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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Haider Noori, Sheak Rashed
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Bangla song genre recognition using artificial neural network Akter, Mariam; Sultana, Nishat; Haider Noori, Sheak Rashed; Hasan, Md Zahid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2413-2422

Abstract

Music has a control over human moods and it can make someone calm or excited. It allows us to feel all emotions we experience. Nowadays, people are often attached with their phones and computers listening to music on Spotify, SoundCloud, or any other internet platform. Music information retrieval plays an important role for music recommendation according to lyrics, pitch, pattern of choices, and genre. In this study, we have tried to recognize the music genre for a better music recommendation system. We have collected an amount of 1820 Bangla songs from six different genres including Adhunik, rock, hip hop, Nazrul, Rabindra, and folk music. We have started with some traditional machine learning algorithms having k-nearest neighbor, logistic regression, random forest, support vector machine, and decision tree but ended up with a deep learning algorithm named artificial neural network with an accuracy of 78% for recognizing music genres from six different genres. All mentioned algorithms are experimented with transformed mel-spectrograms and mean chroma frequency values of that raw amplitude data. But we found that music tempo having beats per minute value with two previous features present better accuracy.
A novel automated feature selection based approach to recognize cauliflower disease Shakil, Rashiduzzaman; Akter, Bonna; Javed Mehedi Shamrat, F M; Haider Noori, Sheak Rashed
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5359

Abstract

Cauliflower disease is a primary cause of reduced cauliflower yield. Preventing cauliflower disease requires early diagnosis. In the scope of this study, we suggested an agro-medical expert system that would make it easier to diagnose cauliflower disease. In this method, a digital image must be taken off the phone or handled device to diagnose cauliflower sickness. A data augmentation technique was initially used to construct a vast data set. The disease-affected parts of the cauliflower were then segmented using k-means clustering. Following that, ten statistical and gray-level co-occurrence matrix (GLCM) features were retrieved from the segmented pictures. After choosing the top n features (N ranged from 5 to 10), the synthetic minority oversampling technique (SMOTE) approach was used to handle training datasets with different amounts of each feature. After that, we utilized five machine learning (ML) algorithms and evaluated their performance using seven performance evaluation matrices for both augmented and non-augmented datasets. The same procedure was performed on both datasets. Then, we use both datasets to test how well the classifier works. Logistic regression (LR) is the most accurate method for the top nine features in the augmented dataset (90.77%).
Co-Authors Akter, Bonna Akter, Mariam Hasan, Md Zahid Javed Mehedi Shamrat, F M Shakil, Rashiduzzaman Sultana, Nishat