6,510 research outputs found
Artificial Intelligence in Engineering Risk Analytics
Risks exist in every aspect of our lives, and can mean different things to different people. While negative in general they always cause a great deal of potential damage and inconvenience for stakeholders. Recent engineering risks include the Fukushima nuclear plant disaster from the 2011 tsunami, a year that also saw earthquakes in New Zealand, tornados in the US, and floods in both Australia and Thailand. Earthquakes, tornados (not to mention hurricanes) and floods are repetitive natural phenomenon. But the October 2011 floods in Thailand were the worst in 50 years, impacting supply chains including those of Honda, Toyota, Lenovo, Fujitsu, Nippon Steel, Tesco, and Canon. Human-induced tragedies included a clothing factory fire in Bangladesh in 2012 that left over 100 dead. Wal-Mart and Sears supply chains were downstream customers. The events of Bhopal in 1984, Chernobyl in 1986, Exxon Valdez in 1989, and the Gulf oil spill of 2010 were tragic accidents. There are also malicious events such as the Tokyo Sarin attach in 1995, The World Trade Center and Pentagon attacks in 2001, and terrorist attacks on subways in Madrid (2004), London (2005), and Moscow (2010). The news brings us reports of such events all too often. The next step up in intensity is war, which seems to always be with us in some form somewhere in the world. Complex human systems also cause problems. The financial crisis resulted in recession in all aspects of the economy. Risk and analytics has become an important topic in today’s more complex, interrelated global environment, replete with threats from natural, engineering, economic, and technical sources (Olson and Wu, 2015)
Resilience Measurement – Financial Survival Bag Concept Using Rough Fuzzy Set Approach
The current Covid-19 pandemic has starkly revealed the importance of being resilient to enable an organization to stay in business. A resilient performance measurement model to constantly measure an organization’s financial resilience is thus necessary to ensure the continued survivability of the organization. The purpose of this research is to develop a resilience measurement model to measure and unify various metrics into a single unit-less index. This paper is an extension of work on the financial survival bag concept and the measures and metrics from [1].  The financial resilience measurement model was developed using the rough fuzzy set method for any participating SME manufacturer. This model intends to solve the research gaps from previous research conducted on resilience measurement to estimate the duration an organization can survive based on its current resilience result and to gauge the interaction of risk/ disruption with resilience capabilities. A case study was conducted and the evaluation concurred with the findings of the proposed model as the results reflected their current resilience level. In essence, this research has managed to offer a new way of measurement for resilience to evaluate the financial resilience of any SME manufacturer in Malaysia
Advances in Data Mining Knowledge Discovery and Applications
Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications
A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications
Enterprise financial risk analysis aims at predicting the enterprises' future
financial risk.Due to the wide application, enterprise financial risk analysis
has always been a core research issue in finance. Although there are already
some valuable and impressive surveys on risk management, these surveys
introduce approaches in a relatively isolated way and lack the recent advances
in enterprise financial risk analysis. Due to the rapid expansion of the
enterprise financial risk analysis, especially from the computer science and
big data perspective, it is both necessary and challenging to comprehensively
review the relevant studies. This survey attempts to connect and systematize
the existing enterprise financial risk researches, as well as to summarize and
interpret the mechanisms and the strategies of enterprise financial risk
analysis in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. This paper provides a
systematic literature review of over 300 articles published on enterprise risk
analysis modelling over a 50-year period, 1968 to 2022. We first introduce the
formal definition of enterprise risk as well as the related concepts. Then, we
categorized the representative works in terms of risk type and summarized the
three aspects of risk analysis. Finally, we compared the analysis methods used
to model the enterprise financial risk. Our goal is to clarify current
cutting-edge research and its possible future directions to model enterprise
risk, aiming to fully understand the mechanisms of enterprise risk
communication and influence and its application on corporate governance,
financial institution and government regulation
Artificial Intelligence and Cognitive Computing
Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that
Sustainable Assessment in Supply Chain and Infrastructure Management
In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management
Artificial Intelligence Enabled Project Management: A Systematic Literature Review
In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
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