scholarly journals Clustering: Finding Patterns in the Darkness

2021 ◽  
pp. 1-28
Author(s):  
Hector Menendez
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Industry 4.0 ◽  
Expert Knowledge ◽  
Large Datasets ◽  
Decision Making Process ◽  
Clustering Problem ◽  
Multiple Strategies ◽  
Data Similarity ◽  
The World

Machine learning is changing the world and fuelling Industry 4.0. These statistical methods focused on identifying patterns in data to provide an intelligent response to specific requests. Although understanding data tends to require expert knowledge to supervise the decision-making process, some techniques need no supervision. These unsupervised techniques can work blindly but they are based on data similarity. One of the most popular areas in this field is clustering. Clustering groups data to guarantee that the clusters’ elements have a strong similarity while the clusters are distinct among them. This field started with the K-means algorithm, one of the most popular algorithms in machine learning with extensive applications. Currently, there are multiple strategies to deal with the clustering problem. This review introduces some of the classical algorithms, focusing significantly on algorithms based on evolutionary computation, and explains some current applications of clustering to large datasets.

scholarly journals A System for Enhancing Human-level Performance in COVID-19 Antibody Detection

2021 ◽  
Author(s):  
Victor Henrique Alves Ribeiro ◽  
Gabriela Steinhaus ◽  
Evair Borges Severo ◽  
José Raniery Ferreira Junior ◽  
Luiz José Lucas Barbosa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sensitivity And Specificity ◽  
Real World ◽  
Antibody Detection ◽  
Decision Making Process ◽  
Remote Laboratory ◽  
The World ◽  
Level Performance

The world currently suffers from the global COVID-19 pandemic. Billions of people have been impacted, and millions of casualties have already occurred. Therefore, it is of extreme importance to identify individuals contaminated by SARS-CoV-2, allowing governments to plan actions to reduce further impacts. In this context, this work employed machine learning to improve the detection of SARS-CoV-2 antibodies in blood exams. Models have been developed in a real-world scenario with 500 thousand exams and were deployed in a remote laboratory for experiments. Results indicate that the models averaged sensitivity and specificity of 95%, and thus, they could aid COVID-19 antibody detection and the decision-making process of biomedical specialists.

scholarly journals Airport 4.0 – Technology Adoption Framework for Airports (TAFA)

2022 ◽  
Author(s):  
Jia Hao Tan ◽  
Tariq Masood
Keyword(s):  
Decision Making ◽  
Technology Adoption ◽  
Technology Implementation ◽  
Industry 4.0 ◽  
Decision Making Process ◽  
Structured Interviews ◽  
Health Concerns ◽  
The World ◽  
Industry Survey

Airports have taken centre stage in the fight against the ongoing COVID-19 pandemic, and adoption of technologies has been instrumental in helping airport operators to mitigate operational and health concerns relating to the pandemic. A novel framework for the adoption of Industry 4.0 technologies was developed based on the insights gathered from an industry survey of 102 airport operators and managers around the world and 17 semi-structured interviews. The framework provides a ‘three-proof’ approach (proof of technology, proof of operations and proof of business) to guide airport operators in their decision-making process in adopting Industry 4.0 technologies in airports. This framework is further verified through a case study of the technology implementation efforts of a leading Asian airport.

scholarly journals HOW INDUSTRY 4.0 RESHAPES THE WORLD: RECOMMENDATIONS BASED ON COMPLEX GRAPH NETWORK ANALYSIS

2021 ◽  
Vol 1 ◽  
pp. 1755-1764
Author(s):  
Rongyan Zhou ◽  
Julie Stal-Le Cardinal
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Network Models ◽  
Global Supply Chain ◽  
Great Opportunity ◽  
Strategic Research ◽  
Complex Graph ◽  
The World ◽  
Tremendous Challenge

Abstract Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed.

scholarly journals Use of Linked Data principles for semantic management of scanned documents

2016 ◽  
Vol 28 (2) ◽  
pp. 241-251 ◽  
Author(s):  
Luciane Lena Pessanha Monteiro ◽  
Mark Douglas de Azevedo Jacyntho
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Web Application ◽  
Linked Data ◽  
World Wide ◽  
Decision Making Process ◽  
Whole Process ◽  
The World ◽  
Scanned Documents ◽  
The Web

The study addresses the use of the Semantic Web and Linked Data principles proposed by the World Wide Web Consortium for the development of Web application for semantic management of scanned documents. The main goal is to record scanned documents describing them in a way the machine is able to understand and process them, filtering content and assisting us in searching for such documents when a decision-making process is in course. To this end, machine-understandable metadata, created through the use of reference Linked Data ontologies, are associated to documents, creating a knowledge base. To further enrich the process, (semi)automatic mashup of these metadata with data from the new Web of Linked Data is carried out, considerably increasing the scope of the knowledge base and enabling to extract new data related to the content of stored documents from the Web and combine them, without the user making any effort or perceiving the complexity of the whole process.

Simulation of consumers decision-making process using agent-based model approach

2019 ◽  
Vol 10 (06) ◽  
pp. 1950037
Author(s):  
Daniel Soto Forero ◽  
Yony F. Ceballos ◽  
German Sànchez Torres
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Real Data ◽  
World Market ◽  
Decision Making Process ◽  
Agent Based ◽  
Dynamic Social Network ◽  
Proposed Model ◽  
The World ◽  
Model Approach

This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network. The proposed model use theories and tools from the psychology of consumer behavior, social networks and complex dynamical systems like the Consumat framework and fuzzy logic. The model has been adjusted using real data, tested with the automobile market and it can recreate trends like those described in the world market.

scholarly journals Cancer Surgery in the Elderly

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Gopal C. Kowdley ◽  
Nishant Merchant ◽  
James P. Richardson ◽  
Justin Somerville ◽  
Myriam Gorospe ◽  
...  
Keyword(s):  
Decision Making ◽  
Elderly Patients ◽  
The Elderly ◽  
Cancer Surgery ◽  
Decision Making Process ◽  
Benefit Ratio ◽  
Patients With Cancer ◽  
The World ◽  
Surgery In The Elderly ◽  
Surgical Treatments

The proportions both of elderly patients in the world and of elderly patients with cancer are both increasing. In the evaluation of these patients, physiologic age, and not chronologic age, should be carefully considered in the decision-making process prior to both cancer screening and cancer treatment in an effort to avoid ageism. Many tools exist to help the practitioner determine the physiologic age of the patient, which allows for more appropriate and more individualized risk stratification, both in the pre- and postoperative periods as patients are evaluated for surgical treatments and monitored for surgical complications, respectively. During and after operations in the oncogeriatric populations, physiologic changes occuring that accompany aging include impaired stress response, increased senescence, and decreased immunity, all three of which impact the risk/benefit ratio associated with cancer surgery in the elderly.

scholarly journals A Turing test for free will

2012 ◽  
Vol 370 (1971) ◽  
pp. 3597-3610 ◽  
Author(s):  
Seth Lloyd
Keyword(s):  
Decision Making ◽  
Quantum Mechanics ◽  
Free Will ◽  
Decision Maker ◽  
Turing Test ◽  
Decision Making Process ◽  
Theory Of Computation ◽  
Alan Turing ◽  
The World ◽  
Decision Making Processes

Before Alan Turing made his crucial contributions to the theory of computation, he studied the question of whether quantum mechanics could throw light on the nature of free will. This paper investigates the roles of quantum mechanics and computation in free will. Although quantum mechanics implies that events are intrinsically unpredictable, the ‘pure stochasticity’ of quantum mechanics adds randomness only to decision-making processes, not freedom. By contrast, the theory of computation implies that, even when our decisions arise from a completely deterministic decision-making process, the outcomes of that process can be intrinsically unpredictable, even to—especially to—ourselves. I argue that this intrinsic computational unpredictability of the decision-making process is what gives rise to our impression that we possess free will. Finally, I propose a ‘Turing test’ for free will: a decision-maker who passes this test will tend to believe that he, she, or it possesses free will, whether the world is deterministic or not.

scholarly journals HYBRID MODE OF BIBLIOMETRY AND TECHNIQUE OF PROSPECTIVE SCENARIOS FOR INDUSTRY 4.0 ASSOCIATED WITH INTELLECTUAL CAPITAL

2021 ◽  
Vol 9 (5) ◽  
pp. 534-549
Author(s):  
Laudiceia Normando de Souza ◽  
Ana Eleonora Almeida Paixão ◽  
Cleide Ane Barbosa da Cruz ◽  
Teresinha Fonseca
Keyword(s):  
Decision Making ◽  
Strategic Planning ◽  
Intellectual Capital ◽  
Industry 4.0 ◽  
Economic Value ◽  
Decision Making Process ◽  
Hybrid Mode ◽  
Organizational Process ◽  
Technological Advances ◽  
Scenario Technique

The prospective scenarios technique conducts strategic planning as a futuristic signpost for the management goals of Industry 4.0 in its technological advances, directed towards the development of productive digitalization and creation of value connected to Intellectual Capital as an aggregator of economic value in the organizational process. The objective of this research is to propose a hybrid modality of bibliometrics and the prospective scenario technique for Industry 4.0 associated with Intellectual Capital. In the methodological stages of this study, the insertion of the Bibliometric Laws of Lotka, Bradford, and Zipf and its informative potential stand out, aiming to assist in the decision-making process of strategic planners.

scholarly journals CHALLENGES FACED BY THE WTO’S ECONOMIC SYSTEM IN TRADE IN SERVICES NEGOTIATIONS

2019 ◽  
Vol 4 (8) ◽  
pp. 121-125
Author(s):  
Nino Parsadanishvili
Keyword(s):  
Decision Making ◽  
International Trade ◽  
World Trade Organization ◽  
World Trade ◽  
Developed Countries ◽  
Decision Making Process ◽  
Special Focus ◽  
Trade In Services ◽  
Least Developed Countries ◽  
The World

resent paper focuses on current crises in international trade in services negotiations from the perspective of consideration of trading interests of developing and least developed countries in line with the operational agenda of the World Trade Organization (WTO). Through the analysis of the existing international legal texts and scholarly works particular attention is paid to the different rounds of trade in services negotiations in parallel to the consideration of the results of relevant ministerial conferences of the World Trade Organization, drawing attention to the situation with regards of consideration of the interests of developing and least developed country members of the WTO. Special focus is paid to the complexity of the decision making process and it’s complication over time due to increased participation of parties concerned in the process of trade in services negotiations resulting in no progress in the overall process. Next to analyzing the challenges faced by the WTO in trade in services negotiations, especially in terms of considering the interests of developing and least developed countries, paper shows the ways that could be used during 2020 Kazakhstan Ministerial Conference of the World Trade Organization for finding solutions to simplify the decision making process and establish freer international trade in services by the way of either implying new approaches in interpreting the existing multilateral treaties that deal with trade in services between all member states of the WTO or deepening the discussions on a new plurilateral agreement helping the organization to overcome the stagnated process of trade in services negotiations and therefore ensuring the compliance with it’s own operational goals.

scholarly journals COVID-19 Future Predictions Using 4 Supervised Machine Learning Models

2022 ◽  
pp. 61-66
Author(s):  
Aditi Vadhavkar ◽  
Pratiksha Thombare ◽  
Priyanka Bhalerao ◽  
Utkarsha Auti
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machine ◽  
Supervised Machine Learning ◽  
Perioperative Outcomes ◽  
Support Vector ◽  
Learning Models ◽  
Death Rates ◽  
The World ◽  
Machine Learning Models

Forecasting Mechanisms like Machine Learning (ML) models having been proving their significance to anticipate perioperative outcomes in the domain of decision making on the future course of actions. Many application domains have witnessed the use of ML models for identification and prioritization of adverse factors for a threat. The spread of COVID-19 has proven to be a great threat to a mankind announcing it a worldwide pandemic throughout. Many assets throughout the world has faced enormous infectivity and contagiousness of this illness. To look at the figure of undermining components of COVID-19 we’ve specifically used four Machine Learning Models Linear Regression (LR), Least shrinkage and determination administrator (LASSO), Support vector machine (SVM) and Exponential smoothing (ES). The results depict that the ES performs best among the four models employed in this study, followed by LR and LASSO which performs well in forecasting the newly confirmed cases, death rates yet recovery rates, but SVM performs poorly all told the prediction scenarios given the available dataset.

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