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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ourdani, Nabil
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Computer Science & IT Engineering

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Towards a new approach to maximize tax collection using machine learning algorithms Ourdani, Nabil; Chrayah, Mohamed; Aknin, Noura
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp737-746

Abstract

Efficient tax debt collection is a challenge for Moroccan local tax authorities. This article explores the potential of machine learning techniques and novel strategies to enhance efficiency in this process. We present a practical use case demonstrating the application of machine learning for taxpayer segmentation, improving accuracy in identifying high-risk debtors. Using a comprehensive dataset of tax payment behavior, we showcase the effectiveness of machine learning algorithms in segmenting taxpayers based on their likelihood of non-compliance or debt accumulation. We also investigate innovative strategies that integrate behavioral economics principles to enable better targeted interventions. Real-world case studies in local tax debt collection highlight the impact of these strategies. The findings underscore the transformative potential of machine learning techniques and novel strategies in improving the efficiency of local tax debt collection. Accurate identification of high-risk debtors and tailored enforcement actions help maximize revenue while minimizing resource waste. This research contributes to the existing knowledge by providing insights into the implementation of machine learning techniques and novel strategies in tax debt collection. It emphasizes the importance of data-driven approaches and highlights how local tax authorities can drive efficiency and optimize revenue collection by embracing these advancements.
Adaptive kernel integration in visual geometry group 16 for enhanced classification of diabetic retinopathy stages in retinal images Hiri, Mustafa; Ourdani, Nabil; Chrayah, Mohamed; Alsadoon, Abeer; Aknin, Noura
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1484-1495

Abstract

Diabetic retinopathy (DR) is a major cause of vision impairment globally, with early detection remaining a significant challenge. The limitations of current diagnostic methods, particularly in identifying early-stage DR, highlight a pressing need for more accurate diagnostic technologies. In response, our research introduces an innovative model that enhances the visual geometry group 16 (VGG16) architecture with adaptive kernel techniques. Traditionally, the VGG16 model deploys consistent kernel sizes throughout its convolutional layers. In this study, multiple convolutional branches with varying kernel sizes (3×3, 5×5, and 7×7) were seamlessly integrated after the 'block5_conv1' layer of VGG16. These branches were adaptively merged using a softmax-weighted combination, enabling the model to automatically prioritize kernel sizes based on the image's intricate features. To combat the challenge of imbalanced datasets, the synthetic minority over-sampling technique (SMOTE) was employed before training, harmonizing the distribution of the five DR stages. Our results are promising, showing a training accuracy above 94.17% and a validation accuracy over 90.24%, our model significantly outperforms traditional methods. This study represents a significant stride in applying adaptive kernels to deep learning for precise medical imaging tasks. The model's accuracy in classifying DR stages highlights its potential as a valuable diagnostic tool, paving the way for future enhancements in DR detection and management.
Co-Authors Aknin, Noura Alsadoon, Abeer Chrayah, Mohamed Hiri, Mustafa