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.

Artificial Intelligence as a Technological Innovation to Accelerate Economic Development

2019 ◽  
Vol 12 (3) ◽  
pp. 125-133
Author(s):  
S. V. Shchurina ◽  
A. S. Danilov
Keyword(s):  
Artificial Intelligence ◽  
Economic Development ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Retail Trade ◽  
High Tech ◽  
Industrial Complex ◽  
Technological Gap ◽  
The Government

The subject of the research is the introduction of artificial intelligence as a technological innovation into the Russian economic development. The relevance of the problem is due to the fact that the Russian market of artificial intelligence is still in the infancy and the necessity to bridge the current technological gap between Russia and the leading economies of the world is coming to the forefront. The financial sector, the manufacturing industry and the retail trade are the drivers of the artificial intelligence development. However, company managers in Russia are not prepared for the practical application of expensive artificial intelligence technologies. Under these circumstances, the challenge is to develop measures to support high-tech projects of small and medium-sized businesses, given that the technological innovation considered can accelerate the development of the Russian economy in the energy sector fully or partially controlled by the government as well as in the military-industrial complex and the judicial system.The purposes of the research were to examine the current state of technological innovations in the field of artificial intelligence in the leading countries and Russia and develop proposals for improving the AI application in the Russian practices.The paper concludes that the artificial intelligence is a breakthrough technology with a great application potential. Active promotion of the artificial intelligence in companies significantly increases their efficiency, competitiveness, develops industry markets, stimulates introduction of new technologies, improves product quality and scales up manufacturing. In general, the artificial intelligence gives a new impetus to the development of Russia and facilitates its entry into the five largest world’s economies.

scholarly journals Macro Patterns and Trends of U.S. Consumer Technological Innovation Diffusion Rates

Systems ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Albert Joseph Parvin ◽  
Mario G. Beruvides
Keyword(s):  
Innovation Diffusion ◽  
Technological Innovation ◽  
Research Effort ◽  
Decision Makers ◽  
Macro Level ◽  
Technological Innovations ◽  
Exploratory Data ◽  
Diffusion Rates ◽  
Technological Innovation Diffusion ◽  
Aid Decision

Macro-level trends and patterns are commonly used in business, science, finance, and engineering to provide insights and estimates to assist decision-makers. In this research effort, macro-level trends and patterns were explored on the diffusion rates of technological innovations, a component of a sorely under-studied question in technology assessment: When should a technological innovation be abandoned? A quantitative exploratory data analysis (EDA)-based approach was employed to examine diffusion market data of 42 U.S. consumer technological innovations from the early 1900s to the 2010s to extract general macro-level knowledge on technological innovation diffusion rates. A goal of this effort is to grow diffusion rate knowledge to enable the development of general macro-based forecasting tools. Such tools would aid decision-makers in making informed and proactive decisions on when to abandon a technological innovation. This research offers several significant contributions to the macro-level understanding of the boundaries and likelihood of achieving a range of technological innovation diffusion rates. These contributions include the determination that the frequency of diffusion rates are positively skewed when ordered from slowest to fastest, and the identification and ranking of probability density functions that best represent the rates of technological innovation diffusion.

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 Medical Physics Contributes to The Advancement in Medicine

2021 ◽  
Vol 14 ◽  
pp. 1-7
Author(s):  
Kwan Hoong Ng ◽  
Jeannie Hsiu Ding Wong ◽  
Chai Hong Yeong ◽  
Hafiz Mohd Zin ◽  
Noriah Jamal
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Medicine ◽  
Big Data ◽  
New Technologies ◽  
Medical Physics ◽  
Knowledge And Skills ◽  
X Ray ◽  
Medical Physicists ◽  
Medicine Today ◽  
Improve Health

Medical physics is the application of physics principles and techniques in medicine. Medical physicists are actively applying their knowledge and skills in the prevention, diagnosis and treatment of diseases to improve health via research and clinical practice. In this paper, we present the roles of medical physicists in the three primary fields, namely, diagnostic imaging, radiotherapy and nuclear medicine.  Medical physicists have been playing a crucial role in the advancement of new technologies that have revolutionised medicine today. This includes the continuous development of medical imaging and radiotherapy techniques since the discovery of X-ray and radioactivity. The last decade has seen tremendous development in the field that allows for better diagnosis and targeted treatment of various diseases. In the era of big data and artificial intelligence, while medical physicists continue to ensure that the application of the technologies in medicine is optimal and safe, it is paramount for the profession to evolve and be equipped with new skills to continue to contribute to the advancement of medicine.

scholarly journals Application Problems and Solutions of Intelligent Airport Service Robots

2020 ◽  
Vol 198 ◽  
pp. 04030
Author(s):  
Dai Yanyan ◽  
Chen Meng
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Big Data ◽  
Large Scale ◽  
New Technologies ◽  
Effective Means ◽  
Third Party ◽  
Service Robots ◽  
Service Robot ◽  
Operation Conditions

With the development of new technologies such as artificial intelligence, big data, and cloud computing, the “intelligent airport” is considered to be an effective means to solve or alleviate the current industry problems such as large-scale airport business, the large number of operating entities, and the complicated operation conditions. This paper is about the collaboration between universities and enterprises based on the concept of service design. Relying on big data and cloud computing technology, this paper addresses the problems of airport service robots in inquiries, blind spots of security inspection, and full monomer smart navigation diffluence, combined with the basic technology of service robot artificial intelligence and the third-party interface to design solutions to effectively solve the problems of process.

scholarly journals A Model to Evaluate the Organizational Readiness for Big Data Adoption

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Javaneh Ramezani ◽  
Mahdi Nasrollahi
Keyword(s):  
Principal Component Analysis ◽  
Big Data ◽  
Literature Review ◽  
New Technologies ◽  
Principal Component ◽  
Organizational Readiness ◽  
Decision Makers ◽  
Comprehensive Literature Review ◽  
The Hierarchical Structure ◽  
Best Worst Method

Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best- Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption.

Big Data and Machine Learning

2018 ◽  
pp. 225-239
Author(s):  
Fernando Enrique Lopez Martinez ◽  
Edward Rolando Núñez-Valdez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Complete Solution ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Healthcare Organizations ◽  
High Level

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.

Origin of Cyber Warfare and How the Espionage Changed

2021 ◽  
pp. 145-153
Author(s):  
Maria Luisa Nardi
Keyword(s):  
Artificial Intelligence ◽  
International Relations ◽  
Technological Innovation ◽  
Digital Technology ◽  
New Technologies ◽  
Cyber Warfare ◽  
Individual Rights ◽  
Information Warfare ◽  
Political Field ◽  
Over Time

International politics is faced with new and vital issues, linked to aspects such as individual rights, the holding of democracy, the effects of worldwide policies, as well as the geopolitics of technology. The intertwining of technology and international relations is now a fact. Exploring the new and different political challenges posed by new technologies is a factor of transformation of the global society that influence on its actors. Today, an application of technological innovation, digital technology, and artificial intelligence is a steady political field. The focus of this work is to describe over time the notion of information warfare, which has matured and manifested into a form that has a colossal impact on how the contemporary wars are fought, but this has also resulted in the downgrading of strategic side of information warfare or cyber warfare to a decisive tactical force multiplier capable of turning the tides in war.

Disruptions of the Fourth Industrial Revolution

2021 ◽  
pp. 189-199
Author(s):  
Idris Olayiwola Ganiyu ◽  
Ola Olusegun Oyedele ◽  
Evelyn Derera
Keyword(s):  
Artificial Intelligence ◽  
Technological Innovation ◽  
Industrial Revolution ◽  
Technological Innovations ◽  
Fourth Industrial Revolution ◽  
Skill Gaps ◽  
The World ◽  
The Future ◽  
The Impact ◽  
And Robotics

The Fourth Industrial Revolution has resulted in the disruption of the world of work whereby technological innovation such as artificial intelligence (AI) and robotics. These disruptions may be creative in that as some jobs are lost due to the development of artificial intelligence, new ones are created. This chapter explored the impact of disruptive technological innovations on the future of work. The skill gaps brought about by the emergence of the Fourth Industrial Revolution was also explored in this chapter.

Research Challenges in Big Data Analytics

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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