Breath analysis for detection and trajectory monitoring of acute respiratory distress syndrome in swine

Despite the enormous impact on human health, acute respiratory distress syndrome (ARDS) is ill-defined, and its timely diagnosis is difficult, as is tracking the course of the syndrome. The objective of this pilot study was to explore the utility of breath collection and analysis methodologies to detect ARDS through changes in the volatile organic compound (VOC) profiles present in breath. Five male Yorkshire mix swine were studied and ARDS was induced utilising both direct and indirect lung injury. An automated portable gas chromatography device developed in-house was used for point of care breath analysis and to monitor swine breath hourly, starting from the initiation of the experiment until the development of ARDS, which was adjudicated based on the Berlin criteria at the breath sampling points and confirmed by lung biopsy at the end of the experiment. A total of 67 breath samples (chromatograms) were collected and analyzed. Through machine learning, principal component analysis, and linear discrimination analysis, seven VOCs biomarkers were identified that distinguished ARDS. These represent seven of the nine biomarkers found in our breath analysis study of human ARDS corroborating our findings. We also demonstrated that breath analysis detects changes 1–6 h earlier than the clinical adjudication based on the Berlin criteria. The findings provide proof of concept that breath analysis can be used for the identification of early changes associated with ARDS pathogenesis in swine. Its clinical application could provide intensive care clinicians with a non-invasive diagnostic tool for early detection and continuous monitoring of ARDS.

Fischer Linear Discrimination and Quadratic Discrimination Analysis–Based Data Mining Technique for Internet of Things Framework for Healthcare

The internet of reality or augmented reality has been considered a breakthrough and an outstanding critical mutation with an emphasis on data mining leading to dismantling of some of its assumptions among several of its stakeholders. In this work, we study the pillars of these technologies connected to web usage as the Internet of things (IoT) system's healthcare infrastructure. We used several data mining techniques to evaluate the online advertisement data set, which can be categorized as high dimensional with 1,553 attributes, and the imbalanced data set, which automatically simulates an IoT discrimination problem. The proposed methodology applies Fischer linear discrimination analysis (FLDA) and quadratic discrimination analysis (QDA) within random projection (RP) filters to compare our runtime and accuracy with support vector machine (SVM), K-nearest neighbor (KNN), and Multilayer perceptron (MLP) in IoT-based systems. Finally, the impact on number of projections was practically experimented, and the sensitivity of both FLDA and QDA with regard to precision and runtime was found to be challenging. The modeling results show not only improved accuracy, but also runtime improvements. When compared with SVM, KNN, and MLP in QDA and FLDA, runtime shortens by 20 times in our chosen data set simulated for a healthcare framework. The RP filtering in the preprocessing stage of the attribute selection, fulfilling the model's runtime, is a standpoint in the IoT industry.Index Terms: Data Mining, Random Projection, Fischer Linear Discriminant Analysis, Online Advertisement Dataset, Quadratic Discriminant Analysis, Feature Selection, Internet of Things.

COVRATIO Statistic as a Discrimination Method for Multivariate Normal Distribution

The COVRATIO statistic has been used to identify the presence of outlier in data, which is based on deletion approach, where the determinant of covariance matrix for the full dataset excludes i-th row. This study proposes a novel discrimination method for the multivariate normal (MVN) distribution using the idea of COVRATIO statistic, denoted as . The linear discrimination function (LDF) for MVN distribution will be compared to the statistic. Simulation results showed that the as discrimination method performs better than the LDF with lower misclassification probabilities in all cases considered. The interest in the discrimination method arose in connection with the study of an application to discriminate the shape of the human maxillary dental arches, thus statistic may be considered as an alternative.

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad-Hoc Network (MANET)

A wormhole attack is a type of attack on the network layer which reflects the issue of routing protocols. The classification is performed with several methods of machine learning consisting of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Linear Discrimination Analysis (LDA), Naive Bayes (NB), and Convolutional neural network (CNN). Moreover, for feature extraction, we used the properties of nodes, especially nodes speed in the MANET. We have collected 3997 distinct (normal 3781 and malicious 216) samples that comprise normal and malicious samples. Results of the classification show that the accuracy of KNN, SVM, DT, LDA, NB, and CNN methods are 97.1%, 98.2%, 98.9%, 95.2%, 94.7%, and 96.4%, respectively. Based on our findings, the DT method's accuracy is 98.9% and higher than other methods. In the next priority, SVM, KNN, CNN, LDA, and NB indicate high accuracy, respectively.

Toward Food Freshness Monitoring: Coordination Binding–Based Colorimetric Sensor Array for Sulfur-Containing Amino Acids

Herein, a self-assembled colorimetric chemosensor array composed of off-the-shelf catechol dyes and a metal ion (i.e., Zn2+) has been used for the sulfur-containing amino acids (SCAAs; i.e., glutathione, glutathione disulfide, L–cysteine, DL–homocysteine, and L–cystine). The coordination binding–based chemosensor array (CBSA) fabricated by a competitive assay among SCAAs, Zn2+ ions, and catechol dyes [i.e., pyrocatechol violet (PV), bromopyrogallol red (BPR), pyrogallol red (PR), and alizarin red S (ARS)] yielded fingerprint-like colorimetric changes. We succeeded in the qualification of SCAAs based on pattern recognition [i.e., a linear discrimination analysis (LDA)] with 100% correct classification accuracy. The semiquantification of reduced/oxidized forms of SCAAs was also performed based on LDA. Furthermore, we carried out a spike test of glutathione in food samples using the proposed chemosensor array with regression analysis. It is worth mentioning that we achieved a 91–110% recovery rate in real sample tests, which confirmed the accuracy of the constructed model. Thus, this study represents a step forward in assessing food freshness based on supramolecular analytical methods.

Discrimination of earthquakes and quarry blasts in Mecsek Mountain region (Hungary) and its vicinity by using a linear discrimination function

<p>Contamination of earthquake catalogues with anthropogenic events largely complicates seismotectonic interpretation. It is especially true for relatively low seismicity areas, such as Hungary. In the present study, we analyze the characteristics of earthquakes and blasts of quarries occurred between 2015 and 2020 in the Mecsek Mountains in southern Hungary within 120 km to MORH and KOVH stations.</p><p>The objective of this study was to determine the linear discrimination line between the two classes earthquakes and explosions. We investigated the effectiveness of P/S amplitude ratios using filtered waveforms at different ranges of frequencies. We applied waveform cross-correlation to build correlation matrices at both stations and performed hierarchical cluster analysis to identify event clusters. Because most of the quarry blasts were carried out by ripple-fire technology, we computed spectrograms and examined the spectral ratio between low and high frequencies and the steepness of spectra.</p><p>Classes of earthquakes and quarry blasts have separated well from each other by combining the amplitude ratio, waveform similarity and the different spectral methods. We compare the discrimination parameters and capability of both stations to identify the explosions in analyzed quarries that were misclassified as earthquakes in the Hungarian National Bulletins.</p>

A new parallel bat algorithm for musical note recognition

Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (Developed Bat Algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.