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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Nursing and Health Services (IJNHS)
Akter, Mariam
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Computer Science & IT Engineering Health Professions Nursing Public Health Social Sciences

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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.
Eating Behaviors of Early Childhood at a Selected Upazila in Bangladesh Akter, Mariam; Gharami, Tumpa; Akter, Halima
International Journal of Nursing and Health Services (IJNHS) Vol. 8 No. 5 (2025): International Journal of Nursing and Health Services (IJHNS)
Publisher : Alta Dharma Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35654/ijnhs.v8i5.886

Abstract

Background: Eating behavior plays a vital role in both the prevention and management of chronic illnesses associated with poor nutrition. Children who are overweight tend to display stronger food responsiveness, derive more enjoyment from eating, and often consume more food in response to emotional cues. The aim of this study was to assess eating behaviors among young children in a selected Upazila of Bangladesh. Methods: This cross-sectional study included a total of 103 mothers with children aged between 1 and 5 years. Participants were chosen using a convenience sampling method. Data were collected using a self-administered questionnaire comprising two sections: Part 1 included socio-demographic information, and Part 2 consisted of the Child Eating Behavior Questionnaire (CEBQ). Data analysis was performed using both descriptive and inferential statistics via SPSS version 26. Results: The study found a moderate overall mean score (2.91 ± 0.33) in children's eating behaviors, with variability observed across the eight subscales of the CEBQ. The average age of participating mothers was 27.75 years (SD = 3.816). Statistically significant associations were observed between children’s eating behaviors and various socio-demographic factors, including mothers’ education level (F = 5.519, p = 0.005), fathers’ education level (F = 7.328, p = 0.000), fathers’ occupation (F = 2.687, p = 0.036), and the number of siblings (r = 0.334, p = 0.001). Conclusion: The findings highlight meaningful associations between several demographic variables and children's eating behaviors. These results support the need for policy-level initiatives aimed at promoting food security and balanced nutrition during early childhood, with the goal of reducing malnutrition and supporting healthy development in Bangladesh.
Co-Authors Akter, Halima Gharami, Tumpa Haider Noori, Sheak Rashed Hasan, Md Zahid Sultana, Nishat