scholarly journals Optimization model: the innovation and future of e-ecotourism for sustainability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fauziah Eddyono ◽  
Dudung Darusman ◽  
Ujang Sumarwan ◽  
Fauziah Sunarminto
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Sustainable Tourism ◽  
System Modeling ◽  
Secondary Data ◽  
Simulation Models ◽  
Content Type ◽  
Causal Loop ◽  
Artificial Intelligence Technology ◽  
Intelligent Technology

PurposeThis study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.Design/methodology/approachData analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.FindingsThis study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.Research limitations/implicationsThis study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.Practical implicationsThe paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.Social implicationsSustainable tourism development.Originality/valueThis study finds the expansion of new theory competitiveness of ecotourism destinations.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

Big Data Technology, Evolving Knowledge Skills and Emerging Roles

2016 ◽  
Vol 16 (4) ◽  
pp. 219-224 ◽  
Author(s):  
Alex Smith
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Emerging Technology ◽  
Law Firms ◽  
Support Functions ◽  
Legal Problems ◽  
Artificial Intelligence Technology ◽  
Domain Expertise ◽  
New Generation ◽  
Big Data Technology

AbstractIn a world where articles and tweets are discussing how artificial intelligence technology will replace humans, including lawyers and their support functions in firms, it can be hard to understand what the future holds. This article, written by Alex Smith, is based on his presentation at the British and Irish Association of Law Librarians conference in Dublin 2016 and looks at demystifying the emerging technology boom and identifies the expertise needed to make these tools work and be deployed in law firms. The article then looks at the skills and expertise of the knowledge and information teams, based in law firms, and suggests how they are ideally placed to lead these challenges as a result of their domain expertise and their existing, well defined skills that are essential to this new generation of technology. The article looks at the new technical environment, the emerging areas of products and legal problems, the skills needed for the new roles that this revolution is creating and how this could fit into a reimagined knowledge team.

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 Preface

2021 ◽  
Vol 2050 (1) ◽  
pp. 011001
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industrial Applications ◽  
Program Committee ◽  
Technical Session ◽  
Technical Program ◽  
Content Type ◽  
The World ◽  
Travel Restrictions ◽  
Virtual Conference

Considering the current situation of COVID-19 and travel restrictions, the 3rd International Conference on Industrial Applications of Big Data and Artificial Intelligence (BDAI 2021) which was planned to be held in Wuhan. China from Sept. 23 to 25, 2021 was changed into virtual conference on Sept. 24, 2021 via Tencent Meeting (Voov) software. BDAI 2021 was organized by China University of Geosciences (Wuhan), sponsored by Hong Kong Society of Mechanical Engineers (HKSME). The Technical Program committee received a total of 38 paper submissions from all over the world, among which 20 papers were accepted, and more than 30 participants attended the conference online, they were from China, Australia, Thailand, Malaysia, India, Japan, UK and more. Four renowned speakers given speeches about their latest research and reports. They are: Prof. Dan Zhang from York University, Canada; Prof. Lefei Zhang from Wuhan University. China: Prof. Deze Zeng from China University of Geosciences (Wuhan), China and Assoc. Prof. Simon James Fong from University of Macau. Macau S.A.R., China. The conference also had 1 technical session and 1 poster sessions. This conference aims to provide a platform for researchers and engineers to share their ideas, recent developments, and successful practices in energy engineering. The participants of the conference were from almost every part of the world, with various background such as academia, industry, and well-known entrepreneurs. Each keynote speech lasted 40 minutes, and authors presentation 15 minutes. Each presentation was included with questions and answers. BDAI 2021 became an effective communication platform for all the participants over the world and unlike some that claim international reach this conference was truly international. The conference proceeding is a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers 3 chapters: 1. Artificial Intelligence: 2. Big Data Technology; 3. Robot System. We would like to acknowledge all of those who supported BDAI 2021. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the committee chairs, committee members and reviewers, for their tremendous contribution in conference organization and peer review of the papers. We sincerely hope that BDAI 2021 will be a fomrn for excellent discussions that will put forward new ideas and promote collaborative research and support researchers as they take their work forward. We are sure that the proceedings will serve as an important research source of references and the knowledge, which will lead to not only scientific and engineering progress but also other new products and processes. Dan Zhang, York University, Canada

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.

Reform and innovation of artificial intelligence technology for information service in university physical education

2020 ◽  
pp. 1-11
Author(s):  
Jianye Zhang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Physical Education ◽  
Information Service ◽  
Management Decision ◽  
Negative Effects ◽  
Education Management ◽  
Depth Analysis ◽  
Artificial Intelligence Technology ◽  
The Difference

This article analyzes the reform of information services in university physical education based on artificial intelligence technology and conducts in-depth and innovative research on it. In-depth analysis of the relationship between big data and the development and application of information technology such as the Internet, Internet of Things, cloud computing, to clarify the difference and connection between big data, informatization and intelligence. Artificial intelligence will bring opportunities for changes in data collection, management decision-making, governance models, education and teaching, scientific research services, evaluation and evaluation of physical education in our university. At the same time, big data education management in colleges and universities faces many challenges such as the balance of privacy and freedom, data hegemony, data junk, data standards, and data security, and they have many negative effects. In accordance with the requirements of educational modernization, centering on the goal of intelligent and humanized education management, it aims existing issues in college physical education management.

Artificial intelligence: accelerator or panacea for financial crime?

2019 ◽  
Vol 26 (2) ◽  
pp. 634-646 ◽  
Author(s):  
Peter Yeoh
Keyword(s):  
Artificial Intelligence ◽  
Secondary Data ◽  
Financial Crime ◽  
Public And Private ◽  
Content Type ◽  
Competitive Edge ◽  
Business Cases ◽  
The Usa ◽  
The Uk ◽  
Administrative Sanctions

PurposeThis purpose of this viewpoint is to address the intended good and unintended bad impacts of artificial intelligence (AI) applications in financial crime.Design/methodology/approachThe paper relied primarily on secondary data resources, business cases and relevant laws and regulations, and it used a legal-economics perspective.FindingsCurrent AI systems could function as antidotes or accelerator of financial crime, in particular cybercrime. Research suggests criminal law could be applied via three approaches to curb these cybercrimes. However, others considered this to be an inappropriate mechanism to hold AI agents accountable, as present AI systems were not deemed capable of making ethically informed choices. Instead, administrative sanctions would be considered more appropriate for now. While keeping vigilance against AI malicious acts, regulatory authorities in the USA and the UK have opted largely for the innovation-friendly, market-oriented, permissionless approach over the state-interventionist stance so as to maintain their global competitive edge in this domain.Originality/valueThe paper reinforced the growing arguments that AI applications should be deployed more as panacea for financial crimes rather than being abused as crime accelerators. There equally though is the need for both public and private sectors to be mindful of the unintended negative, harmful consequences to society, especially those connected to cybercrime. This implied the further need to beef up attention and resources to help mitigate these risks.

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