ABSTRACT - Currency recognition plays a vital role in banking, commerce, and assistive technology for visually impaired individuals. Traditional methods, such as manual verification and optical scanning, often suffer from limitations in accuracy and efficiency. This study introduces an advanced currency recognition system utilizing Convolutional Neural Networks (CNNs) to accurately classify Bangladeshi banknotes. A dataset comprising 50,334 images was collected, preprocessed, and used to train a CNN model optimized for high - performance classification. The trained model achieved an accuracy of 98.5%, surpassing conventional image - based currency recognition approaches. T o enable real - time and offline functionality, the model was converted in to T ensorFlow Lite format and integrated into an Android mobile application. The results highlight the effectiveness of deep learning in currency recognition, providing a fast, secure, and accessible solution that enhances financial transactions and assist ive technologies. INTRODUCTION Currency plays a crucial role in financial transactions, and an efficient recognition system is essential for ensuring seamless economic operations.

Conceptual combination is a cognitive process that merges basic concepts, enabling the creation of complex expressions. During this process, the properties of combination (e.g., the whiteness of a peeled apple) can be inherited from basic concepts, newly emerge, or be canceled. However, previous studies have evaluated a limited set of properties and have not examined the generative process. To address this gap, we introduce the Conceptual Combination with Property Type dataset (CCPT), which consists of 12.3K annotated triplets of noun phrases, properties, and property types. Using CCPT, we establish three types of tasks to evaluate LLMs for conceptual combination thoroughly. Our key findings are threefold: (1) Our automatic metric grading property emergence and cancellation closely corresponds with human judgments. (2) LLMs, including OpenAI's o1, struggle to generate noun phrases which possess given emergent properties. (3) Our proposed method, inspired by cognitive psychology model that explains how relationships between concepts are formed, improves performances in all generative tasks. The dataset and experimental code are available at https://github.com/seokwon99/CCPT.git.

Banknote recognition is a major problem faced by visually Challenged people. So we propose a application to help the visually Challenged people to identify the different types of Yemenian currencies through deep learning technique. As money has a significant role in daily life for any business transactions, real-time detection and recognition of banknotes become necessary for a person, especially blind or visually impaired, or for a system that sorts the data. This paper presents a real-time Yemeni currency detection system for visually impaired persons. The proposed system exploits the deep learning approach to facilitate the visually impaired people to prosperously recognize banknotes. For real-time recognition, we have deployed the system into a mobile application.

Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model with new training data. In these cases, we wish to go beyond differences in overall metrics such as accuracy to identify where in the feature space do the differences occur. We formalize this problem of model differencing as one of predicting a dissimilarity function of two ML models' outputs, subject to the representation of the differences being human-interpretable. Our solution is to learn a Joint Surrogate Tree (JST), which is composed of two conjoined decision tree surrogates for the two models. A JST provides an intuitive representation of differences and places the changes in the context of the models' decision logic. Context is important as it helps users to map differences to an underlying mental model of an AI system. We also propose a refinement procedure to increase the precision of a JST. We demonstrate, through an empirical evaluation, that such contextual differencing is concise and can be achieved with no loss in fidelity over naive approaches.

Fake currency, unauthorized imitation money lacking government approval, constitutes a form of fraud. Particularly in Afghanistan, the prevalence of fake currency poses significant challenges and detrimentally impacts the economy. While banks and commercial establishments employ authentication machines, the public lacks access to such systems, necessitating a program that can detect counterfeit banknotes accessible to all. This paper introduces a method using image processing to identify counterfeit Afghan banknotes by analyzing specific security features. Extracting first and second order statistical features from input images, the WEKA machine learning tool was employed to construct models and perform classification with Random Forest, PART, and Na\"ive Bayes algorithms. The Random Forest algorithm achieved exceptional accuracy of 99% in detecting fake Afghan banknotes, indicating the efficacy of the proposed method as a solution for identifying counterfeit currency.

Currency is used almost everywhere to facilitate business. In most developing countries, especially the ones in Africa, tangible notes are predominantly used in everyday financial transactions. One of these countries, Ethiopia, is believed to have one of the world highest rates of blindness (1.6%) and low vision (3.7%). There are around 4 million visually impaired people; With 1.7 million people being in complete vision loss. Those people face a number of challenges when they are in a bus station, in shopping centers, or anywhere which requires the physical exchange of money. In this paper, we try to provide a solution to this issue using AI/ML applications. We developed an Android and IOS compatible mobile application with a model that achieved 98.9% classification accuracy on our dataset. The application has a voice integrated feature that tells the type of the scanned currency in Amharic, the working language of Ethiopia. The application is developed to be easily accessible by its users. It is build to reduce the burden of visually impaired people in Ethiopia.

Generative Adversarial Networks (GAN-s) -- sounds complicated, doesn't it? It is a lot simpler than it sounds. In this article, I will intuitively explain how those programs work, what they are used for, and my view on their future applications. Without further ado, let's get into it. The cheater wants to print banknotes that are indistinguishable from real money.

Robots are impacting multiple industries, and the banking industry is no exception. Financial institutions across the world are beginning to use them for tasks ranging from counting money to handling customer requests in an effort to not only simplify tasks but to transform the branch experience. VTB, a Russian state bank, for example, has recently deployed robots designed by DIIP200 to handle cash-counting duties. An operator directs the robot to load cash from customer revenue, ATM cassettes and point-of-sale cash into trays. "From the moment of loading into special trays for recalculation to the moment of forming ready-made banknote spines, it works independently, performing all digital operations, including laying banknotes in the counting and sorting machine for recalculation, removing processed banknotes (spines of 100 sheets), placing them in the bander machine, and forming rolls with ruined bills and those subject to further examination," a spokesperson for VTB said in an email.

Amazon Redshift ML allows data analysts, developers, and data scientists to train machine learning (ML) models using SQL. In previous posts, we demonstrated how customers can use the automatic model training capability of Amazon Redshift to train their classification and regression models. Redshift ML provides several capabilities for data scientists. It allows you to create a model using SQL and specify your algorithm as XGBoost. It also lets you bring your pre-trained XGBoost model into Amazon Redshift for local inference.

Have you ever been in a situation where you were handing money to the clerks at a supermarket only to find that the money is fake while there was a long line of people behind you waiting to check out? I personally had experienced this situation one time and that embarrassment of being assumed to be an immoral cheapskate just stuck in my head for a long time. This motivated me to conduct this project, building a K-Means Clustering model to detect if a banknote is real or fake. This dataset is about distinguishing genuine and forged banknotes. Data were extracted from images that were taken from genuine and forged banknote-like specimens.