Digital Enterprise Software Productivity Metrics and Enhancing Their Business Impacts Using Machine Learning

2022 ◽  
pp. 413-430
Author(s):  
Vipul Gaurav ◽  
Savita Choudhary
Keyword(s):  
Machine Learning ◽  
Enterprise Software ◽  
Software Productivity ◽  
Digital Enterprise

Transforming Digital Enterprises Towards Web Scale With Machine Learning

2021 ◽  
pp. 241-260
Author(s):  
Tasneem Aamir
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Life Cycle ◽  
Business Models ◽  
It Governance ◽  
Business Services ◽  
Enterprise Transformation ◽  
Modern Application ◽  
Digital Enterprise ◽  
Present Understanding

Digital enterprise transformation focuses on alignment of processes, products, services, business models, and technologies to perceive business value. Digital business integration in an organization utilizes information technology and its tools to drive and manage the life cycle of digital enterprise transformation. It utilizes the practices and approaches of IT governance with modern application tools and APIs. The millennium brought many technological advancements over internet technologies and these technologies operate numerous applications and business services. The span of digital enterprises is expanding and continues to grow with their evolution on a web scale. This chapter is an effort to present understanding about machine learning and automation around businesses intelligence and analytics on a web scale. The chapter provides a brief summary of technologies used in digital enterprise transformation for all the domains of an organization.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

Machine learning

AccessScience ◽  
2018 ◽  
Keyword(s):  
Machine Learning

Talent management and enterprise software: Trials, tribulations, and trends

2008 ◽  
Author(s):  
Tobin V. Anselmi ◽  
Lisa Kobe Cross ◽  
Nathan Mondragon
Keyword(s):  
Talent Management ◽  
Enterprise Software
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