scholarly journals Application of Combined Developments in Processes and Models to the Determination of Hot Metal Temperature in BOF Steelmaking

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 732
Author(s):  
José Díaz ◽  
Francisco Javier Fernández
Keyword(s):  
Machine Learning ◽  
Carbon Footprint ◽  
Process Model ◽  
Relevant Information ◽  
Metal Temperature ◽  
Machine Learning Techniques ◽  
Hot Metal ◽  
Infrared Thermometry ◽  
Temperature Determination ◽  
Process Computer

Nowadays, the steel industry is seeking to reduce its carbon footprint without affecting productivity or profitability. This challenge needs to be supported by continuous improvements in equipment, methods, sensors and models. The present work exposes how the combined development of processes and models (CDPM) has been applied to the improvement of hot metal temperature determination. The synergies that arise when both sides of this research are simultaneously approached are evidenced. A workflow that takes into account the CDPM approach is proposed. First, a thermal model of the process is developed, making it possible to identify that hot metal temperature is a key lever for carbon footprint reduction. Then, three main alternatives for hot metal temperature determination are compared: infrared thermometry, time-series forecasting and machine learning prediction. Despite considering only few process variables, machine learning techniques succeed in extracting relevant information from process databases. An accuracy close to infrared thermometry is obtained, with a much higher applicability. This research shows that process-model alternatives are complementary when judiciously nested in the process computer routines. Combining measurement and modelling techniques, 100% applicability is achieved with an error reduction of 7 °C.

scholarly journals Machine Learning:A Review

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Isonkobong Christopher Udousoro
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Data Interpretation ◽  
Relevant Information ◽  
Machine Learning Techniques ◽  
Predictive Analysis ◽  
Learning Approaches ◽  
Processing Data ◽  
Learning Techniques ◽  
Machine Learning Applications

Due to the complexity of data, interpretation of pattern or extraction of information becomes difficult; therefore application of machine learning is used to teach machines how to handle data more efficiently. With the increase of datasets, various organizations now apply machine learning applications and algorithms. Many industries apply machine learning to extract relevant information for analysis purposes. Many scholars, mathematicians and programmers have carried out research and applied several machine learning approaches in order to find solution to problems. In this paper, we focus on general review of machine learning including various machine learning techniques. These techniques can be applied to different fields like image processing, data mining, predictive analysis and so on. The paper aims at reviewing machine learning techniques and algorithms. The research methodology is based on qualitative analysis where various literatures is being reviewed based  on machine learning.

Extraction of drug–drug interaction using neural embedding

2018 ◽  
Vol 16 (06) ◽  
pp. 1840027 ◽  
Author(s):  
Wen Juan Hou ◽  
Bamfa Ceesay
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Interaction ◽  
Relevant Information ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Learning Approach ◽  
Learning Techniques ◽  
Drug Drug Interaction

Information on changes in a drug’s effect when taken in combination with a second drug, known as drug–drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

scholarly journals SOCIAL MEDIA DATA PROCESSING AND ANALYSIS BY MEANS OF MACHINE LEARNING FOR RAPID DETECTION, ASSESSMENT AND MAPPING THE IMPACT OF DISASTERS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1237-1241
Author(s):  
P. M. Kikin ◽  
A. A. Kolesnikov ◽  
E. A. Panidi
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Relevant Information ◽  
Important Task ◽  
Machine Learning Techniques ◽  
Social Media Data ◽  
Accurate Localization ◽  
The Impact ◽  
Official Sources ◽  
Media Data

Abstract. The main factor determining the possibility of using data obtained from social media as a source of information about the threat of emergencies is their relevance and accuracy. Thus, the important task is the determination of metrics for evaluating these parameters for a specific publication in a social media. It is worth noting the importance of this information channel as a source of eyewitness accounts from the scene. A comparison of social media data and official sources shows that social media contain a significant amount of unique information at different stages of emergency development. Also, when monitoring the situation for a specific event, social media allows to get more relevant information in comparison to official sources. Another important task is to search for emergency messages and their most accurate localization in space. A promising solution for the analysis and processing of social media data during emergency response is the application of artificial intelligence methods, and, particularly, machine learning techniques.

scholarly journals Qmin: A machine learning-based application for mineral chemistry data processing and analysis

2021 ◽  
Author(s):  
Guilherme Ferreira da Silva ◽  
Marcos Vinicius Ferreira ◽  
Iago Sousa Lima Costa ◽  
Renato Borges Bernardes ◽  
Carlos Eduardo Miranda Mota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Relevant Information ◽  
Mineral Chemistry ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Max Planck ◽  
Blind Test ◽  
Chemistry Data ◽  
Learning Techniques

Abstract Mineral chemistry analysis is a valuable tool in several phases of mineralogy and mineral prospecting studies. This type of analysis can point out relevant information, such as concentration of the chemical element of interest in the analyzed phase and, thus, the predisposition of an area for a given commodity. Due to this, considerable amount of data has been generated, especially with the use of electron probe micro-analyzers (EPMA), either in research for academic purposes or in a typical prospecting campaign in the mineral industry. We have identified an efficiency gap when manually processing and analyzing mineral chemistry data, and thus, we envisage this research niche could benefit from the versatility brought by machine learning algorithms. In this paper, we present Qmin, an application that assists in increasing the efficiency of mineral chemistry data processing and analysis stages through automated routines. Our code benefits from a hierarchical structure of classifiers and regressors trained by a Random Forest algorithm developed on a filtered training database extracted from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, maintained by the Max Planck Institute for Chemistry. To test the robustness of our application, we applied a blind test with more than 11,000 mineral chemistry analyses compiled for diamond prospecting within the scope of the Diamante Brasil Project of the Geological Survey of Brazil. The blind test yielded a balanced classifier accuracy of ca. 99% for the minerals known by Qmin. Therefore, we highlight the potential of machine learning techniques in assisting the processing and analysis of mineral chemistry data.

scholarly journals A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems

2021 ◽  
Vol 11 (20) ◽  
pp. 9590
Author(s):  
Hajo Wiemer ◽  
Alexander Dementyev ◽  
Steffen Ihlenfeldt
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Production Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Monitoring Process ◽  
Machine Learning Applications ◽  
Cyber Physical Production Systems ◽  
Automation Technology

With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

scholarly journals A review on prediction of diabetes type 2 by machine learning techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 5638-5651
Author(s):  
Alka Singh
Keyword(s):  
Machine Learning ◽  
Diabetes Type 2 ◽  
Relevant Information ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Information Usage ◽  
Learning Techniques ◽  
Prediction Of Diabetes

Machine learning is considered to be one of the most promising tools when it comes to working with heterogeneous data. It provides a new dimension which enables one to extract relevant data and take decision for the effective functioning of the network, making use of network generated data. Every sphere of our life is now dependent on machine learning. It has flourished in every dimension. Making it versatile and ever demanding. Department of healthcare contains very abundant and sensitive information which is needed to be carefully handled. Diabetes mellitus is increasing exponentially and is spreading like anything in the world. A reliable prediction system should be present for diagnosing diabetes. Variety of machine learning techniques find their use in the examination of data from variant perspectives and summarizing it into effective information. Usage of new patterns is done to elucidate these patterns in order to deliver relevant information for their users. By making use of techniques such as SVM, random forest, logistic regression, naïve bayes etc the prediction of diabetes can be done easily and accurately. In this study we will make use of different machine learning techniques and try to find accurate prediction regarding the same.

scholarly journals Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic (Preprint)

2018 ◽  
Author(s):  
Ravali Mamidi ◽  
Michele Miller ◽  
Tanvi Banerjee ◽  
William Romine ◽  
Amit Sheth
Keyword(s):  
Machine Learning ◽  
Topic Modeling ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
The Public ◽  
Public Sentiment ◽  
Domain Expertise ◽  
Treatment Research ◽  
Negative Sentiment

BACKGROUND To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues. OBJECTIVE The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed. METHODS Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category. RESULTS A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category. CONCLUSIONS Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects,Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research.

scholarly journals Application of model trees and other machine learning techniques for algal growth prediction in Yongdam reservoir, Republic of Korea

2009 ◽  
Vol 12 (3) ◽  
pp. 262-274 ◽  
Author(s):  
Nahm-Chung Jung ◽  
Ioana Popescu ◽  
Peter Kelderman ◽  
Dimitri P. Solomatine ◽  
Roland K. Price
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Piecewise Linear ◽  
Algal Growth ◽  
Machine Learning Techniques ◽  
Least Squares Regression ◽  
Linear Regression Models ◽  
M5 Model Tree ◽  
Growth Prediction ◽  
Learning Techniques

A promising new approach for eco-environmental modelling, such as algal growth prediction, is the data-driven modeling using machine learning techniques: an artificial neural network (ANN) being a typical method. Another method growing in popularity, based on the M5 model tree (MT) algorithm, is the use of piecewise linear regression models at the leaf nodes of the tree. M5 MTs using partial least-squares regression (PLSR) proposed in this paper were tested on a particular dataset and then compared to M5 MTs, MLF- and RBF-ANN and k nearest neighbours (kNN). With the dataset partitioned to periods of algal growth and no growth, M5 MTs using PLSR showed better results for algal growth prediction in the reservoir than using the annual dataset and other algorithms. This gives the idea that the M5-PLSR MTs, in spite of the lack of data, more effectively seeks latent vectors between the closely correlated multivariate dataset partitioned using clustering techniques. M5-PLSR MTs is a promising approach when there is a shortage of data required to build a more transparent learning process model, and a combination with clustering is recommended.

scholarly journals Sports prediction and betting models in the machine learning age: The case of tennis

2021 ◽  
pp. 1-19
Author(s):  
Sascha Wilkens
Keyword(s):  
Machine Learning ◽  
Management Strategies ◽  
Relevant Information ◽  
Sports Betting ◽  
Machine Learning Techniques ◽  
Sports Events ◽  
Wide Range ◽  
High Volatility ◽  
Betting Markets ◽  
Model Ensembles

Machine learning and its numerous variants have meanwhile become established tools in many areas of society. Several attempts have been made to apply machine learning to the prediction of the outcome of professional sports events and to exploit “inefficiencies” in the corresponding betting markets. On the example of tennis, this paper extends previous research by conducting one of the most extensive studies of its kind and applying a wide range of machine learning techniques to male and female professional singles matches. The paper shows that the average prediction accuracy cannot be increased to more than about 70%. Irrespective of the used model, most of the relevant information is embedded in the betting markets, and adding other match- and player-specific data does not lead to any significant improvement. Returns from applying predictions to the sports betting market are subject to high volatility and mainly negative over the longer term. This conclusion holds across most tested models, various money management strategies, and for backing the match favorites or outsiders. The use of model ensembles that combine the predictions from multiple approaches proves to be the most promising choice.

Predictive Analytics for Business Processes in Service Management

2015 ◽  
pp. 366-403
Author(s):  
Yurdaer N. Doganata ◽  
Geetika T. Lakshmanan ◽  
Merve Unuvar
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Service Management ◽  
Business Processes ◽  
Ad Hoc ◽  
Predictive Analytics ◽  
Parallel Execution ◽  
Machine Learning Techniques ◽  
Parallel Flows ◽  
Future Outcomes

Underlying business processes in service management are people intensive and collaborative by nature. We are observing an emerging trend in the service management applications, moving away from rigid process orchestration to leveraging collaboration. Such solutions allow staffers to define their own customized, ad-hoc step flow consisting of the sequence of the activities necessary to handle a service component. These ad-hoc steps introduce uncertainty to the successful completion of a service request. When there is uncertainty, predictive guidance about future outcomes could provide value to the workers handling a time-sensitive service delivery component. Predicting the future outcomes using machine-learning techniques requires effective representation of the process execution traces. This is challenging when process model includes parallel execution flows or repeated executions of some activities. In this chapter, we describe algorithms for training machine learning models when the execution paths include parallel flows and when some activities are repeatedly executed.

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