Imposter Paranoia in the Age of Intelligent Surveillance

Artificial intelligence, deep learning and big data analytics are viewed as the technologies of the future, capable of delivering expert intelligence decisions, risk assessments and predictions within milliseconds. In a world of fakes, they promise to deliver ‘hard facts’ and data-driven ‘truth’, but their solutions resurrect ideologies of purity, embrace bogus science reminiscent of the likes of anthropometry, and create a deeply paranoid world where the Other is increasingly perceived either as a threat or as a potential imposter, or both. Social sorting in the age of intelligent surveillance acquires a whole new meaning. This article explores the possible effects of algorithmic governance on society through a critical analysis of the figure of the imposter in the age of intelligent surveillance. It links a critical analysis of new technologies of surveillance, policing and border control, to the extreme ethnographic example of paranoia within outlaw motorcycle clubs – organizations that are heavily targeted by new and old modes of policing and surveillance, while themselves increasingly embracing the very same logic and technologies themselves. With profound consequences. The article shows how in the quest for power, order, profit, and control, we are sacrificing critical reason and risk becoming as a society not unlike the paranoid criminal organizations.

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Modern Business Intelligence: Big Data Analytics and Artificial Intelligence for Creating the Data-Driven Value

E-Business - Higher Education and Intelligence Applications ◽

10.5772/intechopen.97374 ◽

2021 ◽

Author(s):

Ahmed A.A. Gad-Elrab

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Business Intelligence ◽

Data Analytics ◽

New Technologies ◽

Big Data Analytics ◽

Data Driven ◽

A Value ◽

Challenges And Opportunities ◽

Modern Business

Currently, business intelligence (BI) systems are used extensively in many business areas that are based on making decisions to create a value. BI is the process on available data to extract, analyze and predict business-critical insights. Traditional BI focuses on collecting, extracting, and organizing data for enabling efficient and professional query processing to get insights from historical data. Due to the existing of big data, Internet of Things (IoT), artificial intelligence (AI), and cloud computing (CC), BI became more critical and important process and received more great interest in both industry and academia fields. The main problem is how to use these new technologies for creating data-driven value for modern BI. In this chapter, to meet this problem, the importance of big data analytics, data mining, AI for building and enhancing modern BI will be introduced and discussed. In addition, challenges and opportunities for creating value of data by establishing modern BI processes.

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Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level

10.2172/1431303 ◽

2018 ◽

Cited By ~ 1

Author(s):

A. Ansari ◽

S. Mohaghegh ◽

M. Shahnam ◽

J. F. Dietiker ◽

T. Li

Keyword(s):

Machine Learning ◽

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Big Data ◽

Data Analytics ◽

Model Building ◽

Big Data Analytics ◽

Data Driven ◽

Cell Level

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Big Data Analytics in Education: A Data-Driven Literature Review

2021 International Conference on Advanced Learning Technologies (ICALT) ◽

10.1109/icalt52272.2021.00053 ◽

2021 ◽

Author(s):

Negar Shabihi ◽

Mi Song Kim

Keyword(s):

Big Data ◽

Literature Review ◽

Data Analytics ◽

Big Data Analytics ◽

Data Driven

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Research Challenges in Big Data Analytics

Decision Management ◽

10.4018/978-1-5225-1837-2.ch006 ◽

2017 ◽

pp. 83-99

Author(s):

Sivamathi Chokkalingam ◽

Vijayarani S.

Keyword(s):

Big Data ◽

Data Analytics ◽

Large Scale ◽

New Technologies ◽

Big Data Analytics ◽

Large Data ◽

Data Sets ◽

Data Types ◽

Customer Preferences ◽

Research Challenges

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. Big Data is differentiated from traditional technologies in three ways: volume, velocity and variety of data. Big data analytics is the process of analyzing large data sets which contains a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Since Big Data is new emerging field, there is a need for development of new technologies and algorithms for handling big data. The main objective of this paper is to provide knowledge about various research challenges of Big Data analytics. A brief overview of various types of Big Data analytics is discussed in this paper. For each analytics, the paper describes process steps and tools. A banking application is given for each analytics. Some of research challenges and possible solutions for those challenges of big data analytics are also discussed.

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Big Data Driven Agriculture: Big Data Analytics in Plant Breeding, Genomics, and the Use of Remote Sensing Technologies to Advance Crop Productivity

tppj ◽

10.2135/tppj2018.12.0009 ◽

2019 ◽

Vol 2 (1) ◽

pp. 1-8 ◽

Cited By ~ 10

Author(s):

Nadia Shakoor ◽

Daniel Northrup ◽

Seth Murray ◽

Todd C. Mockler

Keyword(s):

Remote Sensing ◽

Big Data ◽

Plant Breeding ◽

Data Analytics ◽

Big Data Analytics ◽

Crop Productivity ◽

Data Driven

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Big Data and Big Data Analytics

Applications of Big Data in Large- and Small-Scale Systems - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-6673-2.ch001 ◽

2021 ◽

pp. 1-19

Author(s):

Sam Goundar ◽

Akashdeep Bhardwaj ◽

Shavindar Singh ◽

Mandeep Singh ◽

Gururaj H. L.

Keyword(s):

Big Data ◽

Data Analytics ◽

Business Processes ◽

New Technologies ◽

Big Data Analytics ◽

Small Island ◽

Island State ◽

Diverse Data ◽

Analytical Tools ◽

Small Island State

Big data is emerging, and the latest developments in technology have spawned enormous amounts of data. The traditional databases lack the capabilities to handle this diverse data and thus has led to the employment of new technologies, methods, and tools. This research discusses big data, the available big data analytical tools, the need to use big data analytics with its benefits and challenges. Through a research drawing on survey questionnaires, observation of the business processes, interviews and secondary research methods, the organizations, and companies in a small island state are identified to survey which of them use analytical tools to handle big data and the benefits it proposes to these businesses. Organizations and companies that do not use these tools were also surveyed and reasons were outlined as to why these organizations hesitate to utilize such tools.

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Text Mining and Big Data Analytics for Retrospective Analysis of Clinical Texts from Outpatient Care

Cybernetics and Information Technologies ◽

10.1515/cait-2015-0055 ◽

2015 ◽

Vol 15 (4) ◽

pp. 58-77 ◽

Cited By ~ 4

Author(s):

Svetla Boytcheva ◽

Galia Angelova ◽

Zhivko Angelov ◽

Dimitar Tcharaktchiev

Keyword(s):

Blood Pressure ◽

Big Data ◽

Text Mining ◽

Retrospective Analysis ◽

Outpatient Care ◽

Data Analytics ◽

Big Data Analytics ◽

Free Text ◽

Arterial Blood ◽

And Control

Abstract This paper presents the results of an on-going research project for knowledge extraction from large corpora of clinical narratives in Bulgarian language, approximately 100 million of outpatient care notes. Entities with numerical values are mined in the free text and the extracted information is stored in a structured format. The Algorithms for retrospective analyses and big data analytics are applied for studying the treatment and evaluating the diabetes compensation and control of arterial blood pressure.

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