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All Journal International Journal of Advances in Intelligent Informatics Science in Information Technology Letters
Amelia Ritahani Ismail
Department of Computer Science, International Islamic University Malaysia
Computer Science & IT

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Journal : Science in Information Technology Letters

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Automated image captioning with deep neural networks Abdullah Ahmad Zarir; Saad Bashar; Amelia Ritahani Ismail
Science in Information Technology Letters Vol 1, No 1: May 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i1.31

Abstract

Generating natural language descriptions of the content of an image automatically is a complex task. Though it comes naturally to humans, it is not the same when making a machine do the same. But undoubtedly, achieving this feature would remarkably change how machines interact with us. Recent advancement in object recognition from images has led to the model of captioning images based on the relation between the objects in it. In this research project, we are demonstrating the latest technology and algorithms for automated caption generation of images using deep neural networks. This model of generating a caption follows an encoder-decoder strategy inspired by the language-translation model based on Recurrent Neural Networks (RNN). The language translation model uses RNN for both encoding and decoding, whereas this model uses a Convolutional Neural Networks (CNN) for encoding and an RNN for decoding. This combination of neural networks is more suited for generating a caption from an image. The model takes in an image as input and produces an ordered sequence of words, which is the caption.
Text classification of traditional and national songs using naïve bayes algorithm Triyanti Simbolon; Aji Prasetya Wibawa; Ilham Ari Elbaith Zaeni; Amelia Ritahani Ismail
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1215

Abstract

In this research, we investigate the effectiveness of the multinomial Naïve Bayes algorithm in the context of text classification, with a particular focus on distinguishing between folk songs and national songs. The rationale for choosing the Naïve Bayes method lies in its unique ability to evaluate word frequencies not only within individual documents but across the entire dataset, leading to significant improvements in accuracy and stability. Our dataset includes 480 folk songs and 90 national songs, categorized into six distinct scenarios, encompassing two, four, and 31 labels, with and without the application of Synthetic Minority Over-sampling Technique (SMOTE). The research journey involves several essential stages, beginning with pre-processing tasks such as case folding, punctuation removal, tokenization, and TF-IDF transformation. Subsequently, the text classification is executed using the multinomial Naïve Bayes algorithm, followed by rigorous testing through k-fold cross-validation and SMOTE resampling techniques. Notably, our findings reveal that the most favorable scenario unfolds when SMOTE is applied to two labels, resulting in a remarkable accuracy rate of 93.75%. These findings underscore the prowess of the multinomial Naïve Bayes algorithm in effectively classifying small data label categories.
Co-Authors Abdullah Ahmad Zarir Afrujaan Yakath Ali Aji Prasetya Wibawa Ali A Alwan Amir Aatieff Amir Hussin Azrina Md Ralib A’inur A’fifah Amri Nadzurah Zainal Abidin Noor Azura Zakaria Normaziah Abdul Aziz Nur Hidayah Mohd Khalid Omar Abdelaziz Mohammad Saad Bashar Samar Bashath Samar Salem Bashath Triyanti Simbolon Zaeni, Ilham Ari Elbaith