Volume 14 | Issue 6 |

 Abstract

Background: Spinal cord injury (SCI) is a life-altering condition that often leads to long-term physical, psychological, and social challenges. Among the various secondary complications associated with SCI, fatigue is one of the most commonly reported yet under-recognized symptoms. Persistent fatigue may negatively influence an individual's ability to participate in daily activities and may significantly affect their overall quality of life. Aim and Objective: The present study aimed to evaluate the impact of fatigue on participation and quality of life in individuals with spinal cord injury living in Mumbai. Method: A cross-sectional observational study was conducted among 138 individuals diagnosed with spinal cord injury. Participants were recruited through convenience sampling. Fatigue was assessed using the Fatigue Severity Scale (FSS). Participation levels were evaluated using the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P), and quality of life was assessed using the World Health Organization Quality of Life-BREF (WHOQOL-BREF) questionnaire. Functional independence was measured using the Spinal Cord Independence Measure (SCIM). Data were analysed using IBM SPSS software. Descriptive statistics were calculated, and correlation analysis was performed to determine the relationship between fatigue, participation, and quality of life. Results: The findings of the study indicated a high prevalence of fatigue among individuals with spinal cord injury. Higher fatigue levels were significantly associated with reduced participation and poorer quality of life across multiple domains. A moderate negative correlation was observed between fatigue severity and participation scores, as well as between fatigue and quality of life domains. Individuals reporting greater fatigue demonstrated lower functional independence and decreased engagement in social and daily activities. Conclusion: Fatigue has a significant negative impact on participation and quality of life in individuals with spinal cord injury. The findings highlight the importance of assessing fatigue as a routine component of rehabilitation and implementing targeted interventions to manage fatigue in order to improve participation and overall well-being in this population.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Spinal cord injury, fatigue, participation, quality of life, rehabilitation, physiotherapy.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of digital payment systems and online financial transactions has increased the risk of fraudulent activities, resulting in significant financial losses for customers, businesses, and financial institutions. Detecting fraudulent transactions in real time has become a major challenge due to the large volume of online payments processed every day. This project presents an intelligent Fraud Detection in Online Payment System using Machine Learning techniques to identify and prevent fraudulent transactions effectively. The system utilizes historical transaction datasets containing attributes such as transaction ID, transaction time, transaction amount, account balance details, and other relevant features to analyze transaction behavior and identify suspicious patterns. Data preprocessing techniques including data cleaning, feature selection, normalization, and dataset transformation are applied to improve data quality and model performance. The Logistic Regression algorithm is employed as the core classification technique due to its simplicity, efficiency, and suitability for binary classification problems involving fraudulent and genuine transactions. The developed system is integrated with a web-based application that enables real-time fraud detection. Whenever a new transaction is initiated, the system analyzes the transaction details using the trained machine learning model and predicts whether the transaction is fraudulent or genuine based on learned patterns from historical data. The prediction result is instantly displayed through the web interface, allowing administrators and users to take immediate action when suspicious activities are detected. This approach improves fraud detection accuracy, reduces manual monitoring efforts, minimizes financial risks, and provides a scalable and cost-effective solution for enhancing the security of online payment systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fraud detection in online payment, scam online payment, fraud transaction

  License

Creative Commons Attribution 4.0 and The Open Definition