scholarly journals A Novel Decision Support System for Long-Term Management of Bridge Networks

2021 ◽  
Vol 11 (13) ◽  
pp. 5928
Author(s):  
Enes Karaaslan ◽  
Ulas Bagci ◽  
Necati Catbas
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Data Integration ◽  
Support System ◽  
Machine Learning Algorithms ◽  
Structural Safety ◽  
Bridge Management ◽  
Wide Range

Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems.

Decision Support System for Tight Oil Fields Development Achimov Deposits and Their Analogues Using Machine Learning Algorithms

2020 ◽  
Author(s):  
Aleksei Eduardovich Fedorov ◽  
Andrey Aleksandrovich Povalyaev ◽  
Bulat Ildarovich Suleymanov ◽  
Ilshat Rashitovich Dilmuhametov ◽  
Andrey Valerievich Sergeychev
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Oil Fields ◽  
Tight Oil

Machine-learning based wind turbine operating state detection and diagnosis

2020 ◽  
Author(s):  
Angela Meyer
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Lifetime Cost ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Wind Park ◽  
State Boundary ◽  
Detection And Diagnosis

<p>The operation cost for wind parks make up a major fraction of the park’s overall lifetime cost. To facilitate an optimal wind park operation and maintenance, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behaviour in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. We demonstrate the successful detection and diagnosis of anomalous power production for a case study of a German onshore wind park for turbines of 3 MW rated power.</p>

Decision Support System for Tight Oil Fields Development Achimov Deposits and Their Analogues Using Machine Learning Algorithms (Russian)

2020 ◽  
Author(s):  
Aleksei Eduardovich Fedorov ◽  
Andrey Aleksandrovich Povalyaev ◽  
Bulat Ildarovich Suleymanov ◽  
Ilshat Rashitovich Dilmuhametov ◽  
Andrey Valerievich Sergeychev
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Oil Fields ◽  
Tight Oil

scholarly journals An Intelligent System for Prediction of Severity of SARS-Cov-2 Infection and Progression to Critical Illness: Using Machine Learning Models

2021 ◽  
Author(s):  
mohammad reza afrash ◽  
Maryam Yaghoubi ◽  
Fatemeh Rahimi ◽  
Mostafa Shanbehzadeh ◽  
Mohammadkarim Bahadori
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Critical Illness ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Input Variables

Abstract Introduction: The rapid worldwide outbreak of coronavirus disease 2019 (COVID-19) has posed serious and extraordinary challenges to healthcare industries in predicting disease behavior, and outcomes. Aim: This study aimed to develop a Clinical Decision Support System (CDSS) for predicting the severity of SARS-CoV-2 infection and progression to critical illness in a patient with COVID-19 using several machine learning algorithms. Material and Methods: Using a two-center registry, the data of 2482 COVID-19 patients from February 9, 2020, to December 20, 2020, were reviewed. The Relief Feature Selection (RFS) algorithm was used for optimizing the input variables. Then, selected variables feed into ML models including XGBoost, HistGradient Boosting (HGB), Random Forest (RF), and Naïve Bayesian (NB) to construct prediction models. Afterwards, the performance of each combination was compared using some evaluation metrics. Eventually using the best ML model performance, a Clinical Decision Support System (CDSS) was implemented with C# programming language.Results: of the 63 included variables, 15 features were identified as the most important predictors. The experimental results indicated that the HGB classifier with an average classification accuracy of 94.2%, mean specificity of 92.4%, mean sensitivity of 91%, mean F-score of 87.2 %, and finally mean AUC of 87.3 % was selected as the most appropriate machine learning model for predicting the Severity of SARS-CoV-2.Conclusion: The results of this study showed that the hybrid ML algorithms and in particular the RFS-HGB (by optimizing input variables and customizing the structure of the algorithms (can help the frontline clinicians to predict the severity of COVID-19 progression.

scholarly journals Early Detection of Septic Shock Onset Using Interpretable Machine Learners

2021 ◽  
Vol 10 (2) ◽  
pp. 301
Author(s):  
Debdipto Misra ◽  
Venkatesh Avula ◽  
Donna M. Wolk ◽  
Hosam A. Farag ◽  
Jiang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Septic Shock ◽  
Logistic Regression ◽  
Decision Support ◽  
Random Forest ◽  
Decision Support System ◽  
Support System ◽  
Machine Learning Algorithms ◽  
Sepsis Definition ◽  
Time Of Admission

Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient’s progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. Method: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. Results: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. Conclusion: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.

Sistem Pendukung Keputusan SNMPTN Jalur Undangan Dengan Metode Electre

2018 ◽  
Vol 3 ◽  
pp. 14
Author(s):  
Lidia K Simanjuntak ◽  
Tessa Y M Sihite ◽  
Mesran Mesran ◽  
Nuning Kurniasih ◽  
Yuhandri Yuhandri
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Action Selection ◽  
Multi Criteria Decision Making ◽  
Prospective Students ◽  
College Entrance ◽  
Electre Method ◽  
Selection Of

All colleges each year organize the selection of new admissions. Acceptance of prospective students in universities as education providers is done by selecting prospective students based on achievement in school and college entrance selection. To select the best student candidates based on predetermined criteria, then use Multi-Criteria Decision Making (MCDM) or commonly called decision support system. One method in MCDM is the Elimination Et Choix Traduisant la Reality (ELECTRE). The ELECTRE method is the best method of action selection. The ELECTRE method to obtain the best alternative by eliminating alternative that do not fit the criteria and can be applied to the decision SNMPTN invitation path.

PEMILIHAN KEPLING TELADAN MENERAPKAN METODE RANK ORDER CENTROID (ROC) DAN METODE ADDITIVE RATIO ASSESSMENT (ARAS) DI KECAMATAN MEDAN AREA

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Liza Handayani ◽  
Muhammad Syahrizal ◽  
Kennedi Tampubolon
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Rank Order ◽  
Priority Level ◽  
The Village ◽  
Field Area ◽  
Selection Of

The head of the environment is an extension of the head of the village head in assisting or providing services to the community both in the administration of administration in the village and to other problems. It is natural for a kepling to be appreciated for their performance during their special tenure in the kecamatan field area. Previously, the selection of a dipling in a sub-district was very inefficient and seemed unfair for this exemplary selection to use a system to produce an accurate value, and no intentional element. To overcome the process of selecting an exemplary kepling that experiences these obstacles by using an application called a Decision Support System. Decision Support System (SPK) is a system that can solve a problem, and this system is also assisted with several methods, namely the Rank Order Centroid (ROC) method that can assign weight values to each of the criteria based on their priority level. And to do the ranking or determine an exemplary set using the Additive Ratio Assessment (ARAS) method, this method provides decision making that takes decisions based on ranking or the highest value.Keywords: Head of Medan Area Subdistrict, SPK, Centroid Rank Order, Additive Ratio Assessment (ARAS).

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU BERPRESTASI KOTA MEDAN MENERAPKAN METODE PREFERENCES SELECTION INDEX (STUDI KASUS : DINAS PENDIDIKAN KOTA MEDAN)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fajar Syahputra ◽  
Mesran Mesran ◽  
Ikhwan Lubis ◽  
Agus Perdana Windarto
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Formal Education ◽  
Basic Education ◽  
Selection Index ◽  
Main Task ◽  
Selection Of

The teacher is a major milestone in the world of education, the ability and achievement of students cannot be separated from the role of a teacher in teaching and guiding students. Based on the Law of the Republic of Indonesia No. 14 of 2005 concerning Teachers and Lecturers, in Article 1 explained that teachers are professional educators with the main task of educating, teaching, guiding, directing, training, evaluating, and evaluating students in early childhood education through formal education, basic education and education medium. Whereas in Article 4 of the Act, it is explained that the position of teachers as professionals serves to enhance the dignity and role of teachers as learning agents to function to improve the quality of national education.Decision making is an election process, among various alternatives that aim to meet one or several targets. The decision-making system has 4 phases, namely intelligence, design, choice and implementation. These phases are the basis for decision making, which ends with a recommendation.The Preferences Selection Index (PSI) method is a rarely used decision support system method. This method is a method developed by stevanie and Bhatt (2010) to solve the Multi Criteria Decision Making (MCDM). With the right consideration, this method can be one of the tools to determine policies in decision-making systems, especially the selection of outstanding teachers. Determination of policies taken as a basis for decision making, must use criteria that can be defined clearly and objectively.Keywords: Decision Support System, PSI, Selection of Achieving Teachers

Sign in / Sign up

Export Citation Format

Share Document