scholarly journals Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models

2020 ◽  
Vol 135 ◽  
pp. 105393 ◽  
Author(s):  
Ping Hou ◽  
Olivier Jolliet ◽  
Ji Zhu ◽  
Ming Xu
Keyword(s):  
Machine Learning ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Characterization Factors ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals The Brand Effect: A Case Study in Taiwan Second-Hand Smartfhone Market

2021 ◽  
Vol 10 (1-2) ◽  
pp. 30-42
Author(s):  
Guan-Yuan Wang
Keyword(s):  
Machine Learning ◽  
Life Cycle ◽  
Purchase Intention ◽  
Brand Preference ◽  
Learning Models ◽  
Consumer Purchase Intention ◽  
Purchasing Behaviour ◽  
Brand Effect ◽  
Machine Learning Models

Abstract Since the smartphone market is an oligopoly market structure, consumer purchase intention is usually driven by brand preference. This research analyses the customer-to-customer market of second-hand smartphones, pointing out how the brand factor affects the consumers’ purchasing behaviour. It is found that the recovery value and life cycle of Apple smartphones are higher and longer than those of other brands. Moreover, the recovery value of other brand smartphones is significantly driven by the debut date of the Apple smartphones, implicitly forming a consumption cycle. In addition, through machine learning models, the predictability for the recovery value is able to reach 93.55%.

scholarly journals Arangopipe, a tool for machine learning meta-data management

Data Science ◽  
2021 ◽  
pp. 1-15
Author(s):  
Jörg Schad ◽  
Rajiv Sambasivan ◽  
Christopher Woodward
Keyword(s):  
Machine Learning ◽  
Life Cycle ◽  
Open Source ◽  
Data Model ◽  
Application Programming Interface ◽  
Learning Models ◽  
Essential Components ◽  
Application Programming ◽  
Programming Interface ◽  
Machine Learning Models

Experimenting with different models, documenting results and findings, and repeating these tasks are day-to-day activities for machine learning engineers and data scientists. There is a need to keep control of the machine-learning pipeline and its metadata. This allows users to iterate quickly through experiments and retrieve key findings and observations from historical activity. This is the need that Arangopipe serves. Arangopipe is an open-source tool that provides a data model that captures the essential components of any machine learning life cycle. Arangopipe provides an application programming interface that permits machine-learning engineers to record the details of the salient steps in building their machine learning models. The components of the data model and an overview of the application programming interface is provided. Illustrative examples of basic and advanced machine learning workflows are provided. Arangopipe is not only useful for users involved in developing machine learning models but also useful for users deploying and maintaining them.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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