scholarly journals A Brief Taxonomy of Hybrid Intelligence

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 633-643
Author(s):  
Niccolo Pescetelli
Keyword(s):  
Prediction Models ◽  
Collective Intelligence ◽  
Machine Intelligence ◽  
Future Research ◽  
Human Errors ◽  
Wide Range ◽  
Hybrid Intelligence ◽  
Forecasting System ◽  
Problem Solvers

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals Role of Insulin in Health and Disease: An Update

2021 ◽  
Vol 22 (12) ◽  
pp. 6403
Author(s):  
Md Saidur Rahman ◽  
Khandkar Shaharina Hossain ◽  
Sharnali Das ◽  
Sushmita Kundu ◽  
Elikanah Olusayo Adegoke ◽  
...  
Keyword(s):  
Insulin Signaling ◽  
Basic Research ◽  
Future Research ◽  
Protective Effects ◽  
Glucose Levels ◽  
Physiological Processes ◽  
Blood Glucose Levels ◽  
Wide Range ◽  
Health And Disease

Insulin is a polypeptide hormone mainly secreted by β cells in the islets of Langerhans of the pancreas. The hormone potentially coordinates with glucagon to modulate blood glucose levels; insulin acts via an anabolic pathway, while glucagon performs catabolic functions. Insulin regulates glucose levels in the bloodstream and induces glucose storage in the liver, muscles, and adipose tissue, resulting in overall weight gain. The modulation of a wide range of physiological processes by insulin makes its synthesis and levels critical in the onset and progression of several chronic diseases. Although clinical and basic research has made significant progress in understanding the role of insulin in several pathophysiological processes, many aspects of these functions have yet to be elucidated. This review provides an update on insulin secretion and regulation, and its physiological roles and functions in different organs and cells, and implications to overall health. We cast light on recent advances in insulin-signaling targeted therapies, the protective effects of insulin signaling activators against disease, and recommendations and directions for future research.

Modern Downsizing

2021 ◽  
pp. 1970-1983
Author(s):  
Grzegorz Wojtkowiak
Keyword(s):  
Financial Analysis ◽  
Management Tool ◽  
Future Research ◽  
Research Directions ◽  
Good Practices ◽  
Strategic Option ◽  
Wide Range ◽  
Future Research Directions ◽  
Points Of View

The aim of the chapter is to present the concept of downsizing from different points of view: as a strategic option, as a management tool and as a phenomenon. It describes the evolution of the term, its definitions, and different directions of development. A scale and possible outcomes are described on the basis of financial analysis; however it also discusses the role of non-financial aspects. The chapter points out reasons, aims and a wide range of tools that may be used during implementation of downsizing. One of the conclusions of the chapter is to present future research directions aiming at increasing knowledge of managers and providing them with detailed good practices.

Conclusion

2017 ◽  
Author(s):  
Gregory L. Carter ◽  
Maryanne L. Fisher
Keyword(s):  
Sex Ratio ◽  
Operational Sex Ratio ◽  
Future Research ◽  
Female Competition ◽  
Mate Poaching ◽  
Attitudes And Behaviors ◽  
Theoretical Perspectives ◽  
Wide Range ◽  
Mate Copying

This handbook has presented a wide range of theoretical perspectives on the motivations, attitudes, and behaviors involved in female competition. Using a metatheoretical framework, the contributors have examined how, when, and why women compete. This conclusion articulates the book’s main themes, beginning with evidence regarding women as active, competitive individuals and the value of mating information, addressing topics such as women’s competitive choices regarding mate copying, mate poaching, and mate retention. It then considers the role of intrasexual aggression in adolescence in relation to dating and reproduction, the importance of Operational Sex Ratio (OSR) to female competition, the concept of cooperative mothering or allomothering, and infanticide. It also discusses women as competitors in both traditional and novel social arenas as well as the role of women’s physiology in their competitive behaviors. Finally, it suggests directions for future research on topics that warrant further scrutiny.

scholarly journals Why Beliefs About Emotion Matter: An Emotion-Regulation Perspective

2018 ◽  
Vol 28 (1) ◽  
pp. 74-81 ◽  
Author(s):  
Brett Q. Ford ◽  
James J. Gross
Keyword(s):  
Emotion Regulation ◽  
Clinical Outcomes ◽  
Underlying Mechanism ◽  
Future Research ◽  
Wide Range ◽  
The World ◽  
Emotion Beliefs

The world is complicated, and we hold a large number of beliefs about how it works. These beliefs are important because they shape how we interact with the world. One particularly impactful set of beliefs centers on emotion, and a small but growing literature has begun to document the links between emotion beliefs and a wide range of emotional, interpersonal, and clinical outcomes. Here, we review the literature that has begun to examine beliefs about emotion, focusing on two fundamental beliefs, namely whether emotions are good or bad and whether emotions are controllable or uncontrollable. We then consider one underlying mechanism that we think may link these emotion beliefs with downstream outcomes, namely emotion regulation. Finally, we highlight the role of beliefs about emotion across various psychological disciplines and outline several promising directions for future research.

The Economics of Payment Cards

2014 ◽  
Vol 13 (3) ◽  
Author(s):  
Marc Rysman ◽  
Julian Wright
Keyword(s):  
Empirical Research ◽  
Future Research ◽  
Policy Options ◽  
Interchange Fees ◽  
Economics Literature ◽  
Important Policy ◽  
Wide Range ◽  
Payment Cards ◽  
Detailed Evaluation

AbstractThis paper surveys the economics literature on payment cards, focusing on the role of interchange fees, merchant internalization, surcharging and two-sided markets. The paper aims to integrate a wide range of theoretical papers with the relevant empirical research and important policy questions. We address not only the accomplishments of the research so far, but also the most important limitations in the existing theory and gaps where future research should be directed. Drawing on the insights developed, we provide a detailed evaluation of different policy options.

scholarly journals Unconscious influences on decision making: A critical review

2014 ◽  
Vol 37 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Ben R. Newell ◽  
David R. Shanks
Keyword(s):  
Decision Making ◽  
Social Behavior ◽  
Behavioral Economics ◽  
Cognitive Neuroscience ◽  
Critical Review ◽  
Explanatory Power ◽  
Future Research ◽  
Wide Range ◽  
Cognitive Unconscious

AbstractTo what extent do we know our own minds when making decisions? Variants of this question have preoccupied researchers in a wide range of domains, from mainstream experimental psychology (cognition, perception, social behavior) to cognitive neuroscience and behavioral economics. A pervasive view places a heavy explanatory burden on an intelligent cognitive unconscious, with many theories assigning causally effective roles to unconscious influences. This article presents a novel framework for evaluating these claims and reviews evidence from three major bodies of research in which unconscious factors have been studied: multiple-cue judgment, deliberation without attention, and decisions under uncertainty. Studies of priming (subliminal and primes-to-behavior) and the role of awareness in movement and perception (e.g., timing of willed actions, blindsight) are also given brief consideration. The review highlights that inadequate procedures for assessing awareness, failures to consider artifactual explanations of “landmark” results, and a tendency to uncritically accept conclusions that fit with our intuitions have all contributed to unconscious influences being ascribed inflated and erroneous explanatory power in theories of decision making. The review concludes by recommending that future research should focus on tasks in which participants' attention is diverted away from the experimenter's hypothesis, rather than the highly reflective tasks that are currently often employed.

scholarly journals COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina F. Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Abstract Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible-infected-recovered (SIR) and susceptible-exposed-infectious-recovered (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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