Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador

Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision.Instituto de Investigación en Informátic

Multi-texture image segmentation

Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification

An Evaluation of Calibrated and Uncalibrated High-Resolution RGB Data in Time Series Analysis for Coal Spoil Characterisation: A Comparative Study

Minor errors in the spoil deposition process, such as placing stronger materials with higher shear strength over weaker ones, can lead to potential dump failure. Irregular deposition and inadequate compaction complicate coal spoil behaviour, necessitating a robust methodology for temporal monitoring. This study explores using unmanned aerial vehicles (UAV) equipped with red-green-blue (RGB) sensors for efficient data acquisition. Despite their prevalence, raw UAV data exhibit temporal inconsistency, hindering accurate assessments of changes over time. This is attributed to radiometric errors in UAV-based sensing arising from factors such as sensor noise, atmospheric scattering and absorption, variations in sun parameters, and variable characteristics of the sensed object over time. To this end, the study introduces an empirical line calibration with invariant targets, for precise calibration across diverse scenes. Calibrated RGB data exhibit a substantial performance advantage, achieving a 90.7% overall accuracy for spoil pile classification using ensemble (subspace discriminant), representing a noteworthy 7% improvement compared to classifying uncalibrated data. The study highlights the critical role of data calibration in optimising UAV effectiveness for spatio-temporal mine dump monitoring. The developed calibration workflow proves robust and reliable across multiple dates. Consequently, these findings play a crucial role in informing and refining sustainable management practices within the domain of mine waste management

Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador

Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision.Instituto de Investigación en Informátic

Classification software technique assessment

A catalog of software options is presented for the use of local user communities to obtain software for analyzing remotely sensed multispectral imagery. The resources required to utilize a particular software program are described. Descriptions of how a particular program analyzes data and the performance of that program for an application and data set provided by the user are shown. An effort is made to establish a statistical performance base for various software programs with regard to different data sets and analysis applications, to determine the status of the state-of-the-art

Learning by correlation for computer vision applications: from Kernel methods to deep learning

Learning to spot analogies and differences within/across visual categories is an arguably powerful approach in machine learning and pattern recognition which is directly inspired by human cognition. In this thesis, we investigate a variety of approaches which are primarily driven by correlation and tackle several computer vision applications

Extremely randomized trees

This paper proposes anew tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced.Peer reviewe

Advanced vibration analysis for the diagnosis and prognosis of rotating machinery components within condition-based maintenance programs

Machines used in the industrial field may deteriorate with usage and age. Thus it is important to maintain them so as to avoid failure during actual operation which may be dangerous or even disastrous.The literature has focused its attention on the development of optimal maintenance strategies, such as condition-based maintenance (CBM), in order to improve system reliability, to avoid system failures, and to decrease maintenance costs. CBM aims to detect the early occurrence and seriousness of a fault, to estimate the time interval during which the equipment can still operate before failure, and to identify the components which are deteriorating. CBM has been widely and effectively applied to rotating machines, which usually operate by means of bearings. The reliable and continuous work of bearings is important as the break of one of them can compromise the work of the system. Thus the monitoring, prognosis and diagnosis of bearings represent crucial and important tasks to support real-time maintenance programs. This research has carried out a complete analysis of advanced soft computing techniques ranging from the multi-class classification to one-class classification, and of combination strategies based on classifier fusion and selection. The purpose of this analysis was to design and develop high accurate and high robust methodologies to perform the detection, diagnosis and prognosis of defects on rolling elements bearings. We used vibration signals recorded by four accelerometers on a mechanical device including rolling element bearings: the signals were collected both with all faultless bearings and after substituting one faultless bearing with an artificially damaged one. Four defects and three severity levels were considered. This research has brought to the design and development of new classifiers which have proved to be very accurate and thus to represent a valuable alternative to the traditional classifiers. Besides, the high accuracy and the high robustness to noise, shown by the obtained results, prove the effectiveness of the proposed methodologies, which can be thus profitably used to perform automatic prognosis and diagnosis of rotating machinery components within real-time condition-based maintenance programs

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

Combining Ion Mobility Mass Spectrometry and Computational Methods to Study Structures of Biomolecules in the Gas Phase

Characterizing the complex, dynamically regulated networks in cells is critical for the understanding of disease mechanisms and development of therapeutics. Over the last two decades, mass spectrometry (MS) has emerged as a key structural biology tool enabling rapid analysis of complex samples. Native MS has had tremendous success in the structural elucidation of proteins, protein complexes, and protein-ligand interactions. Ion mobility MS (IM-MS), under the native MS category, has gained popularity as a structural biology technique capable of reporting collision cross section (CCS) area of biomolecular ions that can be used as an attribute for identification in bioinformatics workflows and restraint for generating three-dimensional models of proteins. Traveling wave IM (TWIM) is the most used IM platform across research and industry laboratories. However, the amount of information and the accuracy obtained from TWIM measurements have been compromised due to the lack of fundamental understanding of the technology itself. Therefore, in this thesis, novel developments in IM-MS techniques, especially with TWIM, are described that are capable of providing accurate biophysical measurements of proteins and protein complexes in a high throughput manner. In chapter 2, we devise a semi-empirical relationship that can model TWIM arrival time distributions (ATDs) across a range of TWIM conditions. A conformational broadening parameter can be extracted from the semi-empirical formalism that describes the size of the structural heterogeneity of biomolecules in the gas phase. We validated our method by investigating the origins of structural heterogeneity arising in a set of model peptides. The conformational broadening parameter also properly reflected the reduction in structural flexibility when we introduced cross-links in a protein complex. In chapter 3, we described a novel pseudo-trapping phenomenon in TW ion guides that produces aberrant ATDs. This was described using a theoretical model and ion trajectory simulations highlighting that imperfect TW leads to a repetitive pattern of ion motion causing the ions with even small mobility difference to travel with the same mean velocity. Consequently, the ions' transit times through the device were altered detrimentally affecting the calibrated CCS values. In chapter 4 we show new calibration functions capable of generating precise and accurate CCS values from TWIM measurements. Velocity relaxation and travelling wave edge effects are incorporated into the new function termed as blend + radial that outperforms the current calibration function in terms of accuracy, preciseness, and robustness. We benchmarked the new function using a large scale of analyte ions comprised of small molecules and metabolites, peptides, denature proteins, and native proteins. The last chapter showcases the utility of IM-MS platform for high throughput characterization of protein structure and protein-ligand interactions using collision induced unfolding (CIU) experiments. A classification algorithm was built for a single state and multi-state classification of CIU fingerprints, where a state can be defined as charge states of the ions, protein incubation properties, etc. Using our classification workflow, we were able to identify the class of an unknown endogenous lipid in a membrane protein complex. Multi-state classifier boosted the accuracy of the classification model, which was demonstrated using Src-kinase ligand binding experiments and biotherapeutic innovator and biosimilar comparisons. Overall, the developments in the IM-MS methods, especially the theoretical contributions to TWIM technology, described in this thesis will allow the widespread TWIM community to properly utilize the platform in the areas of chemical analysis and structural biology.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153475/1/sugyan_1.pd