Quadratic Discriminant Analysis Research Articles

Gas chromatographic determination of fatty acid composition in breast milk of mothers with different health conditions

AbstractHuman milk (HM) of healthy, well-nourished, lactating mothers is a unique and ideal source of nutritive factors, like hormones, cytokines, chemokines, growth factors that ensures the proper growth and development of infants. Among the main components of HM, fat is an important energy source and a regulatory factor. The quality of milk fat depends on its fatty acid (FA) composition. Gas chromatography coupled with flame ionisation detection is one of the most common methods for analysis of the FA profile of HM. The aim of this study was to evaluate the FA composition of HM, collected from mothers with different health conditions (normal Body Mass Index (nBMI); overweight and obese) using GC-FID method. The results showed that saturated FAs were present in the highest amount in the HM samples, of which palmitic acid was the main representative. The major monounsaturated FA was oleic acid, while linoleic acid was the most abundant of the polyunsaturated FAs (PUFA). Overweight and obese women have lower levels of PUFA in their breast milk. The data were subjected to principal component and quadratic discriminant analysis (QDA). QDA classified nBMI and overweight and obese mother milk samples with 88.24% accuracy. Significant differences were found between normal and overweight and obese HM samples in case of C10:0 and C18:3 FAs. Higher maternal BMI was associated with a higher n-6/n-3 PUFA ratio.

Classification of breast cancer recurrence based on imputed data: a simulation study

Several studies have been conducted to classify various real life events but few are in medical fields; particularly about breast recurrence under statistical techniques. To our knowledge, there is no reported comparison of statistical classification accuracy and classifiers’ discriminative ability on breast cancer recurrence in presence of imputed missing data. Therefore, this article aims to fill this analysis gap by comparing the performance of binary classifiers (logistic regression, linear and quadratic discriminant analysis) using several datasets resulted from imputation process using various simulation conditions. Our study aids the knowledge about how classifiers’ accuracy and discriminative ability in classifying a binary outcome variable are affected by the presence of imputed numerical missing data. We simulated incomplete datasets with 15, 30, 45 and 60% of missingness under Missing At Random (MAR) and Missing Completely At Random (MCAR) mechanisms. Mean imputation, hot deck, k-nearest neighbour, multiple imputations via chained equation, expected-maximisation, and predictive mean matching were used to impute incomplete datasets. For each classifier, correct classification accuracy and area under the Receiver Operating Characteristic (ROC) curves under MAR and MCAR mechanisms were compared. The linear discriminant classifier attained the highest classification accuracy (73.9%) based on mean-imputed data at 45% of missing data under MCAR mechanism. As a classifier, the logistic regression based on predictive mean matching imputed-data yields the greatest areas under ROC curves (0.6418) at 30% missingness while k-nearest neighbour tops the value (0.6428) at 60% of missing data under MCAR mechanism.

OCT-Derived Radiomic Features Predict Anti-VEGF Response and Durability in Neovascular Age-Related Macular Degeneration.

No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution). A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response. The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance. The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained. Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Comparison of discriminant methods and deep learning analysis in plant taxonomy: a case study of Elatine

Elatine is a genus in which, flower and seed characteristics are the most important diagnostic features; i.e. seed shape and the structure of its cover found to be the most reliable identification character. We used a combination of classic discriminant methods by combining with deep learning techniques to analyze seed morphometric data within 28 populations of six Elatine species from 11 countries throughout the Northern Hemisphere to compare the obtained results and then check their taxonomic classification. Our findings indicate that among the discriminant methods, Quadratic Discriminant Analysis (QDA) had the highest percentage of correct matching (mean fit—91.23%); only the deep machine learning method based on Convolutional Neural Network (CNN) was characterized by a higher match (mean fit—93.40%). The QDA method recognized the seeds of E. brochonii and E. orthosperma with 99% accuracy, and the CNN method with 100%. Other taxa, such as E. alsinastrum, E. trianda, E. californica and E. hungarica were matched with an accuracy of at least 95% (CNN). Our results indicate that the CNN obtains remarkably more accurate classifications than classic discriminant methods, and better recognizes the entire taxa pool analyzed. The least recognized species are E. macropoda and E. hexandra (88% and 78% match).

Detection of terbufos in cases of intoxication by means of entomotoxicological analysis using ATR-FTIR spectroscopy combined with chemometrics

The detection of toxic substances in larvae from carcasses in an advanced stage of decomposition may help criminal expertise in elucidating the cause of death in suspected cases of poisoning. Terbufos (Counter®) or O,O-diethyl-S-[(tert-butylsulfanyl)methyl] phosphorodithioate is an insecticide and systemic nematicide, which has very high toxicity from an acute point of view (oral LD50 in rodents ranging from 1.4 to 9.2 mg/kg) that has been marketed irregularly and indiscriminately in Brazil as a rodenticide, often being used to practice homicides. The present study aims to evaluate the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to detect traces of terbufos pesticide in fly larvae (Sarcophagidae). ATR-FTIR spectra of scavenger fly larvae from control (n = 31) and intoxicated (n = 80) groups were collected and submitted to chemometric analysis by means of multivariate classification using principal component analysis with quadratic discriminant analysis (PCA-QDA), successive projections algorithm with quadratic discriminant analysis (SPA-QDA) and genetic algorithm with quadratic discriminant analysis (GA-QDA) in order to distinguish between control and intoxicated groups. All discriminant models showed sensitivity and specificity above 90%, with the GA-QDA model showing the best performance with 98.9% sensitivity and specificity. The proposed methodology proved to be sensitive and promising for the detection of terbufos in scavenger fly larvae from intoxicated rat carcasses. In addition, the non-destructive nature of the ATR-FTIR technique may be useful in preserving the forensic evidence, meeting the precepts of the chain of custody and allowing for counter-proof.

Using statistical modelling to identify precarious urban Settlements and their access to Water and Sanitation services in Brazil

ABSTRACT SDG 6 aims universal coverage of Water and Sanitation services for all, under the principle of leaving no one behind. Thus, measuring progress on service levels cannot be based on averages, at the risk of not capturing the real situation in vulnerable areas such as Urban Precarious Settlements (UPS), to which the lack of appropriate indicators and representative data hinder access to the services. This paper aims to use statistical modelling to propose a new procedure to identify UPS in Brazil, to then measure their access to WS services and compare with general figures. The method was applied to the Federal District of Brazil, testing three statistical techniques: Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression. The latter presented the best predictive performance resulting in a sensitivity of 88,5% and an Area Under the Curve of 0,96. Overall results demonstrated inequalities of access among the poorest, particularly to sanitation.

Implementing Sentinel-2 Data and Machine Learning to Detect Plant Stress in Olive Groves

Olives are an essential crop for Greece and constitute a major economic and agricultural factor. Diseases, pests, and environmental conditions are all factors that can deteriorate the health status of olive crops by causing plant stress. Researchers can utilize remote sensing to assist their actions in detecting these sources of stress and act accordingly. In this experiment, Sentinel-2 data were used to create vegetation indices for commercial olive fields in Halkidiki, Northern Greece. Twelve machine learning algorithms were tested to determine which type would be the most efficient to detect plant stress in olive trees. In parallel, a test was conducted by testing 26 thresholds to determine how setting different thresholds for stress incidence affects model performance and which threshold constitutes the best choice for more accurate classification. The results show that among all tested classification algorithms, the quadratic discriminant analysis provided the best performance of 0.99. The stress incidence threshold used in the current case to generate the best-performing model was 6%, but the results suggest that setting customized thresholds relevant to specific cases would provide optimal results. The best-performing model was used in a one-vs.-rest multiclass classification task to determine the source of the stress between four possible classes: “healthy”, “verticillium”, “spilocaea”, and “unidentified”. The multiclass model was more accurate in detection for the “healthy” class (0.99); the “verticillium” and “unidentified” classes were less accurate (0.76); and “spilocaea” had the lowest score (0.72). Findings from this research can be used by experts as a service to enhance their decision-making and support the application of efficient strategies in the field of precision crop protection.

Deep belief network Assisted quadratic logit boost classifier for brain tumor detection using MR images

Classification is the main work while detecting the brain tumor images. Prior researchers planned to determine the brain tumors via various classification approaches. But, the error rate and detection time of tumor are high. Therefore, brain tumor detection is improved via classification using the Deep Belief Network Assisted Quadratic Logit BoostClassifier (DBNQLBC) technique. The proposed DBNQLBC technique is employed for increasing the accuracy with lesser error and time using classifying the brain images. The features from the input brain MRI images are taken as input in the DBNQLBC technique to carry out the brain tumor detection. DBNQLBC technique comprises the different types of layers namely input layer, hidden layers, and output layer. From the brain MRI images, the input layer gets the features and it is sent to the hidden layer. In the hidden layer, a quadratic logit boostclassifier is applied to classify the extracted features. Boosting classifier uses the quadratic classifier as a sub-classifier to detect the brain tumor through the likelihood measure. The results of sub-classifiers are merged and create a robust one through diminishing logit loss. The Boosting classifier determines the best classification results that provide higher accuracy results. As a result, input MRI images are accurately categorized into normal and abnormal and the outcomes are displayed at the output layer. From this, brain tumor detection is achieved with lower error, time and higher accuracy. Simulation evaluation is carried out using the metrics such as brain tumor detection accuracy, brain tumor detection time, and false-positive rate based on the number of MRI images. The obtained outcomes ensure the presented DBNQLBC technique effectively increases the brain tumor detection accuracy and reduces the time requirement and false-positive rate than the other methods.

Development of FT-NIR Technique to Determine the Ripeness of Sweet Cherries and Sour Cherries

The FT-NIR technique was used for the rapid and non-destructive determination of sweet cherry and sour cherry ripeness. Titratable acidity (A), water-soluble total solids (SSC), total anthocyanin (TA) content and calculated maturity index (SSC/A = MI) were used as reference values. PLS correlations were validated by seven-fold cross-validation (RMSECV for different parameters: DM = 1.25%, w/w; A = 0.14%, w/w; SSC = 0.97%, w/w; TA = 17.5 g/100 mL; MI = 1.66) and test-validation (RMSEP for different parameters: DM = 1.46%, w/w; A = 0.19%, w/w; SSC = 0.99%, w/w; TA = 17.5 g/100 mL; MI = 1.59). Different discriminant analyses and support vector machine (SVM) classifications were performed for each parameter. The quadratic discriminant analysis (QDA) was found to be the best pattern recognition method. A maturity degree (MD) was developed based on the reference values, which classified the samples into mature and immature categories with an accuracy of 98.44%.

Post-Stroke Gait Classification Based on Feature Space Transformation and Data Labeling

Despite scientific and clinical advances, stroke is still considered one of the main causes of disability, including gait disorders. The search for more effective methods of gait re-education in post-stroke patients is one of the most important issues in contemporary neurorehabilitation. In this paper, we propose a transformation of the feature space and definition of class labels in the post-stroke gait problem to more efficiently study related phenomena and assess gait faster. Clustering is used to define two class labels (improvement and recurrence) in the data labeling process. The proposed approach was tested on a real-world dataset consisting of 50 patients (male and female, aged 49–82 years) after ischemic stroke who participated in a gait rehabilitation program. Gait in the study was described using speed, cadence, and stride length and their normalized values. Ten treatment sessions (10 therapy days) were conducted over two weeks (10 working days). The same specialist took measurements, and hence inter-rater reliability can be neglected. Machine learning methods, support vector machine and quadratic discriminant analysis were used to classify post-stroke gait for three cases with different class labels. The proposed novel approach, characterized by its speed of execution and accuracy of classification, may be helpful for screening, better targeting, and rehabilitation monitoring. The proposed approach minimizes clinical testing and supports the work of physicians, physiotherapists, and diagnosticians.

Using molecular phylogenetic and stable isotopic analysis to identify species, geographical origin and production method of mullet roes

The flathead grey mullet, Mugil cephalus (Mugilidae), is one of the most commercially important fish species in Taiwan since mullet roe is considered as a delicacy with a high economic value. Farmed, wild-caught, and imported mullet roes are often not properly labelled in retail stores and merchants can make unethical profits from selling mislabeled products. Therefore, there is an urgent need to identify the provenance and production method of the mullet roes. In this study, we conducted a molecular phylogenetic analysis so as to identify the species and geographical origin of the mullet roes and we used stable isotopic values as a tool to differentiate between the wild-caught and farmed mullet roes. DNA sequence variations of a mitochondrial COI gene from the sampled mullets and mullet roes of wild-caught and farmed individuals, as well as the roe products sold in retail stores in Taiwan, were analyzed within the phylogenetic framework. The results showed that the samples were from four M. cephalus lineages (or putative Mugil species B, C, I, and H) occurring in the Caribbean Sea, Northwestern Pacific, and Southeastern Australia, and the M. liza in the Western Atlantic, respectively. The M. liza samples were from the salted roe products purchased from two retail stores, in which one claimed that the roes were imported from Brazil, whereas the other claimed that the roes were from locally reared flathead grey mullets. The farmed mullet roes had significantly lower δ13C values and less variation in δ15N values when compared with the wild-caught and imported mullet roes. Quadratic discriminant analysis based on the δ13C and δ15N values successfully discriminated the farmed mullet roes from the wild-caught and imported roes with an approximate error rate of 2%. Our study demonstrated that the combined approach of genetic and stable isotopic analyses can effectively authenticate the mullet roe origin and production methods.

Data-Driven Modeling of Aggregate Flexibility Under Uncertain and Non-Convex Device Models

Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into wholesale energy markets. To ease market clearing, system operators allow aggregators to submit bidding models of simple prespecified polytopic shapes. Aggregators need to carefully design and commit to a polytope that best captures their energy flexibility along a day-ahead scheduling horizon. This work puts forth a model-informed data-based optimal flexibility design for aggregators, which deals with the time-coupled, uncertain, and non-convex models of individual loads. The proposed solution first generates efficiently a labeled dataset of (in)-feasible aggregation schedules. The feasible set of the aggregator is then approximated by an ellipsoid upon training a convex quadratic classifier using the labeled dataset. The ellipsoid is subsequently inner approximated by a polytope. Using Farkas’ lemma, the obtained polytope is finally inner approximated by the polytopic shape dictated by the market. Numerical tests show the effectiveness of the proposed flexibility design framework for designing the feasible sets of small- and large-sized aggregators coordinating solar photovoltaics, thermostatically-controlled loads, batteries, and electric vehicles. The tests further demonstrate that it is crucial for the aggregator to consider time-coupling and uncertainties in optimal flexibility design.

Venetian Protected Designation of origin wines traceability: Multi-elemental, isotopes and chemometric analysis

The traceability and authentication of PDOs wines are important issues for safeguarding their production and distribution systems. This paper evaluated seven Venetian PDO wines, i.e., Amarone, Bardolino, Custoza, Pinot Grigio, Recioto, Soave and Valpolicella. For this purpose, 219 wine samples from the Veneto region were characterised by determining 63 elements and six isotope ratios by HR-ICP-MS and MC-ICP-MS. Chemometric tools highlighted As, Ca, Cs, δ11B and 87Sr/86Sr as the most informative variables to differentiate the PDOs. Seven classification methods, such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, k-Nearest Neighbours, Naïve Bayes, Random Forest, Artificial Neural Networking, and Support Vector Machine were tested and perform a correct classification for Amarone, Bardolino, Pinot Grigio and Recioto PDOs. This paper successfully proposes for the first time advanced traceability tools of seven Venetian PDO by the use of an integrated approach of multi-elemental and isotopes followed by chemometrics analysis.

Retinal Shape-Based Classification of Retinal Detachment and Posterior Vitreous Detachment Eyes.

Retinal detachment is a sight-threatening emergency, with more than half of those affected suffering permanent visual impairment. A diagnostic test to identify eyes at risk before vision is threatened would enable exploration of prophylactic treatment. This report presents the use of irregularities in retinal shape, quantified from optical coherence tomography (OCT) images, as a biomarker for retinal detachment. OCT images were taken from posterior and mid-peripheral retina of 264 individuals [97 after a posterior vitreous detachment (PVD), 99 after vitrectomy for retinal detachment and 68 after laser for a retinal tear]. Diagnoses were taken from history, examination and OCT. Retinal irregularity was quantified in the frequency domain, and the distribution of irregularity across the regions of the eye was explored to identify features exhibiting the greatest difference between retinal detachment and PVD eyes. Two of these features plus axial length were used to train a quadratic discriminant analysis classifier. Classifier performance was assessed by its sensitivity and specificity in identifying retinal detachment eyes and visualised with a receiver operating characteristic (ROC) curve. Validation set specificity was 84% (44/52 PVD eyes correctly labelled) and sensitivity 35% (23/64 retinal detachment eyes identified, p = 0.02). Area under the ROC curve was 0.75 (95% confidence intervals 0.58-0.85). Retinal detachment eyes were significantly more irregular than PVD eyes in the superior retina (0.70mm versus 0.49mm, p < 0.05) and supero-temporal retina (1.12mm versus 0.80mm, p < 0.05). Lower sensitivity (16/68, 24%) was seen for eyes with a retinal tear without detachment, that were intermediate in size between retinal detachment and PVD eyes. Axial length on its own was a poor classifier. Neither irregularity nor classification were affected by surgery for retinal detachment or the development of PVD. The classifier identified 1/3 of retinal detachment eyes in this sample. In future work, these features can be evaluated as a test for retinal detachment prior to PVD.

Moisture Content Prediction in Polymer Composites Using Machine Learning Techniques.

The principal objective of this study is to employ non-destructive broadband dielectric spectroscopy/impedance spectroscopy and machine learning techniques to estimate the moisture content in FRP composites under hygrothermal aging. Here, classification and regression machine learning models that can accurately predict the current moisture saturation state are developed using the frequency domain dielectric response of the composite, in conjunction with the time domain hygrothermal aging effect. First, to categorize the composites based on the present state of the absorbed moisture supervised classification learning models (i.e., quadratic discriminant analysis (QDA), support vector machine (SVM), and artificial neural network-based multilayer perceptron (MLP) classifier) have been developed. Later, to accurately estimate the relative moisture absorption from the dielectric data, supervised regression models (i.e., multiple linear regression (MLR), decision tree regression (DTR), and multi-layer perceptron (MLP) regression) have been developed, which can effectively estimate the relative moisture absorption from the dielectric response of the material with an R¬2 value greater than 0.95. The physics behind the hygrothermal aging of the composites has then been interpreted by comparing the model attributes to see which characteristics most strongly influence the predictions.

High nutrient loads hinder successful restoration of natural habitats in freshwater wetlands

Restoration of natural processes in ecosystems is key to mitigate the biodiversity crisis. Here, we evaluate 20 Danish stream‐valley restoration projects—mainly by rewetting—in terms of successful restoration of natural wetland habitats. We used quadratic discriminant analysis and generalized linear models to compare 80 vegetation plots from the restoration projects with &gt;60,000 natural or semi‐natural wetland reference plots. We modeled the influence of time since restoration, grazing, rewetting, and nutrient availability on (1) the probability that study plots belong to natural habitats and (2) their richness of high‐quality‐habitat indicator species. The probability of a restored wetland having developed into a natural wetland habitat—such as an alkaline fen—was generally below 10%. Also, we found only half as many indicator species in restored wetlands than in reference wetlands and we demonstrated that the number of characteristic alkaline fen species did not deviate from what could be expected under the prevailing nutrient conditions. We found a negative effect of soil nutrient availability on the number of high‐quality‐habitat indicator species and the lowest probability of plots being natural wetlands in the most nutrient rich plots. The effect of grazing was only positive in the first years after restoration and only in the most nutrient rich plots, while the effect of rewetting sites to historical hydrological conditions was generally negative. Our findings suggest that unnaturally high nutrient availability is probably the core limiting factor for successful restoration of natural wetlands and their associated plant diversity.

Physical human locomotion prediction using manifold regularization.

Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting the dynamics for complex locomotion patterns is still immature. Therefore, this article proposes unique methods including the calibration-based filter algorithm and kinematic-static patterns identification for predicting those complex activities from fused signals. Different types of signals are extracted from benchmarked datasets and pre-processed using a novel calibration-based filter for inertial signals along with a Bessel filter for physiological signals. Next, sliding overlapped windows are utilized to get motion patterns defined over time. Then, polynomial probability distribution is suggested to decide the motion patterns natures. For features extraction based kinematic-static patterns, time and probability domain features are extracted over physical action dataset (PAD) and growing old together validation (GOTOV) dataset. Further, the features are optimized using quadratic discriminant analysis and orthogonal fuzzy neighborhood discriminant analysis techniques. Manifold regularization algorithms have also been applied to assess the performance of proposed prediction system. For the physical action dataset, we achieved an accuracy rate of 82.50% for patterned signals. While, the GOTOV dataset, we achieved an accuracy rate of 81.90%. As a result, the proposed system outdid when compared to the other state-of-the-art models in literature.

Authentication of Iranian Saffron (Crocus sativus) Using Stable Isotopes δ13C and δ2H and Metabolites Quantification.

Saffron is a very high value-added ingredient used in the food supplement market and contains a high level of safranal. Adding synthetic safranal to saffron, which is significantly cheaper, and falsifying the origin of saffron may represent recurrent fraud. Saffron from different countries was analyzed to determine the stable isotope ratios δ13C and δ2H from safranal by gas chromatography coupled with isotope-ratio mass spectrometry (GC-C/P-IRMS) and the concentration of saffron metabolites with ultra-high performance liquid chromatography coupled with diode array detector (UHPLC-DAD). The isotopic analysis highlighted a higher ratio of δ2H in synthetic safranal than in natural safranal; the mean values were 36‰ (+/− 40) and −210‰ (+/− 35), respectively. The δ13C between Iranian, Spanish and other saffron was significantly different and represents median values of −28.62‰, −30.12‰ and −30.70‰, respectively. Moreover, linear and quadratic discriminant analyses (LDA and QDA) were computed using the two isotope ratios of safranal and the saffron metabolites. A first QDA showed that trans-crocetin and the δ13C of safranal, picrocrocin, and crocin C3 concentrations clearly differentiated Iranian saffron from other origins. A second model identified δ13C, trans-crocetin, crocin C2, crocin C3, and picrocrocin as good predictors to discriminate saffron samples from Iran, Spain, or other origins, with a total ability score classification matrix of 100% and a prediction matrix of 82.5%. This combined approach may be a useful tool to authenticate the origin of unknown saffron.

The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line

The objective of this study was to evaluate differences between the red onion cultivar and breeding line using models based on selected fluorescence spectroscopic data built using machine-learning algorithms from different groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The combination of fluorescence spectroscopy and machine learning is an original approach to the non-destructive and objective discrimination of red onion samples. The selected fluorescence spectroscopic data were used to build models using algorithms from the groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The most satisfactory results were obtained using J48 and LMT (Logistic Model Tree) from the group of Trees, Multilayer Perceptron, and QDA (Quadratic Discriminant Analysis) from Functions, Naive Bayes from Bayes, Logit Boost from Meta, JRip from Rules, and LWL (Locally Weighted Learning) from Lazy. The average accuracy of discrimination of onion bulbs belonging to ‘Asenovgradska kaba’ and a red breeding line equal to 100% was found in the case of models developed using the LMT, Multilayer Perceptron, Naive Bayes, Logit Boost, and LWL algorithms. The TPR (True Positive Rate), Precision, and F-Measure of 1.000 and FPR (False Positive Rate) of 0.000, as well as the Kappa statistic of 1.0, were determined. The results revealed the usefulness of the approach combining fluorescence spectroscopy and machine learning to distinguish red onion cultivars and breeding lines.

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network.

Heart disease is an emerging health issue in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart generate oxygenated blood to all body parts, however sometimes blood vessels become clogged or restrained due to cardiac issues. Past heart diagnosis applications are outdated and suffer from poor performance. Therefore, an intelligent heart disease diagnosis application design is required. In this research work, internet of things (IoT) sensor data with a deep learning-based heart diagnosis application is designed. The heart disease IoT sensor data is collected from the University of California Irvine machine learning repository free open-source dataset which is useful for training the deep graph convolutional network (DG_ConvoNet) deep learning network. The testing data has been collected from the Cleveland Clinic Foundation; it is a collection of 350 real-time clinical instances from heart patients through IoT sensors. The K-means technique is employed to remove noise in sensor data and clustered the unstructured data. The features are extracted to employ Linear Quadratic Discriminant Analysis. DG_ConvoNet is a deep learning process to classify and predict heart diseases. The diagnostic application achieves an accuracy of 96%, sensitivity of 80%, specificity of 73%, precision of 90%, F-Score of 79%, and area under the ROC curve of 75% implementing the proposed model.