Automating Common Data Integration for Improved Data-Driven Decision-Support System in Industrial Construction

2022 ◽  
Vol 36 (2) ◽  
Author(s):  
Lingzi Wu ◽  
Zuofu Li ◽  
Simaan AbouRizk
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Integration ◽  
Support System ◽  
Data Driven ◽  
Industrial Construction

scholarly journals Data-Driven Decision-Support System for Speaker Identification Using E-Vector System

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
He Ma ◽  
Yi Zuo ◽  
Tieshan Li ◽  
C. L. Philip Chen
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Speech Signal ◽  
Speaker Identification ◽  
Data Driven ◽  
Vector System ◽  
Massachusetts Institute Of Technology ◽  
Speech Signal Processing ◽  
Institute Of Technology

Recently, biometric authorizations using fingerprint, voiceprint, and facial features have garnered considerable attention from the public with the development of recognition techniques and popularization of the smartphone. Among such biometrics, voiceprint has a personal identity as high as that of fingerprint and also uses a noncontact mode to recognize similar faces. Speech signal-processing is one of the keys to accuracy in voice recognition. Most voice-identification systems still employ the mel-scale frequency cepstrum coefficient (MFCC) as the key vocal feature. The quality and accuracy of the MFCC are dependent on the prepared phrase, which belongs to text-dependent speaker identification. In contrast, several new features, such as d-vector, provide a black-box process in vocal feature learning. To address these aspects, a novel data-driven approach for vocal feature extraction based on a decision-support system (DSS) is proposed in this study. Each speech signal can be transformed into a vector representing the vocal features using this DSS. The establishment of this DSS involves three steps: (i) voice data preprocessing, (ii) hierarchical cluster analysis for the inverse discrete cosine transform cepstrum coefficient, and (iii) learning the E-vector through minimization of the Euclidean metric. We compare experiments to verify the E-vectors extracted by this DSS with other vocal features measures and apply them to both text-dependent and text-independent datasets. In the experiments containing one utterance of each speaker, the average accuracy of the E-vector is improved by approximately 1.5% over the MFCC. In the experiments containing multiple utterances of each speaker, the average micro-F1 score of the E-vector is also improved by approximately 2.1% over the MFCC. The results of the E-vector show remarkable advantages when applied to both the Texas Instruments/Massachusetts Institute of Technology corpus and LibriSpeech corpus. These improvements of the E-vector contribute to the capabilities of speaker identification and also enhance its usability for more real-world identification tasks.

ADAPTIVE DECISION SUPPORT SYSTEM FOR AUTOMATIC PHYSICAL EFFORT PLAN GENERATION—DATA-DRIVEN APPROACH

2013 ◽  
Vol 44 (2-3) ◽  
pp. 204-221 ◽  
Author(s):  
Krzysztof Brzostowski ◽  
Jarosław Drapała ◽  
Adam Grzech ◽  
Paweł Świątek
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Data Driven ◽  
Physical Effort ◽  
Plan Generation ◽  
Data Driven Approach

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

2014 ◽  
Vol 41 (6Part6) ◽  
pp. 155-155
Author(s):  
Y Luo ◽  
D McShan ◽  
M Schipper ◽  
M Matuszak ◽  
F Kong ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Treatment Planning ◽  
Support System ◽  
Radiation Treatment ◽  
Data Driven ◽  
Radiation Treatment Planning

scholarly journals Implementing data-driven decision support system based on independent educational data mart

2021 ◽  
Vol 11 (6) ◽  
pp. 5301
Author(s):  
Alaa Khalaf Hamoud ◽  
Marwah Kamil Hussein ◽  
Zahraa Alhilfi ◽  
Rabab Hassan Sabr
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Private School ◽  
Evaluation Method ◽  
New Technologies ◽  
Performance Indicator ◽  
Data Driven ◽  
Independent Data ◽  
Data Mart

<span>Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions.</span>

scholarly journals Data-Driven Clinical Decision Support System for Medical Diagnosis and Treatment Recommendation

2019 ◽  
Vol 8 (11) ◽  
pp. 3660-3668
Keyword(s):  
Face Recognition ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Data Driven ◽  
Treatment Recommendation ◽  
Disease Prediction

This paper presents a Data-Driven Clinical Decision Support System (CDSS) using machine learning. The proposed system predicts the possibility of diseases based on the patient’s symptoms. It suggests lab tests and medication related to the disease. Lab test results are analyzed to check the probability of liver and kidney diseases. The proposed system uses face recognition to identify the patient. Face recognition module retrieves the Patient Health Record and provides patient information and health records access to the doctor and medical staff. The system is developed using Python Django for Backend, React.JS for User Interface and PostgreSQL as the relational database. The system uses Logistic Regression for possible disease prediction, Support Vector Machine for liver disease prediction, Random Forest for chronic kidney disease prediction. The result of the proposed data-driven clinical decision support system is compared with a doctor’s disease analysis to measure the effectiveness of the proposed system. This kind of system can help doctors in providing better care and predict the disease at an early stage.

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