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Jul 21, 2020 - Oct 16, 2023

مسؤول شعبة الانظمة والبرامجيات في رئاسة الجامعة التقنية الشمالية مركز الحاسبه الالكترونيه

معهد الادارة التقني نينوى ، قسم تقنيات انظمة الحاسوب [رئيس قسم تقنيات انظمة الحاسوب]

Oct 16, 2023 - Sep 1, 2024

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معهد الادارة التقني نينوى [معاون عميد للشؤون العلميه والطلبه]

Sep 1, 2024 - حالي

معاون عميد للشؤون العلميه والطلبه

A Predictive Analysis of IMDb Movie Reviews Using LSTM and ANN Models

Jun 8, 2024

المجلة Journal of Intelligent Systems and Internet of Things

publisher American Scientific Publishing Group (ASPG)

DOI 10.54216/JISIoT.130223

العدد 2

المجلد 13

The Machine Learning domain has made a major process with the progression of state-of-the-art technologies. Since current algorithms often don’t provide palatable learning performance, it is necessary to continually upgrade them. This paper has illustrated the comparison of the Long Short-Term Memory (LSTM) model and the Artificial Neural Networks (ANN) model in the prediction of the Internet Movie Database (IMDb) website. These evaluations were then related to sentiment assessment approaches to evaluate their predicted accuracy and performances. The results demonstrate that the ANN model outperforms the LSTM model with a high accuracy rate in terms of the prediction accuracy and loss indicators for the IMDb movie review’s sentiment analysis task in terms of the prediction accuracy and loss indicators for the IMDb movie review’s sentiment analysis task. The accuracy of prediction on the test dataset of the ANN model is 83.5 % and the LSTM model is 83.5%. Therefore, it can be concluded that the standard artificial neural network model that was utilized is an appropriate technique for sentiment assessment tasks in IMDb rating text data. © 2024, American Scientific Publishing Group (ASPG). All rights reserved. Author keywords Artificial Neural Networks (ANN); Internet Movie Database (IMDb); Long Short-Term Memory (LSTM); Prediction Accuracy

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The Effect of Vehicles Speed on the Performance of VANET Protocol

Oct 14, 2023 - Oct 14, 2023

الناشر IEEE Explore

DOI https://doi.org/10.1109/ICONNIC59854.2023

الدولة Indonesia

الموقع Kediri

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