generation/book3.html http://blogs.discovermagazine.com/cosmicvariance/2012/06/08/dark-matter-vs-aether/ https://www.forbes.com/sites/startswithabang/2017/04/21/the-failed-experiment-that-changed-the-world https://www.aps.org/publications/apsnews/200711/physicshistory.cfm https://www.aps.org/programs/outreach/history/historicsites/michelson-morley.cfm https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.91.020401 https://books.google.com/books?id=to8OAAAAIAAJ&printsec=frontcover#v=onepage&q&f=false p216 https://www.britannica.com/science/phlogiston https://eic.rsc.org/feature/the-logic-of-phlogiston/2000126.article https://www.acs.org/content/acs/en/education/whatischemistry/landmarks/lavoisier.html https://www.acs.org/content/dam/acsorg/education/whatischemistry/landmarks/lavoisier/antoine-laurent-lavoisier-commemorative-booklet.pdf http://www.chss.uqam.ca/Portals/0/docs/hps5002/Stud_Hist_Phil_Sci_v25n2_p159-190.pdf https://www.jstor.org/stable/3143157?seq=1#page_scan_tab_contents https://www.britannica.com/science/steady-state-theory https://www.google.com/amp/s/futurism.com/steady-state-model-of-the-universe/amp/ https://history.aip.org/exhibits/cosmology/ideas/bigbang.htm https://www.nasa.gov/topics/earth/features/earth20110816.html https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2011GL047450 https://www.hist-geo-space-sci.net/5/135/2014/hgss-5-135-2014.pdf http://www.earth-prints.org/bitstream/2122/2017/1/MANTOVANI.pdf https://www.hist-geo-space-sci.net/5/135/2014/hgss-5-135-2014.pdf https://blogs.scientificamerican.com/history-of-geology/from-the-contracting-earth-to-early-supercontinents/ https://arstechnica.com/science/2014/03/mercury-the-planet-shrinks-as-it-cools ------ Images: https://www.istockphoto.com/photo/maggot-of-fly-for-sport-fisherman-gm106458303-6041350 https://www.istockphoto.com/photo/aristotle-portray-the-philosopher-gm172411889-4331403 https://www.istockphoto.com/vector/house-fly-and-bee-illustrations-gm185111511-19447453 https://www.istockphoto.com/vector/set-of-glass-jars-for-canning-and-preserving-vector-illustration-isolated-on-gm846771750-138853499 https://www.istockphoto.com/photo/dreamy-light-refraction-pastel-soft-pale-background-abstract-defocus-rainbow-gm531186409-55315198 https://en.wikipedia.org/wiki/Celestial_spheres#/media/File:Ptolemaicsystem-small.png https://www.istockphoto.com/photo/fireplace-gm498891142-79892091 https://www.istockphoto.com/vector/burning-bonfire-with-wood-gm871355210-145516179 https://www.istockphoto.com/photo/yellow-color-burning-fire-frame-gm853959940-140333267 https://www.istockphoto.com/photo/burning-charcoal-gm865453156-143575701 https://www.nasa.gov/content/most-colorful-view-of-universe-captured-by-hubble-space-telescope https://www.nasa.gov/mission_pages/chandra/multimedia/distant-quasar-RXJ1131.html https://www.nasa.gov/image-feature/nasa-captures-epic-earth-image https://images.nasa.gov/details-PIA11245.html https://www.istockphoto.com/vector/19th-century-engraving-of-louis-pasteur-at-work-in-his-laboratory-victorian-gm872138750-243617917
Views: 356489 SciShow
What is BUSINESS RULE? What does BUSINESS RULE mean? BUSINESS RULE meaning & explanation
 
04:45
What is BUSINESS RULE? What does BUSINESS RULE mean? BUSINESS RULE meaning - BUSINESS RULE definition - BUSINESS RULE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A business rule is a rule that defines or constrains some aspect of business and always resolves to either true or false. Business rules are intended to assert business structure or to control or influence the behavior of the business. Business rules describe the operations, definitions and constraints that apply to an organization. Business rules can apply to people, processes, corporate behavior and computing systems in an organization, and are put in place to help the organization achieve its goals. For example, a business rule might state that no credit check is to be performed on return customers. Other examples of business rules include requiring a rental agent to disallow a rental tenant if their credit rating is too low, or requiring company agents to use a list of preferred suppliers and supply schedules. While a business rule may be informal or even unwritten, documenting the rules clearly and making sure that they don't conflict is a valuable activity. When carefully managed, rules can be used to help the organization to better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Business rules tell an organization what it can do in detail, while strategy tells it how to focus the business at a macro level to optimize results. Put differently, a strategy provides high-level direction about what an organization should do. Business rules provide detailed guidance about how a strategy can be translated to action. Business rules exist for an organization whether or not they are ever written down, talked about or even part of the organization's consciousness. However it is a fairly common practice for organizations to gather business rules. This may happen in one of two ways. Organizations may choose to proactively describe their business practices, producing a database of rules. While this activity may be beneficial, it may be expensive and time-consuming. For example, they might hire a consultant to comb through the organization to document and consolidate the various standards and methods currently in practice. More commonly, business rules are discovered and documented informally during the initial stages of a project. In this case the collecting of the business rules is incidental. In addition, business projects, such as the launching of a new product or the re-engineering of a complex process, might lead to the definition of new business rules. This practice of incidental, or emergent, business rule gathering is vulnerable to the creation of inconsistent or even conflicting business rules within different organizational units, or within the same organizational unit over time. This inconsistency creates problems that can be difficult to find and fix. Allowing business rules to be documented during the course of business projects is less expensive and easier to accomplish than the first approach, but if the rules are not collected in a consistent manner, they are not valuable. In order to teach business people about the best ways to gather and document business rules, experts in business analysis have created the Business Rules Methodology. This methodology defines a process of capturing business rules in natural language, in a verifiable and understandable way. This process is not difficult to learn, can be performed in real-time, and empowers business stakeholders to manage their own business rules in a consistent manner. Gathering business rules is also called rules harvesting or business rule mining. The business analyst or consultant can extract the rules from IT documentation (like use cases, specifications or system code). They may also organize workshops and interviews with subject matter experts (commonly abbreviated as SMEs). Software technologies designed to capture business rules through analysis of legacy source code or of actual user behavior can accelerate the rule gathering processing.
Views: 2648 The Audiopedia
An Open Source CPU!?
 
12:50
Thanks to Audible for sponsoring this video! To start your free 30-day trial and receive a free audiobook visit https://www.audible.com/linus or text linus to 500 500! Buy noblechairs ICON Series Real Leather Gaming Chair on Amazon at http://geni.us/IFow As making faster CPUs gets more difficult on the hardware side, a group of researchers have looked into improving them on the software side by creating a new instruction set that someday might completely replace x86 and ARM. Check out SiFive: https://www.sifive.com/ Buy more RISC-V knowledge On Amazon: http://geni.us/LXBbYz6 On Amazon: http://geni.us/0URdH0z Discuss on the forum: https://linustechtips.com/main/topic/963444-an-open-source-cpu/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/75969-linus-tech-tips-affiliates-referral-programs-and-sponsors Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips/ Linus Tech Tips posters at http://crowdmade.com/linustechtips Our Test Benches on Amazon: https://www.amazon.com/shop/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxmFU3lWY iTunes Download Link: https://itunes.apple.com/us/album/supernova/id936805712 Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnirvana Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 1102790 Linus Tech Tips
Epping Model Railway Train Show 2016 Part 2 The Devil In The Detail
 
33:13
More super amazing Model Railway Displays by Epping Model Railway at the Brickpit Stadium 2016. This years train show was massive Vs what was offered up in 2015. This video will show these model railway displays. 4 Ways (HO Scale) This cylindrical dual layered layout was really amazing. Built to entertain children and adults it incorporated various themes split around the layout. From Dinosaurs to the Zombie apocalypse and everything in between. Bethungra Spiral (HO Scale) Australian themed layout based on a real rail spiral in NSW at Bethungra. The scenery was amazing and looked just like real life. A variety of train were seen AD60 class were Beyer-Garratt to the XPT High Speed Train and everything in between. Basing a layout on a real rail spiral was amazing and it gave a fantastic view of the train as they moved around the layout. The Charging Moose (On30 American) One of the most beautiful model railways ever built. Geoff Nott and John Montgomery layout depicting a small narrow gauge line somewhere in the American forests serving both the logging and mining industries To see some amazing photos of this display best to follow this NMRA link. http://www.nmra.org.au/Layout_Tours/Charging%20Moose/index.html Goulburn (HO Scale) This Australian themed layout was constructed and run by the Gilford Model Railway Club. This highly detailed railway study is based on the Goulburn station and surrounding environments as it looks today. Locomotives and rolling stock are individually owned by the railway club members. The power was supplied by conventional DC block control. This layout was very popular as it's something that many people could relate to if they have travelled by rail to Goulburn. Hoyt-Clagwell Tractor Factory (On30 Scale) This tiny layout was one of the biggest surprises at the train show. It cleverly sucked you in via a question sheet related to the classic TV comedy show Green Acres. This layout used one of the most unusual ideas as a theme and the way it presented made you look into the layout. It goes to show that you don't need a huge highly detailed railway layout to entertain a audience. Hoyt-Clagwell Tractor Factory was amazing. Over The Fence (HO Scale) A railway layout based on the Newcastle area that's north of Sydney. It features typical Australian trains and rollingstock that are a common sight during the 80's to 90's. This layout was enclosed in a perspex tank that made clear viewing and photography near impossible. Sadly this really distracted from the beauty of the various trains. The only way I could see them clearly was when they were running around the rear of the layout. Children had little or no chance to engage with this layout due to the height it was set at and perspex tank that protected the layout from the viewing public. South Bend And Hilltop (N Scale) This American themed layout was totally amazing due to the number of trains and incredible details on the layout. Presented by the Hills model Railway Society this large layout can run both DC and DCC trains for it's members. The track design is a folded dogbone and the scenic layout incorporates innovative model making methods to reduce weight while remaining strong. As I so often say when N Scale is done well it's incredibly beautiful. This layout was a real joy to watch. Gordon (HO Scale) A layout thats depicting the Australian railway station at Gordon near the top of the Great Dividing Range. The Layout represents Gordon prior to the rationalization of the railways in Australia. Gordon was originally a terminus station on a branch line but eventually it was linked to Melbourne. The trains seen on the layout are from the 1950's to 1980's. Gordon is a little peek back in time to when Australia was a very different place Vs today. Back Of Beyond (N Scale) This beautiful layout represents a small country town somewhere in country NSW Australia. Because of Government decisions the railways have been rationalized so the goods shed and two branch lines lapse into ruin. The layout has lots of buildings done in a variety of methods. The designs in this layout is very common of of the many towns that have derelict railway infrastructure around active mainlines. It's a reminder of very different times seeing the ghosts railways. This layout is another excellent N Scale model. The model detailing's here are extreme. The track ballast even shows areas of loose sleepers. It's an amazing study of how the world looks in a tiny scale. Bethungra Rail Spiral Google Maps Link https://www.google.com.au/maps/place/Bethungra+Rail+Spiral/@-34.7505315,147.8787325,17z/data=!3m1!4b1!4m5!3m4!1s0x6b184cd2b7a85451:0xd209864efc3f7c25!8m2!3d-34.7505315!4d147.8809212 Web Links : http://www.eppingmodelrailway.org.au/ https://en.wikipedia.org/wiki/National_Model_Railroad_Association https://en.wikipedia.org/wiki/Rail_transport_modelling https://en.wikipedia.org/wiki/List_of_model_railways https://en.wikipedia.org/wiki/Bethungra_Spiral
Views: 42037 leokimvideo
Tornado-Proof Suburb
 
01:29
Our proposed site is on the outskirts of Kansas City Missouri, under the most violent of atmospheres in the northern hemisphere. Buildings in this region move, but not usually of their own volition. The uninvited motion takes the form of a shredding violence which often obliterates the home, reducing entire communities to rubble.. The solution requires nothing less than a paradigm shift in home design. A series of hydraulic levers are used to move the housing units in and out of the ground, warping and deflecting the outer skin in response to external stimulation. The mobility also offers the home a chance to aim itself into the prevailing wing to capture maximum breezes or avoid them. Solar cells on the skin rotate and flex to attain maximum solar intensity. A translucent outer skin consisting of clear insulation sandwiched between two layers of Kevlar provides the weather barrier, structure, and diffuse lighting. Neighborhoods are interconnected to collect and share micro climactic information. The basic framework is composed of three basic processes, Sensors (collecting meteorological data from the surroundings); Control system (processing the real-time information, data mining the knowledge base, and making decision on the action taken); Actuators (expressing the decision made in physical transformation of building). Once the alarm has sounded the entire neighborhood simply and safely drifts down into the ground out of harms way. The fundamental question is why build something solid where nature's patterns are clear and predictably destructive?
Views: 21300 macbethdolon
What is DISINTERMEDIATION? What does DISINTERMEDIATION mean? DISINTERMEDIATION meaning
 
01:48
What is DISINTERMEDIATION? What does DISINTERMEDIATION mean? DISINTERMEDIATION meaning - DISINTERMEDIATION definition - DISINTERMEDIATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In economics, disintermediation is the removal of intermediaries from a supply chain, or "cutting out the middlemen" in connection with a transaction or a series of transactions. Instead of going through traditional distribution channels, which had some type of intermediary (such as a distributor, wholesaler, broker, or agent), companies may now deal with customers directly, for example via the Internet. Disintermediation may decrease the total cost of servicing customers and may allow the manufacturer to increase profit margins and/or reduce prices. Disintermediation initiated by consumers is often the result of high market transparency, in that buyers are aware of supply prices direct from the manufacturer. Buyers may choose to bypass the middlemen (wholesalers and retailers) to buy directly from the manufacturer, and pay less. Buyers can alternatively elect to purchase from wholesalers. Often, a business-to-consumer electronic commerce (B2C) company functions as the bridge between buyer and manufacturer. However manufacturers will still incur distribution costs, such as the physical transport of goods, packaging in small units, advertising, and customer helplines, some or all of which would previously have been borne by the intermediary.
Views: 1267 The Audiopedia
What is CUSTOMER INTELLIGENCE? What does CUSTOMER INTELLIGENCE mean?
 
02:55
What is CUSTOMER INTELLIGENCE? What does CUSTOMER INTELLIGENCE mean? CUSTOMER INTELLIGENCE meaning - CUSTOMER INTELLIGENCE definition - CUSTOMER INTELLIGENCE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Customer intelligence (C I) is the process of gathering and analyzing information regarding customers; their details and their activities, in order to build deeper and more effective customer relationships and improve strategic decision making. Customer intelligence is a key component of effective customer relationship management (CRM), and when effectively implemented it is a rich source of insight into the behaviour and experience of a company's customer base. As an example, some customers walk into a store and walk out without buying anything. Information about these customers/prospects (or their visits) may not exist in a traditional CRM system, as no sales are entered on the store cash register. Although no commercial transaction took place, knowing why customers leave the store (perhaps by asking them, or a store employee, to complete a survey) and using this data to make inferences about customer behaviour, is an example of C I. Customer Intelligence begins with reference data – basic key facts about the customer, such as their geographic location. This data is then supplemented with transaction data – reports of customer activity. This can be commercial information (for example purchase history from sales and order processing), interactions from service contacts over the phone and via e-mail. A further subjective dimension can be added, in the form of customer satisfaction surveys or agent data. Finally, a company can use competitor insight and mystery shopping to get a better view of how their service benchmarks in the market. By mining this data, and placing it in context with wider information about competitors, conditions in the industry, and general trends, information can be obtained about customers' existing and future needs, how they reach decisions, and predictions made about their future behavior. Customer Intelligence provides a detailed understanding of the experience customers have in interacting with a company, and allows predictions to be made regarding reasons behind customer behaviors. This knowledge can then be applied to support more effective and strategic decision making – for example, understanding why customers call makes it easier to predict (and plan to reduce) call volumes in a contact centre.
Views: 417 The Audiopedia
What Is The Meaning Of Depletion In Accounting?
 
00:45
Multiplying cost per unit by number of units extracted during the period gives us depletion expense for. Typical these natural resources utilized by definition of depletion in the financial dictionary free online english an accounting and tax term referring to deductions made account for land charge against earnings, based on amount wasting (consumed or extracted) that are taken out intermediate dummiesinstead of, addition to, owning tangible assets, a company may purchase own rights is defined as reduction number quantity. Depletion (accounting) wikipediawhat is depletion? What Definition example my accounting depletion financial definition of. Depletion allowance definition & example forest finance what is timber 'depletion'? Forisk. Free online dictionary of law terms and depletion renewable environmental resources. Depletion is an accounting and tax concept used most often in mining, timber, petroleum, or other similar industries. Depletion (accounting) wikipedia en. Depletion is a periodic charge to expense for the use of natural resources. The information below is 27 dec 2012. What is depletion accounting? Definition and meaning which assets can be depleted dummiesdepletion defined yourdictionarydepletion expense journal entry accounting money zine examples (in natural resource accounting) definition oecd statistics. Thus, it is used in situations where a therefore, any method for calculation of depletion expense must strictly obey the relevant accounting principles. The financial accounting term depletion refers to the allocation of cost an period as units a natural resource are mined, cut, pumped method depreciation. Wine depletion data and its influence on the 3 p's accounting (general guidelines to determine. In extraction and mining industry, entities have fixed assets mines, quarries wells to extract natural resources like coal, oil definition depletion (in resource accounting) for renewable resources, refers the part of harvest, logging, catch so forth above sustainable a allowance is tax deduction allowed in order compensate or 'using up' deposits such as oil, gas, iron, 10 mar 2013 based on actual cost timber, adjusted all capitalized silviculture expenses plus merchantable timber accounts 12 2014 term used wine industry known data refer sales from distributors retail. This method to calculate depletion expense definition. What does amortization mean? To learn definition depletion is the systematic allocation of costs associated with extracting natural resources from a reserve. Wikipedia wiki depletion_(accounting) url? Q webcache. Apr 2013 legal definition of depletion accountingrelated entries accounting in the encyclopedia 19 dec 2007 renewable resources are, by definition, able to sustain or increase and economic 2003 (seea), 'making environmental. The depletion deduction allows an owner or operator to account for the reduction of a product's reserves what is 'depletion' accrual accounting technique used allocate cost extractin
Views: 305 Pan Pan 3