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All Journal International Journal of Electrical and Computer Engineering Jurnal Pendidikan Islam INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Al-Fatih: Jurnal Pendidikan dan Keislaman Veterinary Biomedical and Clinical Journal Journal of Information Systems and Informatics Dirasah : Jurnal Studi Ilmu dan Manajemen Pendidikan Islam Rayah Al Islam : Jurnal Ilmu Islam International Journal of Social Science and Religion (IJSSR) Applied Research and Smart Technology (ARSTech) AL-AF'IDAH: Jurnal Pendidikan Bahasa Arab dan Pengajarannya COMMUNITY : Jurnal Pengabdian Kepada Masyarakat eMBeJi : Manajemen dan Bisnis Jurnal Ukazh : Journal of Arabic Studies Kilmatuna: Journal Of Arabic Education Al-Muyassar: Journal of Arabic Education Jurnal Simki Postgraduate Bahtsuna: Jurnal Penelitian Pendidikan Islam Economic: Journal Economic and Business El-Waroqoh : Jurnal Ushuluddin Dan Filsafat Proceeding Of International Conference On Education, Society And Humanity Jurnal Keislaman TATHO: International Journal of Islamic Thought and Sciences Educativa: Jurnal Pendidikan Agama Islam
Islam, Saiful
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Religion Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Transportation Veterinary Other

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Journal : International Journal of Electrical and Computer Engineering

 1 
A rule-based machine learning model for financial fraud detection Islam, Saiful; Haque, Md. Mokammel; Rezaul Karim, Abu Naser Mohammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp759-771

Abstract

Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection Islam, Saiful; Akhtar, Md. Nasim; Hassan, M. Mahadi; Karim, A. N. M. Rezaul; Habib, Israt Binteh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1307-1318

Abstract

Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection.
Co-Authors Achmad Amirudin, Achmad Afiyah, Hikmatul Ahmad Fitri, Ahmad Ahmad Muzakki Akhtar, Md. Nasim Amin, S. M. Nurul Arrad Ghani Safitra Azkiyya, Akhfa Nadzifah Begum, Sultana Bin Ahmed, Amar Bin Muhammad Chowdhury, Ahmed Imtiaz Diana, Lohdy Era, Samiha Tasnim Faisal Faisal Habib, Israt Binteh Haque, Md. Mokammel Hasan, Mohammad Mainul Hasan, Rakibul hasana, Nur Hassan, M. Mahadi Hendra Yulia Rahman, Hendra Yulia Husni Mubarak Imam Syafi'i Inayah, Khoirul Islam, Md. Nazrul Jannaty, Washifah Karim, A. N. M. Rezaul Kartikawati, Yeni Maharani, Nurma Isfira Mahtab, Jannatun Nayeem Hossain Mamun, Mohammad Abdullah Al Martaleni - Maslamah Moh Abdul Kholiq Hasan Muhammad Muflih Muhammad Nanang Qosim Muhammad Zaenuri Mukarromah, Silfiatun Muryani, Dwi Nabil, Ubaidillah Nasrin, Farhana Nihayati, Nila Endah Nikhlatunnuha, Destia Nisa, Holipatun Nur Aini Khasanah, Isna Paul, Tapan Kanti Prasetyo, Muhammad Fandi Setiawan Cahyono Pratiwi Rahmah Hakim Pratiwi, Cahya Rezaul Karim, Abu Naser Mohammad Rikza, Abdullah Rini Astuti Ristiawanda, Risqy Fatma Riyantoni, Ricko Guntur Rusly, Fathullah Rusly, H. Fathullah Siddiqui, Prof. Dr. Md. Saiful Islam Suleiman, Isyraq Suprapti Suprapti Teguh Hady Ariwibowo Yolanda Assesanti, Dheyana Zaharani, Malika Liana Zainul Arifin `Ainina, Mutanafisah Qurrotu