Analyzing the Performance of Novel Logistic Regression over Linear Regression Algorithms for Predicting Fake Job with Improved Accuracy

This study tries to detect fake job advertisements online using Novel Logistic Regression and compares its accuracy with linear regression. Data collection and model training are essential steps. This study includes two groups and employs Novel Logistic Regression and linear regression machine learn...

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Veröffentlicht in:International Conference on Electronics and Sustainable Communication Systems (Online) S. 1728 - 1732
Hauptverfasser: R, Narayanan, R, Bhavani, Balamanigandan, R, Priscilla, A.AntoSagaya
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 07.08.2024
Schlagworte:
Accuracy
Computational modeling
Employment
Fake Job
Job Seekers
Linear regression
Logistic regression
Logistics
Machine learning
Novel Logistic Regression
Prediction algorithms
Training
Vulnerable
ISSN:2996-5357
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Beschreibung
Zusammenfassung:This study tries to detect fake job advertisements online using Novel Logistic Regression and compares its accuracy with linear regression. Data collection and model training are essential steps. This study includes two groups and employs Novel Logistic Regression and linear regression machine learning methods. Each set comprises 10 samples, totalling 20 samples. An 80% G power value is used for SPSS computations. The factors under consideration are confidence interval (CI) and significance level (alpha). Linear regression and innovative logistic regression models were used to analyse susceptible persons on the internet. Novel logistic regression achieves a higher accuracy rate of 97.92 % compared to the 94.88% accuracy rate of linear regression. A measurement that is accurate, with a mean value and a standard deviation of ±1 at the very least. A statistically significant result was obtained from the t-test due to the fact that the t-value was lower than the significance level of 0.05 (0.002). The analysis and comparison of fraudsters in employment scam is identified and the proposed Novel Logistic Regression outperforms linear regression. The proposed Novel Logistic Regression algorithm obtained an accuracy of 97.92%.
ISSN:2996-5357
DOI:10.1109/ICESC60852.2024.10690033