Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 286270 Last moment tuitions
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Views: 17707 Artificial Intelligence - All in One
#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 435099 Last moment tuitions
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Views: 564 MATLAB ASSIGNMENTS AND PROJECTS
Top tips for data mining success! Watch John Elder present this short tutorial on how to get ahead in data mining. This is extracted from training material produced by Elder Research, Inc. For more information about statistical analysis and data mining, check out the brand new reference book from Elsevier: The Handbook of Statistical Analysis and Data Mining Applications (www.elsevierdirect.com/datamining).
Views: 2773 Elsevier Books
http://www.quiktekinfo.com/web-research-data-mining.html Web Research and Data Mining Services at QuikTek: Providing Relevant Information To help entrepreneurs gain business insight and competitive intelligence, QuikTek provides web research and data mining services. Our experts work round-the-clock to fetch out relevant information on the basis of a certain criteria. For more information about our Web Research and Data Mining Services, visit: http://www.quiktekinfo.com/web-research-data-mining.html
Views: 22 QuikTek Info Services
Mining subgraph patterns is an active area of research. Till now, the focus has primarily been on mining all subgraph patterns in the given database. However, due to the exponential subgraph search space, the number of patterns mined, typically, is too large for any human mediated analysis. Consequently, deriving insights from the mined patterns is hard for domain scientists. In addition, subgraph pattern mining is posed in multiple forms: the function that models if a subgraph is a pattern varies based on the application and the database could be over multiple graphs or a single, large graph. A natural question that therefore arises is the following: Given any graph database type and a subgraph importance function, can we develop a technique to mine k subgraph patterns that best represent all other patterns of interest? We will discuss a generic framework called RESLING that answers this question. Experiments show that RESLING is up to 20 times more representative of the pattern space and 2 orders of magnitude faster than the state-of-the-art techniques. See more on this video at https://www.microsoft.com/en-us/research/video/generic-framework-mining-top-k-representative-subgraph-patterns/
Views: 507 Microsoft Research
DATA WAREHOUSE AND DATA MINING (DWDM) IMPORTANT QUESTIONS #DATA WAREHOUSE #DATA MINING #DWDM #IMPORTANT #QUESTIONS #EXAMS
Views: 2539 best way to study
Predicting Stock Price movement statistically. Here we use historical data to predict the movement of stock price for next day. It is completely mathematically valid. The mathematical model of Brownian motion has several real-world applications. Stock market fluctuations are often cited, although Benoit Mandelbrot rejected its applicability to stock price movements in part because these are discontinuous. This is a momentum indicator used in technical analysis, which compares the stock's closing price to its price over the course of a particular time frame. During an upward trend in the market, a stock's share price will close near its high (highest price traded), and when in a downward-trending market, the security's price will close near the low (lowest price traded). This may determine whether a stock is overbought or oversold, thus predicting a possible momentum change. http://www.garguniversity.com Check out Ebook "Mind Math" from Dr. Garg https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18
Views: 117894 Garg University
The term Data Mining refers to the extraction of vital information by processing a huge amount of data. Data Mining plays a prominent role in predictive analysis and decision making. Companies basically uses these techniques to know the exact customer focus and finalize the marketing goals. DM is also useful in market research, industry research and competitor's analysis. Major activities involved in DM is: • Extract Data from web databases. • Load them into data store systems • Classify stored data in multidimensional database system • Analysis using some automated technical software application. • Presentation of Extracted information useful format like PPT, XLS file For more details: http://bit.ly/1iAor17
Views: 63 Datatude Technologies
Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1999 Quantopian
Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 102227 Siraj Raval
Ambuj Tewari - EECS at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 3877 Michigan Engineering
Basics of Social Network Analysis In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research. The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php The video's content includes: Why Social Network Analysis (SNA)? Enables us to segment data based on user behavior. Understand natural groups that have formed: a. topics b. personal characteristics Understand who are the important people in these groups. Analysing Social Networks: Data Collection Methods: a. Surveys b. Interviews c. Observations Analysis: a. Computational analysis of matrices Relationships: A. is connected to B. SNA Introduction: [from] A. Directed Graph [to] B. e.g. Twitter replies and mentions A. Undirected Graph B. e.g. family relationships What is Social Network Analysis? Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)). Social Network Analysis Basics: Node and Edge Node: “actor” or people on which relationships act Edge: relationship connecting nodes; can be directional Social Network Analysis Basics: Cohesive Sub-group Cohesive Sub-group: a. well-connected group, clique, or cluster, e.g. A, B, D, and E Social Network Analysis Basics: Key Metrics Centrality (group or individual measure): a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only). b. Measure at the individual node or group level. Cohesion (group measure): a. Ease with which a network can connect. b. Aggregate measure of shortest path between each node pair at network level reflects average distance. Density (group measure): a. Robustness of the network. b. Number of connections that exist in the group out of 100% possible. Betweenness (individual measure): a. Shortest paths between each node pair that a node is on. b. Measure at the individual node level. Social Network Analysis Basics: Node Roles: Node Roles: Peripheral – below average centrality, e.g. C. Central connector – above average centrality, e.g. D. Broker – above average betweenness, e.g. E. References and Reading Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492. Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8. Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.
Views: 40267 Alexandra Ott
"Design Mining the Web" -Ranjitha Kumar, Stanford University This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI. Learn more: http://stanford.io/UdmdrX
Views: 2396 stanfordonline
Matz has eight years experience in the field of marketing analytics. He has worked as analytics lead for companies like PwC, Bristol-Myers Squibb, Toys 'R' Us, SAP and dELiA*s. He holds a master's degree specializing in computational analytics from the New Jersey Institute of Technology, and two bachelor's degree in business administration and computer science from Jadavpur University and Manipal University in India (respectively). He offers analytics consulting services in the field of web-analytics, predictive analytics and advanced text mining across a wide group of industries. He has worked on select funded academic research initiatives and continues to explore advanced research projects in the field of application of text mining and advanced predictive analytics. Matz has shared his expertise and case studies in marketing analytics and retail, speaking at conferences including eTail East, The I.E Big Data & Analytics for Retail Summit and IQPC Digital Marketing Metrics & Analytics Summit. Matz Lukmani joined MediaCom in 2014 as Associate Director, Insights & Analytics. Prior to joining the agency, Matz led marketing analytics at dELiA*s Inc. for two years.
Views: 434 murtaza lukmani
Google Tech Talk February 13, 2009 ABSTRACT Presented by Jessica Staddon. Text content can allow unintended inferences. Consider, for example, the numerous people who have published anonymous blogs for venting about their employer only to be identified through seemingly non-identifying posts. Similarly, the US government's "Operation Iraqi Freedom Portal" was assembled as evidence of nuclear weapons presence in Iraq, but removed because it could be used to infer much of the weapon making process. We propose a simple, semi-automated approach to detecting text-based inferences prior to the release of content. Our approach uses association rule mining of the Web to identify keywords that may allow a sensitive topic to be inferred. While the main application of this work is data leak prevention we will also discuss how it might be used to detect bias in product reviews. Finally, if time permits, we will discuss how inference detection can support topic-driven access control. Most of this talk is joint work with Richard Chow and Philippe Golle. Jessica is an area manager at PARC (aka Xerox PARC). She received her PhD in Math from U. C. Berkeley and has held research scientist positions at RSA Labs and Bell Labs. Jessica's background is in applied cryptography, specifically, cryptographic protocols for large, dynamic groups. Her current research interests include the use of data mining to support content privacy. http://www.parc.com/jstaddon
Views: 2757 GoogleTechTalks
In this talk, Andreas speaks about Bitcoin as a system of commerce that is the culmination of decades of research into cryptography and digital currencies, and how it achieves digital scarcity through proof-of-work and decentralized consensus. He argues that Bitcoin is powerful because it is where the laws of mathematics prevail to deliver robust security, financial autonomy, and censorship resistance. THIS IS A COPY OF ORIGINAL VIDEOCLIP THAT CAN BE FOUND HERE: https://www.youtube.com/watch?v=HaJ1hvon0E0 Fair Use Notice: This video might contain some copyrighted material whose use may or may not have not been authorized by the copyright owners. We believe that this not-for-profit, educational, and/or criticism or commentary use on the Web constitutes a fair use of the copyrighted material (as provided for in section 107 of the US Copyright Law). If you wish to use this copyrighted material for purposes that go beyond fair use, you must obtain permission from the copyright owner. In case, you own or reperesent someone who owns any of the material in this video, and would like us to remove your content, we will immediately comply with any copyright owner who wants their material removed or modified, wants us to link to their web site, or wants us to add their photo.
Views: 447 Bitcoin TV
I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP, ether through a full-time position or contract work. You’ll be learning a host of tools like SQL, Python, Hadoop, and even data storytelling, all of which make up the complete data science pipeline. Curriculum for this video: https://github.com/llSourcell/Learn_Data_Science_in_3_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Week 1 - Learn Python - EdX https://www.edx.org/course/introduction-python-data-science-2 - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Week 2 - Statistics & Probability - KhanAcademy https://www.khanacademy.org/math/statistics-probability Week 3 - Data Pre-processing, Data Vis, Exploratory Data Analysis - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis Week 4 - Kaggle Project #1 Week 5-6 - Algorithms & Machine Learning - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/ Week 7 - Deep Learning - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 8 - Kaggle Project #2 Week 9 - Databases (SQL + NoSQL) - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2 Week 10 - Hadoop & Map Reduce + Spark - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf Week 11 - Data Storytelling - Edx https://www.edx.org/course/analytics-storytelling-impact-1 Week 12- Kaggle Project #3 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/ Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 303055 Siraj Raval
This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 5187 tutor2u
In 1999 A. Barabasi and R. Albert suggested the idea of preferential attachment to explain the power law distribution of the vertex degrees in web graphs. Several mathematical models have then appeared incorporating this idea. Among them, the LCD model by B. Bollobas and O. Riordan, the Buckley--Osthus model, the Bollobas--Borgs-Chayes--Riordan model of directed scale-free graphs, etc. Many deep results have been obtained concerning these models. For example, one studies the degree distributions, the diameter, the clustering coefficient, and so on. In our work, we continue studying important statistics of the random web graph in the just-mentioned models. On the one hand, we substantially improve some of the formerly known results. On the other hand, we introduce new characteristics of the web including the number of edges between vertices of given degrees. For these characteristics, we find accurate analytic expressions and we apply them to improve quality of search engine rankings.
Views: 315 Microsoft Research
In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 280366 Siraj Raval
Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow. Code for this video: https://github.com/llSourcell/AI_in_Finance Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://www.datacamp.com/community/tutorials/finance-python-trading http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/ https://www.udacity.com/course/machine-learning-for-trading--ud501 https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 182451 Siraj Raval
Best Assistance for Ph.D. works. Xsys takes care of the process right from the problem formulation till final thesis submission. We provide support for Algorithms, mathematical modelings, simulations, journal writing, thesis writing, DC reports, plagiarism removing, publishing, etc. Domains in IT we work on Image processing, Data mining, Software Engineering, Defect prediction, Networks, Security, WSN, Manet, Vanet, Biomedical, Robotics, Cloud Computing, Load Balancing, OCR, VLSI, Artificial Intelligence, Artificial Neural Networks, Genetic Algorithms, Optimization algorithms (ACO,PSO, etc), etc. We do work on Non-IT requirements such as on HRM, Work-Life Balance, International Management, Reality, Hospitality, Marketing, Sales, etc.
Views: 95 Xsys Software
Abstract: A lot of research was done about clustering attacks of different types using many Machine Learning algorithms, with high rates of success. These were mainly done from the comfort of a research lab, with specific datasets and no performance limitations. In this session I will share my experience with dealing with clustering of attacks in near real-time scenarios where performance is a key factor, and where the reality punches lab statistics in the face. I will discuss some of the challenges we experienced during the research like: 1) Applying a clustering algorithm to a stream of data. 2) Extracting meaningful features from limited data. 3) Translating different features into something we can calculate distance from. Speaker Bio: ilad Yehudai is an algorithm developer and security researcher at Imperva’s web application research group. Gilad develops algorithms and solutions using state-of-the-art machine learning algorithms, and also researches new security threats and vulnerabilities. Gilad holds a B.Sc. and a M.Sc. in Mathematics from Tel Aviv University. He has a very analytical and technical background with experience in both statistics and machine learning. A math geek by day and an avid Snooker player by night (And vice versa).
Views: 231 BSides Leeds
Do data mining One way that some corporations keep ahead of their competition is to do data mining. Businesses derive useful information from huge databases through statistical analysis. Applications of this mathematical algorithm based analysis tool are in the areas of product analysis, consumer research marketing, e-Commerce, stock investment trend and many more. Relational database mining, web mining, text mining, audio and video mining, and social networks mining are some types of data mining. You can relate data mining to geology in the sense that in geology you search for specific minerals (for example gold or lead), while a statistical data miner uses various tools to find useful information from a wide database. It is a way of extracting data from very large and sometimes complex databases to find patterns or trends that a company can use to further their business. Data mining is a labor intensive job wherein a lot of data has to be collected and analyzed. Outsourcing data mining jobs may be more beneficial to companies who do not have the time or manpower to invest in this endeavor. The outsourcing company will take care of collecting the needed data and organizing the data in a well mapped database so that they can easily filter or extract the required information for analysis. But if you have the resources, you can also use a variety of data mining programs out there. Some data mining software are SAS Enterprise Miner, DataDetective, Statistical Data Miner, Statistica, and Weka. You can read more about data mining on the Internet. But just to give you an idea, below are the steps in performing data mining: Define the objectives. This step is basically identifying why you need to perform data mining. What problem brought about a perceived data mining solution and what are the objectives for this project? Gather and organize the data. The bulk of the work in data mining is data gathering and exploring. Data has to be organized in an efficient and effective way for you to be able to process the information properly. Select the data-mining task. There are four basic data mining techniques: classification, regression, clustering and association rule. Choose the ones appropriate to your objectives. Modeling. This is when you actually perform the data mining procedure. Search for patterns in the database by applying your selected data mining techniques in order to create models. Data interpretation and validation. After the actual data mining task, the data gathered is now interpreted, validated, transformed and visualized using statistical techniques. Data deployment. This step can involve a report that is generated showing the patterns found in the data mining activity or the use of the data model on a larger group of data for further analysis. Data mining is an iterative process so you may have to go through several of the steps above a number of times until the results you derive answer your objectives. There was a time when data mining was not widely used by businesses. Now, public and private companies and organizations find data mining an invaluable way for them to keep up and even get ahead of their competitors. Businesses are now able to monitor the kind of customers their products cater to and what their customers’ buying behaviors are. The information mined and modeled from various types of databases is used for competition analysis, market research, economic trending, consumer behavior, industry research, geographical information analysis and so on. Even the FBI and other law enforcement groups use data mining techniques.
Views: 2 How to : Tips and Trick
On August 23-24, 2018 the CMSA hosted our fourth annual Conference on Big Data. The Conference featured many speakers from the Harvard community as well as scholars from across the globe, with talks focusing on computer science, statistics, math and physics, and economics. Speaker: William Stein Title: CoCalc: Making open source data analysis software more collaborative Abstract: I launched https://CoCalc.com in 2013, as an easy web-based way for students and instructors to streamline their use of open source data analysis and presentation software such as R, SageMath, Octave, Jupyter notebooks, and LaTeX. Everything in CoCalc now fully supports realtime synchronized editing, and there is a huge preinstalled software stack. CoCalc now has tens of thousands of active users at hundreds of sites. In this talk, I will explain how you can use CoCalc to enhance your teaching, research and data sharing. I will also describe how CoCalc grew out of courses I taught and a software project I started (SageMath) at the Harvard Mathematics Department 2000-2005.
Views: 2891 Harvard CMSA
Neel Sundaresan, Partner Director, Cloud and Enterprise, Microsoft Description: In the recent years we have seen how Data Science, Machine Learning, and AI, in general, have been influencing and rewriting the rules in everything from commerce to healthcare to finance. Additionally, deploying to the cloud and has opened new opportunities for collecting and using data. We have quickly moved from using data for reporting to prediction to prescription to assisting rewrite the programs that generate the data in the first place. Bots rewriting themselves have got attention for both new productivity opportunities and fear of robot domination. Bootstrapping and program generation are well known in the programming language and compiler community. Traditionally these were done using rule based systems. With massive data on code and coder behavior and interactions, and processing power to create intelligence from this, one can envision new AI that blurs the distance between data and code and code development process. Combining interaction data with program and deployment data i In this talk we will discuss topics related to robotification of various aspects of software development, compiler systems, software processes for code review, deployment etc. through use of data and AI. Bio: Neel Sundaresan is a Partner Director at Microsoft with the Cloud and Enterprise division where he leads the data platform, insights, data science/AI/ML teams. Lately he has been focusing on bringing AI and ML to the world of cloud developers. Prior to Microsoft he headed eBay Research Labs and eBay Data Labs where he created and led technology a diverse research and technology organization focused on areas from machine learning for search and recommendations to computer vision to economics, HCI and distributed systems. Prior to that he co-founded a network CRM company and was also a research manager at IBM Almaden research center. He received his Ph.D. in computer science from Indiana University, Bloomington and a masters in mathematics and in computer science from the Indian Institute of Technology, Mumbai. He has product and research experience in areas from programming languages, compilers, parallel and distributed systems, web mining, search and recommender systems, machine learning, computer vision, and internet economics. He has over 100 research publications and 148 issued patents and is a frequent speaker at academic and industrial conferences. https://www.linkedin.com/in/neel-sundaresan-a964a2/ http://www.meetup.com/SF-Bay-ACM/ http://www.sfbayacm.org/
Views: 154 San Francisco Bay ACM
What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Views: 100867 IT Miner - Tutorials & Travel
Talk Slides: https://drive.google.com/open?id=1nm3jU2sjLxoatWTenffraN3a6xt0QEE8 Deep learning is widely use in several cases with a good match and accuracy, as for example images classifications. But when to come to social networks there is a lot of problems involved, for example how do we represent a network in a neural network without lost node correspondence? Which is the best encode for graphs or is it task dependent? Here I will review the state of art and present the success and fails in the area and which are the perspective. Ana Paula is a Research Staff Member in IBM Research - Brazil, currently work with large amount of data to do Science WITH Data and Science OF Data at IBM Research Brazil. My technical interesting are in data mining and machine learning area specially in graph mining techniques for health and finance data. I am engage in STEAM initiatives to help girls and women to go to math/computer/science are. She is also passion for innovation and thus I become a master inventor at IBM.
Views: 460 PAPIs.io
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Views: 10664 Artificial Intelligence - All in One
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 95353 Stanford
Title: On the Anonymization of Sparse High-Dimensional Data Domain: Data Mining Description: 1, Privacy preservation is the most focussed issue in information publication, on the grounds that the sensitive data shouldn't be disclosed. For this regard, several privacy preservation data mining algorithms are proposed. 2, Generalisation, Bucketisation and Anatomisation techniques are used as a part of this regard. They ensure the privacy of the user,either by modifying quasi identifier values or by including noise. 3, These techniques are well suited for low dimensional data and they expel the most valuable information from the dataset.In this work,we concentrate on protection against identity disclosure in the publication of sparse high dimensional data. 4, The sparse dataset which is scanty has less information distributed in the entire dataset.So,in the first phase we transform the dataset into a band matrix framework by coordinating Genetic algorithm with Cuckoo search algorithm.This makes the nearest rows associated and makes the non zero components near to the diagonal and lessens the search space and also memory. 5, In the other phase a novel anatomisation technique based on disassociation is introduced to safeguard privacy.This technique isolates the quasi identifier values with sensitive attributes and publishes quasi identifiers straightforwardly.Then density based clustering is employed to anonymise the underlying data,ands protects against identity disclosure and increases data utility The adversary cannot relate the sensitive value with high probability.Experimental results demonstrate that this technique decreases information loss, reconstruction error and increases data utility. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Views: 99 InnovationAdsOfIndia
DATA SCIENCE AND BIG DATA - https://drive.google.com/folderview?id=1HcitqhbfSHwp6dofk6wp6JiKSCbB_0Gw ________ Download Notes Here-: https://drive.google.com/drive/folders/10L6DeXnoxmhJl_iErduOlXdLrK3Y4HU-?usp=sharing _____________ Follow Me On Instagram and Facebook-: Instagram-: https://www.instagram.com/iamaakashkamalkar/ Facebook:-www.facebook.com/aakashhearthack
Views: 3146 Engineer Ki Pathsala
Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases Steps of Classification: 1. Model construction: Describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute. The set of tuples used for model construction is training set. The model is represented as classification rules, decision trees, or mathematical formulae. 2. Model usage: For classifying future or unknown objects Estimate accuracy of the model If the accuracy is acceptable, use the model to classify new data MLP- NN Classification Algorithm The MLP-NN algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of “neuronlike” units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. Algorithm of MLP-NN is as follows: Step 1: Initialize input of all weights with small random numbers. Step 2: Calculate the weight sum of the inputs. Step 3: Calculate activation function of all hidden layer. Step 4: Output of all layers For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Views: 796 E2MATRIX RESEARCH LAB
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 27639 5 Minutes Engineering
Please also see http://www.sfbayacm.org/?p=1579 for links to the presentation slides and other related documents. DESCRIPTION: Several web applications like content optimization and online advertising involve recommending items from an inventory for each user visit to maximize some yield metric of interest (e.g. click rates). These are instances of large scale recommender system problems that entail several statistical challenges. We provide a mathematical description of the problem followed by modeling solutions for a content optimization problem that arises in the context of Yahoo! Front Page (www.yahoo.com). In fact, we discuss models to a) serve most popular items, b) serve items that are most popular in different user segments and c) provide personalized item recommendations for each user. Our models are based on time series methods, multi-armed bandit schemes and bilinear random effects model. One class of bilinear random effects model we propose extends reduced rank regression to incomplete matrices, the other class extends matrix factorization to incorporate covariates. Throughout, concepts are illustrated with examples and results obtained from "bucket tests" conducted on a real system. SPEAKER BIOGRAPHY: Deepak Agarwal is currently a Principal research scientist at Yahoo! Research. Prior to joining Yahoo!, he was a member of the statistics department at AT&T Research. He is a statistician interested in scalable modeling approaches for large scale applications. He has done extensive research on large scale hierarchical random effects model, computational advertising, modeling massive social networks with applications to call graph that arise in the telecommunications industry and modeling massive dyadic data that arise in applications like recommender systems. He has won four best paper awards (JSM 2001, SDM 2004, KDD 2007, ICDM 2009) that are directly related to the material of the talk. He has also done research in anomaly detection using a time series approach and computational approaches for scaling spatial scan statistic to large data sets. He regularly serves on program committees of data mining and machine learning conferences. He is currently associate editor for Journal of Americal Statistical Association, the top journal in the field of Statistics. He have given two tutorials on Statistical Challenges in Online Advertising at CIKM 2009 and KDD 2009. Deepak in collaboration with his co-authors have developed algorithms for real recommender systems that have been successfully deployed and thus has experience with both practical and scientific issues that arise in such applications. More info available at http://www.sfbayacm.org/?p=1579
Views: 2396 San Francisco Bay ACM
As algorithms reach ever more deeply into our daily lives, increasing concern that they be “fair” has resulted in an explosion of research in the theory and machine learning communities. This talk surveys key results in both areas and traces the arc of the emerging theory of algorithmic fairness. See more at https://www.microsoft.com/en-us/research/video/the-emerging-theory-of-algorithmic-fairness/
Views: 1483 Microsoft Research
Match the applications to the theorems: (i) Find the variance of traffic volumes in a large network presented as streaming data. (ii) Estimate failure probabilities in a complex systems with many parts. (iii) Group customers into clusters based on what they bought. (a) Projecting high dimensional space to a random low dimensional space scales each vector's length by (roughly) the same factor. (b) A random walk in a high dimensional convex set converges rather fast. (c) Given data points, we can find their best-fit subspace fast. While the theorems are precise, the talk will deal with applications at a high level. Other theorems/applications may be discussed.
Views: 2604 Microsoft Research
* First example of attention routing: web-scale malware detection (Polonium, developed with Symantec, published at SDM 2011) --------------------- Polo Chau's Thesis Defense Ph.D. in Machine Learning Carnegie Mellon University July 30, 2012
Views: 1542 Duen Horng Chau
Simpson's Paradox - How UCBerkeley Sex Discrimination Law Suit was a sham Check out http://www.SkipMba.com for free concise business & analytics concepts! Get Naked Statistics -- http://bit.ly/2earLEh Get Web Analytics -- http://amzn.to/2mQ7TIp Get Standard Deviation -- http://amzn.to/2nGchOt Want more resources? Check out the SkipMBA reading list -- https://goo.gl/n8vekf In this video, we go through an example of the Simpson's Paradox and confounding factor using the ucberkeley sex discrimination example and a business strategy case example. With data being so readily available, we're becoming overly reliant on it. Putting your blind faith in data can be perilous. Often there can be a confounding factor that is hidden within your data that will tell a different story. In this video we show a couple of confounding examples in the Simpson's Paradox. The UCBerkeley sex discrimination law suit is one of the classic simpson's paradox examples (as well as a confounding example). However, we also go through a business strategy case example of how Simpson's Paradox can lead you to make the wrong decisions. Understanding your data analytics correctly is key for business strategy in the new aga - and hence we include this study in the SkipMBA DIY MBA (self mba). For more information, we recommend reading Naked Statistics, Web Analytics 2.0 and Standard Deviations. Simpsons Paradox | Confounding factor | Statistics | Analytics | Data Analytics | Standard Deviations | Web Analytics | Business Strategy | SELF MBA | DIY MBA
Views: 9250 Mango Research
Hemant Purohit Dissertation Defense “MINING BEHAVIOR OF CITIZEN SENSOR COMMUNITIES TO IMPROVE COOPERATION WITH ORGANIZATIONAL ACTORS” Ph.D. Committee: Drs. Amit Sheth, Advisor, TK Prasad, Guozhu Dong, Valerie Shalin, Psychology, Srinivasan Parthasarathy, CSE, Ohio State University, and Patrick Meier, QCRI ABSTRACT: Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award. Additionally, the intent classification technology for identifying seeking-offering of help during a crisis was integrated by the crisis-mapping pioneer Ushahidi’s project, CrisisNET for broader impact. Slideshare: http://www.slideshare.net/knoesis/hemant-purohit-phd-defense-mining-citizen-sensor-communities-for-cooperation-with-organizations NSF SOCS project on organizational sensemaking during emergencies: http://knoesis.org/projects/socs Thesis Webpage: http://www.knoesis.org/aboutus/thesis_defense#video_hemant
Views: 1054 Knoesis Center
If you have dreams to work aborad, 2018 is the right time. This video tells you the list of top 10 Jobs for Indians in Canada with their salaries. And why only Indians, it is for anybody who wishes to immigrate to Canada and looking for job prospects here. Video guide for the Express Entry Step by Step Process: https://youtu.be/g_Zq_fnkc08 How much money would it cost you to apply Canada PR: https://youtu.be/IBB0pVgfuTo 10 most important benefits of Candian PR: https://youtu.be/P8mtkE6kpwk What is Permanent Residency: https://youtu.be/Blzm19eV6wg -~-~~-~~~-~~-~- Video guide to the Step by Step Process for Canada PR ( Express Entry 2018)" https://www.youtube.com/watch?v=g_Zq_fnkc08 -~-~~-~~~-~~-~-
Views: 732265 Dream Abroad
International Journal of Service Science, Management, Engineering, and Technology Ahmad Taher Azar (Benha University, Egypt) and Ghazy Assassa (Benha University, Egypt) http://www.bu.edu.eg/staff/ahmadazar14 Now Available Year Established: 2010 Publish Frequency: Quarterly ISSN: 1947-959X EISSN: 1947-9603 https://www.igi-global.com/journal/international-journal-service-science-management/1132 ___________ Description: The International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) is a multidisciplinary journal that publishes high-quality and significant research in all fields of computer science, information technology, software engineering, soft computing, computational intelligence, operations research, management science, marketing, applied mathematics, statistics, policy analysis, economics, natural sciences, medicine, and psychology, among others. This journal publishes original articles, reviews, technical reports, patent alerts, and case studies on the latest innovative findings of new methodologies and techniques. ___________ Topics Covered: • Agent technologies • Agricultural applications • Agricultural traceability and food safety • Artificial Intelligence • Autonomous systems • Big data technologies and management • Biochemistry • Biomedicine and bioinformatics • Biotechnology • Business Information Systems • Clinical decision support • Cloud Computing • Computational Intelligence • Computational techniques for service operations • Data mining and data security • Decision theory • Disease detection, management and monitoring • Distributed intelligence • Drug discovery • Ecological system modeling • Economic aspects of the service sector • Embedded sensor and mobile database • Evolutionary computing • Expert Systems • Financial innovation • Financial statements analysis • Fraud management • Geographic Information Systems • Heuristics • Image Processing • Information Technology • Intelligent systems and data mining • Life science and medical research • Machine Learning • Management accounting • Markov chains • Models of service systems, services as complex systems • Network management contingency issues • Neuroscience • Optimization Techniques • Pharmaceutical science • Policy, privacy, security, and legal issues regarding services • Reasoning and inferences • Security in software architecture and design • Security patterns • Sensor design, sensor-fusion and sensor-based control • Service design and modeling • Service innovation and marketing • Service oriented architecture and technologies • Service performance measurement and analysis • Service quality measurement, benchmarking, and management • Service risk management • Social Networking • Soft Computing • Software engineering • Stochastic models • Strategic Planning • Supply Chain Management • Systems engineering • Telecommunications and networking technologies • Teleoperation and telerobotics • Venture capital • Virtual Reality • Web informatics • Web intelligence and mining • Web services and technologies • Working capital management
Views: 90 IGI Global