scholarly journals Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning

2018 ◽  
Vol 11 (4) ◽  
pp. 70 ◽  
Author(s):  
Jung-sik Hong ◽  
Hyeongyu Yeo ◽  
Nam-Wook Cho ◽  
Taeuk Ahn
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Area Under The Curve ◽  
High Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Machine Learning Technique ◽  
Novel Approach ◽  
Learning Technique

Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables.

An Efficient Supervised Machine Learning Technique for Forecasting Stock Market Trends

2021 ◽  
pp. 143-162
Author(s):  
Asad Khattak ◽  
Adil Khan ◽  
Habib Ullah ◽  
Muhammad Usama Asghar ◽  
Areeba Arif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Supervised Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Market Trends

scholarly journals Detection of Breast Cancer in the Bio Medical Image using Supervised Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1140-1142
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Medical Image ◽  
Image Sensors ◽  
Supervised Machine Learning ◽  
Machine Learning Technique ◽  
Biomedical Image ◽  
Learning Technique ◽  
Learning Concept

The detection of disease for diagnosis in the medical image becomes easier, when the machine learning concept is used. In this article, the Breast cancer is detected from the biomedical image using supervised machine learning technique. The bio medical image is acquired from the image sensors and that acquired image is pre processed for further processing. From that pre-processed image, the object detection is performed. The next processes are segmentation and feature extraction. Finally, the supervised learning is implemented in the image for image classification. With the help of machine learning, the different cases of cells are also detected, differentiated and spotted for the further diagnosis

scholarly journals Machine learning Techniques for Hotel Online Reputation

2019 ◽  
Vol 8 (9) ◽  
pp. 3004-3007
Keyword(s):  
Machine Learning ◽  
Online Reviews ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Learning Reinforcement ◽  
Online Reputation ◽  
Fake Reviews

Now days when someone decide to book a hotel, previous online reviews of the hotels play a major role in determining the best hotel within the budget of the customer. Previous Online reviews are the most important motivation for the information that are used to analyse public opinion. Because of the high impact of the reviews on business, hotel owners are always highly concerned and focused about the customer feedback and past online reviews. But all reviews are not true and trustworthy, sometime few people may intentionally generate the fake reviews to make some hotel famous of to defame. Therefore it is essential to develop and propose the techniques for analysis of reviews. With the help of various machine learning techniques viz. Supervised machine learning technique, Text mining, Unsupervised machine learning technique, Semi-supervised learning, Reinforcement learning etc we may detect the fake reviews. This paper gives some notions of using machine learning techniques in analysis of past online reviews of hotels, Based on the observation it also suggest the optimal machine learning technique for a particular situation

scholarly journals Estimation of diffusive states from single-particle trajectory in heterogeneous medium using machine-learning methods

2018 ◽  
Vol 20 (37) ◽  
pp. 24099-24108 ◽  
Author(s):  
Yu Matsuda ◽  
Itsuo Hanasaki ◽  
Ryo Iwao ◽  
Hiroki Yamaguchi ◽  
Tomohide Niimi
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Particle Trajectory ◽  
Heterogeneous Medium ◽  
Hidden Markov ◽  
Machine Learning Method ◽  
Learning Method ◽  
Machine Learning Methods ◽  
Novel Approach ◽  
Hybrid Machine

We propose a novel approach to analyze random walks in heterogeneous medium using a hybrid machine-learning method based on a gamma mixture and a hidden Markov model.

scholarly journals Validity of the EQ-5D-5L questionnaire among the general population of Poland

2020 ◽  
Author(s):  
Katarzyna Młyńczak ◽  
Dominik Golicki
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
General Population ◽  
Construct Validity ◽  
Psychometric Properties ◽  
Machine Learning Technique ◽  
Validity Assessment ◽  
Novel Approach ◽  
Learning Technique ◽  
Eq Vas

Abstract Purpose We aim to compare the psychometric properties of the EQ-5D-5L questionnaire with the EQ-5D-3L version and EQ VAS, based on a survey conducted in a sample representing the general adult population of Poland. Methods The survey comprised health-related quality of life (HRQoL) questionnaires: EQ-5D-5L, EQ VAS, SF-12 and EQ-5D-3L, together with demographic and socio-economic characteristics items. The EQ-5D index values were estimated based on a directly measured value set for Poland. The following psychometric properties were analysed: feasibility, distribution of responses, redistribution from EQ-5D-3L to EQ-5D-5L, inconsistencies, ceiling effects, informativity power and construct validity. We proposed a novel approach to the construct validity assessment, based on the use of a machine learning technique known as the random forest algorithm. Results From March to June 2014, 3978 subjects (aged 18–87, 53.2% female) were surveyed. The EQ-5D-5L questionnaire had a lower ceiling effect compared to EQ-5D-3L (38.0% vs 46.6%). Redistribution from EQ-5D-3L to EQ-5D-5L was similar for each dimension, and the mean inconsistency did not exceed 5%. The results of known-groups validation confirmed the hypothesis concerning the relationship between the EQ-5D index values and age, sex and occurrence of diabetes. Conclusions The EQ-5D-5L, in comparison with its EQ-5D-3L equivalent, showed similar or better psychometric properties within the general population of a country. We assessed the construct validity of the questionnaire with a novel approach that was based on a machine learning technique known as the random forest algorithm.

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