An Improved Precision Using an Integrated Neural Network Model and Support Vector Machine Algorithms to Find the Instagram Users Profile Classification

This work proposes a Novel Integrated Neural Network (NINN) for Fake profile detection on Instagram and evaluates its precision against a Support Vector Machine (SVM) algorithm. The goal is to enhance the accuracy of identifying fake profiles on the social media platform using the NINN approach. It is expected that the NINN will provides reliable predictions, with greater accurate results than existing models considered in this article for classifying fake profiles and reducing security threats in Instagram. The research utilized two sample groups, each containing 20 samples. We used the clinic calc tool to perform the calculations and set the significance level at 0.05. This implies that we are ready to tolerate a 5% probability of rejecting the null hypothesis when it is true. We also specified a last beta rate of 0.2 and a 95% confidence interval (CI) for the effect size estimation. NINN are trained using a dataset and a cost function, and their performance is measured by the precision of their output. The model has three components: an input component, a hidden component that implements the NINN algorithm, and an output component that shows the accuracy of the algorithm. After conducting a thorough analysis, the Novel Integrated Neural Networks Algorithm was found to have a precision of 90.91%, while the Support Vector Machine Algorithm had a precision of 88.19%. A statistical analysis was performed to compare the performance of the two algorithms. The analysis used an Independent samples T-Test, which tests the null hypothesis that the means of two populations are equal. The analysis yielded a p-value of p=0.000 (p<0.05), which indicates that the null hypothesis can be rejected with a high level of confidence. Therefore, the difference between the algorithms is statistically significant. In this research article on Automated fake profile classification on Instagram, the Novel Integrated Neural Networks algorithm was found to have a higher prediction precision percentage (90.91%) than the Support Vector Machine algorithm (88.19%). A more precise fake profile detection method was implemented by applying the NINN algorithm along with the SVM algorithm, this method allows users to distinguish fake profiles more accurately. The goal of this study is to overcome the drawbacks of SVM approaches and developed a better approach using NINN to identify fraudulent accounts on Instagram.