Neural PCA and Maximum Likelihood Hebbian Learning on the GPU

This study introduces a novel fine-grained parallel implementation of a neural principal component analysis (neural PCA) variant and the maximum Likelihood Hebbian Learning (MLHL) network designed for modern many-core graphics processing units (GPUs). The parallel implementation as well as the computational experiments conducted in order to evaluate the speedup achieved by the GPU are presented and discussed. The evaluation was done on a well-known artificial data set, the 2D bars data set

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Modeling of a Method of Cellular Technology Processing Systems and Pattern Recognition Images for Fast Recognition of Dynamic Images

Supervised learning has been considered as a hot topic as it is used in different fields that can exploit the advantages of artificial intelligence. This research introduces a new approach using cellular technology for solving various problems of processing and pattern recognition images that are invariant to the orientation, scale, and dynamic changes in real time. On the basis of the notion of geometric type solved the problem of information selection elements in the image recognition of shapes, lines and laser processing of personal identification for handwritten text. Keywords: cellular technology, pattern recognition, figures recognition, neural networ

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Predicting Forex Currency Fluctuations Using a Novel Bio-inspired Modular Neural Network

This thesis explores the intricate interplay of rational choice theory (RCT), brain modularity, and artificial neural networks (ANNs) for modelling and forecasting hourly rate fluctuations in the foreign exchange (Forex) market. While RCT traditionally models human decision-making by emphasising self-interest and rational choices, this study extends its scope to encompass emotions, recognising their significant impact on investor decisions. Recent advances in neuro- science, particularly in understanding the cognitive and emotional processes associated with decision-making, have inspired computational methods to emulate these processes. ANNs, in particular, have shown promise in simulating neuroscience findings and translating them into effective models for financial market dynamics. However, their monolithic architectures of ANNs, characterised by fixed struc- tures, pose challenges in adaptability and flexibility when faced with data perturbations, limiting overall performance. To address these limitations, this thesis proposes a Modular Convolutional orthogonal Recurrent Neural Net- work with Monte Carlo dropout-ANN (MCoRNNMCD-ANN) inspired by recent neuroscience findings. A comprehensive literature review contextualises the challenges associated with monolithic architectures, leading to the identification of neural network structures that could enhance predictions of Forex price fluctuations, such as in the most prominently traded currencies, the EUR/GBP pairing. The proposed MCoRNNMCD-ANN is thoroughly evaluated through a detailed comparative analysis against state-of-the-art techniques, such as BiCuDNNL- STM, CNN–LSTM, LSTM–GRU, CLSTM, and ensemble modelling and single- monolithic CNN and RNN models. Results indicate that the MCoRNNMCD- ANN outperforms competitors. For instance, reducing prediction errors in test sets from 19.70% to an impressive 195.51%, measured by objective evaluation metrics like a mean square error. This innovative neurobiologically-inspired model not only capitalises on modularity but also integrates partial transfer learning to improve forecasting ac- curacy in anticipating Forex price fluctuations when less data occurs in the EUR/USD currency pair. The proposed bio-inspired modular approach, incorporating transfer learning in a similar task, brings advantages such as robust forecasts and enhanced generalisation performance, especially valuable in domains where prior knowledge guides modular learning processes. The proposed model presents a promising avenue for advancing predictive modelling in Forex predictions by incorporating transfer learning principles

Early detection of lung cancer through nodule characterization by Deep Learning

Lung cancer is one of the most frequent cancers in the world with 1.8 million new cases reported in 2012, representing 12.9% of all new cancers worldwide, accounting 1.4 million deaths up to 2008. The importance of early detection and classification of malignant and benign nodules using computed tomography (CT) scans, may facilitate radiologists the tasks of nodule staging assessment and individual therapeutic planning. However, if potential malignant nodules are detected on CT scans, treatments may be less aggressive, not even requiring chemotherapy or radiation therapy after surgery. This Bachelor Thesis focus on the exploration of existing methods and data sets for the automatic classification of lung nodules based on CT images. To this aim, we start by assembling, studying and analyzing some state-of-the-art studies in lung nodule detection, characterization and classification. Furthermore, we report and contextualize state-of-the-art deep learning architectures suited for lung nodule classification. From the public datasets researched, we select a widely used and large data set of lung nodules CT scans, and use it to fine-tune a state-of-theart convolutional neural network. We compare this strategy with training-from-scratch a new shallower neuronal network. Initial evaluation suggests that: (1) Transfer learning is unable to perform correctly due to its inability to adapt between natural images and CT scans domains. (2) Learning from scratch is unable to learn from a small number of samples. However, this first evaluation paves the road towards the design of better classification methods fed by better annotated public-available data sets. In overall, this Project is a mandatory first stage on a hot research topic