Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively.

Download Full-text

Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking

PLoS ONE ◽

10.1371/journal.pone.0252657 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0252657

Author(s):

David Jiménez-Grande ◽

S. Farokh Atashzar ◽

Eduardo Martinez-Valdes ◽

Deborah Falla

Keyword(s):

Neck Pain ◽

Network Topology ◽

Chronic Neck Pain ◽

Classification Performance ◽

Support Vector ◽

Neck Muscle ◽

Linear Discriminant ◽

Upper Trapezius ◽

Intermuscular Coherence

Neuromuscular impairments are frequently observed in patients with chronic neck pain (CNP). This study uniquely investigates whether changes in neck muscle synergies detected during gait are sensitive enough to differentiate between people with and without CNP. Surface electromyography (EMG) was recorded from the sternocleidomastoid, splenius capitis, and upper trapezius muscles bilaterally from 20 asymptomatic individuals and 20 people with CNP as they performed rectilinear and curvilinear gait. Intermuscular coherence was computed to generate the functional inter-muscle connectivity network, the topology of which is quantified based on a set of graph measures. Besides the functional network, spectrotemporal analysis of each EMG was used to form the feature set. With the use of Neighbourhood Component Analysis (NCA), we identified the most significant features and muscles for the classification/differentiation task conducted using K-Nearest Neighbourhood (K-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms. The NCA algorithm selected features from muscle network topology as one of the most relevant feature sets, which further emphasize the presence of major differences in muscle network topology between people with and without CNP. Curvilinear gait achieved the best classification performance through NCA-SVM based on only 16 features (accuracy: 85.00%, specificity: 81.81%, and sensitivity: 88.88%). Intermuscular muscle networks can be considered as a new sensitive tool for the classification of people with CNP. These findings further our understanding of how fundamental muscle networks are altered in people with CNP.

Download Full-text

An Artificial Intelligence-Based Smart System for Early Glaucoma Recognition Using OCT Images

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.20210701.oa3 ◽

2021 ◽

Vol 12 (4) ◽

pp. 32-59

Author(s):

Law Kumar Singh ◽

Pooja ◽

Hitendra Garg ◽

Munish Khanna

Keyword(s):

Vision Loss ◽

Nearest Neighbor ◽

Peripheral Vision ◽

Learning Algorithm ◽

Three Dimensional ◽

Support Vector ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Glaucoma Diagnosis ◽

Detection And Identification

Glaucoma is a progressive and constant eye disease that leads to a deficiency of peripheral vision and, at last, leads to irrevocable loss of vision. Detection and identification of glaucoma are essential for earlier treatment and to reduce vision loss. This motivates us to present a study on intelligent diagnosis system based on machine learning algorithm(s) for glaucoma identification using three-dimensional optical coherence tomography (OCT) data. This experimental work is attempted on 70 glaucomatous and 70 healthy eyes from combination of public (Mendeley) dataset and private dataset. Forty-five vital features were extracted using two approaches from the OCT images. K-nearest neighbor (KNN), linear discriminant analysis (LDA), decision tree, random forest, support vector machine (SVM) were applied for the categorization of OCT images among the glaucomatous and non-glaucomatous class. The largest AUC is achieved by KNN (0.97). The accuracy is obtained on fivefold cross-validation techniques. This study will facilitate to reach high standards in glaucoma diagnosis.

Download Full-text

ECG Signal Classification using Support Vector Machine and Linear Discriminant Analysis

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i5.17201725 ◽

2019 ◽

Vol 7 (5) ◽

pp. 1720-1725

Author(s):

S. Grover ◽

Shailja .

Keyword(s):

Support Vector Machine ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Signal Classification ◽

Support Vector ◽

Ecg Signal ◽

Linear Discriminant

Download Full-text

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

Current Drug Targets ◽

10.2174/1389450119666180809122244 ◽

2019 ◽

Vol 20 (5) ◽

pp. 488-500 ◽

Cited By ~ 6

Author(s):

Yan Hu ◽

Yi Lu ◽

Shuo Wang ◽

Mengying Zhang ◽

Xiaosheng Qu ◽

...

Keyword(s):

Machine Learning ◽

Drug Design ◽

Anticancer Drugs ◽

Nearest Neighbor ◽

Cost Effective ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Activity Prediction ◽

Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Download Full-text

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

Current Bioinformatics ◽

10.2174/1574893615999200711170445 ◽

2020 ◽

Vol 15 ◽

Author(s):

Mohanad Mohammed ◽

Henry Mwambi ◽

Bernard Omolo

Keyword(s):

Colorectal Cancer ◽

Survival Analysis ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Negative Binomial ◽

Sub Saharan Africa ◽

Support Vector ◽

Mutation Status ◽

Linear Discriminant ◽

Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

Download Full-text

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Sensors ◽

10.3390/s19020419 ◽

2019 ◽

Vol 19 (2) ◽

pp. 419 ◽

Cited By ~ 11

Author(s):

Dongdong Du ◽

Jun Wang ◽

Bo Wang ◽

Luyi Zhu ◽

Xuezhen Hong

Keyword(s):

Prediction Models ◽

Extraction Methods ◽

Oxide Semiconductor ◽

Soluble Solids ◽

Support Vector ◽

Least Squares Regression ◽

Accuracy Rate ◽

Linear Discriminant ◽

The Difference ◽

Ripe Stage

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

Download Full-text

Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

Royal Society Open Science ◽

10.1098/rsos.190001 ◽

2019 ◽

Vol 6 (5) ◽

pp. 190001 ◽

Cited By ~ 1

Author(s):

Katherine E. Klug ◽

Christian M. Jennings ◽

Nicholas Lytal ◽

Lingling An ◽

Jeong-Yeol Yoon

Keyword(s):

Heavy Metal ◽

Support Vector Machine ◽

Metal Ions ◽

Heavy Metal Ions ◽

Mie Scattering ◽

Training Data ◽

Scattering Data ◽

Support Vector ◽

Linear Discriminant ◽

Machine Analysis

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

Download Full-text

Detection and Recognition of RF Devices Using Support Vector Machine

International Journal of Interdisciplinary Telecommunications and Networking ◽

10.4018/ijitn.2013100102 ◽

2013 ◽

Vol 5 (4) ◽

pp. 13-20

Author(s):

Shikhar P. Acharya ◽

Ivan G. Guardiola

Keyword(s):

Support Vector Machine ◽

Radio Frequency ◽

Experimental Results ◽

Support Vector ◽

Noise Band ◽

Detection And Identification ◽

Electromagnetic Emissions ◽

Rf Devices ◽

Unintended Electromagnetic Emissions ◽

Detection And Recognition

Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be used as a signature for the purpose of detection and identification. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band. The research herein provides the application of Support Vector Machine (SVM) for detection and identification of RF devices using their UEEs. Experimental Results shows that SVM can detect RF devices within the noise band, and can also identify RF devices using their UEEs.

Download Full-text

Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽

10.3390/s19204523 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4523 ◽

Cited By ~ 1

Author(s):

Carlos Cabo ◽

Celestino Ordóñez ◽

Fernando Sáchez-Lasheras ◽

Javier Roca-Pardiñas ◽

and Javier de Cos-Juez

Keyword(s):

Random Forest ◽

Laser Scanning ◽

Supervised Classification ◽

Computing Time ◽

Principal Component ◽

Point Clouds ◽

Support Vector ◽

Linear Discriminant ◽

Vector Machines ◽

Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

Download Full-text