Quadratic Discriminant Analysis Research Articles

Research on BeiDou B1C Signal Abnormal Monitoring Algorithm Based on Machine Learning

High-precision systems such as civil aviation have put forward higher requirements for navigation systems, including indicators such as accuracy and integrity. Signal distortions and evil waveforms (EWF) generated by the signal-generating hardware on the satellite can severely affect the cross-correlation function of the signal, thereby affecting the integrity of the navigation system. With the further development of the BeiDou Navigation System (BDS), the types of signal distortion are subdivided into three types: analog distortion, subcarrier distortion, and PN code distortion. Traditional multi-correlator methods are no longer applicable under the requirements of modern navigation systems. In this paper, a machine learning-based BeiDou B1C signal anomaly monitoring algorithm is proposed. We detected and classified the signals using a quadratic discriminant analysis (QDA) method. The results show that our method can accurately classify the distortion types under the condition that the accuracy of distortion detection can be greatly improved. Meanwhile, our method is also highly effective and robust.

A quick method for fraud detection in egg labels based on egg centrifugation plasma

The aim of this work was to develop a quick and cheap method for fraud detection in egg labels according to the four legal farming method of the EU. The plasma obtained from egg centrifugation was investigated for this purpose. Initial protein content in egg, plasma protein content, plasma colour parameters (L*, a* and b*) and plasma UV–VIS-NIR (Ultraviolet–Visible-Near-infrared) spectra were evaluated. The classification algorithms applied were SVM (Support-Vector-Machine), LDA (Linear-Discriminant-Analysis) and QDA (Quadratic-Discriminant-Analysis). The analysis of the protein content did not detect differences. Colour parameters and spectral measurements showed significant differences between eggs. Spectra analysis with QDA gave sensitivity of 100% in the calibration set. The validation set scored 87.5% sensitivity and 94.07% specificity using the visible spectra. This work established plasma spectral measurements combined with classification algorithms as a powerful tool to discriminate the four farming systems. This work presents a fast tool for the egg label control.

SERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis.

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection.

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning.

Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.

Subject independent emotion recognition using EEG and physiological signals – a comparative study

PurposeThe aim of this study is to investigate subject independent emotion recognition capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG), electromyography (EMG), electrodermal activity (EDA), temperature, plethysmograph and respiration. The experiments are conducted on both modalities independently and in combination. This study arranges the physiological signals in order based on the prediction accuracy obtained on test data using time and frequency domain features.Design/methodology/approachDEAP dataset is used in this experiment. Time and frequency domain features of EEG and physiological signals are extracted, followed by correlation-based feature selection. Classifiers namely – Naïve Bayes, logistic regression, linear discriminant analysis, quadratic discriminant analysis, logit boost and stacking are trained on the selected features. Based on the performance of the classifiers on the test set, the best modality for each dimension of emotion is identified.Findings The experimental results with EEG as one modality and all physiological signals as another modality indicate that EEG signals are better at arousal prediction compared to physiological signals by 7.18%, while physiological signals are better at valence prediction compared to EEG signals by 3.51%. The valence prediction accuracy of EOG is superior to zygomaticus electromyography (zEMG) and EDA by 1.75% at the cost of higher number of electrodes. This paper concludes that valence can be measured from the eyes (EOG) while arousal can be measured from the changes in blood volume (plethysmograph). The sorted order of physiological signals based on arousal prediction accuracy is plethysmograph, EOG (hEOG + vEOG), vEOG, hEOG, zEMG, tEMG, temperature, EMG (tEMG + zEMG), respiration, EDA, while based on valence prediction accuracy the sorted order is EOG (hEOG + vEOG), EDA, zEMG, hEOG, respiration, tEMG, vEOG, EMG (tEMG + zEMG), temperature and plethysmograph.Originality/valueMany of the emotion recognition studies in literature are subject dependent and the limited subject independent emotion recognition studies in the literature report an average of leave one subject out (LOSO) validation result as accuracy. The work reported in this paper sets the baseline for subject independent emotion recognition using DEAP dataset by clearly specifying the subjects used in training and test set. In addition, this work specifies the cut-off score used to classify the scale as low or high in arousal and valence dimensions. Generally, statistical features are used for emotion recognition using physiological signals as a modality, whereas in this work, time and frequency domain features of physiological signals and EEG are used. This paper concludes that valence can be identified from EOG while arousal can be predicted from plethysmograph.

Alzheimer's disease diagnosis by blood plasma molecular fluorescence spectroscopy (EEM)

Despite tremendous research advances in detecting Alzheimer's disease (AD), traditional diagnostic tests remain expensive, time-consuming or invasive. The search for a low-cost, rapid, and minimally invasive test has marked a new era of research and technological developments toward establishing blood-based AD biomarkers. The current study has employed excitation-emission matrices (EEM) of fluorescence spectroscopy combined with machine learning to diagnose AD using blood plasma samples from 230 individuals (83 AD patients from 147 healthy controls). To evaluate the performance of the classification algorithms, we calculated the commonly used figures of merit (accuracy, sensitivity and specificity) and figures of merit that take into account the samples unbalance and the discrimination power of the models, as F2-score (F2), Matthews correlation coefficient (MCC) and test effectiveness (delta). The classification models achieved satisfactory results: Parallel Factor Analysis with Quadratic Discriminant Analysis (PARAFAC-QDA) with 83.33% sensitivity, 100% specificity, 86.21% F2; and Tucker3-QDA with 91.67% sensitivity, 95.45% specificity and 91.67% F2. In addition, the classifiers show high overall performance with 94.12% accuracy and 0.87 MCC. Regarding the discrimination power between healthy and AD patients, the classification algorithms showed high effectiveness with the mean scores separated by three or more standard deviations. The PARAFAC's spectral profiles and the wavelength values from both models loading profiles can be used in future research to relate this information to plasma AD biomarkers. Our results point to a rapid, low-cost and minimally invasive blood-based method for AD diagnosis.

Sessile droplet array for sensitive profiling of multiple extracellular vesicle immuno-subtypes

The sensitive detection of the multiple immuno-subtypes of cancer-specific extracellular vesicles (EVs) has emerged as a promising method for multiclass cancer diagnosis; however, its limitations in sensitivity, accessibility, and multiple detection of EV subtypes have hindered its further implementation. Here, we present a platform for sensitive EV detection enabled by sessile droplet array (eSD) that exploits enhanced immuno-capture of EVs via evaporation-driven radial flows in a sessile droplet. Compared to a micro-well without internal flows, this platform demonstrates significantly enhanced EV capture and detection by detecting low levels of EVs with a detection limit of 384.7 EVs per microliter, which is undetectable in the micro-well. In addition, using a small sample consumption of ∼0.2 μL plasma per droplet, the platform detects EV immuno-subtypes against seven different antibodies in patient plasma samples of different cancer types (liver, colon, lung, breast and prostate cancers). Further, using the profiling data, the platform exhibits a sensitivity of 100% (95% confidence interval (CI): 83–100%) and a specificity of 100% (95% CI: 40–100%) for the diagnosis of cancer, and classified cancer types with an overall accuracy of 96% (95% CI: 86–100%) using a two-staged algorithm based on quadratic discriminant analysis technique for machine learning.

Ultrasound and Magnetic Resonance Imaging of the Tongue in Obstructive Sleep Apnoea

To examine the geometrical parameters of the tongue in obstructive sleep apnoea (OSA) based on sex, age and BMI parameters and ultrasound (US) and MRI. The presence of OSA and tongue-based obstruction can be predicted using these parameters. Of 100 patients, 64% were diagnosed with OSA according to overnight polysomnography. MRI and US devices were used to measure tongue parameters. The location of the obstruction was identified using drug-induced sleep endoscopy. Statistical analysis was performed using the quadratic discriminant analysis. Men presented higher tongue volumes and axial diameter during Müller’s maneuver (MM) of US and coronal diameter of the MRI. In women, all examined MRI parameters were significantly correlated with apnoea-hypopnea index (AHI). A stronger correlation between BMI and AHI parameters was observed in women than in men. Using our algorithm, which includes tongue parameters and anthropometric values, the presence of OSA could be predicted in 91% with US and 82% with MRI. The detection of tongue-based obstruction was successful in 89% using US and 87% using MRI, whereas tongue-based obstruction was successful in 70% using US. Using MRI and US of the tongue combined with basic anthropometric parameters, the presence of OSA and tongue-based obstruction can be identified with high precision.

Multivariate Discrimination of Some Grapevine Cultivars under Drought Stress in Iran

Grapevine is one of the most important economic crops in horticulture, and drought stress is one of the most significant threatening factors in the world. Therefore, the identification and investigation of cultivars under drought stress are the basic steps and important goals in grapevine-breeding programs. In the present study, the 17 parameters of 14 grapevine cultivars under drought stress were first scaled. Based on the initial information, we divided the 14 grape cultivars according to their resistance to drought stress into four groups: tolerant, semi-tolerant, semi-sensitive, and sensitive. Then, the utilization of multivariate techniques comprising principal component analysis (PCA), along with quadratic discriminant analysis (QDA), were utilized to choose the most substantial and accountable traits for the four groups’ discrimination. For the QDA, the 17 parameters were arranged into four sets. The discrimination for all parameters showed 96% correct classification. The first set includes shoot length (Shoot L), shoot number (Shoot N), leaf area (Leaf A), relative water content (RWC), and chlorophyll a (Chl a) parameters that showed 71.5% correct classification. The second set includes chlorophyll b (Chl b), chlorophyll total, peroxidase (POX), and superoxide dismutase (Sod) parameters that had 75% correct classification. Electrolyte leakage (EL), malondialdehyde (MDA), proline, catalase (CAT), and ascorbate peroxidase (APX) parameters were in the third set and had 87% correct discrimination. The best discrimination was obtained by the combination of the first and third set, including the Shoot L, Shoot N, Leaf A, RWC, Chl a, EL, MDA, proline, CAT, and APX with 100% correct discrimination.

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios.

Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their harvesting and handling operations throughout the supply chain. Recent studies have introduced machine vision systems as a promising candidate for non-destructive evaluations of the ripeness levels of various agricultural and forest products. However, the reported models have been fruit-specific and cannot be applied to other fruit. In this regard, the current study aims to evaluate the feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques. Images of wild pistachios at four ripeness levels were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using the quadratic sequential feature selection method, 16 efficient features were identified and used to estimate the maturity levels of samples. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and an artificial neural network (ANN) were employed to classify samples into four ripeness levels, including initial unripe, secondary unripe, ripe, and overripe. The developed machine vision system achieved a correct classification rate (CCR) of 93.75, 97.5, and 100%, respectively. The high accuracy of the developed models confirms the capability of the low-cost visible imaging system in assessing the ripeness of wild pistachios in a non-destructive, automated, and rapid manner.

Mapping Mediterranean maquis formations using Sentinel-2 time-series

Maquis, which provides numerous ecosystem services and constitutes an integral part of the Mediterranean ecosystem, is highly heterogeneous. However, despite its importance and heterogeneity, maquis is generally mapped as a single class, while forests are mapped for management purposes. Detailed mapping of the maquis formations is necessary to understand their ecology and manage them sustainably. This study presents a method that generates alliance-level maps of the maquis ecosystems through satellite images using various machine learning techniques with different feature combinations and evaluates the proposed approach in the Mediterranean region of Southern Turkey, which has an area of 95,000 km2. Multi-temporal images extract information from vegetation phenology, while topographic and meteorological data are used to improve the classification. Cross-validation is performed using a ground-truth data set of approximately 7500 polygons. Results show that cost-effective and accurate maquis classification at the alliance level is possible using a combination of environmental features, multi-spectral, and multi-temporal satellite images. Adding environmental features to remotely sensed classification has improved the accuracy by 18%. The Random Forest (RF) algorithm improves classification accuracy by 7.3% and 14.6% relative to Support Vector Machine and Quadratic Discriminant Analysis algorithms, respectively. With the help of newly introduced features, we have succeeded in mapping 11 alliances with 64.2–82.7% overall accuracy. We believe the proposed classification approach will help improve the mapping of the shrubland ecosystems, which will significantly affect natural resource management, conservation, and adaptation to climate change.

Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity

Linear and quadratic discriminant analysis are two fundamental classification methods used in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids (MVE), and t-distribution methods are used to estimate the parameter of independent variables on the multivariate normal distribution in order to classify binary dependent variables. The MM and ML methods are popular and effective methods that approximate the distribution parameter and use observed data. However, the MVE and t-distribution methods focus on the resampling algorithm, a reliable tool for high resistance. This paper starts by explaining the concepts of linear and quadratic discriminant analysis and then presents the four other methods used to create the decision boundary. Our simulation study generated the independent variables by setting the coefficient correlation via multivariate normal distribution or multicollinearity, often through basic logistic regression used to construct the binary dependent variable. For application to Pima Indian diabetic dataset, we expressed the classification of diabetes as the dependent variable and used a dataset of eight independent variables. This paper aimed to determine the highest average percentage of accuracy. Our results showed that the MM and ML methods successfully used large independent variables for linear discriminant analysis (LDA). However, the t-distribution method of quadratic discriminant analysis (QDA) performed better when using small independent variables.

Oxidized and Reduced Sulfur Observed by the Sample Analysis at Mars (SAM) Instrument Suite on the Curiosity Rover Within the Glen Torridon Region at Gale Crater, Mars

AbstractThe Mars Science Laboratory (MSL) Curiosity rover has been assessing the habitability and geologic history of Gale crater, Mars since landing in 2012. One of the primary objectives of the mission was to investigate a clay‐bearing unit identified using orbital spectral data, designated the Glen Torridon (GT) region. This region was of particular interest because of its elevated abundance of clay minerals that may have preserved geochemical evidence of ancient habitable environments. The Curiosity rover explored the GT region for ∼750 sols and analyzed eight drilled samples with the Sample Analysis at Mars (SAM) instrument suite using evolved gas analysis‐mass spectrometry. Evolved sulfur‐bearing gases provided insight about the composition of sulfur‐containing compounds in Martian samples. Evolved gases were analyzed by three methods to understand the oxidation state of sulfur in the samples: (a) SO2 evolution temperature, (b) quadratic discriminant analysis comparing SAM data to SAM‐like laboratory investigations, and (c) sulfur isotope values from evolved 34SO2/32SO2. The results of these three methods were consistent with the majority of sulfur in the GT region being in an oxidized state, but two of the eight samples analyzed by SAM were consistent with the presence of small amounts of reduced sulfur. The oxidized and reduced sulfur could have a variety of sources and represents a nonequilibrium assemblage that could have supported putative ancient chemolithotrophic metabolisms.

Sequential feature selection for power system event classification utilizing wide-area PMU data

The increasing penetration of intermittent, non-synchronous generation has led to a reduction in total power system inertia. Low inertia systems are more sensitive to sudden changes and more susceptible to secondary issues that can result in large-scale events. Due to the short time frames involved, automatic methods for power system event detection and diagnosis are required. Wide-area monitoring systems (WAMS) can provide the data required to detect and diagnose events. However, due to the increasing quantity of data, it is almost impossible for power system operators to manually process raw data. The important information is required to be extracted and presented to system operators for real/near-time decision-making and control. This study demonstrates an approach for the wide-area classification of many power system events. A mixture of sequential feature selection and linear discriminant analysis (LAD) is adopted to reduce the dimensionality of PMU data. Successful event classification is obtained by employing quadratic discriminant analysis (QDA) on wide-area synchronized frequency, phase angle, and voltage measurements. The reliability of the proposed method is evaluated using simulated case studies and benchmarked against other classification methods.

Predictive maintenance for offshore oil wells by means of deep learning features extraction

AbstractNowadays, the great diffusion of the Internet of Things and the improvements in Artificial Intelligence techniques have given a rise in the development and application of data‐driven approaches for Predictive Maintenance to reduce the costs linked to the maintenance of industrial machinery. Due to the wide real‐life applications and the strong interest by even more industries, this field is highly attractive for academics and practitioners. So, constructing efficient frameworks to address the Predictive Maintenance problem is an open debate. In this work, we propose a Deep Learning approach for the feature extraction in the offshore oil wells monitoring context, exploiting the public 3 W dataset, which is well‐known in the literature. The dataset is made up of about 2000 multivariate time series labelled according to the corresponding functioning of the well. So, there is a classification task with eight classes, each related to a particular machinery condition. Thanks to the peculiarities of the labels, the proposed framework is valid both for diagnostics and prognostics. In more detail, we compare two different approaches in feature extraction. The first is a statistical approach, widely used in the literature related to the considered dataset; the second is based on Convolutional 1D AutoEncoder. The extracted features are then used as input for several Machine Learning algorithms, namely the Random Forest, Nearest Neighbours, Gaussian Naive Bayes and Quadratic Discriminant Analysis. Different experiments on various time horizons prove the worthiness of the Convolutional AutoEncoder.

ProAll-D: protein allergen detection using long short term memory - a deep learning approach.

BackgroundAn allergic reaction is the immune system's overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach's positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens.MethodThis work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration.ResultsThe Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour's Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %.ConclusionAs the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data.

Seedlessness detection in ‘White Malaga’ table grapes using near-infrared spectroscopy

‘White Malaga’ table grapes are seeded and widely grown in Thailand. They are converted by induction into seedless grapes to increase their value. It is difficult to identify seedlessness in table grapes without destroying the grape berry. The present work thus described a quick and non-destructive method for detecting and predicting seedlessness in ‘White Malaga’ table grapes by using near-infrared (NIR) spectroscopy together with chemometric analysis. The NIR spectra of 280 grape samples were recorded after harvest. Firmness, total soluble solids (TSS), pH, titratable acidity (TA), tartaric acid, number of seeds, and relevant physical properties were analysed. The width and weight of plant growth regulator (PGR) treatments were significantly lower than those in the untreated grapes, while the length, firmness, TA, and tartaric acid were not significantly different. Partial least square (PLS) regression was used to investigate the prediction. Classification models, namely principal component analysis (PCA) and quadratic discriminant analysis (QDA), were used to identify seedlessness. It was found that, QDA, as a representative of linear classification, resulted in the best classification of seeded and seedless performance, where the percentages of predictive ability (%PA), the percentages of model stability (%MS), and the percentages of correctly classified (%CC) were 97.27, 98.57, and 96.23%, respectively, for the training set with no pre-processing. Therefore, the NIR spectroscopy technique can be a non-destructive technique for seedlessness detection in ‘White Malaga’ table grapes.

Applying machine learning methods to predict geology using soil sample geochemistry

In this study we compared various machine learning techniques that used soil geochemistry to aid in geologic mapping. We tested six different sampling methods (undersample, oversample, Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE and Edited Nearest Neighbor (SMOTEENN), and SMOTE and Tomek links (SMOTETomek)). SMOTE performed best with ADASYN and SMOTETomek having slightly lower effectiveness. Nine machine learning algorithms (naïve Bayes, logistic regression, quadratic discriminant analysis, nearest neighbors, radial basis function support-vector machine, artificial neural network, random forest, AdaBoost classifier, and gradient boosting classifier) were compared and AdaBoost classifiers and gradient boosting classifiers were found to be most effective. Finally, we experimented with multiple classifier systems (MCS) testing different combinations of algorithms and various combinatorial functions. It was found that MCS can outperform individual models, and the best MCS combined nearest neighbors, radial basis function support-vector machine, artificial neural network, random forest, AdaBoost classifiers, and gradient boosting classifier, then applied a logistic regression to the probabilities output by the models. Ultimately, we created a tool that is able to adequately predict underlying geology in the study area using soil geochemistry.

IoMT aware data collective quadratic ensembled cat boost module classification algorithm for non-invasive blood glucose monitoring in VLSI design

Internet of Medical Things (IoMT) is the combination of medical devices and utilization by networking technologies. But, the response time and cost were not reduced. In order to address these issues, IoMT Aware Data Collective Quadratic Ensembled Cat Boost Module Classification (IoMT-DCQECBMC) Method is introduced. Initially, IoMT Aware Data Collection is used for gathering data from medical devices. After the data collection process, Quadratic Ensembled Cat Boost Module Classification (QECBM) is carried out in IoMT-DCQECBMC Method to design an efficient VLSI architecture with minimal cost and area. The quadratic classifier is considered the weak learner that categorizes the module for efficient VLSI design. Finally, the weak learners are joined to form the strong classifier to perform non-invasive blood glucose monitoring efficiently. Experimental evaluation is carried out on the factors such as computation cost, area, and accuracy with respect to a number of modules in VLSI circuits. The accuracy of the IoMT-DCQECBMC method is increased by 4% than conventional methods. In addition, the area consumption and computation cost of the proposed IoMT-DCQECBMC method are reduced by 13% to 30% other than existing methods.

Detection of fraud in sesame oil with the help of artificial intelligence combined with chemometrics methods and chemical compounds characterization by gas chromatography–mass spectrometry

The majority of current approaches to identify adulterated edible vegetable oils are of limited practical benefits because they require long analysis times, expensive equipment, and professional training. In this study, a new, simple, accurate, and fast detection method was proposed based on the odor fingerprint obtained by measuring the volatile odors of edible vegetable oils with an electronic nose. The odor fingerprints were obtained for 8 different levels of sunflower and canola oil added to sesame oil, and the samples were analyzed simultaneously by gas chromatography–mass spectrometry (GC-MS). The chemometric methods such as Principal Component Analysis (PCA), Liner Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) were used to analyze the signals from the electronic nose.According to results, low level fraud (25% sunflower oil to 75% sesame oil), which is difficult to detect using the GC-MS method, was detected with very high accuracy via the electronic nose. This indicates that the current approach has the potential to detect and quantify edible oil fraud to improve efficiency and monitoring and to ensure the safety of consumption of edible vegetable oils.