Predicting popular contributors in innovation crowds: the case of My Starbucks Ideas

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chien-Yi Hsiang ◽  
Julia Taylor Rayz
Keyword(s):  
Empirical Evidence ◽  
New Product Development ◽  
Supervised Machine Learning ◽  
User Generated Content ◽  
Machine Learning Techniques ◽  
Customer Engagement ◽  
Text Representation ◽  
Content Type ◽  
Customer Preferences ◽  
Learning Techniques

PurposeThis study aims to predict popular contributors through text representations of user-generated content in open crowds.Design/methodology/approachThree text representation approaches – count vector, Tf-Idf vector, word embedding and supervised machine learning techniques – are used to generate popular contributor predictions.FindingsThe results of the experiments demonstrate that popular contributor predictions are considered successful. The F1 scores are all higher than the baseline model. Popular contributors in open crowds can be predicted through user-generated content.Research limitations/implicationsThis research presents brand new empirical evidence drawn from text representations of user-generated content that reveals why some contributors' ideas are more viral than others in open crowds.Practical implicationsThis research suggests that companies can learn from popular contributors in ways that help them improve customer agility and better satisfy customers' needs. In addition to boosting customer engagement and triggering discussion, popular contributors' ideas provide insights into the latest trends and customer preferences. The results of this study will benefit marketing strategy, new product development, customer agility and management of information systems.Originality/valueThe paper provides new empirical evidence for popular contributor prediction in an innovation crowd through text representation approaches.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

scholarly journals Supervised Machine Learning Techniques: An Overview with Applications to Banking

2021 ◽  
Author(s):  
Linwei Hu ◽  
Jie Chen ◽  
Joel Vaughan ◽  
Soroush Aramideh ◽  
Hanyu Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques

Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques

2015 ◽  
Vol 22 (5) ◽  
pp. 573-590 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Claude Sammut ◽  
S. Travis Waller
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Design Methodology ◽  
Machine Learning Techniques ◽  
Mixing Process ◽  
Content Type ◽  
Practical Solution ◽  
Learning Techniques ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications – This approach can be applied in practice to match experts’ decisions. Originality/value – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.

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