A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification

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Currency security and forensics: a survey

By its definition, the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desire

An empirical approach for currency identification

Currency identification is the application of systematic methods to determine authenticity of questioned currency. However, identification analysis is a difficult task requiring specially trained examiners, the most important challenge is automating the analysis process reducing human labor and time. In this study, an empirical approach for automated currency identification is formulated and a prototype is developed. A two parts feature vector is defined comprised of color features and texture features. Finally the banknote in question is classified by a Feedforward Neural Network (FNN) and a measurement of the similarity between existing samples and suspect banknote is output

Selective Neuron Re-Computation (SNRC) for Error-Tolerant Neural Networks

Artificial Neural networks (ANNs) are widely used to solve classification problems for many machine learning applications. When errors occur in the computational units of an ANN implementation due to for example radiation effects, the result of an arithmetic operation can be changed, and therefore, the predicted classification class may be erroneously affected. This is not acceptable when ANNs are used in many safety-critical applications, because the incorrect classification may result in a system failure. Existing error-tolerant techniques usually rely on physically replicating parts of the ANN implementation or incurring in a significant computation overhead. Therefore, efficient protection schemes are needed for ANNs that are run on a processor and used in resource-limited platforms. A technique referred to as Selective Neuron Re-Computation (SNRC), is proposed in this paper. As per the ANN structure and algorithmic properties, SNRC can identify the cases in which the errors have no impact on the outcome; therefore, errors only need to be handled by re-computation when the classification result is detected as unreliable. Compared with existing temporal redundancy-based protection schemes, SNRC saves more than 60 percent of the re-computation (more than 90 percent in many cases) overhead to achieve complete error protection as assessed over a wide range of datasets. Different activation functions are also evaluated.This research was supported by the National Science Foundation Grants CCF-1953961 and 1812467, by the ACHILLES project PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Science and Innovation and by the Madrid Community research project TAPIR-CM P2018/TCS-4496.Publicad

Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters

We perform image-based denomination recognition of the Pakistani currency notes. There are a total of seven different denominations in the current series of Pakistani notes. Apart from color and texture, these notes differ from one another mainly due to their aspect ratios. Our aim is to exploit this single feature to attain an image-based recognition that is invariant to the most common image variations found in currency notes images. Among others, the most notable image variations are caused by the difference in positions and in-plane orientations of the currency notes in images. While most of the proposed methods for currency denomination recognition only focus on attaining higher recognition rates, our aim is more complex, i.e., attaining a high recognition rate in the presence of image variations. Since, the aspect ratio of a currency note is invariant to such differences, an image-based recognition of currency notes based on aspect ratio is more likely to be translation- and rotation-invariant. Therefore, we adapt a two step procedure that first extracts a currency note from the homogeneous image background via local entropy and range filters. Then, the aspect ratio of the extracted currency note is calculated to determine its denomination. To validate our proposed method, we gathered a new dataset with the largest and most diverse collection of Pakistani currency notes, where each image contains either a single or multiple notes at arbitrary positions and orientations. We attain an overall average recognition rate of 99% which is very encouraging for our method, which relies on a single feature and is suited for real-time applications. Consequently, the method may be extended to other international and historical currencies, which makes it suitable for business and digital humanities application

Weight Re-Mapping for Variational Quantum Algorithms

Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications. The promising outcomes shown by VQCs, such as improved generalization and reduced parameter training requirements, are attributed to the robust algorithmic capabilities of quantum computing. However, the current gradient-based training approaches for VQCs do not adequately accommodate the fact that trainable parameters (or weights) are typically used as angles in rotational gates. To address this, we extend the concept of weight re-mapping for VQCs, as introduced by K\"olle et al. (2023). This approach unambiguously maps the weights to an interval of length $2\pi$, mirroring data rescaling techniques in conventional machine learning that have proven to be highly beneficial in numerous scenarios. In our study, we employ seven distinct weight re-mapping functions to assess their impact on eight classification datasets, using variational classifiers as a representative example. Our results indicate that weight re-mapping can enhance the convergence speed of the VQC. We assess the efficacy of various re-mapping functions across all datasets and measure their influence on the VQC's average performance. Our findings indicate that weight re-mapping not only consistently accelerates the convergence of VQCs, regardless of the specific re-mapping function employed, but also significantly increases accuracy in certain cases

Improving binary classification using filtering based on k-NN proximity graphs

© 2020, The Author(s). One of the ways of increasing recognition ability in classification problem is removing outlier entries as well as redundant and unnecessary features from training set. Filtering and feature selection can have large impact on classifier accuracy and area under the curve (AUC), as noisy data can confuse classifier and lead it to catch wrong patterns in training data. The common approach in data filtering is using proximity graphs. However, the problem of the optimal filtering parameters selection is still insufficiently researched. In this paper filtering procedure based on k-nearest neighbours proximity graph was used. Filtering parameters selection was adopted as the solution of outlier minimization problem: k-NN proximity graph, power of distance and threshold parameters are selected in order to minimize outlier percentage in training data. Then performance of six commonly used classifiers (Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Support Vector Machine and Decision Tree) and one heterogeneous classifiers combiner (DES-LA) are compared with and without filtering. Dynamic ensemble selection (DES) systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of base classifiers, such as, its accuracy in local regions of the feature space around the query instance. In our case the combiner is based on the local accuracy of single classifiers and its output is a linear combination of single classifiers ranking. As results of filtering, accuracy of DES-LA combiner shows big increase for low-accuracy datasets. But filtering doesn’t have sufficient impact on DES-LA performance while working with high-accuracy datasets. The results are discussed, and classifiers, which performance was highly affected by pre-processing filtering step, are defined. The main contribution of the paper is introducing modifications to the DES-LA combiner, as well as comparative analysis of filtering impact on the classifiers of various type. Testing the filtering algorithm on real case dataset (Taiwan default credit card dataset) confirmed the efficiency of automatic filtering approach

Banknote Authentication and Medical Image Diagnosis Using Feature Descriptors and Deep Learning Methods

Banknote recognition and medical image analysis have been the foci of image processing and pattern recognition research. As counterfeiters have taken advantage of the innovation in print media technologies for reproducing fake monies, hence the need to design systems which can reassure and protect citizens of the authenticity of banknotes in circulation. Similarly, many physicians must interpret medical images. But image analysis by humans is susceptible to error due to wide variations across interpreters, lethargy, and human subjectivity. Computer-aided diagnosis is vital to improvements in medical analysis, as they facilitate the identification of findings that need treatment and assist the expert’s workflow. Thus, this thesis is organized around three such problems related to Banknote Authentication and Medical Image Diagnosis. In our first research problem, we proposed a new banknote recognition approach that classifies the principal components of extracted HOG features. We further experimented on computing HOG descriptors from cells created from image patch vertices of SURF points and designed a feature reduction approach based on a high correlation and low variance filter. In our second research problem, we developed a mobile app for banknote identification and counterfeit detection using the Unity 3D software and evaluated its performance based on a Cascaded Ensemble approach. The algorithm was then extended to a client-server architecture using SIFT and SURF features reduced by Bag of Words and high correlation-based HOG vectors. In our third research problem, experiments were conducted on a pre-trained mobile app for medical image diagnosis using three convolutional layers with an Ensemble Classifier comprising PCA and bagging of five base learners. Also, we implemented a Bidirectional Generative Adversarial Network to mitigate the effect of the Binary Cross Entropy loss based on a Deep Convolutional Generative Adversarial Network as the generator and encoder with Capsule Network as the discriminator while experimenting on images with random composition and translation inferences. Lastly, we proposed a variant of the Single Image Super-resolution for medical analysis by redesigning the Super Resolution Generative Adversarial Network to increase the Peak Signal to Noise Ratio during image reconstruction by incorporating a loss function based on the mean square error of pixel space and Super Resolution Convolutional Neural Network layers

Applications of Artificial Intelligence to Cryptography

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other data types in order to assess the cryptographic strength of an encryption algorithm, e.g. to detect patterns of intercepted data streams that are signatures of encrypted data. This includes some of the authors’ prior contributions to the field which is referenced throughout. Applications are presented which include the authentication of high-value documents such as bank notes with a smartphone. This involves using the antenna of a smartphone to read (in the near field) a flexible radio frequency tag that couples to an integrated circuit with a non-programmable coprocessor. The coprocessor retains ultra-strong encrypted information generated using EC that can be decrypted on-line, thereby validating the authenticity of the document through the Internet of Things with a smartphone. The application of optical authentication methods using a smartphone and optical ciphers is also briefly explored