Search results for "Machine"
showing 10 items of 2592 documents
Emotion Based Music Recommendation System
2020
Nowadays, music platforms provide easy access to large amounts of music. They are working continuously to improve music organization and search management thereby addressing the problem of choice and simplify exploring new music pieces. Recommendation systems gain more and more popularity and help people to select appropriate music for all occasions. However, there is still a gap in personalization and emotions driven recommendations. Music has a great influence on humans and is widely used for relaxing, mood regulation, destruction from stress and diseases, to maintain mental and physical work. There is a wide range of clinical settings and practices in music therapy for wellbeing support.…
Matemātiskās un statistiskās datu analīzes metodes cukura diabēta pētījumos
2020
Šajā darbā aprakstītas matemātiskās un statistiskās datu analīzes metodes, kas pielietotas cukura diabēta pētījumos. Tā kā cukura diabēts ir viena no izplatītākajām slimībām pasaulē, un tās slimnieku skaits ar katru gadu palielinās, ir nepieciešams izstrādāt tādus matemātiskos modeļus, kas prognozētu personas iespējamību saslimt, tādejādi spējot laicīgi veikt preventīvus pasākumus. Darbā apskatītās metodes tika implementētas brīvpiekļuves programmā R, un ar to palīdzību tika analizēti Latvijā ievākti dati, kā arī mašīnmācīšanās algoritmu izstrādē pasaulē populārā iebūvēto datu kopa PimaIndiansDatabase.
Inducing Rules of Ensemble Music Performance : A Machine Learning Approach
2013
Previous research in expressive music performance has described how solo musicians intuitively shape each note in relation to local/global score contexts. However, expression in ensemble performances, where each individual voice is played simultaneously with other voices, has been little explored. We present an exploratory study in which the performance of a string quartet is recorded and analysed by a computer. We use contact microphones to acquire four audio signals from which a set of audio descriptors is extracted individually for each musician. Moreover, we use motion capture to extract bowing descriptors (bow velocity/force) from each of the four performers. The gathered multimodal da…
A Spotlight on the Role of Radiomics and Machine-Learning Applications in the Management of Intracranial Meningiomas: A New Perspective in Neuro-Onco…
2022
Background: In recent decades, the application of machine learning technologies to medical imaging has opened up new perspectives in neuro-oncology, in the so-called radiomics field. Radiomics offer new insight into glioma, aiding in clinical decision-making and patients’ prognosis evaluation. Although meningiomas represent the most common primary CNS tumor and the majority of them are benign and slow-growing tumors, a minor part of them show a more aggressive behavior with an increased proliferation rate and a tendency to recur. Therefore, their treatment may represent a challenge. Methods: According to PRISMA guidelines, a systematic literature review was performed. We included selected a…
Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices
2021
Mental stress has been identified as the root cause of various physical and psychological disorders. Therefore, it is crucial to conduct timely diagnosis and assessment considering the severe effects of mental stress. In contrast to other health-related wearable devices, wearable or portable devices for stress assessment have not been developed yet. A major requirement for the development of such a device is a time-efficient algorithm. This study investigates the performance of computer-aided approaches for mental stress assessment. Machine learning (ML) approaches are compared in terms of the time required for feature extraction and classification. After conducting tests on data for real-t…
Determinantes Sociales de la Salud, Modos de Transporte, y su Relación con Riesgo de Accidentalidad en Jóvenes residentes de la Región Metropolitana …
2022
Antecedentes y tema de trabajo: La movilidad y la circulación determinan dinámicas que caracterizan la vida humana. Estas actividades son tan importantes que se consideran un derecho humano universal, constituyéndose en fuente de constante preocupación para investigadores, gobiernos e instituciones. Las limitaciones al ejercicio de este derecho son un detrimento para la calidad de vida, especialmente cuando conlleva un número inaceptable de muertes y lesiones que hoy se consideran de carácter pandémico. Los eventos desfavorables para la movilidad y la circulación suelen denominarse como “suceso”, “incidente”, “choque”, “colisión”, y quizás el termino más empleado sea “accidente”. Ni experto…
Compensated transfer entropy as a tool for reliably estimating information transfer in physiological time series
2013
We present a framework for the estimation of transfer entropy (TE) under the conditions typical of physiological system analysis, featuring short multivariate time series and the presence of instantaneous causality (IC). The framework is based on recognizing that TE can be interpreted as the difference between two conditional entropy (CE) terms, and builds on an efficient CE estimator that compensates for the bias occurring for high dimensional conditioning vectors and follows a sequential embedding procedure whereby the conditioning vectors are formed progressively according to a criterion for CE minimization. The issue of IC is faced accounting for zero-lag interactions according to two a…
Analysis of Somatosensory Cortical Responses to Different Electrotactile Stimulations as a Method Towards an Objective Definition of Artificial Senso…
2022
Sensory feedback is a critical component in many human-machine interfaces (e.g., bionic limbs) to provide missing sensations. Specifically, electrotactile stimulation is a popular feedback modality able to evoke configurable sensations by modulating pulse amplitude, duration, and frequency of the applied stimuli. However, these sensations coded by electrotactile parameters are thus far predominantly determined by subjective user reports, which leads to heterogeneous and unstable feedback delivery. Thus, a more objective understanding of the impact that different stimulation parameters induce in the brain, is needed. Analysis of cortical responses to electrotactile afference might be an effe…
Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospecti…
2022
IntroductionPreeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning algorithms demonstrate promising potential, while there is a controversial discussion about whether machine learning methods should be recommended preferably, compared to traditional statistical models.MethodsWe employed both logistic regression and six machine learning methods as binary predictive models for a dataset containing 733 women diagnosed with preeclampsia. Participants were grouped by four different pregnancy outcomes. After the imputation of missing values, statistic…