Details

Autor(en) / Beteiligte

• Gadiparthi, Manjunath
• Reddy, E Srinivasa

Titel

Optimizing the Quality of Predicting the ill effects of Intensive Human Exposure to Social Networks using Ensemble Method

Ist Teil von

• Informatica (Ljubljana), 2022-10, Vol.46 (7), p.41-46

Ort / Verlag

Ljubljana: Slovenian Society Informatika / Slovensko drustvo Informatika

Erscheinungsjahr

2022

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• People may quickly obtain information through a variety of channels including social media, blogs, websites, and other online resources. These platforms have made it possible for information to be shared more easily. As a result, the amount of time that individuals spend on various social networking applications has increased. This research predicts the effects of human exposure to social networks in the near future. In this work a competent model for predicting the ill-effects is provided, that is both accurate and efficient. This model represents a combination of the independent models that have been operating independently so far. Thus each of these models makes a forecast and ultimate selection is decided based on whether or not there exists a majority of convergence of results from the operation of various models. By employing the majority voting system, this strategy attempts to take the benefits of the predictions produced by all the models while also to reduce the inaccuracies generated by each model. Theoretically, this model should outperform the use of individual models in terms of performance. Important features are extracted from the datasets using the proposed model, and the extracted features are then classified using an ensemble model that consists of four popular machine learning models: support vector machines (SVMs), logistic regression (logistic regression), random forest (random forest classification), and neural networks (NN). We have analyzed our prediction performance with the existing methods for number of times by changing the train set and test set data. In all the cases our novel method has been predicting with 3% to 4% improved performance in accuracy, precision, F-1 score, and specificity. From the dataset it has been achievable to attain the highest training and testing accuracy from among the existing models.