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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Edukasi Matematika dan Sains Research of Islamic Economics (RIE)
Mahmud, Imran
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics

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Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization Hridoy, Rashidul Hasan; Arni, Arindra Dey; Ghosh, Shomitro Kumar; Chakraborty, Narayan Ranjan; Mahmud, Imran
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2181-2190

Abstract

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.
Analisis Problematika Pembelajaran Daring Terhadap Siswa Pada Mata Pelajaran Matematika di SMA Negeri 1 Kabila Mahmud, Imran; Pomalato, Sarson W. Dj.; Majid, Majid
JEMS: Jurnal Edukasi Matematika dan Sains Vol. 12 No. 2 (2024)
Publisher : Universitas PGRI Madiun

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Abstract

Proses pembelajaran di sekolah pada masa pandemi Covid-19 mempunyai banyak permasalahan yang dihadapi. Pandemi Covid-19 yang melanda dunia termasuk Indonesia mengharuskan mengambil sikap dalam mencegah penularan yang lebih luas, termasuk sektor pendidikan. Berkaitan dengan hal tersebut Kementerian Pendidikan dan Kebudayaan mengambil sikap tegas melalui beberapa surat edaran berkaitan tentang kebijakan pendidikan dalam masa darurat penyebaran Covid-19. Tulisan ini membahas tentang pelaksanaan pendidikan dalam masa pandemi Covid-19 berkaitan dengan pembelajaran daring. Proses pembelajaran daring merupakan solusi untuk melaksanakan pembelajaran. Pembelajaran daring melibatkan guru dan peserta didik dimana guru memberikan materi secara online dan peserta didik pun menerima materi secara online pula. Tujuan penulisan artikel ini untuk mempelajari dan memahami permasalahan dalam kegiatan pemebelajaran di masa pandemi yakni pembelajaran daring agar peserta didik bisa mengikutinya dengan aktif dan menarik terutama dalam menerima materi pada pelajaran matematika. Hasil kajian ini membuktikan bahwa pembelajaran daring di masa pandemi covid-19 ini menimbulkan berbagai tanggapan dan perubahan pada sistem belajar yang dapat mempengaruhi proses pemebelajaran khususnya pelajaran matematika serta tingkat perkembangan peserta didik dalam merespon materi yang disampaikan
The Impact of Basel Standards on Default Risk: A Case of Islamic Banks in Bangladesh Ahmed, Md. Adnan; Mahmud, Imran
Research of Islamic Economics Vol. 3 No. 1 (2025): JULY 2025
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/rie.v3i1.460

Abstract

Default risk is a major concern for banks and is shaped by both internal and external factors. Regulatory frameworks like Basel III aim to mitigate such risks. This study investigates the impact of Basel III standards on the default risk of Islamic banks in Bangladesh, focusing on three key indicators: Capital Adequacy Ratio (CAR), Liquidity Coverage Ratio (LCR), and Net Stable Funding Ratio (NSFR). The research covers all Islamic banks in Bangladesh and utilizes secondary data from annual reports. Default risk is assessed using the z-score, where a higher score indicates a lower probability of insolvency. Control variables include credit risk, investment propensity, off-balance sheet exposure, economic growth, and lending rates. A Random Effects Model is employed, with Panel-Corrected Standard Errors (PCSE) applied to address heteroskedasticity, autocorrelation, and cross-sectional dependency. Findings reveal that CAR, LCR, and NSFR significantly reduce default risk, highlighting the effectiveness of Basel III measures in strengthening financial stability. This study uniquely emphasizes Islamic banks and explores the alignment between globally recognized regulatory standards and Sharia-compliant banking. The results offer valuable insights for regulators, policymakers, and bank managers striving to balance regulatory compliance with the principles of Islamic finance.
BonoNet: a deep convolutional neural network for recognizing bangla compound characters Ahmed, Kazi Rifat; Jahan, Nusrat; Masud, Adiba; Tasnim, Nusrat; Sharmin, Sazia; Mim, Nusrat Jahan; Mahmud, Imran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4171-4180

Abstract

The bangla alphabet includes vowels, consonants, and compound symbols. The compound nature of bangla is a product of combining two or more root bangla characters into one graph. They are difficult to differentiate because they have a sophisticated geometric shape and an immense variety of scripts used by different places and individuals. This is one of the greatest challenges in creating effective optical character recognition (OCR) systems for bangla. In this paper, a deep convolutional neural network (DCNN)-based system is presented to identify bangla compound characters with high precision. The model was trained using the AIBangla dataset. It has about 171 classes of bangla compound characters. A DCNN system, BonoNet, was designed to classify compound characters. BonoNet outperformed all other state-of-the-art architecture on the test set and improved over current state-of-the-art architecture methods. BonoNet will greatly improve the automation and analysis of the bangla language by accurately identifying these compound complex characters.
Co-Authors Ahmed, Kazi Rifat Ahmed, Md. Adnan Arni, Arindra Dey Chakraborty, Narayan Ranjan Ghosh, Shomitro Kumar Hridoy, Rashidul Hasan Jahan, Nusrat Majid, Majid Masud, Adiba Mim, Nusrat Jahan Sarson W DJ Pomalato Sharmin, Sazia Tasnim, Nusrat