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Article
Publication date: 27 October 2020

Fuad Ali Mohammed Al-Yarimi, Nabil Mohammed Ali Munassar and Fahd N. Al-Wesabi

Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are…

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Abstract

Purpose

Digital 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/approach

Considering 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.

Findings

From 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/value

The 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.

Details

Data Technologies and Applications, vol. 54 no. 5
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 8 August 2024

Jitender Kumar and Vinki Rani

This paper aims to identify the factors influencing the adoption of financial technology (FinTech) services among Indian residents. Moreover, it compares the awareness levels…

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Abstract

Purpose

This paper aims to identify the factors influencing the adoption of financial technology (FinTech) services among Indian residents. Moreover, it compares the awareness levels among both male and female users to offer a comprehensive insight into FinTech adoption.

Design/methodology/approach

The research comprises two cross-sectional surveys utilizing self-administered questionnaires: Study A involves 411 male participants and Study B involves 473 female users in FinTech adoption. This article used a “Statistical Package for Social Science (SPSS) followed by partial least squares-structural equation modeling (PLS-SEM)” for data analysis.

Findings

The exciting finding reveals that attitude and personal innovativeness have a significant impact, while technology anxiety shows a statistically insignificant impact on awareness in both studies. Surprisingly, the socio-demographic factor significantly impacts awareness (in Study A) and has an insignificant impact on awareness in Study B. Moreover, both studies reveal that awareness significantly impacts perceived usefulness and ease of use. Additionally, the outcomes confirm a positive relation between awareness, perceived usefulness, ease of use and FinTech adoption in both studies.

Practical implications

The present research will offer valuable insights to all FinTech service providers and stakeholders, aiding them in planning and designing relevant policies.

Originality/value

As far as the researchers are aware, this study stands as the initial survey into FinTech that specifically examines the impact of gender on technology adoption. The divergence in awareness and adoption rates between males and females and the authors’ insightful findings illuminate the context's uniqueness. Moreover, this article offers a robust model for using FinTech services from the perspective of a developing economy.

Details

International Journal of Accounting & Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1834-7649

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