Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction

2020 ◽  
Vol 54 (5) ◽  
pp. 685-701
Author(s):  
Fuad Ali Mohammed Al-Yarimi ◽  
Nabil Mohammed Ali Munassar ◽  
Fahd N. Al-Wesabi
Keyword(s):  
Supervised Learning ◽  
Computation Time ◽  
Classification Error ◽  
Clinical Practices ◽  
Content Type ◽  
A Value ◽  
Correlated Patterns ◽  
Constructive Models ◽  
Minimum Classification Error ◽  
Supervised Learning And Classification

PurposeDigital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.Design/methodology/approachConsidering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.FindingsFrom the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.Originality/valueThe authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.

Dynamics of Visual Supervised Learning: A Statistical-Physics Approach

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 165-165
Author(s):  
A Unzicker ◽  
M Jüttner ◽  
I Rentschler
Keyword(s):  
Supervised Learning ◽  
Statistical Physics ◽  
Energy Gap ◽  
Feature Space ◽  
Mathematical Structure ◽  
Thermodynamic System ◽  
Classification Performance ◽  
Classification Error ◽  
Wide Range ◽  
Supervised Learning And Classification

We analysed human supervised learning and classification performance for compound Gabor gray-level patterns. We found that internal visual representations for supervised learning and classification may not be constructed in a smooth process of gradual development (Jüttner and Rentschler, 1996 Vision Research in press). Rather, it seemed that certain learning states (‘stereotypes’) recur that may be considered as ‘perceptual hypotheses’. Such effects have a transient character and cannot, therefore, be studied on the basis of cumulative learning data, which allow smoothing at the expense of temporal resolution. Thus, we analyse classification behaviour in terms of the evolution of a thermodynamic system, that is a system characterised by Gibbs statistics. Here it is assumed that a classification error occurs when a noise-influenced decision process passes an ‘energy gap’ related to the distance of signals in feature space. This approach has been extended to a wide range of distance-based models, originated by different fields, such as classical psychometrics, signal detection theory, technical pattern recognition, and connectionism. We made use of the finding that all these models can be related to a uniform mathematical structure (Unzicker et al, 1995 Perception24 Supplement, 95). The subjects' performance can then be described as a cooling process that reveals adaptive feature extraction during learning.

Graduates giving back – a mentoring program for MBA students

2015 ◽  
Vol 4 (3) ◽  
pp. 200-212 ◽  
Author(s):  
Ann Darwin
Keyword(s):  
Higher Education ◽  
South Australia ◽  
Value Added ◽  
Master Of Business Administration ◽  
Mentoring Program ◽  
Education Students ◽  
Content Type ◽  
Mba Students ◽  
A Value ◽  
The University

Purpose – The purpose of this paper is to discuss the challenges and obstacles encountered in the implementation of a mentoring program for Master of Business Administration (MBA) students at the University of South Australia (UniSA) Business School. The paper starts with an exploration into the need for a mentoring program, the trial and subsequent four years of implementation. The paper also explores the network model of mentoring and the reasons why this, rather than a more traditional model, was chosen for the program’s implementation. Design/methodology/approach – This exploratory case study uses data from over 600 students and their alumni mentors over a five-year period to evaluate and improve the program as well as cultivating a critical community of adult learners. Findings – Feedback from students indicates that the mentoring program is regarded by most as a value-added feature of their early learning as it offers support, if and when it is required, from those who have been there before. Research limitations/implications – Results are limited to one institution. However, as research into mentoring for higher education students is thin on the ground, this study contributes to our understanding of the positive impacts of mentoring on student success. Practical implications – This paper emphasizes the importance of business leaders giving back to their alma mater through mentoring current MBA students. It shows how mentoring can support learning and management development. Originality/value – This is an original study which explores ways to increase the learning of higher education students for positive social outcomes.

scholarly journals Strategic account management as a value co-creation selling model in the pharmaceutical industry

2017 ◽  
Vol 32 (2) ◽  
pp. 310-325 ◽  
Author(s):  
Francois Pilon ◽  
Elias Hadjielias
Keyword(s):  
Pharmaceutical Industry ◽  
Pharmaceutical Company ◽  
Value Added ◽  
Pharmaceutical Companies ◽  
Design Data ◽  
Content Type ◽  
Qualitative Research Design ◽  
Account Management ◽  
A Value ◽  
Key Accounts

Purpose This study aims to explore the dynamics enabling strategic account management (SAM) to function as a value co-creation selling model in the pharmaceutical industry. Design/methodology/approach Using an inductive qualitative research design, data are collected within 11 industry customers in Canada. This work focuses on hospitals as strategic accounts of pharmaceutical companies, exploring SAM value co-creation in the “hospital-pharmaceutical company” relationship. Findings The findings suggest the presence of two key dimensions that can enable a value co-creation SAM model in the hospital-pharmaceutical relationship: “customer-tailored value-added initiatives” and “relationship enhancers”. Customer-tailored value-added initiatives explain the activities that are central to the hospital-pharmaceutical company relationship and can lead to the provision of value added that is unique to the hospital. Relationship enhancers explain the activities that can help strengthen hospital-pharmaceutical company relations in the pursuit of enhanced value-added interactions between the two parties. The research demonstrates a cyclical relationship between “customer-tailored value-added initiatives” and “relationship enhancers”, leading to value co-creation through a SAM model. Practical implications The study informs pharmaceutical industry practitioners on how to improve their value proposition through new, more sustainable selling practices. It offers information on implementing a value co-creation SAM model, which can enable pharmaceutical companies to sustain long-lasting value-added relationships with key accounts such as hospitals. Originality/value The study contributes to the field of SAM by conceptualizing SAM as a value co-creation system. It introduces new knowledge in pharmaceutical marketing by offering empirical insight on the applicability and use of SAM in the hospital-pharmaceutical company dyad.

scholarly journals Value-driven engineering of E-maintenance platforms

2014 ◽  
Vol 25 (4) ◽  
pp. 568-598 ◽  
Author(s):  
Marco Macchi ◽  
Adolfo Crespo Márquez ◽  
Maria Holgado ◽  
Luca Fumagalli ◽  
Luis Barberá Martínez
Keyword(s):  
Information And Communication Technologies ◽  
Cost Benefit ◽  
Communication Technologies ◽  
Industrial Applications ◽  
Maintenance Management ◽  
Content Type ◽  
Positive Effects ◽  
A Value ◽  
New Information ◽  
Information And Communication

Purpose – The purpose of this paper is to propose a methodology for the engineering of E-maintenance platforms that is based on a value-driven approach. Design/methodology/approach – The methodology assumes that a value-driven engineering approach would help foster technological innovation for maintenance management. Indeed, value-driven engineering could be easily adopted at the business level, with subsequent positive effects on the industrial applications of new information and communication technologies solutions. Findings – The methodology combines a value-driven approach with the engineering in the maintenance scope. The methodology is tested in a manufacturing case to prove its potential to support the engineering of E-maintenance solutions. In particular, the case study concerns the investment in E-maintenance solutions developed in the framework of a Supervisory Control and Data Acquisition system originally implemented for production purposes. Originality/value – Based on literature research, the paper presents a methodology that is implemented considering three different approaches (business theories, value-driven engineering and maintenance management). The combination of these approaches is novel and overcomes the traditional view of maintenance as an issue evaluated from a cost-benefit perspective.

Computational predictions for predicting the performance of steel 1 panel shear wall under explosive loads

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aydin Shishegaran ◽  
Behnam Karami ◽  
Elham Safari Danalou ◽  
Hesam Varaee ◽  
Timon Rabczuk
Keyword(s):  
Steel Plate ◽  
Plate Thickness ◽  
Ensemble Model ◽  
Maximum Deflection ◽  
Explosive Load ◽  
Statistical Parameters ◽  
Content Type ◽  
A Value ◽  
Error Terms ◽  
Better Than

Purpose The resistance of steel plate shear walls (SPSW) under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the conventional weapons effect program (CONWEP) model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a multiple linear regression (MLR), multiple Ln equation regression (MLnER), gene expression programming (GEP), adaptive network-based fuzzy inference (ANFIS) and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads. Design/methodology/approach The SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads. Findings The resistance of SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads. Originality/value The resistance of SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads.

Balancing risk and trust for strategic alliance formation decisions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zachary A. Collier ◽  
Matthew D. Wood ◽  
Dale A. Henderson
Keyword(s):  
Value Of Information ◽  
Strategic Alliance ◽  
Selection Process ◽  
Subjective Assessment ◽  
Quantitative Model ◽  
Information Gathering ◽  
Content Type ◽  
Alliance Partner ◽  
Analysis Process ◽  
A Value

PurposeTrust entails the assumption of risk by the trustor to the extent that the trustee may act in a manner unaligned with the trustor's interests. Before a strategic alliance is formed, each firm formulates a subjective assessment regarding whether the other firm will behave in a trustworthy manner and not act opportunistically. To inform this partner analysis and selection process, the authors leverage the concept of value of information to quantify the benefit of information gathering activities on the trustworthiness of a potential trustee.Design/methodology/approachIn this paper, the authors develop a decision model that explicitly operationalizes trust as the subjective probability that a trustee will act in a trustworthy manner. The authors integrate the concept of value of information related to information gathering activities, which would inform a trustor about a trustee's trustworthiness.FindingsTrust inherently involves some degree of risk, and the authors find that there is practical value in carrying out information gathering activities to facilitate the partner analysis process. The authors present a list of trustworthiness indicators, along with a scoring sheet, to facilitate learning more about a potential strategic alliance partner.Originality/valueThe need for a quantitative model that can support risk-based strategic alliance decision-making for partner analysis represents a research gap in the literature. The modeling of strategic alliance partner analysis decisions from a value of information (VOI) perspective adds a contribution to the trust literature.

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

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