Nowadays, smartphones seamlessly blend into every aspect of our lives, including as handheld assistants for individuals with disabilities. Therefore, this research addresses the need for a robust system that can classify Kazakh banknotes. By capitalizing on the availability of smartphones and the ability to integrate detectors with classifiers this study introduces classifiers of Kazakh banknote images specifically designed for banknotes ranging from 500 KZT to 20,000 KZT. It compares traditional and hybrid machine learning (ML) approaches, utilizing a dataset of diverse banknote images, aiming for both lightweight and high accuracy. Competitive performance is demonstrated by the traditional approach, enhanced by thoughtful feature engineering. The hybrid approach, utilizing features from a pre-trained ResNet-18 model, showcases remarkable accuracy and robustness. Evaluation metrics reveal significant achievements, with the traditional approach attaining 94.00% accuracy and the hybrid approach excelling at 99.11%. Model stacking, combining classifiers from both approaches, outperforms individual classifiers, achieving 95.00% and 99.55% accuracy for the traditional and hybrid ML approaches, respectively. Our methodology’s comparable outcome in classifying Thai banknotes and coffee beans roasting levels demonstrates their versatility in image classification tasks that rely on color differentiation, showcasing the potential beyond banknote recognition.

This systematic survey explores the landscape of fuzzy computer vision techniques in biomedical research using articles from the Scopus database over the past decade. With a focus on methodologies, applications, and challenges, the survey aims to guide future research at the intersection of fuzzy logic and computer vision in biomedicine. Emphasizing applications such as dental image analysis and brain tumor detection, the paper showcases the collaborative potential of deep learning and fuzzy logic in enhancing biomedical image analysis. Despite notable advancements, challenges like model interpretability and scalability persist. The survey concludes by proposing future research directions, underscoring the pivotal role of fuzzy computer vision in advancing biomedical research.

The field of deep learning is rapidly advancing and impacting various industries, including banking. However, there are still challenges when it comes to accurately identifying the denomination of currencies, especially when dealing with issues like variation within the same class of currency and inconsistent lighting conditions. One notable problem is the lack of available data for Kazakhstan's currency. This research paper introduces the Kazakhstan Banknotes Dataset (KZ-BD), which is a unique collection of 4200 carefully annotated images covering 14 different categories. The dataset includes high-resolution images of authentic Kazakhstan Tenge in both coin and paper note forms, ranging from 1 to 20,000 tenge denominations. Each image has undergone strict de-identification and validation procedures, and the dataset is openly accessible to artificial intelligence researchers. This contribution addresses the data gap in deep learning research related to currency identification by offering a comprehensive dataset for Kazakhstan's currency, enabling better evaluation and fine-tuning of machine learning models with real-world data.

When it comes to financial transactions and counterfeit detection systems, banknote classification is crucial. In this paper, we propose an approach for the classification of Kazakhstan banknote images integrating Learning Vector Quantization (LVQ) with Statistical Texture Feature Extraction using the Gray Level Co-occurrence matrix (GLCM). Our methodology demonstrates effectiveness in accurately classifying banknote images, as evidenced by experimental results. The comprehensive testing scenarios show promising outcomes, with the combination of GLCM and Color Histogram under the LVQ algorithm achieving a high accuracy of 94.87 percent at distance 1 and 90°. These findings indicate the robustness and practical viability of the proposed method for authenticating banknotes.