scholarly journals Artificial Intelligence Driven Resiliency with Machine Learning and Deep Learning Components

2019 ◽  
Vol 4 (2) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Homeland Security ◽  
Decision Makers ◽  
Actual Behavior ◽  
Direct Impact ◽  
Data Infrastructure ◽  
Government Organizations ◽  
The Right ◽  
Real Challenge

The future of any business from banking, e-commerce, real estate, homeland security, healthcare, and marketing, the stock market, manufacturing, education, and retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology in recent years has been a real challenge for all businesses. To manage that, a significant number of organizations are exploring the Big Data (BD) infrastructure that helps them to take advantage of new opportunities while saving costs. Timely transformation of information is also critical for the survivability of an organization. Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment. It is no longer enough to rely on a sampling of information about the organizations’ customers. The decision-makers need to get vital insights into the customers’ actual behavior, which requires enormous volumes of data to be processed. We believe that Big Data infrastructure is the key to successful Artificial Intelligence (AI) deployments and accurate, unbiased real-time insights. Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL. In this article, we discuss these topics.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals A Discussion on How to Apply Smart Data for Precision Marketing in the Global Economy: Asian Context

2021 ◽  
Vol 1 (2) ◽  
pp. 70-84
Author(s):  
Sandy Zhu
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Global Economy ◽  
Digital Marketing ◽  
Core Concepts ◽  
Artificial Intelligence Technology ◽  
Smart Data ◽  
Asian Context ◽  
The Right ◽  
Full Swing

The aim of the research is to provide support for the application of smart data, precision marketing, and business analysis and in so doing, it is aimed to contribute to the further sustainable development of the economy. At present, intelligent technologies such as artificial intelligence and big data are developing in full swing, and various application scenarios are gradually being launched. Smart data is a new sort of database in combination with artificial intelligence and big data technology, which makes artificial intelligence technology and big data the core concepts and the foundation of digital smart data. With smart data, companies could apply precision marketing to better reach their target consumers, push notifications at the right time, advertise the products and services consumers are interested in, and establish personalised marketing communication with each consumer in order to increase marketing efficiency. Undoubtedly, precision marketing has become the top priority in the development of the digital marketing industry, and it is becoming increasingly popular. The paper is based on this perspective and starts with an overview of smart data. The definition and development status of smart data are first reviewed, followed by an analysis of the application of smart data technology and precision marketing in digital marketing.

scholarly journals Interpreting, analyzing and distributing information: A big data framework for competitive intelligence

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Eduardo Luis Casarotto ◽  
Guilherme Cunha Malafaia ◽  
Marta Pagán Martínez ◽  
Erlaine Binotto
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Technological Innovation ◽  
New Technologies ◽  
Decision Makers ◽  
Strategic Decision ◽  
Competitive Intelligence ◽  
Technological Innovations ◽  
Data Sets ◽  
Data Framework

This paper aimed to develop a data-based technological innovation frameworkfocused on the competitive intelligence process. Technological innovations increasinglytransform the behavior of societies, affecting all sectors. Solutions such as cloud computing, theInternet of Things, and artificial intelligence provide and benefit from a vast generation of data:large data sets called Big Data. The use of new technologies in all sectors increases in the faceof such innovation and technological mechanisms of management. We advocated that the use ofBig Data and the competitive intelligence process could help generate or maintain a competitiveadvantage for organizations. We based the proposition of our framework on the concepts of BigData and competitive intelligence. Our proposal is a theoretical framework for use in thecollection, treatment, and distribution of information directed to strategic decision-makers. Itssystematized architecture allows the integration of processes that generate information fordecision making.

scholarly journals Dealing Crisis Management using AI

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Crisis Management ◽  
Social Life ◽  
Decision Makers ◽  
Advanced Technology ◽  
Business Operations ◽  
The World ◽  
Tools And Techniques ◽  
Management Domains

Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.

scholarly journals Dealing Crisis Management using AI

2021 ◽  
Vol 11 (05) ◽  
pp. 15-22
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Crisis Management ◽  
Social Life ◽  
Decision Makers ◽  
Advanced Technology ◽  
Business Operations ◽  
The World ◽  
Tools And Techniques ◽  
Management Domains

Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.

Machine Learning and Deep Learning Technologies

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Model Building ◽  
Learning Technologies ◽  
Decision Makers ◽  
Human Intervention ◽  
Learning Techniques ◽  
Tools And Techniques

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using machine learning, which is the application of advanced deep learning techniques on big data. This paper aims to analyse some of the different machine learning and deep learning algorithms and methods, aswell as the opportunities provided by the AI applications in various decision making domains.

scholarly journals Applying AI and Big Data for Sensitive Operations and Disaster Management

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Big Data ◽  
Disaster Management ◽  
Social Life ◽  
Decision Makers ◽  
Advanced Technology ◽  
Business Operations ◽  
The World ◽  
Tools And Techniques

Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various sensitive operations and disaster management.

scholarly journals How gaming, artificial intelligence and big data is changing education

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Teo Peihan Janine
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Learning By Doing ◽  
Retention Rates ◽  
Learning Needs ◽  
Strategy Games ◽  
Puzzle Games ◽  
The Right

With 263 million children and youth out of school, there is a need for a highly scalable way to provide quality education to the underprivileged. Solve Education!(SE!)’s solution is combining an addictive game with the “Learning-by-Doing” Principle. Leveraging artificial intelligence and big data analysis, SE! explores the possibility of combining multi-user online strategy games with casual puzzle games to increase user retention rates, and in the process educating the users effectively over a longer period of time. Game mechanics are used to increase user retention by boosting motivation, while big data analysis allows SE! to understand the users’ in-game behavior and how they learn best. Artificial intelligence helps to deliver the right content to the user at the right time, optimizing the learning process, and enabling in-game adaption to the users’ learning needs 

scholarly journals Understanding Cumulative Hazards in a Rustbelt City: Integrating GIS, Archaeology, and Spatial History

Urban Science ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 83
Author(s):  
Dan Trepal ◽  
Don Lafreniere
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Environmental Sustainability ◽  
Historical Record ◽  
Decision Makers ◽  
Landscape Model ◽  
Final Model ◽  
Data Infrastructure ◽  
Spatial History ◽  
Spatial Big Data

We combine the Historical Spatial Data Infrastructure (HSDI) concept developed within spatial history with elements of archaeological predictive modeling to demonstrate a novel GIS-based landscape model for identifying the persistence of historically-generated industrial hazards in postindustrial cities. This historical big data approach draws on over a century of both historical and modern spatial big data to project the presence of specific persistent historical hazards across a city. This research improves on previous attempts to understand the origins and persistence of historical pollution hazards, and our final model augments traditional archaeological approaches to site prospection and analysis. This study also demonstrates how models based on the historical record, such as the HSDI, complement existing approaches to identifying postindustrial sites that require remediation. Our approach links the work of archaeologists more closely to other researchers and to municipal decision makers, permitting closer cooperation between those involved in archaeology, heritage, urban redevelopment, and environmental sustainability activities in postindustrial cities.

scholarly journals Leadership in Higher Education – coping with AI and the turbulence of our times

2018 ◽  
Vol 12 (1) ◽  
pp. 683-694
Author(s):  
Mariana Nicolae ◽  
Elena E. Nicolae
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Big Data ◽  
Decision Makers ◽  
Human Intelligence ◽  
Higher Education System ◽  
Leadership In Higher Education ◽  
National Media ◽  
The World ◽  
Human Decision

Abstract Today’s world is clearly fractured whether we are looking at it through economic, political, cultural or educational lenses. This is in no way something new. The world has always been in this state, but the speed with which it reacted to real or perceived threats and tried to change accordingly was barely perceivable and, therefore, easier to adopt and adapt to. Today those changes happen with incredible speed and our reactions to them may not be informed or educated and are usually taken by leaders who are, at best, controversial and at worst obviously partial to their own, petty interests against the greater public good they vowed to serve. What can higher education do in such a world? Artificial intelligence (AI) is making huge progress and, although education at all levels is lagging behind in meaningfully adopting AI and working with it, the educational system is expected to react to a world divided by the fear of AI using big data, claiming jobs, and ushering in the era of loss of human supremacy or by the glorification of AI which is only a tool, fast developing indeed, but permanently controlled by human intelligence. Even if that human intelligence is concentrated into fewer and fewer human decision makers thus contributing to the already huge gap of inequality existing in today’s world. The present paper will explore issues related to the way in which the leadership of higher education chooses to handle today’s challenges and will use the home university of the authors to illustrate what happens in Romanian universities. The discussion will be informed by the authors’ own experience in the higher education system as well as by an analysis of various discourses and narratives belonging to different stakeholders, discussing those issues in various inter/national media. The paper will offer some recommendations.

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