• Extracting Knowledge from Text - Pedro Domingos

    Title: Extracting Knowledge from Text w/ Tractable Markov Logic & Symmetry-Based Semantic Parsing Abstract: Building very large commonsense knowledge bases and reasoning with them is a long-standing dream of AI. Today that knowledge is available in text; all we have to do is extract it. Text, however, is extremely messy, noisy, ambiguous, incomplete, and variable. A formal representation of it needs to be both probabilistic and relational, either of which leads to intractable inference and therefore poor scalability. In the first part of this talk I will describe tractable Markov logic, a language that is restricted enough to be tractable yet expressive enough to represent much of the commonsense knowledge contained in text. Even then, transforming text into a formal representation of its ...

    published: 26 Aug 2014
  • Machine Learning Knowledge Extraction MAKE it short

    MAKE stands for MAchine Learning & Knowledge Extraction. Machine learning deals with understanding intelligence for the design and development of algorithms that can learn from data and improve over time. The original definition was “the artificial generation of knowledge from experience”. The challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in such data, which are often hidden and not accessible to a human. Today, machine learning is the fastest growing technical field, having many application domains, e.g. health, Industry 4.0, recommender systems, speech recognition, autonomous driving (Google car), etc. The grand challenge is in decision making under uncertainty, and probabilistic inference enormously influenced artificial intelligence and s...

    published: 13 May 2017
  • Extracting Knowledge from Informal Text

    The internet has revolutionized the way we communicate, leading to a constant flood of informal text available in electronic format, including: email, Twitter, SMS and also informal text produced in professional environments such as the clinical text found in electronic medical records. This presents a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large scale data-analysis applications by extracting machine-processable information from unstructured text at scale. In this talk I will discuss several challenges and opportunities which arise when applying NLP and IE to informal text, focusing specifically on Twitter, which has recently rose to prominence, challenging the mainstream news media as the dominant source of real-tim...

    published: 08 Aug 2016
  • Extracting Theobromine from Cocoa

    Theobromine is an interesting molecule and is very similar to caffeine. It appears in a high concentration in cocoa (a small amount of caffeine is also in cocoa). In this video, I am extracting theobromine from cocoa. In the future, I might prepare cocoa powder from raw cocoa pods. Link to procedure: https://goo.gl/o7uvc3 Other reference (Chemplayer): https://youtu.be/Q5NmjKMqC74 ------------------------------------------ Nile Red Shop: https://www.nilered.ca (for keychains and beakers) Patreon: https://goo.gl/3h353G Facebook: https://goo.gl/uyxvJV Twitter: https://goo.gl/uCmnV4 Personal Instagram: https://goo.gl/EdBq4b

    published: 16 Mar 2017
  • Analyzing the Web from Start to Finish - Knowledge Extraction using KNIME - Bernd Wiswedel - #1

    Slides: http://goo.gl/Gsyvxk züri ML meetup #1, february 25th 2014 Bernd Wiswedel, Co-Founder and CTO at KNIME Analyzing the Web from Start to Finish - Knowledge Extraction from a Web Forum using KNIME Abstract: Bernd will give us an introduction to KNIME, an open source graphical workbench for the entire analysis process from data access and preprocessing to analytics, visualization and reporting. As a practical example, he will show an application in which KNIME was used to analyze a public discussion forum. It includes the steps of web crawling, web analytics, topic detection and description of user interaction. http://www.knime.org/ http://en.wikipedia.org/wiki/KNIME 1st Züri Machine Learning Meetup 25th February 2014, ETH Zurich, Switzerland

    published: 11 Mar 2014
  • Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark

    published: 22 Feb 2016
  • Machine learning for autonomous systems and knowledge extraction from big data - Lyudmila Mihaylova

    CIS Seminar Series (http://cis.eecs.qmul.ac.uk/seminars.html) Machine learning for autonomous systems and knowledge extraction from big data Lyudmila S Mihaylova, University of Sheffield Where: Eng 209 Host: Andrea Cavallaro Abstract This talk focuses on methods for detection, tracking and decision making for autonomous or semi-autonomous surveillance systems. The data usually arrive from multiple heterogeneous sensors such as radar, LIDAR or cameras (optical and infrared). Methods for small and large groups comprised of hundreds or thousands of objects (normally referred to as cluster/crowd tracking) will be presented. Groups are structured objects characterised with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport...

    published: 27 Nov 2014
  • Mining the Knowledge of Scientific Publications

    Horacio Saggion: Associate Professor - Natural Language Processing Research Group María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic

    published: 11 Jul 2016
  • CHEM117 04 Liquid Liquid Extraction Fundamentals

    published: 02 Oct 2015
  • Knowledge extraction and semantic linking in the Encyclopedia of Life

    Anne Thessen Boston Python - Parsing with PLY and Lightning Talks 04/25/2013 Microsoft NERD, Cambridge, MA

    published: 27 Apr 2013
  • Extraction with Mark Niemczyk, Ph.D.

    Extraction with Mark Niemczyk, Ph.D.

    published: 20 Sep 2013
  • Data Mining KDD Process

    KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.

    published: 22 May 2015
  • Multilingual Knowledge Extraction From Emails

    published: 07 May 2015
  • Knowledge Extraction for Retail

    Rafael Pous: Associate Professor - Ubiquitous Computing Applications Lab. María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic

    published: 13 Jul 2016
  • Knowledge Extraction

    published: 06 Jun 2017
  • Doozer: Knowledge Extraction from Community-Generated Content

    Doozer is an on demand domain model creation tool, designed by Christopher Thomas at Knoesis Research Center. (http://knoesis.org) Project page: http://wiki.knoesis.org/index.php/Doozer

    published: 27 Oct 2011
  • Knowledge Extraction Process

    Knowledge extraction process employed by Triumph India Software Services.

    published: 18 Sep 2012
  • Technique of Extraction and Washing

    Introduction on to the technique of liquid-liquid extraction and washing at the University of Manitoba. Undergraduate organic chemistry laboratory program under Dr. Horace Luong's supervision.

    published: 17 Dec 2016
  • The MdM Knowledge Extraction Strategic Research Program at DTIC-UPF

    Xavier Serra, Scientific Director of the DTIC-UPF María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic

    published: 11 Jul 2016
  • ML Lunch (Oct 7, 2013): Extracting Knowledge from Informal Text

    Speaker: Alan Ritter Abstract The internet has revolutionized the way we communicate, leading to a constant flood of informal text available in electronic format, including: email, Twitter, SMS and the clinical text found in electronic medical records. This presents a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large scale data-analysis applications by extracting machine-processable information from unstructured text at scale. In this talk I will discuss several challenges and opportunities which arise when applying NLP and IE to informal text, focusing specifically on Twitter, which has recently rose to prominence, challenging the mainstream news media as the dominant source of realtime information on current events. I w...

    published: 07 Oct 2013
  • Knowledge extraction from the wastewater treatment plant operation - Y. Kim

    knowledge extraction from the historical database of wastewater treatment plant operation using various data mining techniques. y. Kim, H. S. Kim, M. S. Kim, W. piao, D. Kang, C. Kim, Republic of Korea Chan Moon,Kim - Industrial Instrumentation & Control Professional Engineer Engineering Services Department, Korea Water Resources Corporation In this conference we will discuss the new developments in IT & Water. These developments are very important for the further evolution of the water sector. IT applications in the water sector cover a broad field of interest. On one hand, IT applications have the ability to integrate the water sector from a high strategic level with connections to security and energy services. on the other hand IT applications have the ability to improve the perform...

    published: 26 Nov 2012
  • Extraction of Knowledge from Email to Aid in Expertise Discovery - Dr Thomas Jackson

    Seminar given at by Dr Thomas Jackson at the Department Information Science Research Day at Loughborough University in July 2010. The talk describes the Extraction of Knowledge from Email to Aid in Expertise Discovery and how the research will move forward to classify and categorise email vaults.

    published: 11 Jan 2011
  • Aspirin to Acetaminophen - Part 1 of 6: Extracting Aspirin from Pills

    UPDATE: In this video, I am showing you guys what the "proper" procedure would be, but it is by no means the fastest. If you want the fastest way, follow chemplayers method. I just prefer to not evaporate solvents, but that takes extra time. I would use a gravity filtration though in both cases though. It's just as fast and doesn't have as many problems as a vacuum one. Still do a recrystallization though. Without a recryst/hot filtration, the ASA is still pretty dirty. Hey guys, today we are starting on the journey from Aspirin to Tylenol. Tell me what you guys wanna see by voting here: http://www.strawpoll.me/11567284 ChemPlayer video: https://youtu.be/oztnjTukbpE My old aspirin extraction video: https://youtu.be/LvYetXXnTmI ------------------------------------------ Patreo...

    published: 03 Nov 2016
  • Knowledge Discovery Framework

    published: 03 Sep 2013
Extracting Knowledge from Text - Pedro Domingos

Extracting Knowledge from Text - Pedro Domingos

  • Order:
  • Duration: 1:04:37
  • Updated: 26 Aug 2014
  • views: 5541
videos
Title: Extracting Knowledge from Text w/ Tractable Markov Logic & Symmetry-Based Semantic Parsing Abstract: Building very large commonsense knowledge bases and reasoning with them is a long-standing dream of AI. Today that knowledge is available in text; all we have to do is extract it. Text, however, is extremely messy, noisy, ambiguous, incomplete, and variable. A formal representation of it needs to be both probabilistic and relational, either of which leads to intractable inference and therefore poor scalability. In the first part of this talk I will describe tractable Markov logic, a language that is restricted enough to be tractable yet expressive enough to represent much of the commonsense knowledge contained in text. Even then, transforming text into a formal representation of its meaning remains a difficult problem. There is no agreement on what the representation primitives should be, and labeled data in the form of sentence-meaning pairs for training a semantic parser is very hard to come by. In the second part of the talk I will propose a solution to both these problems, based on concepts from symmetry group theory. A symmetry of a sentence is a syntactic transformation that does not change its meaning. Learning a semantic parser for a language is discovering its symmetry group, and the meaning of a sentence is its orbit under the group (i.e., the set of all sentences it can be mapped to by composing symmetries). Preliminary experiments indicate that tractable Markov logic and symmetry-based semantic parsing can be powerful tools for scalably extracting knowledge from text.
https://wn.com/Extracting_Knowledge_From_Text_Pedro_Domingos
Machine Learning Knowledge Extraction MAKE it short

Machine Learning Knowledge Extraction MAKE it short

  • Order:
  • Duration: 3:21
  • Updated: 13 May 2017
  • views: 163
videos
MAKE stands for MAchine Learning & Knowledge Extraction. Machine learning deals with understanding intelligence for the design and development of algorithms that can learn from data and improve over time. The original definition was “the artificial generation of knowledge from experience”. The challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in such data, which are often hidden and not accessible to a human. Today, machine learning is the fastest growing technical field, having many application domains, e.g. health, Industry 4.0, recommender systems, speech recognition, autonomous driving (Google car), etc. The grand challenge is in decision making under uncertainty, and probabilistic inference enormously influenced artificial intelligence and statistical learning. The inverse probability allows to infer unknowns, learn from data and make predictions to support decision making. Whether in social networks, recommender systems, smart health or smart factory applications, the increasingly complex data sets require efficient, useful and useable intelligence for knowledge discovery and knowledge extraction. A synergistic combination of methodologies and approaches of two domains offer ideal conditions towards unraveling these challenges and to foster new, efficient and user-friendly machine learning algorithms and knowledge extraction tools: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), aiming at augmenting human intelligence with computational intelligence and vice versa. Successful Machine Learning & Knowledge extraction needs a concerted international effort without boundaries, supporting collaborative and integrative cross-disciplinary research between experts from 7 fields: in short: 1-data, 2-learning, 3-graphs, 4-topology, 5-entropy, 6-visualization, and 7-privacy; see http://hci-kdd.org/about-the-holzinger-group https://cd-make.net/about/ http://www.mdpi.com/journal/make/about Andreas Holzinger, 14.05.2017
https://wn.com/Machine_Learning_Knowledge_Extraction_Make_It_Short
Extracting Knowledge from Informal Text

Extracting Knowledge from Informal Text

  • Order:
  • Duration: 59:59
  • Updated: 08 Aug 2016
  • views: 544
videos
The internet has revolutionized the way we communicate, leading to a constant flood of informal text available in electronic format, including: email, Twitter, SMS and also informal text produced in professional environments such as the clinical text found in electronic medical records. This presents a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large scale data-analysis applications by extracting machine-processable information from unstructured text at scale. In this talk I will discuss several challenges and opportunities which arise when applying NLP and IE to informal text, focusing specifically on Twitter, which has recently rose to prominence, challenging the mainstream news media as the dominant source of real-time information on current events. I will describe several NLP tools we have adapted to handle Twitter�s noisy style, and present a system which leverages these to automatically extract a calendar of popular events occurring in the near future (http://statuscalendar.cs.washington.edu). I will further discuss fundamental challenges which arise when extracting meaning from such massive open-domain text corpora. Several probabilistic latent variable models will be presented, which are applied to infer the semantics of large numbers of words and phrases and also enable a principled and modular approach to extracting knowledge from large open-domain text corpora.
https://wn.com/Extracting_Knowledge_From_Informal_Text
Extracting Theobromine from Cocoa

Extracting Theobromine from Cocoa

  • Order:
  • Duration: 11:54
  • Updated: 16 Mar 2017
  • views: 61610
videos
Theobromine is an interesting molecule and is very similar to caffeine. It appears in a high concentration in cocoa (a small amount of caffeine is also in cocoa). In this video, I am extracting theobromine from cocoa. In the future, I might prepare cocoa powder from raw cocoa pods. Link to procedure: https://goo.gl/o7uvc3 Other reference (Chemplayer): https://youtu.be/Q5NmjKMqC74 ------------------------------------------ Nile Red Shop: https://www.nilered.ca (for keychains and beakers) Patreon: https://goo.gl/3h353G Facebook: https://goo.gl/uyxvJV Twitter: https://goo.gl/uCmnV4 Personal Instagram: https://goo.gl/EdBq4b
https://wn.com/Extracting_Theobromine_From_Cocoa
Analyzing the Web from Start to Finish - Knowledge Extraction using KNIME - Bernd Wiswedel - #1

Analyzing the Web from Start to Finish - Knowledge Extraction using KNIME - Bernd Wiswedel - #1

  • Order:
  • Duration: 33:59
  • Updated: 11 Mar 2014
  • views: 8383
videos
Slides: http://goo.gl/Gsyvxk züri ML meetup #1, february 25th 2014 Bernd Wiswedel, Co-Founder and CTO at KNIME Analyzing the Web from Start to Finish - Knowledge Extraction from a Web Forum using KNIME Abstract: Bernd will give us an introduction to KNIME, an open source graphical workbench for the entire analysis process from data access and preprocessing to analytics, visualization and reporting. As a practical example, he will show an application in which KNIME was used to analyze a public discussion forum. It includes the steps of web crawling, web analytics, topic detection and description of user interaction. http://www.knime.org/ http://en.wikipedia.org/wiki/KNIME 1st Züri Machine Learning Meetup 25th February 2014, ETH Zurich, Switzerland
https://wn.com/Analyzing_The_Web_From_Start_To_Finish_Knowledge_Extraction_Using_Knime_Bernd_Wiswedel_1
Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark

Relationship Extraction from Unstructured Text Based on Stanford NLP with Spark

  • Order:
  • Duration: 27:06
  • Updated: 22 Feb 2016
  • views: 6406
videos
https://wn.com/Relationship_Extraction_From_Unstructured_Text_Based_On_Stanford_Nlp_With_Spark
Machine learning for autonomous systems and knowledge extraction from big data - Lyudmila Mihaylova

Machine learning for autonomous systems and knowledge extraction from big data - Lyudmila Mihaylova

  • Order:
  • Duration: 49:59
  • Updated: 27 Nov 2014
  • views: 390
videos
CIS Seminar Series (http://cis.eecs.qmul.ac.uk/seminars.html) Machine learning for autonomous systems and knowledge extraction from big data Lyudmila S Mihaylova, University of Sheffield Where: Eng 209 Host: Andrea Cavallaro Abstract This talk focuses on methods for detection, tracking and decision making for autonomous or semi-autonomous surveillance systems. The data usually arrive from multiple heterogeneous sensors such as radar, LIDAR or cameras (optical and infrared). Methods for small and large groups comprised of hundreds or thousands of objects (normally referred to as cluster/crowd tracking) will be presented. Groups are structured objects characterised with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of interacting components such as crowds of people. Large groups pose different challenges compared with small group tracking due to the fact that sensors do not provide sufficient information for each individual target. Groups and extended objects generate multiple measurements which require advanced methods to cope with measurements origin uncertainty and large data sets. This talk will present recent results with Sequential Monte Carlo methods such as convolution particle filters and Box Particle filters. Challenges and future trends are also discussed.
https://wn.com/Machine_Learning_For_Autonomous_Systems_And_Knowledge_Extraction_From_Big_Data_Lyudmila_Mihaylova
Mining the Knowledge of Scientific Publications

Mining the Knowledge of Scientific Publications

  • Order:
  • Duration: 36:37
  • Updated: 11 Jul 2016
  • views: 92
videos
Horacio Saggion: Associate Professor - Natural Language Processing Research Group María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic
https://wn.com/Mining_The_Knowledge_Of_Scientific_Publications
CHEM117 04   Liquid Liquid Extraction Fundamentals

CHEM117 04 Liquid Liquid Extraction Fundamentals

  • Order:
  • Duration: 26:46
  • Updated: 02 Oct 2015
  • views: 37166
videos
https://wn.com/Chem117_04_Liquid_Liquid_Extraction_Fundamentals
Knowledge extraction and semantic linking in the Encyclopedia of Life

Knowledge extraction and semantic linking in the Encyclopedia of Life

  • Order:
  • Duration: 9:42
  • Updated: 27 Apr 2013
  • views: 108
videos https://wn.com/Knowledge_Extraction_And_Semantic_Linking_In_The_Encyclopedia_Of_Life
Extraction with Mark Niemczyk, Ph.D.

Extraction with Mark Niemczyk, Ph.D.

  • Order:
  • Duration: 29:16
  • Updated: 20 Sep 2013
  • views: 2002
videos
Extraction with Mark Niemczyk, Ph.D.
https://wn.com/Extraction_With_Mark_Niemczyk,_Ph.D.
Data Mining   KDD Process

Data Mining KDD Process

  • Order:
  • Duration: 3:08
  • Updated: 22 May 2015
  • views: 16565
videos
KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
https://wn.com/Data_Mining_Kdd_Process
Multilingual Knowledge Extraction From Emails

Multilingual Knowledge Extraction From Emails

  • Order:
  • Duration: 2:36
  • Updated: 07 May 2015
  • views: 25
videos
https://wn.com/Multilingual_Knowledge_Extraction_From_Emails
Knowledge Extraction for Retail

Knowledge Extraction for Retail

  • Order:
  • Duration: 36:00
  • Updated: 13 Jul 2016
  • views: 35
videos
Rafael Pous: Associate Professor - Ubiquitous Computing Applications Lab. María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic
https://wn.com/Knowledge_Extraction_For_Retail
Knowledge Extraction

Knowledge Extraction

  • Order:
  • Duration: 13:03
  • Updated: 06 Jun 2017
  • views: 20
videos
https://wn.com/Knowledge_Extraction
Doozer: Knowledge Extraction from Community-Generated Content

Doozer: Knowledge Extraction from Community-Generated Content

  • Order:
  • Duration: 11:23
  • Updated: 27 Oct 2011
  • views: 185
videos
Doozer is an on demand domain model creation tool, designed by Christopher Thomas at Knoesis Research Center. (http://knoesis.org) Project page: http://wiki.knoesis.org/index.php/Doozer
https://wn.com/Doozer_Knowledge_Extraction_From_Community_Generated_Content
Knowledge Extraction Process

Knowledge Extraction Process

  • Order:
  • Duration: 0:10
  • Updated: 18 Sep 2012
  • views: 79
videos
Knowledge extraction process employed by Triumph India Software Services.
https://wn.com/Knowledge_Extraction_Process
Technique of Extraction and Washing

Technique of Extraction and Washing

  • Order:
  • Duration: 11:41
  • Updated: 17 Dec 2016
  • views: 1047
videos
Introduction on to the technique of liquid-liquid extraction and washing at the University of Manitoba. Undergraduate organic chemistry laboratory program under Dr. Horace Luong's supervision.
https://wn.com/Technique_Of_Extraction_And_Washing
The MdM Knowledge Extraction Strategic Research Program at DTIC-UPF

The MdM Knowledge Extraction Strategic Research Program at DTIC-UPF

  • Order:
  • Duration: 50:53
  • Updated: 11 Jul 2016
  • views: 72
videos
Xavier Serra, Scientific Director of the DTIC-UPF María deMaeztu DTIC-UPF Workshop on Data Driven Knowledge Extraction, 28-29 June 2016, Barcelona To know more about the María de Maeztu Strategic Research Program and projects, visit http://www.upf.edu/mdm-dtic To know more about the department, visit http://www.upf.edu/mdm-dtic
https://wn.com/The_Mdm_Knowledge_Extraction_Strategic_Research_Program_At_Dtic_Upf
ML Lunch (Oct 7, 2013): Extracting Knowledge from Informal Text

ML Lunch (Oct 7, 2013): Extracting Knowledge from Informal Text

  • Order:
  • Duration: 42:43
  • Updated: 07 Oct 2013
  • views: 608
videos
Speaker: Alan Ritter Abstract The internet has revolutionized the way we communicate, leading to a constant flood of informal text available in electronic format, including: email, Twitter, SMS and the clinical text found in electronic medical records. This presents a big opportunity for Natural Language Processing (NLP) and Information Extraction (IE) technology to enable new large scale data-analysis applications by extracting machine-processable information from unstructured text at scale. In this talk I will discuss several challenges and opportunities which arise when applying NLP and IE to informal text, focusing specifically on Twitter, which has recently rose to prominence, challenging the mainstream news media as the dominant source of realtime information on current events. I will describe several NLP tools we have adapted to handle Twitter’s noisy style, and present a system which leverages these to automatically extract a calendar of popular events occurring in the near future. I will further discuss fundamental challenges which arise when extracting meaning from such massive open-domain text corpora. Several probabilistic latent variable models will be presented, which are applied to infer the semantics of large numbers of words and phrases and also enable a principled and modular approach to extracting knowledge from large open-domain text corpora. For more ML Lunch talks: http://www.cs.cmu.edu/~learning/
https://wn.com/Ml_Lunch_(Oct_7,_2013)_Extracting_Knowledge_From_Informal_Text
Knowledge extraction from the wastewater treatment plant operation - Y. Kim

Knowledge extraction from the wastewater treatment plant operation - Y. Kim

  • Order:
  • Duration: 20:17
  • Updated: 26 Nov 2012
  • views: 642
videos
knowledge extraction from the historical database of wastewater treatment plant operation using various data mining techniques. y. Kim, H. S. Kim, M. S. Kim, W. piao, D. Kang, C. Kim, Republic of Korea Chan Moon,Kim - Industrial Instrumentation & Control Professional Engineer Engineering Services Department, Korea Water Resources Corporation In this conference we will discuss the new developments in IT & Water. These developments are very important for the further evolution of the water sector. IT applications in the water sector cover a broad field of interest. On one hand, IT applications have the ability to integrate the water sector from a high strategic level with connections to security and energy services. on the other hand IT applications have the ability to improve the performance of a single process, or part of a process, by improving the design or control with detailed models. IT applications should lead to better water quality, lower environmental impact and more efficient management, control, monitoring and maintenance of water systems, infrastructure and water treatment processes. Due to fast communication and new ways of personal interaction with stakeholders and customers, IT applications will not only support the water sector in carrying out its primary tasks but also in its communication with customers and stakeholders.
https://wn.com/Knowledge_Extraction_From_The_Wastewater_Treatment_Plant_Operation_Y._Kim
Extraction of Knowledge from Email to Aid in Expertise Discovery - Dr Thomas Jackson

Extraction of Knowledge from Email to Aid in Expertise Discovery - Dr Thomas Jackson

  • Order:
  • Duration: 13:01
  • Updated: 11 Jan 2011
  • views: 283
videos
Seminar given at by Dr Thomas Jackson at the Department Information Science Research Day at Loughborough University in July 2010. The talk describes the Extraction of Knowledge from Email to Aid in Expertise Discovery and how the research will move forward to classify and categorise email vaults.
https://wn.com/Extraction_Of_Knowledge_From_Email_To_Aid_In_Expertise_Discovery_Dr_Thomas_Jackson
Aspirin to Acetaminophen - Part 1 of 6: Extracting Aspirin from Pills

Aspirin to Acetaminophen - Part 1 of 6: Extracting Aspirin from Pills

  • Order:
  • Duration: 15:50
  • Updated: 03 Nov 2016
  • views: 69539
videos
UPDATE: In this video, I am showing you guys what the "proper" procedure would be, but it is by no means the fastest. If you want the fastest way, follow chemplayers method. I just prefer to not evaporate solvents, but that takes extra time. I would use a gravity filtration though in both cases though. It's just as fast and doesn't have as many problems as a vacuum one. Still do a recrystallization though. Without a recryst/hot filtration, the ASA is still pretty dirty. Hey guys, today we are starting on the journey from Aspirin to Tylenol. Tell me what you guys wanna see by voting here: http://www.strawpoll.me/11567284 ChemPlayer video: https://youtu.be/oztnjTukbpE My old aspirin extraction video: https://youtu.be/LvYetXXnTmI ------------------------------------------ Patreon: https://www.patreon.com/user?u=2448989&ty=h Facebook: https://www.facebook.com/NileRed1/ Twitter: https://twitter.com/NileRed2 Personal Instagram: https://www.instagram.com/nilered2/
https://wn.com/Aspirin_To_Acetaminophen_Part_1_Of_6_Extracting_Aspirin_From_Pills
Knowledge Discovery Framework

Knowledge Discovery Framework

  • Order:
  • Duration: 1:45
  • Updated: 03 Sep 2013
  • views: 127
videos
https://wn.com/Knowledge_Discovery_Framework