Safety performance prediction and modification strategies for construction projects via machine learning techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hamidreza Abbasianjahromi ◽  
Mehdi Aghakarimi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Performance Prediction ◽  
Construction Projects ◽  
Safety Performance ◽  
Performance Criteria ◽  
Second Phase ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Content Type

PurposeUnsafe behavior accounts for a major part of high accident rates in construction projects. The awareness of unsafe circumstances can help modify unsafe behaviors. To improve awareness in project teams, the present study proposes a framework for predicting safety performance before the implementation of projects.Design/methodology/approachThe machine learning approach was adopted in this work. The proposed framework consists of two major phases: (1) data collection and (2) model development. The first phase involved several steps, including the identification of safety performance criteria, using a questionnaire to collect data, and converting the data into useful information. The second phase, on the other hand, included the use of the decision tree algorithm coupled with the k-Nearest Neighbors algorithm as the predictive tool along with the proposing modification strategies.FindingsA total of nine safety performance criteria were identified. The results showed that safety employees, training, rule adherence and management commitment were key criteria for safety performance prediction. It was also found that the decision tree algorithm is capable of predicting safety performance.Originality/valueThe main novelty of the present study is developing an integrated model to propose strategies for the safety enhancement of projects in the case of incorrect predictions.

scholarly journals Anticipation of Living Status of Hepatitis B Patient by using Machine Learning

2021 ◽  
Vol 9 (11) ◽  
pp. 924-930
Author(s):  
Mr. G Ragu
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Hepatitis B ◽  
Decision Tree ◽  
Age Groups ◽  
Public Health Care ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Visualization Process ◽  
Exploratory Data

Abstract: Recently the methods of Data mining and machine learning are widely used in medical field. These methods/techniques have given better results in the prediction of respective diseases. Hepatitis B is a Liver inflammation; it can affect people of all age groups. Lakhs of people across the globe are thought to be affected by Hepatitis B. Early prediction of Hepatitis B with accurate results can save many people. Hepatitis B is a tough challenge for public health care system because of limited clinical diagnosis in the early stages of disease. This paper presents the decision tree algorithm to diagnose the Hepatitis B. The proposed algorithm includes collection of datasets, pre-processing, EDA (Exploratory Data Analysis), Feature Selection, data visualizing, Interpreting, saving and evaluating the model. After the data visualization process decision tree algorithm is implemented to diagnose the disease along with the patient chances of living. Keywords: Hepatitis B virus, Machine Learning, Decision Tree, Public Health, EDA

scholarly journals CLINICAL APPLICATIONS OF MACHINE LEARNING ON COVID-19: THE USE OF A DECISION TREE ALGORITHM FOR THE ASSESSMENT OF PERCEIVED STRESS IN MEXICAN HEALTHCARE PROFESSIONALS

2020 ◽  
Author(s):  
Juan Luis Delgado-Gallegos ◽  
Gener Avilés-Rodriguez ◽  
Gerardo R. Padilla-Rivas ◽  
María De los Ángeles Cosio-León ◽  
Héctor Franco-Villareal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Perceived Stress ◽  
Healthcare Professionals ◽  
Statistical Correlation ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Managing Stress ◽  
Applications Of Machine Learning ◽  
C5.0 Decision Tree

AbstractStress and anxiety have shown to be indirect effects of the COVID-19 pandemic, therefore managing stress becomes essential. One of the most affected populations by the pandemic are healthcare professionals. Thus, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. In our study, we used a machine learning prediction model to help measure perceived stress; a C5.0 decision tree algorithm was used to analyze and classify datasets obtained from healthcare professionals of the northeast region of Mexico. Our analysis showed that 6 out of 102 instances were incorrectly classified. Missing two cases for mild, three for moderate and 1 for severe (accuracy of 94.1%), statistical correlation analysis was performed to ensure integrity of the method, in addition we concluded that severe stress cases can be related mostly to high levels of Xenophobia and Compulsive stress.

scholarly journals Phishing Scam Detection using Machine Learning

2019 ◽  
Vol 9 (1S) ◽  
pp. 114-118
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithm ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Approaches ◽  
Decision Tree Algorithm ◽  
Sensitive Data ◽  
Tree Algorithm ◽  
Testing Issue

As a wrongdoing of utilizing specialized intends to take sensitive data of clients and users in the internet, phishing is as of now an advanced risk confronting the Internet, and misfortunes due to phishing are developing consistently. Recognition of these phishing scams is a very testing issue on the grounds that phishing is predominantly a semantics based assault, which particularly manhandles human vulnerabilities, anyway not system or framework vulnerabilities. Phishing costs. As a product discovery plot, two primary methodologies are generally utilized: blacklists/whitelists and machine learning approaches. Every phishing technique has different parameters and type of attack. Using decision tree algorithm we find out whether the attack is legitimate or a scam. We measure this by grouping them with diverse parameters and features, thereby assisting the machine learning algorithm to edify.

scholarly journals Heart Disease Prediction Using Machine Learning Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 2603-2606
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Tree Classifier

Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basis of kernel and it also groups the dataset using hyperplane. The main objective of this project is to try and reduce the number of occurrences of the heart diseases in patients

scholarly journals Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System

2021 ◽  
Vol 335 ◽  
pp. 04002
Author(s):  
Casper Kaun ◽  
N.Z Jhanjhi ◽  
Wei Wei Goh ◽  
Sanath Sukumaran
Keyword(s):  
Machine Learning ◽  
Knowledge Management ◽  
Decision Tree ◽  
Training Model ◽  
Pilot Test ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Knowledge Quality ◽  
Knowledge Intensive ◽  
Intensive System

Knowledge is an important asset for an organisation as it facilitates organisational growth. To facilitate knowledge creation and sharing, this is where a knowledge-intensive system is required. One key area that hinders the effective use of knowledge-intensive systems in an organisation is the lack of knowledge quality. This causes the system to be underutilised, and as a result, knowledge will not be captured or shared effectively. Recent KM findings identified that machine learning could be beneficial to knowledge management. A literature review was conducted to identify knowledge of quality attributes and machine learning algorithms. From the findings, it was identified that the decision tree algorithm has a strong potential at classifying knowledge quality. An experiment was then devised to identify the training model required and measure its effectiveness using a pilot test. This involved using a knowledge-intensive system and mapping its variables to the respective knowledge quality attributes. From the experimentation result, the training model is then devised before implemented in a pilot test. The pilot test involved collecting knowledge using the same knowledge-intensive system before running the training model. From the results, it was identified that the decision tree could classify knowledge quality though the results yielded four different outputs at classifying knowledge quality. It was concluded that machine learning is beneficial in the area of knowledge management.

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