scholarly journals Near Miss Archive: A Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

2021 ◽  
Vol 13 (15) ◽  
pp. 8456
Author(s):  
Silvia Maria Ansaldi ◽  
Patrizia Agnello ◽  
Annalisa Pirone ◽  
Maria Rosaria Vallerotonda
Keyword(s):  
Machine Learning ◽  
Continuous Improvement ◽  
Management System ◽  
Safety Management ◽  
Near Miss ◽  
Machine Learning Techniques ◽  
Near Misses ◽  
Robust Model ◽  
Learning Techniques ◽  
Relation Model

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses, and possible anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in 5 years by some 100 inspectors throughout Italy. This paper discusses in detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, machine learning techniques have been used for preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

scholarly journals Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

2021 ◽  
Author(s):  
Silvia Maria Ansaldi ◽  
Patrizia Agnello ◽  
Annalisa Pirone ◽  
Maria Rosaria Vallerotonda
Keyword(s):  
Machine Learning ◽  
Continuous Improvement ◽  
Management System ◽  
Safety Management ◽  
Near Miss ◽  
Machine Learning Techniques ◽  
Near Misses ◽  
Robust Model ◽  
Learning Techniques ◽  
Relation Model

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

IOT Based Water Management System For Crops Using Conventional Machine Learning Techniques

2021 ◽  
Author(s):  
Tiramareddy Manasa Swetha ◽  
Tekkali Yogitha ◽  
Manche Kuruba Sai Hitha ◽  
Puppala Syamanthika ◽  
S S Poorna ◽  
...  
Keyword(s):  
Water Management ◽  
Machine Learning ◽  
Management System ◽  
Machine Learning Techniques ◽  
Water Management System ◽  
Learning Techniques ◽  
Conventional Machine

scholarly journals Prediction of defects using machine learning techniques in order to improve quality management system – A case study

2021 ◽  
Vol 343 ◽  
pp. 05010
Author(s):  
Adina Sârb ◽  
Cristina Burja Udrea ◽  
Daniela Nagy – Oniţa ◽  
Liliana Itul ◽  
Maria Popa
Keyword(s):  
Machine Learning ◽  
Quality Management ◽  
Management System ◽  
Quality Management System ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Learning Techniques ◽  
Different Types ◽  
The Future

According to ISO 9000, a quality management system is part of a set of related or interacting elements of an organization that sets policies and objectives, as well as the processes necessary to achieve the quality objectives. Quality is the extent to which a set of intrinsic characteristics of an object meets the requirements. Based on these definitions, the factory, considered in this paper, S.C. APULUM S.A.,decided to implement a quality management system since 1998. Subsequently, the organization’s attention is focus on the continuous improvement of the implemented quality management system. The purpose of this paper is to study the percent of specified defects specific to ceramic products in the future to improve the quality management system. In this regard, machine learning techniques were applied for defects forecasting for different types of products: mugs, pressed plates and jiggered plates. The experimental evaluation was performed on real data sets that contain percentages about different types of defects collected in 2018-2019. The experimental results show that for each type of product exists an algorithm that forecasts the future defects.

scholarly journals Comparing Classical and Modern Machine Learning Techniques for Monitoring Pedestrian Workers in Top-View Construction Site Video Sequences

2020 ◽  
Vol 10 (23) ◽  
pp. 8466
Author(s):  
Marcel Neuhausen ◽  
Dennis Pawlowski ◽  
Markus König
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Kalman Filter ◽  
Safety Management ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Learning Approaches ◽  
Construction Sites ◽  
Learning Techniques ◽  
Different Characteristics

Keeping an overview of all ongoing processes on construction sites is almost unfeasible, especially for the construction workers executing their tasks. It is difficult for workers to concentrate on their work while paying attention to other processes. If their workflows in hazardous areas do not run properly, this can lead to dangerous accidents. Tracking pedestrian workers could improve the productivity and safety management on construction sites. For this, vision-based tracking approaches are suitable, but the training and evaluation of such a system requires a large amount of data originating from construction sites. These are rarely available, which complicates deep learning approaches. Thus, we use a small generic dataset and juxtapose a deep learning detector with an approach based on classical machine learning techniques. We identify workers using a YOLOv3 detector and compare its performance with an approach based on a soft cascaded classifier. Afterwards, tracking is done by a Kalman filter. In our experiments, the classical approach outperforms YOLOv3 on the detection task given a small training dataset. However, the Kalman filter is sufficiently robust to compensate for the drawbacks of YOLOv3. We found that both approaches generally yield a satisfying tracking performances but feature different characteristics.

A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints

2020 ◽  
pp. 073563312096921
Author(s):  
Şeyhmus Aydoğdu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Management System ◽  
Learning Management System ◽  
Modeling Technique ◽  
Machine Learning Techniques ◽  
Student Modeling ◽  
Student Model ◽  
Learning Techniques

Student modeling is one of the most important processes in adaptive systems. Although learning is individual, a model can be created based on patterns in student behavior. Since a student model can be created for more than one student, the use of machine learning techniques in student modeling is increasing. Artificial neural networks (ANNs), which form one group of machine learning techniques, are among the methods most frequently used in learning environments. Convolutional neural networks (CNNs), which are specific types of these networks, are used effectively for complex problems such as image processing, computer vision and speech recognition. In this study, a student model was created using a CNN due to the complexity of the learning process, and the performance of the model was examined. The student modeling technique used was named LearnerPrints. The navigation data of the students in a learning management system were used to construct the model. Training and test data were used to analyze the performance of the model. The classification results showed that CNNs can be used effectively for student modeling. The modeling was based on the students’ achievement and used the students’ data from the learning management system. The study found that the LearnerPrints technique classified students with an accuracy of over 80%.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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