Public Services Data Analytics using Artificial Intelligence Solutions derived from Telecommunications Systems

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Christophe Gaie ◽  
Markus Mueck
Keyword(s):  
Artificial Intelligence ◽  
Data Analytics ◽  
Public Services

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

CONCEPTUAL DESIGN OF BOMB DETECTOR AS ANTICIPATION OF TERRORISM THREAT TO PUBLIC SERVICES

2021 ◽  
Vol 7 (1) ◽  
pp. 170
Author(s):  
Iris Sumariyanto ◽  
Asep Adang Supriyadi ◽  
I Nengah Putra A
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Design ◽  
Systems Engineering ◽  
Public Services ◽  
Mass Casualties ◽  
Intelligence System ◽  
Three Stages ◽  
Metal Detectors ◽  
Security Surveillance ◽  
Processing Device

<p>Acts of terrorism are crimes and serious violations of human rights, also the threat of violence that can cause mass casualties and destruction of vital strategic objects. This is an urgent threat that needs to be prepared by designing a bomb detector conceptual design as anticipation of the threat of terrorism in public services. This study aims to obtain operational requirements and conceptual design of bomb detectors as detection of terrorism threats in public services. This study uses a mixed-method with a systems engineering approach and a life cycle model to produce a technological design. The results of operational requirements are sensors, standards, artificial intelligence, integration capability, reliability, calibration mode, portable, and easy to maintain. The configuration design is divided into three stages, namely, 1) sensors including a camera security surveillance system vector image, metal detectors, explosive detectors, and A-jamming; 2) as a processing device, processes an order with the help of an artificial intelligence system; and 3)  a security computer (surveillance), early warning, and mobile information to provide information to related agencies, especially the anti-terror unit.</p>

Predictive Analysis for Digital Marketing Using Big Data

2017 ◽  
pp. 259-283
Author(s):  
Balamurugan Balusamy ◽  
Priya Jha ◽  
Tamizh Arasi ◽  
Malathi Velu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Cognitive Computing ◽  
Internet Resources ◽  
To Come

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

Sign in / Sign up

Export Citation Format

Share Document