Feature signature prediction of a boring process using neural network modeling with confidence bounds

Prediction of machine tool failure has been very important in modern metal cutting operations in order to meet the growing demand for product quality and cost reduction. This paper presents the study of building a neural network model for predicting the behavior of a boring process during its full life cycle. This prediction is achieved by the fusion of the predictions of three principal components extracted as features from the joint time–frequency distributions of energy of the spindle loads observed during the boring process. Furthermore, prediction uncertainty is assessed using nonlinear regression in order to quantify the errors associated with the prediction. The results show that the implemented Elman recurrent neural network is a viable method for the prediction of the feature behavior of the boring process, and that the constructed confidence bounds provide information crucial for subsequent maintenance decision making based on the predicted cutting tool degradation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45845/1/170_2005_Article_114.pd

Improving Diagnostics with Deep Forest Applied to Electronic Health Records

An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations

Safeguarding Privacy Through Deep Learning Techniques

Over the last few years, there has been a growing need to meet minimum security and privacy requirements. Both public and private companies have had to comply with increasingly stringent standards, such as the ISO 27000 family of standards, or the various laws governing the management of personal data. The huge amount of data to be managed has required a huge effort from the employees who, in the absence of automatic techniques, have had to work tirelessly to achieve the certification objectives. Unfortunately, due to the delicate information contained in the documentation relating to these problems, it is difficult if not impossible to obtain material for research and study purposes on which to experiment new ideas and techniques aimed at automating processes, perhaps exploiting what is in ferment in the scientific community and linked to the fields of ontologies and artificial intelligence for data management. In order to bypass this problem, it was decided to examine data related to the medical world, which, especially for important reasons related to the health of individuals, have gradually become more and more freely accessible over time, without affecting the generality of the proposed methods, which can be reapplied to the most diverse fields in which there is a need to manage privacy-sensitive information

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

Data exploration process based on the self-organizing map

With the advances in computer technology, the amount of data that is obtained from various sources and stored in electronic media is growing at exponential rates. Data mining is a research area which answers to the challange of analysing this data in order to find useful information contained therein. The Self-Organizing Map (SOM) is one of the methods used in data mining. It quantizes the training data into a representative set of prototype vectors and maps them on a low-dimensional grid. The SOM is a prominent tool in the initial exploratory phase in data mining. The thesis consists of an introduction and ten publications. In the publications, the validity of SOM-based data exploration methods has been investigated and various enhancements to them have been proposed. In the introduction, these methods are presented as parts of the data mining process, and they are compared with other data exploration methods with similar aims. The work makes two primary contributions. Firstly, it has been shown that the SOM provides a versatile platform on top of which various data exploration methods can be efficiently constructed. New methods and measures for visualization of data, clustering, cluster characterization, and quantization have been proposed. The SOM algorithm and the proposed methods and measures have been implemented as a set of Matlab routines in the SOM Toolbox software library. Secondly, a framework for SOM-based data exploration of table-format data - both single tables and hierarchically organized tables - has been constructed. The framework divides exploratory data analysis into several sub-tasks, most notably the analysis of samples and the analysis of variables. The analysis methods are applied autonomously and their results are provided in a report describing the most important properties of the data manifold. In such a framework, the attention of the data miner can be directed more towards the actual data exploration task, rather than on the application of the analysis methods. Because of the highly iterative nature of the data exploration, the automation of routine analysis tasks can reduce the time needed by the data exploration process considerably.reviewe

The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography

[EN] The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs.This research received financial support from grants PID2021-00X128525-IV0, PID2021123804OB-I00 and TED2021-129996B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU), SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2021/286 from Generalitat Valenciana.Cano, J.; Fácila, L.; Gracia-Baena, JM.; Zangróniz, R.; Alcaraz, R.; Rieta, JJ. (2022). The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography. Biosensors. 12(5):1-14. https://doi.org/10.3390/bios1205028911412

Applications and implementation of neuro-connectionist architectures.

by H.S. Ng.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 91-97).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- Neuro-connectionist Network --- p.2Chapter 2 --- Related Works --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.1.1 --- Kruskal's Algorithm --- p.5Chapter 2.1.2 --- Prim's algorithm --- p.6Chapter 2.1.3 --- Sollin's algorithm --- p.7Chapter 2.1.4 --- Bellman-Ford algorithm --- p.8Chapter 2.1.5 --- Floyd-Warshall algorithm --- p.9Chapter 3 --- Binary Relation Inference Network and Path Problems --- p.11Chapter 3.1 --- Introduction --- p.11Chapter 3.2 --- Topology --- p.12Chapter 3.3 --- Network structure --- p.13Chapter 3.3.1 --- Single-destination BRIN architecture --- p.14Chapter 3.3.2 --- Comparison between all-pair BRIN and single-destination BRIN --- p.18Chapter 3.4 --- Path Problems and BRIN Solution --- p.18Chapter 3.4.1 --- Minimax path problems --- p.18Chapter 3.4.2 --- BRIN solution --- p.19Chapter 4 --- Analog and Voltage-mode Approach --- p.22Chapter 4.1 --- Introduction --- p.22Chapter 4.2 --- Analog implementation --- p.24Chapter 4.3 --- Voltage-mode approach --- p.26Chapter 4.3.1 --- The site function --- p.26Chapter 4.3.2 --- The unit function --- p.28Chapter 4.3.3 --- The computational unit --- p.28Chapter 4.4 --- Conclusion --- p.29Chapter 5 --- Current-mode Approach --- p.32Chapter 5.1 --- Introduction --- p.32Chapter 5.2 --- Current-mode approach for analog VLSI Implementation --- p.33Chapter 5.2.1 --- Site and Unit output function --- p.33Chapter 5.2.2 --- Computational unit --- p.34Chapter 5.2.3 --- A complete network --- p.35Chapter 5.3 --- Conclusion --- p.37Chapter 6 --- Neural Network Compensation for Optimization Circuit --- p.40Chapter 6.1 --- Introduction --- p.40Chapter 6.2 --- A Neuro-connectionist Architecture for error correction --- p.41Chapter 6.2.1 --- Linear Relationship --- p.42Chapter 6.2.2 --- Output Deviation of Computational Unit --- p.44Chapter 6.3 --- Experimental Results --- p.46Chapter 6.3.1 --- Training Phase --- p.46Chapter 6.3.2 --- Generalization Phase --- p.48Chapter 6.4 --- Conclusion --- p.50Chapter 7 --- Precision-limited Analog Neural Network Compensation --- p.51Chapter 7.1 --- Introduction --- p.51Chapter 7.2 --- Analog Neural Network hardware --- p.53Chapter 7.3 --- Integration of analog neural network compensation of connectionist net- work for general path problems --- p.54Chapter 7.4 --- Experimental Results --- p.55Chapter 7.4.1 --- Convergence time --- p.56Chapter 7.4.2 --- The accuracy of the system --- p.57Chapter 7.5 --- Conclusion --- p.58Chapter 8 --- Transitive Closure Problems --- p.60Chapter 8.1 --- Introduction --- p.60Chapter 8.2 --- Different ways of implementation of BRIN for transitive closure --- p.61Chapter 8.2.1 --- Digital Implementation --- p.61Chapter 8.2.2 --- Analog Implementation --- p.61Chapter 8.3 --- Transitive Closure Problem --- p.63Chapter 8.3.1 --- A special case of maximum spanning tree problem --- p.64Chapter 8.3.2 --- Analog approach solution for transitive closure problem --- p.65Chapter 8.3.3 --- Current-mode approach solution for transitive closure problem --- p.67Chapter 8.4 --- Comparisons between the different forms of implementation of BRIN for transitive closure --- p.71Chapter 8.4.1 --- Convergence Time --- p.71Chapter 8.4.2 --- Circuit complexity --- p.72Chapter 8.5 --- Discussion --- p.73Chapter 9 --- Critical path problems --- p.74Chapter 9.1 --- Introduction --- p.74Chapter 9.2 --- Problem statement and single-destination BRIN solution --- p.75Chapter 9.3 --- Analog implementation --- p.76Chapter 9.3.1 --- Separated building block --- p.78Chapter 9.3.2 --- Combined building block --- p.79Chapter 9.4 --- Current-mode approach --- p.80Chapter 9.4.1 --- "Site function, unit output function and a completed network" --- p.80Chapter 9.5 --- Conclusion --- p.83Chapter 10 --- Conclusions --- p.85Chapter 10.1 --- Summary of Achievements --- p.85Chapter 10.2 --- Future development --- p.88Chapter 10.2.1 --- Application for financial problems --- p.88Chapter 10.2.2 --- Fabrication of VLSI Implementation --- p.88Chapter 10.2.3 --- Actual prototyping of Analog Integrated Circuits for critical path and transitive closure problems --- p.89Chapter 10.2.4 --- Other implementation platform --- p.89Chapter 10.2.5 --- On-line update of routing table inside the router for network com- munication using BRIN --- p.89Chapter 10.2.6 --- Other BRIN's applications --- p.90Bibliography --- p.9