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%.

Explainable machine learning: A case study on impedance tube measurements

2021 ◽  
Vol 263 (3) ◽  
pp. 3223-3234
Author(s):  
Merten Stender ◽  
Mathies Wedler ◽  
Norbert Hoffmann ◽  
Christian Adams
Keyword(s):  
Machine Learning ◽  
Absorption Coefficient ◽  
Specimen Thickness ◽  
Learning Models ◽  
Impedance Tube ◽  
Frequency Regime ◽  
Hidden Patterns ◽  
Model Diagnosis ◽  
Machine Learning Models

Machine learning (ML) techniques allow for finding hidden patterns and signatures in data. Currently, these methods are gaining increased interest in engineering in general and in vibroacoustics in particular. Although ML methods are successfully applied, it is hardly understood how these black box-type methods make their decisions. Explainable machine learning aims at overcoming this issue by deepening the understanding of the decision-making process through perturbation-based model diagnosis. This paper introduces machine learning methods and reviews recent techniques for explainability and interpretability. These methods are exemplified on sound absorption coefficient spectra of one sound absorbing foam material measured in an impedance tube. Variances of the absorption coefficient measurements as a function of the specimen thickness and the operator are modeled by univariate and multivariate machine learning models. In order to identify the driving patterns, i.e. how and in which frequency regime the measurements are affected by the setup specifications, Shapley additive explanations are derived for the ML models. It is demonstrated how explaining machine learning models can be used to discover and express complicated relations in experimental data, thereby paving the way to novel knowledge discovery strategies in evidence-based modeling.

scholarly journals GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction

Processes ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 23
Author(s):  
Kexin Bi ◽  
Dong Zhang ◽  
Tong Qiu ◽  
Yizhen Huang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Gas Chromatography Mass Spectrometry ◽  
Learning Models ◽  
Fingerprint Image ◽  
Whole Process ◽  
Evaluation Problem ◽  
Potential Issue ◽  
Machine Learning Models

Food flavor quality evaluation is attracting continuous attention, but a suitable evaluation system is severely lacking. Gas chromatography-mass spectrometry/olfactometry (GC-MS/O) is widely used to solve the food flavor evaluation problem, but the olfactometry evaluation is unfeasible to be carried out in large batches and is unreliable due to potential issue of an operator or systematic laboratory effect. Thus, a novel fingerprint modeling and profiling process was proposed based on several machine learning models including convolutional neural network (CNN). The fingerprint template was created by the data analysis of existing GC-MS spectrum dataset. Then the fingerprint image generation program was applied for structuring the complex instrumental data. Food olfactometry result was obtained by a machine learning method based on CNN using fingerprint image as the input. The case study on peanut oil samples demonstrated the model accuracy of around 93%. By structure optimization and further dataset expansion, the whole process has the potential to be utilized by sensory laboratories for aroma analysis instead of humans.

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