Breath and cardiac sounds are two major bio sound signals. In this, heart sounds are produced by movement of some body parts such as heart valve, leaflets and the blood flow through the vessels, whereas lung sounds generates due to the air in and out flow through airways during breathing cycle. These two signals are recorded from chest region. These two signals have very high clinical importance for the patient who is critically ill. The lung functions and the cardiac cycles are continuously monitored for such patients with the help of the bio sound signal captured using suitable sensing mechanism or with auscultation techniques. But these two signals mostly superimpose with each other, so the separation of these heart sound signals (HSS) and the lung sound signals (LSS) is of great research interest. There are so many different techniques proposed for this purpose. In this paper, a study is carried out on different algorithms used for the separation of HSS from LSS, and also the results of major four separation algorithms are compared.

The beauty industry has seen rapid growth in multiple countries and due to its applications in entertainment, the analysis and assessment of facial attractiveness have received attention from scientists, physicians, and artists because of digital media, plastic surgery, and cosmetics. An analysis of techniques is used in the assessment of facial beauty that considers facial ratios and facial qualities as elements to predict facial beauty. Here, the facial landmarks are extracted to calculate facial ratios according to Golden Ratios and Symmetry Ratios, and an ablation study is performed to find the best performing feature set from extracted ratios. Subsequently, Gray Level Covariance Matrix (GLCM), Hu’s Moments, and Color Histograms in the HSV space are extracted as texture, shape, and color features, respectively. Another ablation study is performed to find out which feature performs the best when concatenated with the facial landmarks. Experimental results show that the concatenation of primary facial characteristics with facial landmarks improved the prediction score of facial beauty. Four models are trained, K‐Nearest Neighbors (KNN), Linear Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) on a dataset of 5500 frontal facial images, and amongst them, KNN performs the best for the concatenated features achieving a Pearson’s Correlation Coefficient of 0.7836 and a Mean Squared Error of 0.0963. Our analysis also provides us with insights into how different machine learning models can understand the concept of facial beauty.

This paper introduces the extended description of a database that contains emotional speech in the Russian language of younger school age (8–12-year-old) children and describes the results of validation of the database based on classical machine learning algorithms, such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). The validation is performed using standard procedures and scenarios of the validation similar to other well-known databases of children’s emotional acting speech. Performance evaluation of automatic multiclass recognition on four emotion classes “Neutral (Calm)—Joy—Sadness—Anger” shows the superiority of SVM performance and also MLP performance over the results of perceptual tests. Moreover, the results of automatic recognition on the test dataset which was used in the perceptual test are even better. These results prove that emotions in the database can be reliably recognized both by experts and automatically using classical machine learning algorithms such as SVM and MLP, which can be used as baselines for comparing emotion recognition systems based on more sophisticated modern machine learning methods and deep neural networks. The results also confirm that this database can be a valuable resource for researchers studying affective reactions in speech communication during child-computer interactions in the Russian language and can be used to develop various edutainment, health care, etc. applications.

Electrocardiogram (ECG) is a biological signal that plays a significant role in the detection of heart diseases. Translating the QRS complex is a standout amongst the most imperative parts of ECG signals and its processing and flag preparing. R wave is the real segment in the intricate, which has a basic job in the analysis of heart Rate abnormalities and furthermore in deciding pulse fluctuation (HRV). In this paper, we are utilizing MATLAB to pin the information from the crude MAT-documents from physio bank and Kubios HRV programming to dissect the information. Three unique methodologies were taken for consideration for the investigation, Time-area analysis, Frequency space analysis, and non-linear analysis methods. The correct utilization of MATLAB capacities (both implicit and user characterized), toolbox and Simulink software can lead us to work with raw ECG signals for preparing, separating and examine by simulation with extraordinary accuracy and ease. The product acknowledges distinctive info information groups for electrocardiogram (ECG) information and beat-to-beat RR interim information. The product figures all the normally utilized time-area and recurrence space HRV parameters and more than 40 investigation parameters. The examination results are spared as an ASCII content record, MATLAB MAT-document, or as a PDF report. The product is essentially a direct result of its reduced graphical UI. The product can be utilized uninhibitedly on Windows and Linux working frameworks.

The paper presents a description of the methodological approach for assessing the formation of the emotional sphere of children aged 5-16 years with typical and atypical development. The purpose of the Child's Emotional Development Method (CEDM) is to assess the emotional development of the children by determining the ability to express their own emotions, emotions adequacy, and recognition of the emotional states of others. The approach is based on adapted methods and scales, assessment criteria and methodological approaches tested in a number of studies, depending on the age of the children, their developmental characteristics, language and cultural characteristics of the country of residence of the child. The approach includes two blocks - information about the development of the child obtaining from parents / legal representatives; methods for children testing, including interview methods, psychological tests, play situations. The assessment uses a score on the Likert scale. A pilot approbation of the methodological approach was carried out on 30 children aged 8-16 years - typically developing (TD), with autism spectrum disorders (ASD) and Down syndrome (DS). The results of the study formed the basis for the creation of the Scale of Emotional Sphere Development of Children and Adolescents.

Automatic control plays a vital role in industrial operation. In process industries, in order to have an improved and stable control system, we need a robust tuning method. In this paper Particle Swarm Optimization (PSO) based algorithm is proposed for the optimization of a PID controller for level control process. A two tank system is considered. Initially a PID controller is designed using an Internal Model Control (IMC). The results are compared with the PSO based controller setting. The performance of the controller is compared and analyzed by time domain specification. In order to validate the robustness of PID controller, disturbance is imposed. The system is simulated using MATLAB. The results show that the proposed method provides better controller performance.