Computing expert's intelligence:a case in bio-medicine and a case in musicology

The research work is presented here in two parts. The one comprises research on the applicability of machine learning techniques for the early identification of chromosomal abnormalities. It is shown in part I that the ANNs achieve better results than other existing methods in terms of diagnostic rate (DR) of chromosomal abnormalities (100% DR of T21) at a lower false positive rate. The work presented in part II of the thesis has been done under a three-years research project for the analysis of the folk music of Cyprus and the Eastern Mediterranean Countries and it was funded by the Research Promotion Foundation of the Republic of Cyprus. The COSFIRE filters that have been found effective for 2D and 3D signals, had been adapted for 1D music signals and their effectiveness in different applications has been studied and the results are reported.The ultimate objective of this thesis is the development of machine learning techniques that can be validated in real data in medicine and musicology and that can have practical value

Filter-based approach for ornamentation detection and recognition in singing folk music

This is a Conference paper presented by the authors at the CAiP 2015; 16th International Conference on Computer Analysis of Images and Patterns, held in Malta from the 2 to 4 September, 2015.Ornamentations in music play a significant role for the emotion which a performer or a composer aims to create. The automated identification of ornamentations enhances the understanding of music, which can be used as a feature for tasks such as performer identification or mood classification. Existing methods rely on a pre-processing step that performs note segmentation. We propose an alternative method by adapting the existing two-dimensional COSFIRE filter approach to onedimension (1D) for the automatic identification of ornamentations in monophonic folk songs. We construct a set of 1D COSFIRE filters that are selective for the 12 notes of the Western music theory. The response of a 1D COSFIRE filter is computed as the geometric mean of the differences between the fundamental frequency values in a local neighbourhood and the preferred values at the corresponding positions. We apply the proposed 1D COSFIRE filters to the pitch tracks of a song at every position along the entire signal, which in turn give response values in the range [0,1]. The 1D COSFIRE filters that we propose are effective to recognize meaningful musical information which can be transformed into symbolic representations and used for further analysis. We demonstrate the effectiveness of the proposed methodology in a new data set that we introduce, which comprises five monophonic Cypriot folk tunes consisting of 428 ornamentations. The proposed method is effective for the detection and recognition of ornamentations in singing folk music.This research was funded from the Republic of Cyprus through the Cyprus research promotion foundation and also supported by the University of Cyprus by the research grant ANΘPΩΠIΣTIKEΣ / ANΘPΩ / 0311(BE) / 19.peer-reviewe

Intelligent Noninvasive Diagnosis of Aneuploidy:Raw Values and Highly Imbalanced Dataset

The objective of this paper is to introduce a noninvasive diagnosis procedure for aneuploidy and to minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method by using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets' with a total of 122 362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus. This study, unlike previous studies published by the authors for a similar problem differs in three basic principles: 1) the training of the artificial neural networks is done by using the markers' values in their raw form (unprocessed), 2) a balanced training dataset is created and used by selecting only a representative number of euploids for the training phase, and 3) emphasis is given to the financials and suggest hierarchy and necessity of the available tests. The proposed artificial neural networks models were optimized in the sense of reaching a minimum false positive rate and at the same time securing a 100% detection rate for Trisomy 21. These systems correctly identify other aneuploidies (Trisomies 13&18, Turner, and Triploid syndromes) at a detection rate greater than 80%. In conclusion, we demonstrate that artificial neural network systems can contribute in providing noninvasive, effective early screening for fetal aneuploidies with results that compare favorably to other existing methods

CHRONOscope: application for the interactive visualization of carbon-14 and beryllium-10 atmospheric data

Information about the global climate, the carbon cycle, changes in solar activity, and a number of other atmospheric processes are preserved in the carbon-14 and the beryllium-10 records. However, these isotope datasets are large and cumbersome to work with. We have designed a self-contained, easy-to-use application that allows for more efficient analysis of different periods and patterns of interest. For several applications in atmospheric modelling, a pre-processing stage is applied to the isotope datasets in order to interpolate the data and mitigate their low temporal resolution. In CHRONOscope, we included linear and non-linear methods of interpolation with interactive parameter optimization. The resultant interpolated data can be extracted for further use. The main functionalities of CHRONOscope include the importation and superimposition of external data, quick navigation through the data with the use of markers, expression of the carbon-14 results in both Δ14C and yr BP form, separation of the data by source, and the visualization of associated error bars. We make this free software available in standalone applications for both Windows and Mac operating systems

Trainable Filters for the Identification of Anomalies in Cosmogenic Isotope Data

Extreme bursts of radiation from space result in rapid increases in the concentration of radiocarbon in the atmosphere. Such rises, known as Miyake Events, can be detected through the measurement of radiocarbon in dendrochronological archives. The identification of Miyake Events is important because radiation impacts of this magnitude pose an existential threat to satellite communications and aeronautical avionics and may even be detrimental to human health. However, at present, radiocarbon measurements on tree-ring archives are generally only available at decadal resolution, which smooths out the effect of a possible radiation burst. The Miyake Events discovered so far, in tree-rings from the years 3372-3371 BCE, 774-775 CE, and 993-994 CE, have essentially been found by chance, but there may be more. In this paper, we use signal processing techniques, in particular COSFIRE, to train filters with data on annual changes in radiocarbon (Delta C-14) around those dates. Then, we evaluate the trained filters and attempt to detect similar Miyake Events in the past. The method that we propose is promising, since it identifies the known Miyake Events at a relatively low false positive rate. Using the findings of this paper, we propose a list of 26 calendar years that our system persistently indicates are Miyake Event-like. We are currently examining a short-list of five of the newly identified dates and intend to perform single-year radiocarbon measurements over them. Signal processing techniques, such as COSFIRE filters, can be used as guidance tools since they are able to identify similar patterns of interest, even if they vary in time or in amplitude

First Trimester Noninvasive Prenatal Diagnosis:A Computational Intelligence Approach

The objective of this study is to examine the potential value of using machine learning techniques such as artificial neural network (ANN) schemes for the noninvasive estimation, at 11-13 weeks of gestation, the risk for euploidy, trisomy 21 (T21), and other chromosomal aneuploidies (O.C.A.), from suitable sonographic, biochemical markers, and other relevant data. A database(1) consisted of 51,208 singleton pregnancy cases, while undergoing first trimester screening for aneuploidies has been used for the building, training, and verification of the proposed method. From all the data collected for each case from the mother and the fetus, the following 9 are considered by the collaborating obstetricians as the most relevant to the problem in question: maternal age, previous pregnancy with T21, fetal crown-rump length, serum free beta-hCG in multiples of the median (MoM), pregnancy-associated plasma protein-A in MoM, nuchal translucency thickness, nasal bone, tricuspid flow, and ductus venosus flow. The dataset was randomly divided into a training set that was used to guide the development of various ANN schemes, support vector machines, and k-nearest neighbor models. An evaluation set used to determine the performance of the developed systems. The evaluation set, totally unknown to the proposed system, contained 16,898 cases of euploidy fetuses, 129 cases of T21, and 76 cases of O.C.A. The best results were obtained by the ANN system, which identified correctly all T21 cases, i.e., 0% false negative rate (FNR) and 96.1% of euploidies, i.e., 3.9% false positive rate (FPR), meaning that no child would have been born with T21 if only that 3.9% of all pregnancies had been sent for invasive testing. The aim of this work is to produce a practical tool for the obstetrician which will ideally provide 0% FNR and to recommend the minimum possible number of cases for further testing such as invasive. In conclusion, it was demonstrated that ANN schemes can provide an effective early screening for fetal aneuploidies at a low FPR with results that compare favorably to those of existing systems