scholarly journals Simulation of energy consumption in urban areas

2020 ◽  
Vol 152 ◽  
pp. 02006
Author(s):  
Nikolay Garyaev
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Energy Consumption ◽  
Urban Areas ◽  
Energy Supply ◽  
Digital Data ◽  
Successful Implementation ◽  
Digital Models ◽  
Balanced Distribution ◽  
The Way

One of the problems that may arise in the way of successful implementation of energy supply in urban areas is the difficulty of analyzing and interpreting a large amount of digital data received from various sensors. This problem may adversely affect the performance of energy organizations. The purpose of this study is to study modern tools to solve the problem of processing big data using technologies of simulation and artificial intelligence. This study is dedicated to the development of innovative digital models for the balanced distribution of energy consumption in urban areas.

Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

2019 ◽  
pp. 22-41
Author(s):  
Onur Dogan ◽  
Omer Faruk Gurcan
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Digital Data ◽  
Rapid Urbanization ◽  
Data Resource ◽  
Parallel Data ◽  
Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

Records Management in the Fourth Industrial Revolution

2021 ◽  
pp. 1-24
Author(s):  
Adeyinka Tella ◽  
Oluwakemi Titilola Olaniyi ◽  
Aderinola Ololade Dunmade
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Industrial Revolution ◽  
Records Management ◽  
Technological Advancement ◽  
Fourth Industrial Revolution ◽  
Industrial Revolutions ◽  
The Way

The chapter looked at records management in the fourth industrial revolution (4IR) with the challenges and the way forward. The chapter discussed the industrial revolutions, records management, and the fourth industrial revolution (4IR), and described the advancement in records management in the 4IR based on the 4IR tools and technologies including artificial intelligence, blockchain, internet of things (IoT), robotics, and big data. The chapter also identified and discussed the benefits of technological advancement in the management of records; challenges of records management at the wake of 4IR and charted the way forward. In the context of document and records management, and taking into account all characteristics of the 4IR technologies and tools as well as its underlying technologies and concepts, the chapter concluded that the 4IR tools can be used to save time to create and process records, secure records from being damaged or destroyed, confirm the integrity of records, among others.

scholarly journals Digital Economy and Anti-Corruption: New Digital Models

2019 ◽  
Vol 71 ◽  
pp. 03004
Author(s):  
E.L. Sidorenko ◽  
A.A. Lykov
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Crime Prevention ◽  
Digital Technologies ◽  
Digital Economy ◽  
Digital Models ◽  
Research Results ◽  
Corruption Prevention

The authors of this paper consider promising areas of the corruption prevention using the latest digital technologies: Blockchain, Internet of Things, Artificial Intelligence and Big Data. The purpose of this research is the analysis of advantages of the digital economy development in terms of solving social problems and crime prevention. The authors also show functional digital models of the anti-corruption compliance are defined. In addition, the research results include the determination of some shortcomings of the proposed models associated with the imperfection of the current legislation.

scholarly journals Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications

2019 ◽  
Vol 3 (2) ◽  
pp. 32 ◽  
Author(s):  
Ifeyinwa Angela Ajah ◽  
Henry Friday Nweke
Keyword(s):  
Big Data ◽  
Success Factors ◽  
Database Management System ◽  
Unstructured Data ◽  
Digital Data ◽  
Business Strategies ◽  
Modern World ◽  
Successful Implementation ◽  
Business Analytics ◽  
Business World

Big data and business analytics are trends that are positively impacting the business world. Past researches show that data generated in the modern world is huge and growing exponentially. These include structured and unstructured data that flood organizations daily. Unstructured data constitute the majority of the world’s digital data and these include text files, web, and social media posts, emails, images, audio, movies, etc. The unstructured data cannot be managed in the traditional relational database management system (RDBMS). Therefore, data proliferation requires a rethinking of techniques for capturing, storing, and processing the data. This is the role big data has come to play. This paper, therefore, is aimed at increasing the attention of organizations and researchers to various applications and benefits of big data technology. The paper reviews and discusses, the recent trends, opportunities and pitfalls of big data and how it has enabled organizations to create successful business strategies and remain competitive, based on available literature. Furthermore, the review presents the various applications of big data and business analytics, data sources generated in these applications and their key characteristics. Finally, the review not only outlines the challenges for successful implementation of big data projects but also highlights the current open research directions of big data analytics that require further consideration. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

NATURE–INSPIRED INTELLIGENCE: A REVIEW OF SELECTED METHODS AND APPLICATIONS

2009 ◽  
Vol 18 (04) ◽  
pp. 487-516 ◽  
Author(s):  
VASSILIOS VASSILIADIS ◽  
GEORGIOS DOUNIAS
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Optimization Problems ◽  
Biological Systems ◽  
Artificial Immune Systems ◽  
Rapid Evolution ◽  
Successful Implementation ◽  
Human Immune System ◽  
Survey Paper ◽  
The Way

The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques take advantage of the way that biological systems deal with real–world situations. Specifically, they simulate the way real biological systems, such as the human brain, ant colonies and human immune system work, when solving complex real–world situations. In this survey paper, we briefly present a number of selected NII approaches and we point particular suitable areas of application for each of them. Specifically, five major categories of nature inspired approaches are presented, namely, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), DNA computing, artificial immune systems and membrane computing. Applications include problems related to optimization (financial, industrial and medical), task scheduling, system design (optimization of the system's parameters), image processing and data processing (feature selection and classification). We also refer to collaboration between NII techniques and classical AI methodologies, such as neural networks, genetic algorithms, fuzzy logic, etc. The current survey states that NII techniques are likely to become the next step in the rapid evolution of artificial intelligence tools.

scholarly journals A Study on the Design of Children Intelligent Guardianship Product Based on Artificial Intelligence Technology

2020 ◽  
Vol 179 ◽  
pp. 02050
Author(s):  
Yan-Xia Qu ◽  
Ming-Feng Wang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Child Care ◽  
Design Process ◽  
Rapid Development ◽  
Parental Supervision ◽  
Parental Experience ◽  
Artificial Intelligence Technology ◽  
Intelligent Child ◽  
The Way

The rapid development of AI has affected the design process. The ability to analyze big data and AI’s efficiency, rapidity will bring great changes to the monitoring products especially for children. At present, the vast majority of intelligent child care products are based on the parental experience, designed in the aspect of parental supervision, and the children who use the product are often neglected. So change the way of designing, from the perspective of children using Intelligence technology, the ultimate child care products can play the most important role.

scholarly journals How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
Ken Redekop
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Intensive Care ◽  
Best Practice ◽  
Investment Decisions ◽  
Cost Savings ◽  
Economic Evaluations ◽  
Successful Implementation ◽  
Practice Methods

Abstract Background Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. Methods The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions. Results When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (− 0.5%, − €886) and to improve patient-ventilator interaction (− 3%, − €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings. Conclusions We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.

How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care. (Preprint)

2021 ◽  
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
Ken Redekop
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Intensive Care ◽  
Best Practice ◽  
Investment Decisions ◽  
Cost Savings ◽  
Lymphocytic Leukemia ◽  
Economic Evaluations ◽  
Successful Implementation ◽  
Practice Methods

BACKGROUND Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have actually been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions to increase the likelihood of successful implementation, but recommendations about their use are lacking. OBJECTIVE The aim of this study was to develop and apply a framework that positions best-practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. METHODS The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Hereafter, economic evaluations were used to determine critical thresholds and guide investment decisions. RESULTS Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs resulted in an unfavorable cost-effectiveness ratio for potential users. In the intensive care, analytics reduced mortality and per-patient costs when used to identify infections (-0.5%, -€886) and also to improve patient-ventilator interaction (-3%, -€264). Both analytics hold the potential to save money but the return on investment for developers of analytics that identify infections strongly depends on infection rate; a higher rate implies greater cost-savings. CONCLUSIONS We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.

E-governance: Learning from Karnataka

2019 ◽  
Vol 65 (2) ◽  
pp. 416-429
Author(s):  
Anil Kumar Vaddiraju ◽  
S. Manasi
Keyword(s):  
Service Delivery ◽  
Case Studies ◽  
Urban Areas ◽  
Traffic Management ◽  
The State ◽  
Records Management ◽  
Successful Implementation ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
The Way

The technological changes of the 20th and 21st centuries, the growth of computer technologies, digital technologies and telecommunications have changed the way the state conducts its functions and delivers governance. Whether or not they have improved the welfare function of the state, the way governance is delivered has been altered. In this article, we discuss the application of electronic governance (e-governance) in Karnataka with the help of three case studies. We discuss the cases of land records management in rural and urban areas and initiatives in Bengaluru traffic management. The case studies indicate that e-governance improves service delivery and that there are points to be gleaned from the successful implementation of the same in Karnataka. Finally, we argue that while there is necessity for optimism regarding the application of technology in service-delivery functions, the overall digitisation of economy may be something qualitatively different.

Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

2022 ◽  
pp. 1090-1109
Author(s):  
Onur Dogan ◽  
Omer Faruk Gurcan
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Digital Data ◽  
Rapid Urbanization ◽  
Data Resource ◽  
Parallel Data ◽  
Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

Sign in / Sign up

Export Citation Format

Share Document