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Item Adaptive multilayer extreme learning machines(Elsevier Ltd., 2024-12-12) Filelis-Papadopoulos, Christos K.; Morrison, John P.; O’Reilly, Philip; Science Foundation IrelandExtreme learning machines is a neural network type that has been utilized in tasks such as regression and classification, due to their efficient training process, which is based on pseudoinverse matrices and randomized weights, avoiding the computationally intensive backpropagation. In order to further improve their performance and reduce their complexity with respect to number of required hyperparameters, especially in the case of multiple layer architectures, a novel multilayer adaptive approach, based on residual networks, is proposed. This approach constructs the network iteratively with respect to error minimization and parsimony using a recursive pseudoinverse matrix framework. A new block approach, using mixed precision arithmetic and Graphics Processing Units (GPU) is proposed. The proposed technique is coupled with a new adaptive penalty criterion to ensure adequate numbers of neurons are included in each layer, while avoiding highly correlated basis. Adaptive regularization, along with scaling, is also incorporated to ensure Symmetric Positive Definiteness (SPD) of the Gram matrix. Several random number distributions for the proposed approach are examined and discussed. Handling of large datasets is discussed and a new batch variant is proposed. The proposed scheme is evaluated for regression and classification tasks in a multitude of datasets and is compared with other neural network architectures.Item Solving complex optimisation problems by machine learning(MDPI, 2024) Prestwich, Steven D.; Science Foundation Ireland; Horizon 2020Most optimisation research focuses on relatively simple cases: one decision maker, one objective, and possibly a set of constraints. However, real-world optimisation problems often come with complications: they might be multi-objective, multi-agent, multi-stage or multi-level, and they might have uncertainty, partial knowledge or nonlinear objectives. Each has led to research areas with dedicated solution methods. However, when new hybrid problems are encountered, there is typically no solver available. We define a broad class of discrete optimisation problem called an influence program, and describe a lightweight algorithm based on multi-agent multi-objective reinforcement learning with sampling. We show that it can be used to solve problems from a wide range of literatures: constraint programming, Bayesian networks, stochastic programming, influence diagrams (standard, limited memory and multi-objective), and game theory (multi-level programming, Bayesian games and level-k reasoning). We expect it to be useful for the rapid prototyping of solution methods for new hybrid problems.Item Road pavement health monitoring system using smartphone sensing with a two-stage machine learning model(Elsevier, 2024-08-17) Zhao, Kai; Xu, Shuoshuo; Loney, James; Visentin, Andrea; Li, Zili; Science Foundation Ireland; European Regional Development FundDrive-by road pavement monitoring, using smartphone sensing, has faced longstanding challenges in adoption due to low accuracy and limited applicability. This stems from significant uncertainties during data collection in real-world scenarios, making it prohibitively difficult in applying conventional machine learning models to the detection of road pavement anomalies. This paper presents a two-stage machine learning approach that extracts potential anomalies from the dataset and classifies them into four typical road feature categories. Unlike time-series data analysis, this approach transforms time-series into geospatial series, allowing the analysis to be time-independent thereby capable of detecting road anomalies regardless of driving speeds. Additionally, a framework for a road pavement health monitoring system is proposed to collect data, integrate the machine learning engine, and visualise road anomalies. The developed system was tested on two shuttle buses with normal smartphones, which achieved 87% overall accuracy compared against manual inspection.Item Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves(Elsevier, 2024-09-03) Van Truong, Hoang; Tran, Khanh-Tung; Vu, Xuan-Son; Nguyen, Duy-Khuong; Bhuyan, Monowar; Nguyen, Hoang D.; Science Foundation IrelandThis paper investigates a novel graph-based representation of sound waves inspired by the physical phenomenon of correlated vibrations. We propose a Wave2Graph framework for integrating multiple acoustic representations, including the spectrum of frequencies and correlations, into various neural computing architectures to achieve new state-of-the-art performances in sound classification. The capability and reliability of our end-to-end framework are evidently demonstrated in voice pathology for low-cost and non-invasive mass-screening of medical conditions, including respiratory illnesses and Alzheimer’s Dementia. We conduct extensive experiments on multiple public benchmark datasets (ICBHI and ADReSSo) and our real-world dataset (IJSound: Respiratory disease detection using coughs and breaths). Wave2Graph framework consistently outperforms previous state-of-the-art methods with a large magnitude, up to 7.65% improvement, promising the usefulness of graph-based representation in signal processing and machine learning.Item iSee: A case-based reasoning platform for the design of explanation experiences(Elsevier, 2024-08-08) Caro-Martínez; Recio-García, Juan A.; Díaz-Agudo, Belén; Darias, Jesus M.; Wiratunga, Nirmalie; Martin, Kyle; Wijekoon, Anjana; Nkisi-Orji, Ikechukwu; Corsar, David; Pradeep, Preeja; Bridge, Derek G.; Liret, Anne; CHIST-ERA; Engineering and Physical Sciences Research Council; Irish Research Council; Science Foundation Ireland; Agence Nationale de la Recherche; Ministerio de Ciencia e InnovaciónExplainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI systems. However, deciding on the best explanation approach for a given AI problem is currently a challenging decision-making task. This paper presents the iSee project, which aims to address some of the XAI challenges by providing a unifying platform where personalized explanation experiences are generated using Case-Based Reasoning. An explanation experience includes the proposed solution to a particular explainability problem and its corresponding evaluation, provided by the end user. The ultimate goal is to provide an open catalog of explanation experiences that can be transferred to other scenarios where trustworthy AI is required.