scholarly journals Machine learning-based multimedia services for business model evaluation

2021 ◽  
pp. 107605
Author(s):  
Xiaoying Zhong ◽  
Xuejiao Tian ◽  
K. Deepa Thilak ◽  
Anbarasan M
Keyword(s):  
Machine Learning ◽  
Business Model ◽  
Model Evaluation ◽  
Multimedia Services

scholarly journals Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers

2021 ◽  
Author(s):  
Beniamino Di Martino ◽  
Dario Branco ◽  
Luigi Colucci Cante ◽  
Salvatore Venticinque ◽  
Reinhard Scholten ◽  
...  
Keyword(s):  
Business Model ◽  
Model Evaluation ◽  
Business Models ◽  
Real Case ◽  
Sustainable Mobility ◽  
Semantic Framework ◽  
Knowledge Based ◽  
Survey Results ◽  
Energy Prosumers

AbstractThis paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

scholarly journals Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction

2021 ◽  
Vol 4 (3) ◽  
pp. 251524592110268
Author(s):  
Roberta Rocca ◽  
Tal Yarkoni
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Model Evaluation ◽  
Evaluation Criteria ◽  
Model Performance ◽  
Scientific Models ◽  
Practical Utility ◽  
Model Performance Evaluation ◽  
Unseen Data ◽  
Reliable Model

Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic and are affected by a number of shortcomings (e.g., failure to assess models’ ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields such as machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility, identify concrete steps the community could take to promote the development of such benchmarks, and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology and encourage researchers to place heavier emphasis on the practical utility of scientific models.

scholarly journals Exploratory analysis on prediction of loan privilege for customers using random forest

2018 ◽  
Vol 7 (2.21) ◽  
pp. 339 ◽  
Author(s):  
K Ulaga Priya ◽  
S Pushpa ◽  
K Kalaivani ◽  
A Sartiha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Data Model ◽  
Model Evaluation ◽  
Banking Industry ◽  
Performance Parameters ◽  
Training Set ◽  
Test Set ◽  
Learning Technique ◽  
Analytical Processing

In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.  

scholarly journals The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Yuna Shin ◽  
Heesuk Lee ◽  
Young-Joo Lee ◽  
Dae Keun Seo ◽  
Bomi Jeong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Variable Selection ◽  
Roc Curve ◽  
Model Evaluation ◽  
Selection Method ◽  
The Other ◽  
Evaluation Tool ◽  
Evaluation Tools ◽  
Variable Selection Method ◽  
Boruta Algorithm

This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging, Boosting, and SVM. In order to compare the performance of the models, this study measured the accuracy, specificity, sensitivity, and AUC, which are representative model evaluation tools. The ROC curve is created by plotting association of sensitivity and (1-specificity). The AUC that is area of ROC curve represents sensitivity and specificity. This measure has two competitive advantages compared to other evaluation tools. One is that it is scale-invariant. It means that purpose of AUC is how well the model predicts. The other is that the AUC is classification-threshold-invariant. It shows that the AUC is independent of threshold because it is plotted association of sensitivity and (1-specificity) obtained by threshold. We chose AUC as a final model evaluation tool with two advantages. Also, variable selection was conducted using the Boruta algorithm. In addition, we tried to distinguish the better model by comparing the model with the variable selection method and the model without the variable selection method. As a result of the analysis, Boruta algorithm as a variable selection method suggested PO4-P, DO, BOD, NH3-N, Susp, pH, TOC, Temp, TN, and TP as significant explanatory variables. A comparison was made between the model with and without these selected variables. Among the models without variable selection method, the accuracy of RF analysis was highest, and ANN analysis showed the highest AUC. In conclusion, ANN analysis using the variable selection method showed the best performance among the models with and without variable selection method.

scholarly journals Predictive Model Evaluation for PHM

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Chunsheng Yang ◽  
Yanni Zou ◽  
Jie Liu ◽  
Kyle R Mulligan
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Real World ◽  
Predictive Models ◽  
Model Evaluation ◽  
High Performance ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Real World Applications ◽  
Prognostic Application

In the past decades, machine learning techniques or algorithms, particularly, classifiers have been widely applied to various real-world applications such as PHM. In developing high-performance classifiers, or machine learning-based models, i.e. predictive model for PHM, the predictive model evaluation remains a challenge. Generic methods such as accuracy may not fully meet the needs of models evaluation for prognostic applications. This paper addresses this issue from the point of view of PHM systems. Generic methods are first reviewed while outlining their limitations or deficiencies with respect to PHM. Then, two approaches developed for evaluating predictive models are presented with emphasis on specificities and requirements of PHM. A case of real prognostic application is studies to demonstrate the usefulness of two proposed methods for predictive model evaluation. We argue that predictive models for PHM must be evaluated not only using generic methods, but also domain-oriented approaches in order to deploy the models in real-world applications.

scholarly journals Sustainability-Oriented Business Model Evaluation—A Literature Review

2021 ◽  
Vol 13 (19) ◽  
pp. 10908
Author(s):  
Anika Süß ◽  
Kristina Höse ◽  
Uwe Götze
Keyword(s):  
Literature Review ◽  
Business Model ◽  
Model Evaluation ◽  
Business Models ◽  
Model Development ◽  
Indicator System ◽  
Future Research ◽  
Sustainable Business ◽  
Simulation Based ◽  
Sustainable Business Models

Since the need of sustainable development is indisputable, companies are forced to strive for resources, processes, and products that are sustainable. Thus, their business models as the main representation of their activities should be designed in an ecologically, economically, and socially beneficial way. However, designing and developing sustainable business models is closely linked to their evaluation. Sustainable business model evaluation as a vital part of business model development has been addressed in literature in the past with increasing frequency. As a consequence, the plethora of different approaches of sustainability-oriented business model evaluation calls for a systematic literature review. Thus, in this study, we reviewed existing articles on sustainability-oriented business model evaluation and identified four main categories of evaluation methods: single indicators (I), indicator system/framework (II), simulation-based evaluation (III), and multi criteria decision-making (IV). By analyzing and structuring the proposed approaches, their benefits and limitations are revealed, pointing out gaps and future research needs for successfully designing and evaluating business models today and in the future.

scholarly journals Tech Start-up Incubation Program: Business Model Evaluation on Government Based Incubator in Indonesia

TEM Journal ◽  
2021 ◽  
pp. 283-291
Author(s):  
Tifa Noer Amelia ◽  
Armanu Thoyib ◽  
Gugus Irianto ◽  
Ainur Rofiq
Keyword(s):  
Business Model ◽  
Support System ◽  
Model Evaluation ◽  
System Analysis ◽  
Qualitative Approach ◽  
Early Stages ◽  
Start Up ◽  
Start Ups

This research evaluates business model in an incubator company using a framework that consists of 21 assessment components. It is required to examine and measure the efficiency of the incubation program. The incubation program is an intensive mentoring service for start-ups and connects them with the related support system. Analysis conducted using a qualitative approach and matrix scoring generated from N-Vivo. Bekraf Incubator is a government-based incubator in Indonesia suitable for early stages of start-up. Bekraf Incubator successfully demonstrates an effective and flexible business model by focusing on 16 sub-sectors under their incubation program.

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