IBM Watson projects :: eight exciting projects that put artificial intelligence into practice for optimal business performance.
IBM Watson Projects demonstrate projects focused on cognitive computing & analytical capabilities of IBM Watson. You will explore designing smart business solutions and understand the effectiveness of a supply chain using projects including healthcare dialog system, sentimental analysis on socia...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | IBM Watson Projects demonstrate projects focused on cognitive computing & analytical capabilities of IBM Watson. You will explore designing smart business solutions and understand the effectiveness of a supply chain using projects including healthcare dialog system, sentimental analysis on social media, personalized recommendation systems and more. |
| Beschreibung: | 1 online resource (332 pages) |
| ISBN: | 9781789346695 178934669X |
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: The Essentials of IBM Watson -- Definition and objectives -- IBM Cloud prerequisites -- Exploring the Watson interface -- The menu bar -- Menu icon -- IBM Cloud -- Catalog -- Docs -- Support -- Manage -- Profile -- avatar -- Online glossary, let's chat, and feedback -- What about Watson? -- The Watson dashboard -- Menu bar -- Quick start information bar -- Search, add, filter, and sort -- Content panel area -- Basic tasks refresher -- The first step -- Explore -- Watson prompts -- Predict -- Assemble -- Social media -- Refine -- Saving the original -- Add -- some data -- Refine -- Summary -- Chapter 2: A Basic Watson Project -- The problem defined -- Getting started -- Gathering data -- Building your Watson project -- Loading your data -- Data review -- What does this mean? -- Improving your score with Refine -- Refine or Explore -- Creating a prediction -- Top predictors -- Main Insight page -- Details page -- An insight -- Reviewing the results -- Summary -- Chapter 3: An Automated Supply Chain Scenario -- The problem defined -- Getting started -- Gathering and reviewing data -- Building the Watson project -- Loading your data -- Reviewing the data -- Refining the data -- Creating a prediction -- Supply chain prediction -- Predictors -- Main insights -- Reviewing the results -- Sharing with a dashboard -- Adding a new visualization -- Summary -- Chapter 4: Healthcare Dialoguing -- The problem defined -- What is dialoguing? -- Leveraging (new) data to identify risk -- Getting started -- Gathering and reviewing data -- Building the project -- Reviewing the results -- Exploring the dialog data -- Collecting the data -- Moving on -- Recap -- Results -- Data quality of the prediction -- Data quality report. | |
| 505 | 8 | |a More predictive strength -- More detail -- Assembling a story -- Testing your story -- Summary -- Chapter 5: Social Media Sentiment Analysis -- The problem defined -- Social media and IBM Watson Analytics -- Getting started -- Creating a Watson Analytics social media project -- Building the project -- Project creation step by step -- Adding topics -- Social media investigative themes -- Adding dates -- Languages -- Sources -- Reviewing the results -- Deeper dive -- conversation clusters -- Navigation -- Topics -- Another look -- Sentiment -- Sentiment terms -- Geography -- Sources and sites -- Influential authors -- Author interests -- Games and shopping -- Behavior -- Demographics -- The sentiment dictionary -- The data -- Summary -- Chapter 6: Pattern Recognition and Classification -- The problem defined -- Data peeking -- Starting a pattern recognition and classification project -- Investigation -- Coach me -- More with Watson Analytics -- The insight bar -- Modifying a visualization -- Additional filtering -- Item-based calculations -- Navigate -- Compare -- Simply trending -- Developing the pattern recognition and classification project -- Quality -- The Watson Analytics data quality report -- Creating the prediction -- The prediction workflow -- Understanding the workflow step by step -- Reviewing the results -- Displaying top predictors and predictive strength -- Summary -- Chapter 7: Retail and Personalized Recommendations -- The problem defined -- Product recommendation engines -- Recommendations from Watson Analytics -- The data at a glance -- Starting the project -- Range filter -- Save me -- Developing the project -- Reviewing the results -- Targets -- Summary ribbon -- The top predictors -- Sharing the insights -- Summary -- Chapter 8: Integration for Sales Forecasting -- The problem defined -- Product forecasting. | |
| 505 | 8 | |a Systematic forecasting -- IBM Planning Analytics -- Our data -- Creating the forecast -- Starting the project -- Developing the project -- Visualizations and data requirements -- More questioning -- Time Series -- Other visualization options -- Reviewing the results -- Summary -- Chapter 9: Anomaly Detection in Banking Using AI -- Defining the problem -- Banking use cases -- Corruption -- Cash -- Billing -- Check tampering -- Skimming -- Larceny -- Financial statement fraud -- Starting the project -- The data -- Developing the project -- The first question -- Using Excel for sorting and filtering the data -- Back to Watson -- Check numbers -- Reviewing the results -- Collecting -- Telling the story -- Summary -- Chapter 10: What's Next -- Chapter-by-chapter summary -- Chapter 1 -- The Essentials of IBM Watson -- Chapter 2 -- A Basic Watson Project -- Chapter 3 -- An Automated Supply Chain Scenario -- Chapter 4 -- Healthcare Dialoguing -- Chapter 5 -- Social Media Sentiment Analysis -- Chapter 6 -- Pattern Recognition And Classification -- Chapter 7 -- Retail And Personalized Recommendations -- Chapter 8 -- Integration for Sales Forecasting -- Chapter 9 -- Anomaly Detection in Banking With AI -- Suggested next steps -- Packt Publishing books, blogs, and video courses -- Learning IBM Watson Analytics -- LinkedIn groups -- Product documentation -- IBM websites -- Experiment -- Summary -- Other Books You May Enjoy -- Index. | |
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| contents | Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: The Essentials of IBM Watson -- Definition and objectives -- IBM Cloud prerequisites -- Exploring the Watson interface -- The menu bar -- Menu icon -- IBM Cloud -- Catalog -- Docs -- Support -- Manage -- Profile -- avatar -- Online glossary, let's chat, and feedback -- What about Watson? -- The Watson dashboard -- Menu bar -- Quick start information bar -- Search, add, filter, and sort -- Content panel area -- Basic tasks refresher -- The first step -- Explore -- Watson prompts -- Predict -- Assemble -- Social media -- Refine -- Saving the original -- Add -- some data -- Refine -- Summary -- Chapter 2: A Basic Watson Project -- The problem defined -- Getting started -- Gathering data -- Building your Watson project -- Loading your data -- Data review -- What does this mean? -- Improving your score with Refine -- Refine or Explore -- Creating a prediction -- Top predictors -- Main Insight page -- Details page -- An insight -- Reviewing the results -- Summary -- Chapter 3: An Automated Supply Chain Scenario -- The problem defined -- Getting started -- Gathering and reviewing data -- Building the Watson project -- Loading your data -- Reviewing the data -- Refining the data -- Creating a prediction -- Supply chain prediction -- Predictors -- Main insights -- Reviewing the results -- Sharing with a dashboard -- Adding a new visualization -- Summary -- Chapter 4: Healthcare Dialoguing -- The problem defined -- What is dialoguing? -- Leveraging (new) data to identify risk -- Getting started -- Gathering and reviewing data -- Building the project -- Reviewing the results -- Exploring the dialog data -- Collecting the data -- Moving on -- Recap -- Results -- Data quality of the prediction -- Data quality report. More predictive strength -- More detail -- Assembling a story -- Testing your story -- Summary -- Chapter 5: Social Media Sentiment Analysis -- The problem defined -- Social media and IBM Watson Analytics -- Getting started -- Creating a Watson Analytics social media project -- Building the project -- Project creation step by step -- Adding topics -- Social media investigative themes -- Adding dates -- Languages -- Sources -- Reviewing the results -- Deeper dive -- conversation clusters -- Navigation -- Topics -- Another look -- Sentiment -- Sentiment terms -- Geography -- Sources and sites -- Influential authors -- Author interests -- Games and shopping -- Behavior -- Demographics -- The sentiment dictionary -- The data -- Summary -- Chapter 6: Pattern Recognition and Classification -- The problem defined -- Data peeking -- Starting a pattern recognition and classification project -- Investigation -- Coach me -- More with Watson Analytics -- The insight bar -- Modifying a visualization -- Additional filtering -- Item-based calculations -- Navigate -- Compare -- Simply trending -- Developing the pattern recognition and classification project -- Quality -- The Watson Analytics data quality report -- Creating the prediction -- The prediction workflow -- Understanding the workflow step by step -- Reviewing the results -- Displaying top predictors and predictive strength -- Summary -- Chapter 7: Retail and Personalized Recommendations -- The problem defined -- Product recommendation engines -- Recommendations from Watson Analytics -- The data at a glance -- Starting the project -- Range filter -- Save me -- Developing the project -- Reviewing the results -- Targets -- Summary ribbon -- The top predictors -- Sharing the insights -- Summary -- Chapter 8: Integration for Sales Forecasting -- The problem defined -- Product forecasting. Systematic forecasting -- IBM Planning Analytics -- Our data -- Creating the forecast -- Starting the project -- Developing the project -- Visualizations and data requirements -- More questioning -- Time Series -- Other visualization options -- Reviewing the results -- Summary -- Chapter 9: Anomaly Detection in Banking Using AI -- Defining the problem -- Banking use cases -- Corruption -- Cash -- Billing -- Check tampering -- Skimming -- Larceny -- Financial statement fraud -- Starting the project -- The data -- Developing the project -- The first question -- Using Excel for sorting and filtering the data -- Back to Watson -- Check numbers -- Reviewing the results -- Collecting -- Telling the story -- Summary -- Chapter 10: What's Next -- Chapter-by-chapter summary -- Chapter 1 -- The Essentials of IBM Watson -- Chapter 2 -- A Basic Watson Project -- Chapter 3 -- An Automated Supply Chain Scenario -- Chapter 4 -- Healthcare Dialoguing -- Chapter 5 -- Social Media Sentiment Analysis -- Chapter 6 -- Pattern Recognition And Classification -- Chapter 7 -- Retail And Personalized Recommendations -- Chapter 8 -- Integration for Sales Forecasting -- Chapter 9 -- Anomaly Detection in Banking With AI -- Suggested next steps -- Packt Publishing books, blogs, and video courses -- Learning IBM Watson Analytics -- LinkedIn groups -- Product documentation -- IBM websites -- Experiment -- Summary -- Other Books You May Enjoy -- Index. |
| ctrlnum | (OCoLC)1056912561 |
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| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1056912561 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:29:11Z |
| institution | BVB |
| isbn | 9781789346695 178934669X |
| language | English |
| oclc_num | 1056912561 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (332 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt, |
| record_format | marc |
| spelling | Miller, James. IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance. Birmingham : Packt, 2018. 1 online resource (332 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (EBSCO, viewed October 17, 2018). Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: The Essentials of IBM Watson -- Definition and objectives -- IBM Cloud prerequisites -- Exploring the Watson interface -- The menu bar -- Menu icon -- IBM Cloud -- Catalog -- Docs -- Support -- Manage -- Profile -- avatar -- Online glossary, let's chat, and feedback -- What about Watson? -- The Watson dashboard -- Menu bar -- Quick start information bar -- Search, add, filter, and sort -- Content panel area -- Basic tasks refresher -- The first step -- Explore -- Watson prompts -- Predict -- Assemble -- Social media -- Refine -- Saving the original -- Add -- some data -- Refine -- Summary -- Chapter 2: A Basic Watson Project -- The problem defined -- Getting started -- Gathering data -- Building your Watson project -- Loading your data -- Data review -- What does this mean? -- Improving your score with Refine -- Refine or Explore -- Creating a prediction -- Top predictors -- Main Insight page -- Details page -- An insight -- Reviewing the results -- Summary -- Chapter 3: An Automated Supply Chain Scenario -- The problem defined -- Getting started -- Gathering and reviewing data -- Building the Watson project -- Loading your data -- Reviewing the data -- Refining the data -- Creating a prediction -- Supply chain prediction -- Predictors -- Main insights -- Reviewing the results -- Sharing with a dashboard -- Adding a new visualization -- Summary -- Chapter 4: Healthcare Dialoguing -- The problem defined -- What is dialoguing? -- Leveraging (new) data to identify risk -- Getting started -- Gathering and reviewing data -- Building the project -- Reviewing the results -- Exploring the dialog data -- Collecting the data -- Moving on -- Recap -- Results -- Data quality of the prediction -- Data quality report. More predictive strength -- More detail -- Assembling a story -- Testing your story -- Summary -- Chapter 5: Social Media Sentiment Analysis -- The problem defined -- Social media and IBM Watson Analytics -- Getting started -- Creating a Watson Analytics social media project -- Building the project -- Project creation step by step -- Adding topics -- Social media investigative themes -- Adding dates -- Languages -- Sources -- Reviewing the results -- Deeper dive -- conversation clusters -- Navigation -- Topics -- Another look -- Sentiment -- Sentiment terms -- Geography -- Sources and sites -- Influential authors -- Author interests -- Games and shopping -- Behavior -- Demographics -- The sentiment dictionary -- The data -- Summary -- Chapter 6: Pattern Recognition and Classification -- The problem defined -- Data peeking -- Starting a pattern recognition and classification project -- Investigation -- Coach me -- More with Watson Analytics -- The insight bar -- Modifying a visualization -- Additional filtering -- Item-based calculations -- Navigate -- Compare -- Simply trending -- Developing the pattern recognition and classification project -- Quality -- The Watson Analytics data quality report -- Creating the prediction -- The prediction workflow -- Understanding the workflow step by step -- Reviewing the results -- Displaying top predictors and predictive strength -- Summary -- Chapter 7: Retail and Personalized Recommendations -- The problem defined -- Product recommendation engines -- Recommendations from Watson Analytics -- The data at a glance -- Starting the project -- Range filter -- Save me -- Developing the project -- Reviewing the results -- Targets -- Summary ribbon -- The top predictors -- Sharing the insights -- Summary -- Chapter 8: Integration for Sales Forecasting -- The problem defined -- Product forecasting. Systematic forecasting -- IBM Planning Analytics -- Our data -- Creating the forecast -- Starting the project -- Developing the project -- Visualizations and data requirements -- More questioning -- Time Series -- Other visualization options -- Reviewing the results -- Summary -- Chapter 9: Anomaly Detection in Banking Using AI -- Defining the problem -- Banking use cases -- Corruption -- Cash -- Billing -- Check tampering -- Skimming -- Larceny -- Financial statement fraud -- Starting the project -- The data -- Developing the project -- The first question -- Using Excel for sorting and filtering the data -- Back to Watson -- Check numbers -- Reviewing the results -- Collecting -- Telling the story -- Summary -- Chapter 10: What's Next -- Chapter-by-chapter summary -- Chapter 1 -- The Essentials of IBM Watson -- Chapter 2 -- A Basic Watson Project -- Chapter 3 -- An Automated Supply Chain Scenario -- Chapter 4 -- Healthcare Dialoguing -- Chapter 5 -- Social Media Sentiment Analysis -- Chapter 6 -- Pattern Recognition And Classification -- Chapter 7 -- Retail And Personalized Recommendations -- Chapter 8 -- Integration for Sales Forecasting -- Chapter 9 -- Anomaly Detection in Banking With AI -- Suggested next steps -- Packt Publishing books, blogs, and video courses -- Learning IBM Watson Analytics -- LinkedIn groups -- Product documentation -- IBM websites -- Experiment -- Summary -- Other Books You May Enjoy -- Index. IBM Watson Projects demonstrate projects focused on cognitive computing & analytical capabilities of IBM Watson. You will explore designing smart business solutions and understand the effectiveness of a supply chain using projects including healthcare dialog system, sentimental analysis on social media, personalized recommendation systems and more. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Intelligence artificielle. artificial intelligence. aat Mathematical theory of computation. bicssc Artificial intelligence. bicssc Machine learning. bicssc Natural language & machine translation. bicssc COMPUTERS General. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1905962 Volltext |
| spellingShingle | Miller, James IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance. Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: The Essentials of IBM Watson -- Definition and objectives -- IBM Cloud prerequisites -- Exploring the Watson interface -- The menu bar -- Menu icon -- IBM Cloud -- Catalog -- Docs -- Support -- Manage -- Profile -- avatar -- Online glossary, let's chat, and feedback -- What about Watson? -- The Watson dashboard -- Menu bar -- Quick start information bar -- Search, add, filter, and sort -- Content panel area -- Basic tasks refresher -- The first step -- Explore -- Watson prompts -- Predict -- Assemble -- Social media -- Refine -- Saving the original -- Add -- some data -- Refine -- Summary -- Chapter 2: A Basic Watson Project -- The problem defined -- Getting started -- Gathering data -- Building your Watson project -- Loading your data -- Data review -- What does this mean? -- Improving your score with Refine -- Refine or Explore -- Creating a prediction -- Top predictors -- Main Insight page -- Details page -- An insight -- Reviewing the results -- Summary -- Chapter 3: An Automated Supply Chain Scenario -- The problem defined -- Getting started -- Gathering and reviewing data -- Building the Watson project -- Loading your data -- Reviewing the data -- Refining the data -- Creating a prediction -- Supply chain prediction -- Predictors -- Main insights -- Reviewing the results -- Sharing with a dashboard -- Adding a new visualization -- Summary -- Chapter 4: Healthcare Dialoguing -- The problem defined -- What is dialoguing? -- Leveraging (new) data to identify risk -- Getting started -- Gathering and reviewing data -- Building the project -- Reviewing the results -- Exploring the dialog data -- Collecting the data -- Moving on -- Recap -- Results -- Data quality of the prediction -- Data quality report. More predictive strength -- More detail -- Assembling a story -- Testing your story -- Summary -- Chapter 5: Social Media Sentiment Analysis -- The problem defined -- Social media and IBM Watson Analytics -- Getting started -- Creating a Watson Analytics social media project -- Building the project -- Project creation step by step -- Adding topics -- Social media investigative themes -- Adding dates -- Languages -- Sources -- Reviewing the results -- Deeper dive -- conversation clusters -- Navigation -- Topics -- Another look -- Sentiment -- Sentiment terms -- Geography -- Sources and sites -- Influential authors -- Author interests -- Games and shopping -- Behavior -- Demographics -- The sentiment dictionary -- The data -- Summary -- Chapter 6: Pattern Recognition and Classification -- The problem defined -- Data peeking -- Starting a pattern recognition and classification project -- Investigation -- Coach me -- More with Watson Analytics -- The insight bar -- Modifying a visualization -- Additional filtering -- Item-based calculations -- Navigate -- Compare -- Simply trending -- Developing the pattern recognition and classification project -- Quality -- The Watson Analytics data quality report -- Creating the prediction -- The prediction workflow -- Understanding the workflow step by step -- Reviewing the results -- Displaying top predictors and predictive strength -- Summary -- Chapter 7: Retail and Personalized Recommendations -- The problem defined -- Product recommendation engines -- Recommendations from Watson Analytics -- The data at a glance -- Starting the project -- Range filter -- Save me -- Developing the project -- Reviewing the results -- Targets -- Summary ribbon -- The top predictors -- Sharing the insights -- Summary -- Chapter 8: Integration for Sales Forecasting -- The problem defined -- Product forecasting. Systematic forecasting -- IBM Planning Analytics -- Our data -- Creating the forecast -- Starting the project -- Developing the project -- Visualizations and data requirements -- More questioning -- Time Series -- Other visualization options -- Reviewing the results -- Summary -- Chapter 9: Anomaly Detection in Banking Using AI -- Defining the problem -- Banking use cases -- Corruption -- Cash -- Billing -- Check tampering -- Skimming -- Larceny -- Financial statement fraud -- Starting the project -- The data -- Developing the project -- The first question -- Using Excel for sorting and filtering the data -- Back to Watson -- Check numbers -- Reviewing the results -- Collecting -- Telling the story -- Summary -- Chapter 10: What's Next -- Chapter-by-chapter summary -- Chapter 1 -- The Essentials of IBM Watson -- Chapter 2 -- A Basic Watson Project -- Chapter 3 -- An Automated Supply Chain Scenario -- Chapter 4 -- Healthcare Dialoguing -- Chapter 5 -- Social Media Sentiment Analysis -- Chapter 6 -- Pattern Recognition And Classification -- Chapter 7 -- Retail And Personalized Recommendations -- Chapter 8 -- Integration for Sales Forecasting -- Chapter 9 -- Anomaly Detection in Banking With AI -- Suggested next steps -- Packt Publishing books, blogs, and video courses -- Learning IBM Watson Analytics -- LinkedIn groups -- Product documentation -- IBM websites -- Experiment -- Summary -- Other Books You May Enjoy -- Index. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Intelligence artificielle. artificial intelligence. aat Mathematical theory of computation. bicssc Artificial intelligence. bicssc Machine learning. bicssc Natural language & machine translation. bicssc COMPUTERS General. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 |
| title | IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance. |
| title_auth | IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance. |
| title_exact_search | IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance. |
| title_full | IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance. |
| title_fullStr | IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance. |
| title_full_unstemmed | IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance. |
| title_short | IBM Watson projects : |
| title_sort | ibm watson projects eight exciting projects that put artificial intelligence into practice for optimal business performance |
| title_sub | eight exciting projects that put artificial intelligence into practice for optimal business performance. |
| topic | Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Intelligence artificielle. artificial intelligence. aat Mathematical theory of computation. bicssc Artificial intelligence. bicssc Machine learning. bicssc Natural language & machine translation. bicssc COMPUTERS General. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast |
| topic_facet | Artificial intelligence. Intelligence artificielle. artificial intelligence. Mathematical theory of computation. Machine learning. Natural language & machine translation. COMPUTERS General. COMPUTERS Intelligence (AI) & Semantics. Artificial intelligence |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1905962 |
| work_keys_str_mv | AT millerjames ibmwatsonprojectseightexcitingprojectsthatputartificialintelligenceintopracticeforoptimalbusinessperformance |