Data Analytics in der Produktion*/A maturity model for the classification of real-world applications of data analytics in manufacturing environment

2018 ◽  
Vol 108 (07-08) ◽  
pp. 543-548
Author(s):  
T. Pschybilla ◽  
D. Baumann ◽  
S. Manz ◽  
W. Wenger ◽  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Data Analytics ◽  
Use Cases ◽  
Maturity Model ◽  
Manufacturing Environment ◽  
Smart Services ◽  
Real World Applications

Mit der fortschreitenden Digitalisierung in der Produktion werden konstant ansteigende Datenmengen generiert. Eine besondere Rolle kommt dabei dem Gebiet der Data Analytics zu, welches die Gewinnung von Wissen aus Daten und damit die Entscheidungsfindung unterstützen kann. Im Beitrag wird ein Reifegradmodell zur Einordnung von Anwendungsfällen der Data Analytics in der Produktion vorgestellt und an einem Beispiel der Smart Services der Trumpf GmbH + Co. KG angewendet.   With the progressing digitization in manufacturing, continuously increasing amounts of data are being generated. The field of data analytics plays an important role in this context by advancing the acquisition of knowledge from data and thus decision-making. This paper presents a maturity model for the classification of data analytics use cases in manufacturing. The model is applied to an example of Smart Services at Trumpf GmbH + Co. KG.

Has Dynamic Programming Improved Decision Making?

2019 ◽  
Vol 11 (1) ◽  
pp. 833-858 ◽  
Author(s):  
John Rust
Keyword(s):  
Decision Making ◽  
Dynamic Programming ◽  
Real World ◽  
Operations Research ◽  
Decision Rules ◽  
Decision Problems ◽  
Optimal Decision ◽  
Sequential Decision ◽  
Success Stories ◽  
Real World Applications

Dynamic programming (DP) is a powerful tool for solving a wide class of sequential decision-making problems under uncertainty. In principle, it enables us to compute optimal decision rules that specify the best possible decision in any situation. This article reviews developments in DP and contrasts its revolutionary impact on economics, operations research, engineering, and artificial intelligence with the comparative paucity of its real-world applications to improve the decision making of individuals and firms. The fuzziness of many real-world decision problems and the difficulty in mathematically modeling them are key obstacles to a wider application of DP in real-world settings. Nevertheless, I discuss several success stories, and I conclude that DP offers substantial promise for improving decision making if we let go of the empirically untenable assumption of unbounded rationality and confront the challenging decision problems faced every day by individuals and firms.

PROVIDING TIMELY UPDATED SEQUENTIAL PATTERNS IN DECISION MAKING

2010 ◽  
Vol 09 (06) ◽  
pp. 873-888 ◽  
Author(s):  
TZUNG-PEI HONG ◽  
CHING-YAO WANG ◽  
CHUN-WEI LIN
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Real World ◽  
Experimental Results ◽  
Sequential Patterns ◽  
The Past ◽  
Large Databases ◽  
Real World Applications ◽  
Critical Task ◽  
Maintenance Process

Mining knowledge from large databases has become a critical task for organizations. Managers commonly use the obtained sequential patterns to make decisions. In the past, databases were usually assumed to be static. In real-world applications, however, transactions may be updated. In this paper, a maintenance algorithm for rapidly updating sequential patterns for real-time decision making is proposed. The proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus greatly reducing the number of database rescans and improving performance. Experimental results verify the performance of the proposed approach. The proposed algorithm provides real-time knowledge that can be used for decision making.

scholarly journals Neural Networks in Credit Risk Classification of Companies in the Construction Sector

2018 ◽  
Vol 2 (2) ◽  
pp. 63-77 ◽  
Author(s):  
Aleksandra Wójcicka
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Credit Risk ◽  
Financial Institutions ◽  
Real World ◽  
Decision Making Process ◽  
Construction Sector ◽  
Real World Data ◽  
Input Variables

The financial sector (banks, financial institutions, etc.) is the sector most exposed to financial and credit risk, as one of the basic objectives of banks' activity (as a specific enterprise) is granting credit and loans. Because credit risk is one of the problems constantly faced by banks, identification of potential good and bad customers is an extremely important task. This paper investigates the use of different structures of neural networks to support the preliminary credit risk decision-making process. The results are compared among the models and juxtaposed with real-world data. Moreover, different sets and subsets of entry data are analyzed to find the best input variables (financial ratios).

scholarly journals Logics and practices of transparency and opacity in real-world applications of public sector machine learning

2017 ◽  
Author(s):  
Michael Veale
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Public Sector ◽  
Real World ◽  
Learning Systems ◽  
Short Paper ◽  
Public Servants ◽  
Real World Applications ◽  
Sector Decision ◽  
Public Sector Decision Making

Presented as a talk at the 4th Workshop on Fairness, Accountability and Transparency in Machine Learning (FAT/ML 2017), Halifax, Nova Scotia, Canada.Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors. Given concerns around accountability in these domains, and amidst accusations of intentional or unintentional bias, there have been increased calls for transparency of these technologies. Few, however, have considered how logics and practices concerning transparency have been understood by those involved in the machine learning systems already being piloted and deployed in public bodies today. This short paper distils insights about transparency on the ground from interviews with 27 such actors, largely public servants and relevant contractors, across 5 OECD countries. Considering transparency and opacity in relation to trust and buy-in, better decision-making, and the avoidance of gaming, it seeks to provide useful insights for those hoping to develop socio-technical approaches to transparency that might be useful to practitioners on-the-ground.

scholarly journals ProtTox: Toxin identification from Protein Sequences

2020 ◽  
Author(s):  
Sathappan Muthiah ◽  
Debanjan Datta ◽  
Mohammad Raihanul Islam ◽  
Patrick Butler ◽  
Andrew Warren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Synthetic Biology ◽  
Real World ◽  
Protein Sequences ◽  
Structural Diversity ◽  
Manual Annotation ◽  
Real World Applications ◽  
Automated Machine Learning

AbstractToxin classification of protein sequences is a challenging task with real world applications in healthcare and synthetic biology. Due to an ever expanding database of proteins and the inordinate cost of manual annotation, automated machine learning based approaches are crucial. Approaches need to overcome challenges of homology, multi-functionality, and structural diversity among proteins in this task. We propose a novel deep learning based method ProtTox, that aims to address some of the shortcomings of previous approaches in classifying proteins as toxins or not. Our method achieves a performance of 0.812 F1-score which is about 5% higher than the closest performing baseline.

scholarly journals AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 982 ◽  
Author(s):  
Khaled Almgren ◽  
Murali Krishnan ◽  
Fatima Aljanobi ◽  
Jeongkyu Lee
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Marketing Strategies ◽  
Multimedia Data ◽  
Visual Features ◽  
Image Detection ◽  
Real World Application ◽  
Real World Applications ◽  
Scanned Images

The processing and analyzing of multimedia data has become a popular research topic due to the evolution of deep learning. Deep learning has played an important role in addressing many challenging problems, such as computer vision, image recognition, and image detection, which can be useful in many real-world applications. In this study, we analyzed visual features of images to detect advertising images from scanned images of various magazines. The aim is to identify key features of advertising images and to apply them to real-world application. The proposed work will eventually help improve marketing strategies, which requires the classification of advertising images from magazines. We employed convolutional neural networks to classify scanned images as either advertisements or non-advertisements (i.e., articles). The results show that the proposed approach outperforms other classifiers and the related work in terms of accuracy.

scholarly journals A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 1603
Author(s):  
Jinli Zhang ◽  
Tong Li ◽  
Zongli Jiang ◽  
Xiaohua Hu ◽  
Ali Jazayeri
Keyword(s):  
Real World ◽  
Structural Features ◽  
Information Networks ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Real World Applications ◽  
Different Types ◽  
Multiple Challenges ◽  
Real World Datasets

There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.

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