Classification of Lung Sounds with Deep Learning

Lung diseases are among the diseases that seriously threaten human health, and many deaths today are caused by lung diseases. Thanks to the lung sounds, important inferences can be made about lung diseases. Doctors often use the auscultation technique to evaluate patients with lung conditions. However, this technique has some drawbacks. For example, this may lead to a misdiagnosis if the doctor has not received a good medical education. In addition, since the lung sounds are nonstationary, the analysis and recognition process is complex. Therefore, the development of automatic recognition systems will help in making more precise and accurate diagnoses. Many studies based on traditional sound processing routines have been proposed to diagnose lung diseases and to assist professionals in their diagnosis. In this study, a method based on deep learning has been proposed for the classification of lung sounds. For this purpose, the Convolutional Neural Network (CNN) has been designed. In addition, experiments are carried out using different machine learning methods based on feature extraction. Experiments to evaluate the effectiveness of different methods are carried out using the ICBHI 2017 data set consisting of four classes commonly used in the literature. On average, 64.5% accuracy is obtained from the proposed method. In addition, when the results obtained from the proposed method are compared with the latest methods in the literature, it is seen that it has a better performance in terms of classification success

Recognition of Human Emotions by Machine Learning Method Using Turkish Music Stimuli

Music is an audio signal consisting of a wide variety of complex components which vary according to time and frequency. It is widely accepted in the literature that music evokes a wide variety of emotions in the audience. When a person says that the music they are listening to contains sad or happy feelings, this may not reveal the feeling they actually feel. However, according to the emotion felt during listening to music, fluctuations in electrical activity of the brain can more accurately reveal the structure of perceived true emotion. Detecting human emotions using brain signals has been the subject of current research in many areas. In this study, the problem of detection human emotions while listening to music has been discussed. Experiments are carried out both on our own dataset and on the DEAP dataset, which is widely used in the literature. Different types of Turkish music’s were played to the participants. By examining the electrical waves that occur in their brain's surface, happy, sad, relaxing and angry mood states were recognized. Participants were asked to listen to music from different types in a noiseless environment. To classification the emotions, electroencephalography (EEG) signals were saved primarily from different channels. Certain features have been extracted from these signals. Extracted features have been classified using machine learning algorithms for Support Vector Machines (SVM), K nearest neighbor (KNN), and Artificial Neural Networks (ANN). The best accuracy rate was obtained by ANN from algorithms used to train the data set and classify human emotions. According to the results obtained, it was observed that the used method performed well

Sub-Image Histogram Equalization using Coot Optimization Algorithm for Segmentation and Parameter Selection

Contrast enhancement is very important in terms of assessing images in an objective way. Contrast enhancement is also significant for various algorithms including supervised and unsupervised algorithms for accurate classification of samples. Some contrast enhancement algorithms solve this problem by addressing the low contrast issue. Mean and variance based sub-image histogram equalization (MVSIHE) algorithm is one of these contrast enhancements methods proposed in the literature. It has different parameters which need to be tuned in order to achieve optimum results. With this motivation, in this study, we employed one of the most recent optimization algorithms, namely, coot optimization algorithm (COA) for selecting appropriate parameters for the MVSIHE algorithm. Blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE) metrics are used for evaluating fitness of the coot swarm population. The results show that the proposed method can be used in the field of biomedical image processing.Comment: 14 page

Measurement of charged particle spectra in minimum-bias events from proton-proton collisions at root s =13 TeV

Pseudorapidity, transverse momentum, and multiplicity distributions are measured in the pseudorapidity range vertical bar eta vertical bar 0.5 GeV in proton-proton collisions at a center-of-mass energy of root s = 13 TeV. Measurements are presented in three different event categories. The most inclusive of the categories corresponds to an inelastic pp data set, while the other two categories are exclusive subsets of the inelastic sample that are either enhanced or depleted in single diffractive dissociation events. The measurements are compared to predictions from Monte Carlo event generators used to describe high-energy hadronic interactions in collider and cosmic-ray physics.Peer reviewe

Search for resonant pair production of Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at 13 TeV

A search for a narrow-width resonance decaying into two Higgs bosons, each decaying into a bottom quark-antiquark pair, is presented. The search is performed using proton-proton collision data corresponding to an integrated luminosity of 35.9 fb$^{-1}$ at $\sqrt{s}=$ 13 TeV recorded by the CMS detector at the LHC. No evidence for such a signal is observed. Upper limits are set on the product of the production cross section for the resonance and the branching fraction for the selected decay mode in the resonance mass range from 260 to 1200 GeV

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press