Movie Rating Prediction and Viewers’ Sentiment Trend Analysis Using YouTube Trailer Comments

Sahu, Sandipan; Kumar, Raghvendra; Shafi, Pathan Mohd

doi:10.1007/978-981-19-9512-5_12

Cited by 2 publications

(1 citation statement)

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“…It works by iteratively training a weak classifier on the misclassified data points from the previou iteration, and then combining the results to make the final prediction. − XGBoost classifier is a gradient boosting algorithm that is used for both regression and classification problems [48]. It operates by constructing numerous DT sequentially, with each subsequent tree aimed at rectifying the mistakes of its predecessor.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of explainable AI models for predicting lung cancer using diverse datasets

Makubhai,

R Pathak,

R Chandre

2024

IJ-AI

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Lung cancer prediction is crucial for early detec-tion and treatment, and explainable AI models have gained attention for their interpretability. This study aims to compare various explainable AI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, prepro-cessed, and used to train models such as Logistic Regression, SVC-Linear, SVC-rbf, Decision Tree, Random Forest, AdaBoost Classifier, and XGBoost Classifier. Preliminary results indicate that Random Forest achieved the highest accuracy of 98.9% across multiple datasets. Evaluation metrics such as accuracy, precision, recall, and F1 score were utilized, along with interpretability techniques like feature importance rankings and rule extraction methods. The study's findings will aid in identifying effective and interpretable AI models, facilitating early detection and treatment decisions for lung cancer.

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Section: Discussionmentioning

confidence: 99%